hwb.ipynb

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      "name": "hwb.ipynb",
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        "<a href=\"https://colab.research.google.com/github/aa25desh/graph/blob/master/hwb.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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      "source": [
        "### 1\n",
        "import numpy as np # A module for handling numerical arrays \n",
        "import os # A module for interacting with your operating system (e.g., listing directories)\n",
        "import gzip # A module to handle gzipped files\n",
        "import scipy # A module with scientific functions (such as statistical tests)\n",
        "import pandas # A module that handles general data arrays, in a way that is similar to R\n",
        "import matplotlib as mpl # A module for plotting\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import seaborn as sns # A module to plot prettier plots\n",
        "sns.set_style('white')\n",
        "sns.set_style('ticks')\n",
        "sns.set_context('notebook')\n",
        "import h5py # A module for interacting with hdf5 data format.\n",
        "import allel; print('scikit-allel', allel.__version__) # A module to handle genetic data\n",
        "\n",
        "# Check version of scikit-allel -- this notebook may not work with older versions\n",
        "from distutils.version import LooseVersion # I import this module just to check that we have the\n",
        "                                           # correct version of allel -- otherwise things\n",
        "                                           # will fail\n",
        "assert LooseVersion(allel.__version__) >=  LooseVersion('1.2.0'), \\\n",
        "                \"This notebook may not work with this version of scikit-allel, requires 1.2.0\"\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "scikit-allel 1.2.1\n"
          ],
          "name": "stdout"
        }
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b8UmWjbYFHeC",
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      "source": [
        "This is an ipython notebook. Lectures about Python, useful both for beginners and experts, can be found at http://scipy-lectures.github.io.\n",
        "\n",
        "I recommend installing the [Anaconda](https://store.continuum.io/cshop/academicanaconda) distribution. Make sure not to pay for it! Click Anaconda Academic License; it should be free for those with academic e-mail addresses. \n",
        "\n",
        "\n",
        "Open the notebook by (1) copying this file into a directory, (2) in that directory typing\n",
        "ipython notebook\n",
        "and (3) selecting the notebook.\n",
        "\n",
        "This notebook uses the scikit-allel package. Follow instructions [here](https://anaconda.org/conda-forge/scikit-allel) to install it. \n",
        "\n",
        "Note: if you have an older version of anaconda installed, you should start by updating anaconda. \n",
        "\n",
        "\n",
        "# The Hardy-Weinberg principle\n",
        "In this exercise, we will warm up by manipulating some data from the 1000 Genomes Project and explore one of the most widely-used population genetics concepts, the Hardy-Weinberg principle. \n",
        "\n",
        "## Motivation\n",
        "If we want to be doing population genetics, we'll have to come up with a definition of what a \"population\" is. We already have an idea of what a diploid species is: it's a set of individuals that can reproduce together. Within a species, we may find multiple groups whose individuals *tend* to reproduce together, because biology, geography, or cultural factors favor matings within the group. This tendency can increase the genetic similarity of individuals within the groups. \n",
        "We'll say that a *population* is a group whose individuals reproduce preferentially with each other because of an inheritable feature. The inheritable feature is typically not genetic: it can involve geographic factors (eg, Peoples of the Americas), as well as political (e.g., Canadians), cultural (e.g., French Canadians), or a mix of all these factors (e.g., Montreal Canadians fans). None of these groups are in reproductive isolation, but individuals are statistically more likely to reproduce with members of their group, and so they form populations in the sense of population genetics. But it should be clear that not all these groups are useful to study from a genetics perspective. \n",
        "\n",
        "This brings up the question of why we need to define populations in the first place. In short, we define populations to help us build models of genetic diversity and evolution, when we think that each population can be described more simply than the whole species. For example, we often assume that indiviudals within a population behave in a relatively uniform manner. A perfectly uniform population never truly exists, and all our models are vast simplifications, but in many cases these simple models are sufficient to understand the biology that we care about. \n",
        "\n",
        "A particularly simple population is one where partners choose each other entirely randomly among all individuals. When that happens, the mathematical modelling gets much easier, because we don't have to model all the individual idiosyncracies. This is extremely convenient, and the random mating assumption plays a a central role in population genetics. The first thing we might want to do when looking at genetic data, therefore, is to check whether we can get away with this random mating assumption.\n",
        "\n",
        "Conveniently, the random mating assumption makes a very clear prediction about the genetic diversity in a population: the chromosomes inherited from each parent should be no more similar, nor dissimilar, than any two chromosomes sampled at random in the population. Therefore, if neutral alleles $a$ and $A$ segregate in a parental population, with frequencies $p_a+p_A=1$, the probability of genotypes in the descendants is \n",
        "\n",
        "$p_{AA}=p_A^2$, \n",
        "\n",
        "$p_{aA}=2 p_ap_A,$ \n",
        "\n",
        "$p_{aa}=p_ap_a$. \n",
        "\n",
        "You can verify that the expected proportion of $A$ alleles in the second generation is just  $p_A^{offspring}=p_{AA}+\\frac{p_{aA}}{2}=p_A$. Therefore, both the expected frequencies of alleles and of genotypes are conserved over time--this is the Hardy-Weinberg principle. \n",
        "\n",
        "Even without looking at the parental population, we can sometimes see that the genotype distribution does not follow the Hardy-Weinberg principle. If the counts of alleles in a population are:\n",
        "\n",
        "\n",
        "$\\#_{AA}=100$\n",
        "\n",
        "$\\#_{aA}=0$\n",
        "\n",
        "$\\#_{aa}=100,$\n",
        "\n",
        "we know that it's unlikely to have occurred in a randomly mating population--we can't find a $p_a$ and $p_A$ that match the data. If the data is consistent with the HW principle, we say that the allele is at Hardy-Weinberg equilibrium. We'll explore a few situations where Hardy-Weinberg equilibrium is not respected, but let's first look at some data and see what we find.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "You can download the data file at:\n",
        "ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/\n",
        "\n",
        "Download \"ALL.chr22.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz\" to your computer and take note of where you put it. "
      ]
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      "source": [
        "### 2\n",
        "vcf_file_name = 'ALL.chr22.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf.gz'\n",
        "vcf_file_path = os.path.join(os.sep, 'Users', 'aa25desh' , 'data1000gen', vcf_file_name) # Change this path as \n",
        "                                                                    # necessary!\n",
        "\n",
        "# I used a relative path here, because I stored the data file in a directory close to the \n",
        "# notebooks. To find the file I just need to go up one directory ('..'), then go in the \n",
        "# \"data/1000G\" directory to use an absolute path, I would write something like\n",
        "# vcf_file = os.path.join(os.sep,'Users','simon', 'data', '1000G', vcf_file_name) \n",
        "                                                            \n",
        "import gzip \n",
        "for line in gzip.open(vcf_file_path,'rt'):\n",
        "    if not line.startswith(\"##\"): # Comment lines in vcf files start with ##. This skips comment lines\n",
        "        print('\\t'.join(line.split()[0:10])) # The first line after the comment lines is the header line, starting w/ #\n",
        "                                             # It gives information about what data is contained in each column.\n",
        "                                             \n",
        "    if not line.startswith(\"#\"): # After the header, we find data lines, which do not start with #\n",
        "                                 # We stop after we have read one such line  \n",
        "        break;\n",
        "        \n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tHG00096\n",
            "22\t16050075\trs587697622\tA\tG\t100\tPASS\tAC=1;AF=0.000199681;AN=5008;NS=2504;DP=8012;EAS_AF=0;AMR_AF=0;AFR_AF=0;EUR_AF=0;SAS_AF=0.001;AA=.|||;VT=SNP\tGT\t0|0\n"
          ],
          "name": "stdout"
        }
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "id": "O7y3gptwFHea",
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      "source": [
        "If you are not familiar with the vcf format, read about it here: http://samtools.github.io/hts-specs/VCFv4.2.pdf. It is a very commonly used format to present sequencing data for occurence_countiple individuals.\n",
        "\n",
        "When we read the vcf file, we are reading lines of text. To analyze the data, we would need to parse the file, that is, tell python which character on each line corresponds to a position, to a genotype, to a chomosome number, etc. We would need to do this in a computationally efficient manner, because reading the file itelf is computationally demanding. And we would need to handle all kinds of possible problems with the data, such as missing genotypes, multiallelic SNPs, etc.\n",
        "\n",
        "As is often the case in python, someone has already solved that problem for us and made it available in the scikit-allel package. \n",
        "Hopefully you were able to install the scikit-allel package -- if you did, you just saved yourself hours of pain.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQhOig_tFHei",
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      "source": [
        "Our first step will be to convert the vcf file in hdf5 format used by scikit-allel. This format takes a bit more space on disk than the compressed vcf format, but it's a lot easier (and faster) to access specific subsets of the data. It will take a few minutes to convert the file the first time we run this, but afterwards things will be much faster."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zuBbsogdFHel",
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      "source": [
        "### 3\n",
        "h5_file_name = \"chr22.h5\"\n",
        "h5_file_path =  os.path.join(os.sep, 'Users', 'aa25desh' , 'data1000gen', h5_file_name)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bY-L8av_FHeu",
        "colab_type": "text"
      },
      "source": [
        "Try to read the hdf5 file. If we don't find it, we'll get an OSError. In that case, we'll create the file from the vcf. This took about 6 minutes on my computer.\n",
        "\n",
        "If you rerun the notebook a second time, the hdf5 file should exist and this should run much faster."
      ]
    },
    {
      "cell_type": "code",
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        "id": "3FEuQLj-FHex",
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      "source": [
        "### 4\n",
        "try:\n",
        "    callset = h5py.File(h5_file_path, mode='r')\n",
        "except OSError:\n",
        "    allel.vcf_to_hdf5(vcf_file_path, h5_file_path, fields='*', overwrite=True)\n",
        "    callset = h5py.File(h5_file_path, mode='r')\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TgIvgBP7FHe7",
        "colab_type": "text"
      },
      "source": [
        "The allel package uses a variety of data structures to encode the data to save on memory and allow for fast computation. This may cause a steep learning curve, but it will hopefully pay off in the end.  \n",
        "\n",
        "The 'callset' variable is a dictionary that contains the different types of data. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y7H6SL6oFHe-",
        "colab_type": "code",
        "colab": {},
        "outputId": "eb2cafd6-2d69-4c43-8adb-cdf9ec97f174"
      },
      "source": [
        "### 5\n",
        "list(callset.keys())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['calldata', 'samples', 'variants']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j6wS2fVVFHfJ",
        "colab_type": "text"
      },
      "source": [
        "Broadly speaking, we have genotypes stored in 'calldata', meta-information about variants in 'variants', and information about samples IDs in 'samples': "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYD-_oJmFHfL",
        "colab_type": "code",
        "colab": {},
        "outputId": "b8b80a9b-1ce5-4e5d-a49e-e8ae85564f3a"
      },
      "source": [
        "### 6\n",
        "callset['samples']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<HDF5 dataset \"samples\": shape (2504,), type \"|O\">"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kh5F8slAFHfS",
        "colab_type": "text"
      },
      "source": [
        "Information about samples is stored as an HDF5 dataset, with 2504 elements (that is, the number of samples).  HDF5 arrays are memory-efficient because they don't load the data in memory unless it is needed. This is why the output is only giving us general information about the dataset, but is not printing the actual data. If we want to see the data, we can ask for specific elements using the array 'slice' notation. This will produce a regular in-memory array:"
      ]
    },
    {
      "cell_type": "code",
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        "id": "j0__pzHjFHfV",
        "colab_type": "code",
        "colab": {},
        "outputId": "201d0681-8cc3-4d9a-97da-6e2a27b4558d"
      },
      "source": [
        "### 7\n",
        "callset['samples'][:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['HG00096', 'HG00097', 'HG00099', 'HG00100', 'HG00101', 'HG00102',\n",
              "       'HG00103', 'HG00105', 'HG00106', 'HG00107'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3PNNGMRBFHfd",
        "colab_type": "text"
      },
      "source": [
        "The hdf5 format is hierarchical: whereas the 'sample' category only contained one type of data, the 'calldata' category has more information."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YrJB9rIhFHff",
        "colab_type": "code",
        "colab": {},
        "outputId": "ae891c89-c917-40b2-bbb4-d440de1c181a"
      },
      "source": [
        "### 8\n",
        "callset['calldata']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<HDF5 group \"/calldata\" (1 members)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pn2UyUe6FHfm",
        "colab_type": "text"
      },
      "source": [
        "callset['calldata'] is an hdf5 'group', rather than a dataset. This is also stored as a dictionary so we can look at its keys to see the sub-categories:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ejFh30L1FHfn",
        "colab_type": "code",
        "colab": {},
        "outputId": "c9894090-893a-43c4-806f-1fb73f1872e5"
      },
      "source": [
        "### 9\n",
        "callset['calldata'].keys()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<KeysViewHDF5 ['GT']>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QzGyPJZwFHfr",
        "colab_type": "text"
      },
      "source": [
        "Now this is still slightly annoying: when we ask for the list of keys, the ouput is just a new data structure ('KeyView'), but doesn't show us the data itslef. Once again, this is designed to save memory. If we want to see the contents, we have to convert it to an in-memory format."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6BagP4RaFHfs",
        "colab_type": "code",
        "colab": {},
        "outputId": "b1bd453f-b0a6-4dcd-abd1-d56f0e650585"
      },
      "source": [
        "### 10\n",
        "list(callset['calldata'].keys())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['GT']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pYQOxxS5FHfw",
        "colab_type": "text"
      },
      "source": [
        "The genotypes are stored under the key 'calldata/GT'. We could refer to the data itself as callset['calldata']['GT'], or, more simply, "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cvPZay9JFHfx",
        "colab_type": "code",
        "colab": {},
        "outputId": "b65f6bf1-f4da-444a-8f6e-99cd1786cb70"
      },
      "source": [
        "### 11\n",
        "callset['calldata/GT']"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<HDF5 dataset \"GT\": shape (1103547, 2504, 2), type \"|i1\">"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VTzKowR6FHf2",
        "colab_type": "text"
      },
      "source": [
        "So we can see that the array has dimension (1103547, 2504, 2). We have:\n",
        "\n",
        "1 103 547 snps,\n",
        "\n",
        "2504 individuals,\n",
        "\n",
        "2 genotypes per snp and per individual. \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NlGNQZDlFHf5",
        "colab_type": "text"
      },
      "source": [
        "This is stored as an hdf5 dataset. Because genotype data is where most of the analysis is taking place, scikit-allel has a specific storage format for genotyping data that is  similar to a numpy array, but has additional properties convenient for genotype data. For example, it has a function to count the number of distinct alleles for a given snp (this takes about a minute)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Ab58UD7FHf6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 12\n",
        "genotypes = allel.GenotypeChunkedArray(callset['calldata/GT'])\n",
        "allele_counts = genotypes.count_alleles()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nU03_HamFHf9",
        "colab_type": "text"
      },
      "source": [
        "Given the matrix of allele_counts, we also have methods to identify whether a site is biallelic. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l34X20jPFHf-",
        "colab_type": "code",
        "colab": {},
        "outputId": "9b6b854e-fe4b-41be-d85d-3ad54f1ea763"
      },
      "source": [
        "### 13\n",
        "is_biallelic = allele_counts.is_biallelic_01()\n",
        "is_biallelic"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ChunkedArrayWrapper shape=(1103547,) dtype=bool chunks=(1048576,)\n",
              "   nbytes=1.1M cbytes=38.7K cratio=27.8\n",
              "   compression=gzip compression_opts=1\n",
              "   values=h5py._hl.dataset.Dataset>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-dS4rohwFHgC",
        "colab_type": "text"
      },
      "source": [
        "We'll discard the non-biallelic sites, because they make everything so messy. When discarding sites, it's always a good idea to count how many things we are discarding. We'll do this a fair amount, so we might as well define a function that does this for us:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v1Z3NvEeFHgE",
        "colab_type": "code",
        "colab": {},
        "outputId": "fd2d7a10-3711-4c0d-ebe0-a149e5b5450b"
      },
      "source": [
        "### 14\n",
        "def report_filter(filter_vector):\n",
        "    kept = np.sum(filter_vector)\n",
        "    length = filter_vector.shape[0]\n",
        "    print(\"kept %d out of %d. Ratio kept: %2.2f \" % (kept, length, kept/length))\n",
        "\n",
        "report_filter(is_biallelic)    "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "kept 1093133 out of 1103547. Ratio kept: 0.99 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mrTkxGlTFHgL",
        "colab_type": "text"
      },
      "source": [
        "Now we can extract only the snps that are biallelic. We'll want to keep track of the SNP positions, the genotypes, and the allele counts. The 'compress' function allows us to extract the part of the array for which a condition evaluated to True."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "erBJkQYMFHgN",
        "colab_type": "code",
        "colab": {},
        "outputId": "6d25f4c1-15a6-4308-deaa-c8ea41982c7c"
      },
      "source": [
        "### 15\n",
        "print(\"original\")\n",
        "print(callset['variants']['ALT'][:11])\n",
        "print(\"\\n compressed\")\n",
        "print(np.compress(is_biallelic,callset['variants']['ALT'], axis = 0)[:11])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "original\n",
            "[['G' '' '']\n",
            " ['A' '' '']\n",
            " ['T' '' '']\n",
            " ['T' '' '']\n",
            " ['A' '' '']\n",
            " ['A' '' '']\n",
            " ['A' '' '']\n",
            " ['T' '' '']\n",
            " ['T' '' '']\n",
            " ['<CN0>' '<CN2>' '<CN3>']\n",
            " ['A' '' '']]\n",
            "\n",
            " compressed\n",
            "[['G' '' '']\n",
            " ['A' '' '']\n",
            " ['T' '' '']\n",
            " ['T' '' '']\n",
            " ['A' '' '']\n",
            " ['A' '' '']\n",
            " ['A' '' '']\n",
            " ['T' '' '']\n",
            " ['T' '' '']\n",
            " ['A' '' '']\n",
            " ['T' '' '']]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z_DSbFXGFHgR",
        "colab_type": "text"
      },
      "source": [
        "We can do the same for the other arrays we will need. This also takes a couple of seconds because we need to make copies of the arrays. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bdLEUa69FHgS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 16\n",
        "genotypes_biallelic = genotypes.compress(is_biallelic)\n",
        "allele_counts_biallelic_all_alleles = allele_counts.compress(is_biallelic)\n",
        "pos_biallelic = callset['variants/POS'][:].compress(is_biallelic)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5ESHVZWDFHgV",
        "colab_type": "text"
      },
      "source": [
        "Let's have a look at the resulting genotype and genotype count arrays"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YykZ7A9UFHgZ",
        "colab_type": "code",
        "colab": {},
        "outputId": "e837f9d4-cfc6-49ad-9cff-2b777ecc6ac9"
      },
      "source": [
        "### 17\n",
        "genotypes_biallelic[0:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div class=\"allel allel-DisplayAs2D\"><span>&lt;GenotypeArray shape=(10, 2504, 2) dtype=int8&gt;</span><table><thead><tr><th></th><th style=\"text-align: center\">0</th><th style=\"text-align: center\">1</th><th style=\"text-align: center\">2</th><th style=\"text-align: center\">3</th><th style=\"text-align: center\">4</th><th style=\"text-align: center\">...</th><th style=\"text-align: center\">2499</th><th style=\"text-align: center\">2500</th><th style=\"text-align: center\">2501</th><th style=\"text-align: center\">2502</th><th style=\"text-align: center\">2503</th></tr></thead><tbody><tr><th style=\"text-align: center; background-color: white; border-right: 1px solid black; \">0</th><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">...</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td></tr><tr><th style=\"text-align: center; background-color: white; border-right: 1px solid black; \">1</th><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">...</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td></tr><tr><th style=\"text-align: center; background-color: white; border-right: 1px solid black; \">2</th><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">...</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td></tr><tr><th style=\"text-align: center; background-color: white; border-right: 1px solid black; \">...</th><td style=\"text-align: center\" colspan=\"12\">...</td></tr><tr><th style=\"text-align: center; background-color: white; border-right: 1px solid black; \">7</th><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">...</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td></tr><tr><th style=\"text-align: center; background-color: white; border-right: 1px solid black; \">8</th><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">...</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td></tr><tr><th style=\"text-align: center; background-color: white; border-right: 1px solid black; \">9</th><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">...</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td><td style=\"text-align: center\">0/0</td></tr></tbody></table></div>"
            ],
            "text/plain": [
              "<GenotypeArray shape=(10, 2504, 2) dtype=int8>\n",
              "0/0 0/0 0/0 0/0 0/0 ... 0/0 0/0 0/0 0/0 0/0\n",
              "0/0 0/0 0/0 0/0 0/0 ... 0/0 0/0 0/0 0/0 0/0\n",
              "0/0 0/0 0/0 0/0 0/0 ... 0/0 0/0 0/0 0/0 0/0\n",
              "...\n",
              "0/0 0/0 0/0 0/0 0/0 ... 0/0 0/0 0/0 0/0 0/0\n",
              "0/0 0/0 0/0 0/0 0/0 ... 0/0 0/0 0/0 0/0 0/0\n",
              "0/0 0/0 0/0 0/0 0/0 ... 0/0 0/0 0/0 0/0 0/0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8D7YKKBFHgi",
        "colab_type": "code",
        "colab": {},
        "outputId": "2a92e0e9-3cdd-4c55-d6b1-138f4531f9da"
      },
      "source": [
        "### 18\n",
        "allele_counts_biallelic_all_alleles[0:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div class=\"allel allel-DisplayAs2D\"><span>&lt;AlleleCountsArray shape=(10, 9) dtype=int32&gt;</span><table><thead><tr><th></th><th style=\"text-align: center\">0</th><th style=\"text-align: center\">1</th><th style=\"text-align: center\">2</th><th style=\"text-align: center\">3</th><th style=\"text-align: center\">4</th><th style=\"text-align: center\">5</th><th style=\"text-align: center\">6</th><th style=\"text-align: center\">7</th><th style=\"text-align: center\">8</th></tr></thead><tbody><tr><th style=\"text-align: center; background-color: white; border-right: 1px solid black; \">0</th><td style=\"text-align: center\">5007</td><td style=\"text-align: center\">   1</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td></tr><tr><th style=\"text-align: center; background-color: white; border-right: 1px solid black; \">1</th><td style=\"text-align: center\">4976</td><td style=\"text-align: center\">  32</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td></tr><tr><th style=\"text-align: center; background-color: white; border-right: 1px solid black; \">2</th><td style=\"text-align: center\">4970</td><td style=\"text-align: center\">  38</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td></tr><tr><th style=\"text-align: center; background-color: white; border-right: 1px solid black; \">...</th><td style=\"text-align: center\" colspan=\"10\">...</td></tr><tr><th style=\"text-align: center; background-color: white; border-right: 1px solid black; \">7</th><td style=\"text-align: center\">5006</td><td style=\"text-align: center\">   2</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td></tr><tr><th style=\"text-align: center; background-color: white; border-right: 1px solid black; \">8</th><td style=\"text-align: center\">5007</td><td style=\"text-align: center\">   1</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td></tr><tr><th style=\"text-align: center; background-color: white; border-right: 1px solid black; \">9</th><td style=\"text-align: center\">5007</td><td style=\"text-align: center\">   1</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td><td style=\"text-align: center\">   0</td></tr></tbody></table></div>"
            ],
            "text/plain": [
              "<AlleleCountsArray shape=(10, 9) dtype=int32>\n",
              "5007    1    0    0    0    0    0    0    0\n",
              "4976   32    0    0    0    0    0    0    0\n",
              "4970   38    0    0    0    0    0    0    0\n",
              "...\n",
              "5006    2    0    0    0    0    0    0    0\n",
              "5007    1    0    0    0    0    0    0    0\n",
              "5007    1    0    0    0    0    0    0    0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xogPEWI_FHgp",
        "colab_type": "text"
      },
      "source": [
        "This has plenty of columns because we used to have multiallelic variants. We can compress to extract columns as well:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IeaLM1YkFHgr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 19\n",
        "relevant_column = np.array([False] * allele_counts_biallelic_all_alleles.shape[1])\n",
        "relevant_column[0:2] = True\n",
        "allele_counts_biallelic = allele_counts_biallelic_all_alleles.compress(relevant_column, axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "utGDcIFQFHgu",
        "colab_type": "code",
        "colab": {},
        "outputId": "049fce74-88c6-4612-b98f-b137982ad136"
      },
      "source": [
        "### 20\n",
        "allele_counts_biallelic[0:10]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[5007,    1],\n",
              "       [4976,   32],\n",
              "       [4970,   38],\n",
              "       [5007,    1],\n",
              "       [5007,    1],\n",
              "       [5006,    2],\n",
              "       [5003,    5],\n",
              "       [5006,    2],\n",
              "       [5007,    1],\n",
              "       [5007,    1]], dtype=int32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NEmzKgqpFHgy",
        "colab_type": "text"
      },
      "source": [
        "Given the allele counts, it is easy to compute the frequency of the alternate allele:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAaILXIDFHgy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 21\n",
        "alt_allele_freqs = allele_counts_biallelic[:,1] / allele_counts_biallelic[:].sum(axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GP8en5q6FHg1",
        "colab_type": "text"
      },
      "source": [
        "In this notebook, we don't really care about whether a genotype is listed as 1/0 or 0/1 -- we only care about whether we have 0, 1, or 2 copies of the alternate allele. We can convert genotype data into integer value alternate allele count using to_n_alt "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YU5PvwP9FHg3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 22\n",
        "\n",
        "genotypes_012 = genotypes_biallelic.to_n_alt(fill=-1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eYjoXD2OFHg9",
        "colab_type": "text"
      },
      "source": [
        "### Testing for Hardy-Weinberg\n",
        "There are many distributions of genotype counts that can produce the same number of reference and alternate alleles. \n",
        "If I have 8 copies of the A allele and 8 copies of the a allele divided among 8 diploid individuals, we could have the following configuration:\n",
        "\n",
        "AA AA aA Aa Aa Aa aa aa\n",
        "\n",
        "which obeys the Hardy-Weinberg proportions perfectly.\n",
        "\n",
        "\n",
        "$p_{A}=\\frac{1}{2}$\n",
        "\n",
        "$p_{a}=\\frac{1}{2}$\n",
        "\n",
        "$p_{aa}=\\frac{1}{4} = p_a^2$\n",
        "\n",
        "$p_{AA}=\\frac{1}{4} = p_A^2$\n",
        "\n",
        "$p_{aA}=\\frac{1}{2} = 2 p_a p_A$\n",
        "\n",
        "\n",
        "\n",
        "Let's first code functions that will compute the HW genotype proportions:\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ccjFesQwFHg_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 23\n",
        "def proportion_hom_ref(frequencies):\n",
        "    \"\"\"computes the proportion of homozygote reference genotypes at a given alternate allele \n",
        "    frequency. This should work when frequency is a number or an array of frequencies.\n",
        "    The output should have the same dimension as the input.\n",
        "    \"\"\"\n",
        "    return (1-frequencies)**2 #s/n.*/n .../\n",
        "\n",
        "def proportion_het(frequencies):\n",
        "    \"\"\"computes the proportion of heterozygote genotypes at a given aleternate allele frequency.\n",
        "    This should work when frequency is a number or a vector of frequency.\n",
        "    The output should have the same dimension as the input.\n",
        "    \"\"\"\n",
        "    return 2*frequencies*(1-frequencies) #s/n.*/n .../\n",
        "\n",
        "\n",
        "\n",
        "def proportion_hom_alt(frequencies):\n",
        "    \"\"\"computes the proportion of homozygote alternate genotypes at a given alternate allele frequency..\n",
        "    This should work when frequency is a number or a vector of frequency.\n",
        "    The output should have the same dimension as the input.\n",
        "    \"\"\"\n",
        "    return frequencies**2 #s/n.*/n .../\n",
        "    "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CfGduuQuFHhD",
        "colab_type": "text"
      },
      "source": [
        "These are expectations under a theoretical model. The next step is to look at the data to see if the model describes the data well. The first step is to count the genotype data "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJ8zlHPHFHhD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 24\n",
        "homalt_counts = (genotypes_012==2).sum(axis = 1)\n",
        "het_counts = (genotypes_012==1).sum(axis = 1)\n",
        "homref_counts = (genotypes_012==0).sum(axis = 1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F8e-K7NJFHhK",
        "colab_type": "text"
      },
      "source": [
        "In the next two code boxes, we'll compute the number of aa, aA, and AA at each site, and see how these proportions compare to the expectation. The resulting graph is called a \"De finetti\" diagram. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VELZQZpAFHhL",
        "colab_type": "code",
        "colab": {},
        "outputId": "053caf1f-8dc0-464e-eab9-3419f46f7474"
      },
      "source": [
        "### 25\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "####\n",
        "# First plot the Hardy-Weinberg expectation curves. These plots shouldn't use the data at all -- we'll just generate a\n",
        "# list of frequencies, p_range, and you'll plot the expected proportion of each genotype count for each frequency.   \n",
        "\n",
        "\n",
        "\n",
        "number_of_genotypes = homref_counts + het_counts + homalt_counts \n",
        "\n",
        "###########\n",
        "# Then plot the observed proportion of each genotype versus the allele frequency. \n",
        "# Each snp is represented by three snps.  \n",
        "plt.plot(alt_allele_freqs, np.array(homref_counts) / number_of_genotypes, 'ro', label = \"homozygote reference\" )\n",
        "plt.plot(alt_allele_freqs, np.array(het_counts) / number_of_genotypes, 'o', color = 'orange', label = \"heterozygote\" )\n",
        "plt.plot(alt_allele_freqs, np.array(homalt_counts) / number_of_genotypes, 'bo', label = \"homozygote alternate\")\n",
        "plt.legend(loc='upper center')\n",
        "plt.ylim([0,1])\n",
        "plt.xlabel(\"alternate allele frequency\")\n",
        "plt.ylabel(\"proportion of genotypes\")\n",
        "\n",
        "\n",
        "p_range = np.arange(0,1,.01) # A sorted list of frequencies\n",
        "\n",
        "\n",
        "plt.plot(p_range, proportion_hom_ref(p_range), 'crimson' ) # homozygote reference expectations \n",
        "plt.plot(p_range, proportion_het(p_range),  color = 'orangered' ) # heterozygote expectations \n",
        "plt.plot(p_range, proportion_hom_alt(p_range), 'aqua') # homozygote alternate expectations \n",
        "plt.title(\"De finetti diagram for 1000 Genomes chromosome 22\")\n",
        "\n",
        "\n",
        "            "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'De finetti diagram for 1000 Genomes chromosome 22')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nXmVhiuEFHhO",
        "colab_type": "text"
      },
      "source": [
        "When we don't have HW equilibrium, it suggests that the two alleles are not inherited independently--either the parents tend to be more related (if we have an excess of homozygotes), or they tend to be less related (if we have an excess of heterozygotes). The former is much more common! \n",
        "\n",
        "However, HW disequilibrium can mean something much less biological--it can indicate that our sequencer is biased and produces too many or too few homozygotes. This is also something that we would like to know, and detecting these issues is one of the main applications of the Hardy-Weinberg principle. \n",
        "\n",
        "Even if there is no such problem, we don't expect the Hardy-Weinberg proportions to hold exactly because the number of individuals is finite. When you look at the scatter of points on your figure, it's hard to tell which points truly deviate from HW, and which are complete outliers. \n",
        "\n",
        "To test each site for Hardy-Weinberg equilibrium, we need to build a statistical test. Our null model is that the two alleles of each individual are drawn with probability $p_a$ and $p_A=1-p_a$. Our observations are the numbers $N_{aa}$, $N_{aA}$ and $N_{AA}$ of each genotype, with $N_{aa}+N_{aA}+N_{AA}=N$. We don't quite know the probabilty $p_a$, but we can estimate it from the data:\n",
        "\n",
        "$$\\hat p_a=\\frac{2N_{aa}+N_{aA}}{2N}=1-\\hat p_A.$$\n",
        "We then expect to have \n",
        "\n",
        "$N_{aa}\\simeq e_{aa}=N \\hat p_a^2$\n",
        "\n",
        "$N_{AA}\\simeq e_{AA}= N \\hat p_{A}^2$\n",
        "\n",
        "$N_{Aa,~aA} \\simeq e_{Aa} = 2 N \\hat p_{a} \\hat p_{A}.$\n",
        "\n",
        "A popular way to test the significance of departures from this distribution is to consider the test statistic\n",
        "\n",
        "$X^2= \\frac{(N_{aa}-e_{aa})^2}{e_{aa}}+ \\frac{(N_{Aa}-e_{Aa})^2}{e_{Aa}}+ \\frac{(N_{AA}-e_{AA})^2}{e_{AA}}.$\n",
        "\n",
        "Intuitively, this statistic measures the departure from our null model. Perhaps less intuitively, it follows a $\\chi^2_1$ (a chi-squared distribution with one degree of freedom) if the null model is true and we have enough data. \n",
        "\n",
        "\n",
        "\"Enough data\" means that we should expect at least a few counts in each category, so it would make sense to request at least 5 counts in both aa and AA genotypes. I.e., $e_{aa}=p_a^2*N>5$ leading to $p_a>\\sqrt{5/N},$ and similarly $p_A>\\sqrt{5/N}.$\n",
        "\n",
        "**This would be a good place to complete the chisquared notebook, which explains why this statistical test is appropriate**\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "*Additional optional reading about Hardy-Weinberg testing* \n",
        "http://link.springer.com/protocol/10.1007%2F978-1-61779-555-8_6"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1Q658BbXFHhP",
        "colab_type": "text"
      },
      "source": [
        "To perform a chi-squared test, we first need to compute the expectations for each genotype:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cTD-F1K7FHhQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 26\n",
        "\n",
        "        \n",
        "        \n",
        "hom_ref_expectation = allele_counts_biallelic[:,0]**2 / (4.*np.array(number_of_genotypes)) # the expected number of homozygote references #s/=.*$/= .../ \n",
        "het_expectation = allele_counts_biallelic[:,0] * allele_counts_biallelic[:,1] / (2.*np.array(number_of_genotypes))  # the expected number of hets #s/=.*$/= .../ \n",
        "hom_alt_expectation = allele_counts_biallelic[:,1]**2 / (4.*np.array(number_of_genotypes) ) # the expected number of homozygote nonreferences #s/=.*$/= .../ \n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-_sDPp8FHhU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 27\n",
        "expectations = np.vstack([hom_ref_expectation,het_expectation, \n",
        "                                                  hom_alt_expectation] ).transpose()\n",
        "\n",
        "\n",
        "observations = np.vstack([homref_counts, het_counts, \n",
        "                                                  homalt_counts] ).transpose()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWqBzq6uFHhX",
        "colab_type": "code",
        "colab": {},
        "outputId": "1fd53c75-dd5a-46ad-f32f-0fe2bd14a108"
      },
      "source": [
        "### 28\n",
        "print(\"expectations for snp 0\")\n",
        "print(expectations[0,:])\n",
        "print(\"observations for snp 0\")\n",
        "print(np.array(observations[0,:]))\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "expectations for snp 0\n",
            "[2.50300010e+03 9.99800319e-01 9.98402556e-05]\n",
            "observations for snp 0\n",
            "[2503    1    0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NH9kuN_GFHhc",
        "colab_type": "text"
      },
      "source": [
        "Performing the chi-squared test is now very straightforward:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CWcSKZQOFHhd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 29\n",
        "hw_test_results = scipy.stats.chisquare(observations, expectations, axis = 1, ddof=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AsQzqXJTFHhh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 30\n",
        "hw_p_values = hw_test_results[1]\n",
        "excess_het = het_counts/expectations[:,1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hjY_QRC2FHhj",
        "colab_type": "code",
        "colab": {},
        "outputId": "3c8e7d11-775b-482d-b581-71a02f012ab4"
      },
      "source": [
        "### 31\n",
        "n_inspect = 30\n",
        "for i in range(n_inspect):\n",
        "    print(\"genotypes\\taa\\taA\\tAA\")\n",
        "    print(\"observed\\t%d\\t%d\\t%d\" % (homref_counts[i], het_counts[i], homalt_counts[i]))\n",
        "    print(\"expected\\t%2.1f\\t%2.1f\\t%2.1f\" % (expectations[i,0], expectations[i,1], expectations[i,2]))\n",
        "    print(\"chi-squared p-value,\\t\", hw_p_values[i],\"\\n\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "genotypes\taa\taA\tAA\n",
            "observed\t2503\t1\t0\n",
            "expected\t2503.0\t1.0\t0.0\n",
            "chi-squared p-value,\t 0.9920260702938559 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2472\t32\t0\n",
            "expected\t2472.1\t31.8\t0.1\n",
            "chi-squared p-value,\t 0.747603804974118 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2467\t36\t1\n",
            "expected\t2466.1\t37.7\t0.1\n",
            "chi-squared p-value,\t 0.023133620432969956 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2503\t1\t0\n",
            "expected\t2503.0\t1.0\t0.0\n",
            "chi-squared p-value,\t 0.9920260702938559 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2503\t1\t0\n",
            "expected\t2503.0\t1.0\t0.0\n",
            "chi-squared p-value,\t 0.9920260702938559 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2502\t2\t0\n",
            "expected\t2502.0\t2.0\t0.0\n",
            "chi-squared p-value,\t 0.984049751812969 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2499\t5\t0\n",
            "expected\t2499.0\t5.0\t0.0\n",
            "chi-squared p-value,\t 0.9601144369577925 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2502\t2\t0\n",
            "expected\t2502.0\t2.0\t0.0\n",
            "chi-squared p-value,\t 0.984049751812969 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2503\t1\t0\n",
            "expected\t2503.0\t1.0\t0.0\n",
            "chi-squared p-value,\t 0.9920260702938559 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2503\t1\t0\n",
            "expected\t2503.0\t1.0\t0.0\n",
            "chi-squared p-value,\t 0.9920260702938559 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2502\t2\t0\n",
            "expected\t2502.0\t2.0\t0.0\n",
            "chi-squared p-value,\t 0.984049751812969 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2503\t1\t0\n",
            "expected\t2503.0\t1.0\t0.0\n",
            "chi-squared p-value,\t 0.9920260702938559 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2503\t1\t0\n",
            "expected\t2503.0\t1.0\t0.0\n",
            "chi-squared p-value,\t 0.9920260702938559 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2503\t1\t0\n",
            "expected\t2503.0\t1.0\t0.0\n",
            "chi-squared p-value,\t 0.9920260702938559 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2467\t36\t1\n",
            "expected\t2466.1\t37.7\t0.1\n",
            "chi-squared p-value,\t 0.023133620432969956 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2503\t1\t0\n",
            "expected\t2503.0\t1.0\t0.0\n",
            "chi-squared p-value,\t 0.9920260702938559 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2466\t37\t1\n",
            "expected\t2465.2\t38.7\t0.2\n",
            "chi-squared p-value,\t 0.028267993119365915 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2478\t26\t0\n",
            "expected\t2478.1\t25.9\t0.1\n",
            "chi-squared p-value,\t 0.7939783384804816 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2498\t6\t0\n",
            "expected\t2498.0\t6.0\t0.0\n",
            "chi-squared p-value,\t 0.9521365428300308 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2503\t1\t0\n",
            "expected\t2503.0\t1.0\t0.0\n",
            "chi-squared p-value,\t 0.9920260702938559 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2503\t1\t0\n",
            "expected\t2503.0\t1.0\t0.0\n",
            "chi-squared p-value,\t 0.9920260702938559 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2485\t19\t0\n",
            "expected\t2485.0\t18.9\t0.0\n",
            "chi-squared p-value,\t 0.848861558929411 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2502\t2\t0\n",
            "expected\t2502.0\t2.0\t0.0\n",
            "chi-squared p-value,\t 0.984049751812969 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2503\t1\t0\n",
            "expected\t2503.0\t1.0\t0.0\n",
            "chi-squared p-value,\t 0.9920260702938559 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2503\t1\t0\n",
            "expected\t2503.0\t1.0\t0.0\n",
            "chi-squared p-value,\t 0.9920260702938559 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2484\t20\t0\n",
            "expected\t2484.0\t19.9\t0.0\n",
            "chi-squared p-value,\t 0.8409789246492491 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2501\t3\t0\n",
            "expected\t2501.0\t3.0\t0.0\n",
            "chi-squared p-value,\t 0.9760718411878782 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2503\t1\t0\n",
            "expected\t2503.0\t1.0\t0.0\n",
            "chi-squared p-value,\t 0.9920260702938559 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2497\t7\t0\n",
            "expected\t2497.0\t7.0\t0.0\n",
            "chi-squared p-value,\t 0.9441602550338726 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2503\t1\t0\n",
            "expected\t2503.0\t1.0\t0.0\n",
            "chi-squared p-value,\t 0.9920260702938559 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4PgFlaqZFHhl",
        "colab_type": "text"
      },
      "source": [
        "Something here should make you feel a bit uncomfortable - we don't fulfil the conditions under which the chi-square test is accurate. So we'd want to restrict the search to places where expected counts in each category are at least 5. \n",
        "\n",
        "*Discussion question*\n",
        "\n",
        "Does it mater if the number of *observations* is less than 5? Why?\n",
        "\n",
        "\n",
        "So we'll now only consider snps that have enough observations to justify the chi-square test.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v9FcGUP6FHhm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 32\n",
        "is_common = expectations.min(axis=1)>5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "miS0m6yTFHho",
        "colab_type": "code",
        "colab": {},
        "outputId": "dffca2cb-9a00-4226-cef4-b610b623642f"
      },
      "source": [
        "### 33\n",
        "n_inspect = 100\n",
        "for i in range(n_inspect):\n",
        "    if is_common[i]:\n",
        "        print(\"genotypes\\taa\\taA\\tAA\")\n",
        "        print(\"observed\\t%d\\t%d\\t%d\" % (homref_counts[i], het_counts[i], homalt_counts[i]))\n",
        "        print(\"expected\\t%2.1f\\t%2.1f\\t%2.1f\" % (expectations[i,0], expectations[i,1], expectations[i,2]))\n",
        "        print(\"HW p-value,\\t\", hw_p_values[i],\"\\n\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "genotypes\taa\taA\tAA\n",
            "observed\t1995\t455\t54\n",
            "expected\t1972.6\t499.7\t31.6\n",
            "HW p-value,\t 7.572006862208043e-06 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t1797\t707\t0\n",
            "expected\t1846.9\t607.2\t49.9\n",
            "HW p-value,\t 1.9423005588494258e-16 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t746\t1149\t609\n",
            "expected\t696.4\t1248.3\t559.4\n",
            "HW p-value,\t 6.925639404283662e-05 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2072\t394\t38\n",
            "expected\t2056.1\t425.9\t22.1\n",
            "HW p-value,\t 0.00017897567991284358 \n",
            "\n",
            "genotypes\taa\taA\tAA\n",
            "observed\t2144\t349\t11\n",
            "expected\t2146.7\t343.5\t13.7\n",
            "HW p-value,\t 0.4243555044945112 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bbrCEOp1FHht",
        "colab_type": "text"
      },
      "source": [
        "A lot of these sites are significantly departing from the Hardy-Weinberg assumptions. Most of them have too many homozygotes, as we would expect under inbreeding, but a few sites have way too many hets. What do you think is going on?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CtFhyAGpFHhu",
        "colab_type": "text"
      },
      "source": [
        "If the snps obey our null model, the distribution of p-values should be uniformly distributed between 0 and 1.\n",
        "\n",
        "*Convince yourself that this expectation should hold true, mathematically or by simulating it*\n",
        "\n",
        "We can inspect this visually. First we'll extract the p-values for positions where the chi-square test is applicable.  \n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JwNHs5NbFHhv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 34\n",
        "common_p_values  = hw_p_values.compress(is_common)\n",
        "common_positions = pos_biallelic.compress(is_common) \n",
        "common_excess_het = excess_het.compress(is_common) \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6e63KuJTFHhy",
        "colab_type": "text"
      },
      "source": [
        "Then we plot the distribution of p-values."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oSYV669fFHhz",
        "colab_type": "code",
        "colab": {},
        "outputId": "f8473fd0-0cf9-4b0e-d03c-37b3d229820b"
      },
      "source": [
        "### 35\n",
        "plt.hist(common_p_values, bins=100);\n",
        "plt.xlabel(\"p_values\")\n",
        "plt.ylabel(\"number of observations\")\n",
        "plt.title(\"distribution of chi-squared p-values\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'distribution of chi-squared p-values')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-uwAm3v3FHh2",
        "colab_type": "text"
      },
      "source": [
        "How do you interpret this figure? \n",
        "\n",
        "We'd now like to distinguish between sites who fail Hardy-Weingberg because of technical reasons from those who fail because of biological or population genetic reasons. One thing to look for is whether SNPs that fail the test clump together: sequencing artifacts tend to come in batches, and if we find a few bad regions, we'll have a hint that the errors are likely to be technical.  \n",
        "\n",
        "Let's plot the excess of heterozygosity vs. genomic position"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-p1t9fnIFHh3",
        "colab_type": "code",
        "colab": {},
        "outputId": "19e2d169-7aa7-444f-e8c5-3914c2ab4d9b"
      },
      "source": [
        "### 36\n",
        "plt.plot(common_positions, common_excess_het[:],'o',markersize=1)\n",
        "plt.xlabel(\"genomic position\")\n",
        "plt.ylabel(\"excess heterozygosity\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'excess heterozygosity')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rZB1QOjQFHh8",
        "colab_type": "text"
      },
      "source": [
        "Zooming in"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_aHJu5RBFHh-",
        "colab_type": "code",
        "colab": {},
        "outputId": "296e5f0d-c8fb-4246-9b6f-cc70b042c1a0"
      },
      "source": [
        "### 37\n",
        "plt.plot(common_positions[:10000], common_excess_het[:10000],'o',markersize=1)\n",
        "plt.xlabel(\"genomic position\")\n",
        "plt.ylabel(\"excess heterozygosity\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'excess heterozygosity')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O94oEQNzFHiD",
        "colab_type": "text"
      },
      "source": [
        "There appears to be regions that behave poorly. These could be regions undergoing selection, but most likely they are regions of poor call quality. Because it's a large number of sites, it's a concern. Maybe we want to report those to the 1000 Genomes project?\n",
        "\n",
        "Before we do this, it would make sense to look at whether the problem was identified already. The 1000 genomes project has a \"mask\" file that tells users of the regions that should be considered trustworthy, and which should be considered with caution. \n",
        "\n",
        "This file is at:\n",
        "ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/20130502/supporting/accessible_genome_masks/20141020.strict_mask.whole_genome.bed\n",
        "\n",
        "from the accompanying readme file:\n",
        "\n",
        "*The StrictMask directory uses a more stringent definition. This definition uses\n",
        "a narrower band for coverage, requiring that total coverage should be within 50%\n",
        "of the average, that no more than 0.1% of reads have mapping quality of zero, \n",
        "and that the average mapping quality for the position should be 56 or greater.\n",
        "This definition is quite stringent and focuses on the most unique regions of the \n",
        "genome. The average total depth of coverage across Phase 3 samples is 17920. Thus, sites\n",
        "with a depth of coverage of <8960 or >26880 were excluded.\n",
        "Overall, this strict mask results in about 6.8% of bases marked N, 1.1% marked\n",
        "L, 0.5% marked H, 16.8% marked Z, and 3.1% marked Q. The remaining 71.7% of\n",
        "passed are marked passed (P) - corresponding to 76.9% of the non-N bases.*\n",
        "\n",
        "The bed file format is often used to describe genomic regions; specifications can be found here: \n",
        "\n",
        "http://genome.ucsc.edu/FAQ/FAQformat.html\n",
        "There's a suite of tools to handle bed files, https://github.com/arq5x/bedtools2. But here we'll want to manipulate it ourselves, so we'll write our own parser. \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UnQk6OndFHiE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 38\n",
        "bed_file = os.path.join(os.sep, 'Users', 'aa25desh' , 'data1000gen', '20141020.strict_mask.whole_genome.bed')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJVyPcnDFHiG",
        "colab_type": "code",
        "colab": {},
        "outputId": "11d8a2b3-035a-483d-ef26-b31b39da9848"
      },
      "source": [
        "### 39\n",
        "mask_bed = pandas.read_csv(bed_file, sep='\\t', header=None)\n",
        "mask_bed"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>chr1</td>\n",
              "      <td>754450</td>\n",
              "      <td>754464</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>chr1</td>\n",
              "      <td>754480</td>\n",
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              "      <td>strict</td>\n",
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              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>chr1</td>\n",
              "      <td>755215</td>\n",
              "      <td>755246</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>chr1</td>\n",
              "      <td>756782</td>\n",
              "      <td>756872</td>\n",
              "      <td>strict</td>\n",
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              "    <tr>\n",
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              "      <td>756922</td>\n",
              "      <td>strict</td>\n",
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              "    <tr>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <td>4185731</td>\n",
              "      <td>chrY</td>\n",
              "      <td>28801544</td>\n",
              "      <td>28801571</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4185732</td>\n",
              "      <td>chrY</td>\n",
              "      <td>28801572</td>\n",
              "      <td>28801573</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4185733</td>\n",
              "      <td>chrY</td>\n",
              "      <td>28801765</td>\n",
              "      <td>28801841</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4185734</td>\n",
              "      <td>chrY</td>\n",
              "      <td>28801988</td>\n",
              "      <td>28802003</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4185735</td>\n",
              "      <td>chrY</td>\n",
              "      <td>28802041</td>\n",
              "      <td>28802057</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4185736 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            0         1         2       3\n",
              "0        chr1    754450    754464  strict\n",
              "1        chr1    754480    754512  strict\n",
              "2        chr1    755215    755246  strict\n",
              "3        chr1    756782    756872  strict\n",
              "4        chr1    756880    756922  strict\n",
              "...       ...       ...       ...     ...\n",
              "4185731  chrY  28801544  28801571  strict\n",
              "4185732  chrY  28801572  28801573  strict\n",
              "4185733  chrY  28801765  28801841  strict\n",
              "4185734  chrY  28801988  28802003  strict\n",
              "4185735  chrY  28802041  28802057  strict\n",
              "\n",
              "[4185736 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWRoAxu5FHiI",
        "colab_type": "text"
      },
      "source": [
        "We only care about the entries relevant to chromosome 22"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEA081YRFHiJ",
        "colab_type": "code",
        "colab": {},
        "outputId": "b55a2271-3d61-4018-85b4-3529ed97149a"
      },
      "source": [
        "### 40\n",
        "mask_bed = mask_bed.loc[mask_bed[0] == 'chr22']\n",
        "n_features = mask_bed.shape[0]\n",
        "print(\"number of features:\", n_features)\n",
        "mask_bed"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "number of features: 55707\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>3832260</td>\n",
              "      <td>chr22</td>\n",
              "      <td>16554726</td>\n",
              "      <td>16554908</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3832261</td>\n",
              "      <td>chr22</td>\n",
              "      <td>16848370</td>\n",
              "      <td>16848383</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3832262</td>\n",
              "      <td>chr22</td>\n",
              "      <td>16848391</td>\n",
              "      <td>16848394</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3832263</td>\n",
              "      <td>chr22</td>\n",
              "      <td>16848606</td>\n",
              "      <td>16848635</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3832264</td>\n",
              "      <td>chr22</td>\n",
              "      <td>16848647</td>\n",
              "      <td>16848654</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3887962</td>\n",
              "      <td>chr22</td>\n",
              "      <td>51185455</td>\n",
              "      <td>51185457</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3887963</td>\n",
              "      <td>chr22</td>\n",
              "      <td>51185460</td>\n",
              "      <td>51185563</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3887964</td>\n",
              "      <td>chr22</td>\n",
              "      <td>51185606</td>\n",
              "      <td>51185614</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3887965</td>\n",
              "      <td>chr22</td>\n",
              "      <td>51187902</td>\n",
              "      <td>51187951</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3887966</td>\n",
              "      <td>chr22</td>\n",
              "      <td>51188129</td>\n",
              "      <td>51188160</td>\n",
              "      <td>strict</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>55707 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             0         1         2       3\n",
              "3832260  chr22  16554726  16554908  strict\n",
              "3832261  chr22  16848370  16848383  strict\n",
              "3832262  chr22  16848391  16848394  strict\n",
              "3832263  chr22  16848606  16848635  strict\n",
              "3832264  chr22  16848647  16848654  strict\n",
              "...        ...       ...       ...     ...\n",
              "3887962  chr22  51185455  51185457  strict\n",
              "3887963  chr22  51185460  51185563  strict\n",
              "3887964  chr22  51185606  51185614  strict\n",
              "3887965  chr22  51187902  51187951  strict\n",
              "3887966  chr22  51188129  51188160  strict\n",
              "\n",
              "[55707 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NpkWFYhjFHiQ",
        "colab_type": "text"
      },
      "source": [
        "The .bed format has the particularity that the start coordinate is exclusive, and the end coordinates is exclusive, so that the first segment runs from 754451 to 754464, inclusively. This was decided so that lengths can be computed directly by taking the difference of the two columns. I disapprove of that convention."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "166tScl_FHiQ",
        "colab_type": "text"
      },
      "source": [
        "Now, let's overlay the two pieces of information."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-XJ60lsRFHiR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 41\n",
        "starts = np.array(mask_bed[1]) + 1\n",
        "ends = np.array(mask_bed[2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wPcxoP_KFHiU",
        "colab_type": "code",
        "colab": {},
        "outputId": "84fcd42f-5f10-4e15-dfb6-86560dfcd6ec"
      },
      "source": [
        "### 42\n",
        "start_at_snp = 0\n",
        "n_snps_to_plot = 20000\n",
        "start_position = common_positions[0]\n",
        "end_position = common_positions[n_snps_to_plot]\n",
        "plt.plot(common_positions[:n_snps_to_plot], common_excess_het[:n_snps_to_plot],'o',markersize=1)\n",
        "plt.xlabel(\"genomic position\")\n",
        "plt.ylabel(\"excess heterozygosity\")\n",
        "for i in range(len(starts)):\n",
        "    feature_start = starts[i] \n",
        "    feature_end = ends[i]\n",
        "    if (feature_start < start_position and feature_end > start_position) \\\n",
        "    or ( start_position < feature_start < end_position ):\n",
        "        plt.plot([feature_start, feature_end], [1,1], 'r-', lw=2)\n",
        "plt.plot([feature_start, feature_end], [1,1],\n",
        "         'r-', lw=2, label=\"good regions\") # Add one more to produce a legend\n",
        "plt.xlim([start_position, end_position])\n",
        "plt.legend(loc = \"upper right\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x1c1dab8990>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xgj8tp1wFHiW",
        "colab_type": "code",
        "colab": {},
        "outputId": "f380a1a3-47fa-4821-ee9c-e0516aa2b72b"
      },
      "source": [
        "### 43\n",
        "in_mask = (common_positions<0) # Initialize with all False\n",
        "i = 0\n",
        "for _index, feature in mask_bed.iterrows():\n",
        "    i+=1\n",
        "    if i%10000 == 0:\n",
        "        print(\"processed\", i, \"features out of\", n_features)\n",
        "\n",
        "    start = feature[1]\n",
        "    end = feature[2]\n",
        "      \n",
        "    in_mask = np.logical_or(in_mask, np.logical_and(common_positions>=start, \n",
        "                                                    common_positions<=end))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "processed 10000 features out of 55707\n",
            "processed 20000 features out of 55707\n",
            "processed 30000 features out of 55707\n",
            "processed 40000 features out of 55707\n",
            "processed 50000 features out of 55707\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ltPsnSfZFHiY",
        "colab_type": "code",
        "colab": {},
        "outputId": "0780b83f-fcf7-4aab-84af-641241d120d3"
      },
      "source": [
        "### 44\n",
        "report_filter(in_mask)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "kept 67799 out of 115108. Ratio kept: 0.59 \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6oj6RrusFHib",
        "colab_type": "text"
      },
      "source": [
        "We'll refer to genotypes within the 'strict' bed file with the prefix 'strict_'."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P3e5wPxuFHid",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 45\n",
        "strict_positions = common_positions.compress(in_mask)\n",
        "strict_excess_het = common_excess_het.compress(in_mask)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SgwYHIplFHig",
        "colab_type": "text"
      },
      "source": [
        "We can repeat the plotting, but only with the strict variants"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJYHaJHfFHig",
        "colab_type": "code",
        "colab": {},
        "outputId": "63a5cdc4-a248-413d-8cd9-e72349f05be7"
      },
      "source": [
        "### 46\n",
        "start_at_snp = 0\n",
        "n_snps_to_plot = 20000\n",
        "start_position = strict_positions[0]\n",
        "end_position = strict_positions[n_snps_to_plot]\n",
        "plt.plot(strict_positions[:n_snps_to_plot], strict_excess_het[:n_snps_to_plot],'o',markersize=1)\n",
        "plt.xlabel(\"genomic position\")\n",
        "plt.ylabel(\"excess heterozygosity\")\n",
        "for i in range(len(starts)):\n",
        "    feature_start = starts[i] \n",
        "    feature_end = ends[i]\n",
        "    if (feature_start < start_position and feature_end > start_position) \\\n",
        "    or ( start_position < feature_start < end_position ):\n",
        "        plt.plot([feature_start, feature_end], [1,1], 'r-', lw=2)\n",
        "plt.plot([feature_start, feature_end], [1,1],\n",
        "         'r-', lw=2, label=\"good regions\") # Add one more to produce a legend\n",
        "plt.xlim([start_position, end_position])\n",
        "plt.legend(loc = \"upper right\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x1c59e25f10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h80Ge0-5FHii",
        "colab_type": "text"
      },
      "source": [
        "To plot the De Finetti diagram again, but only on the strict snps, we need to extract some more information: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ucf8p5VUFHij",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 47\n",
        "strict_homref_counts = homref_counts.compress(is_common).compress(in_mask) \n",
        "strict_het_counts = het_counts.compress(is_common).compress(in_mask)\n",
        "strict_homalt_counts = homalt_counts.compress(is_common).compress(in_mask)\n",
        "strict_alt_allele_freqs = alt_allele_freqs.compress(is_common).compress(in_mask)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tBJMvM76FHik",
        "colab_type": "code",
        "colab": {},
        "outputId": "533930b2-bd99-4764-cac7-73b9fec7e87d"
      },
      "source": [
        "### 48\n",
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "####\n",
        "# First plot the Hardy-Weinberg expectation curves. These plots shouldn't use the data at all -- we'll just generate a\n",
        "# list of frequencies, p_range, and you'll plot the expected proportion of each genotype count for each frequency.   \n",
        "\n",
        "\n",
        "\n",
        "strict_number_of_genotypes = strict_homref_counts + strict_het_counts + strict_homalt_counts \n",
        "\n",
        "###########\n",
        "# Then plot the observed proportion of each genotype versus the allele frequency. \n",
        "# Each snp is represented by three snps.  \n",
        "plt.plot(strict_alt_allele_freqs, np.array(strict_homref_counts) / strict_number_of_genotypes, 'ro', label = \"homozygote reference\" )\n",
        "plt.plot(strict_alt_allele_freqs, np.array(strict_het_counts) / strict_number_of_genotypes, 'o', color = 'orange', label = \"heterozygote\" )\n",
        "plt.plot(strict_alt_allele_freqs, np.array(strict_homalt_counts) / strict_number_of_genotypes, 'bo', label = \"homozygote alternate\" )\n",
        "plt.legend(loc='upper center')\n",
        "plt.ylim([0,1])\n",
        "plt.xlabel(\"alternate allele frequency\")\n",
        "plt.ylabel(\"proportion of genotypes\")\n",
        "\n",
        "\n",
        "p_range = np.arange(0,1,.01) # A sorted list of frequencies\n",
        "\n",
        "\n",
        "plt.plot(p_range, proportion_hom_ref(p_range), 'crimson' ) # homozygote reference expectations \n",
        "plt.plot(p_range, proportion_het(p_range),  color = 'orangered' ) # heterozygote expectations \n",
        "plt.plot(p_range, proportion_hom_alt(p_range), 'aqua' ) # homozygote alternate expectations \n",
        "\n",
        "\n",
        "\n",
        "            "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x1c5b7c5f50>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ciSFQ3N1FHim",
        "colab_type": "text"
      },
      "source": [
        "Things should look much better now! Do you think that alleles satisfy Hardy-Weinberg equilibrium? \n",
        "\n",
        "One way to check is to run again our statistical test and plot the distribution of p-values "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KcU83grnFHin",
        "colab_type": "code",
        "colab": {},
        "outputId": "3ee38e41-fe6f-48a3-c01b-8b9df6798b2a"
      },
      "source": [
        "### 49\n",
        "strict_p_values = common_p_values.compress(in_mask)\n",
        "plt.hist(strict_p_values, bins=100);\n",
        "plt.xlabel(\"p_values\")\n",
        "plt.ylabel(\"number of observations\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'number of observations')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QgyWL8RwFHio",
        "colab_type": "text"
      },
      "source": [
        "We still are enriched for departures from Hardy-Weinberg.  We can again plot the Hardy-Weinberg departures versus genomic positions:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZejOakTpFHip",
        "colab_type": "text"
      },
      "source": [
        "We still have a bit too few heterozygotes, but now we have fewer clumps of crummy data. \n",
        "\n",
        "Then we are finally left to explore our population genetic hypothesis--the idea that the excess of homozygotes is caused by population structure. \n",
        "\n",
        "One thing that we can do to verify this further is calculate the HW statistic within each population comprising the 1000 Genomes Project. \n",
        "\n",
        "The information about 1000 Genomes populations is [here](goo.gl/9Hb83M)\n",
        "(full path: \n",
        "ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/release/20130502/integrated_call_samples_v3.20130502.ALL.panel)\n",
        "\n",
        "\n",
        "\\>head integrated_call_samples_v3.20130502.ALL.panel\n",
        "\n",
        "sample\tpop\tsuper_pop\tgender\t\t\n",
        "\n",
        "HG00096\tGBR\tEUR\tmale\n",
        "\n",
        "HG00097\tGBR\tEUR\tfemale\n",
        "\n",
        "HG00099\tGBR\tEUR\tfemale\n",
        "\n",
        "HG00100\tGBR\tEUR\tfemale\n",
        "\n",
        "HG00101\tGBR\tEUR\tmale\n",
        "\n",
        "HG00102\tGBR\tEUR\tfemale\n",
        "\n",
        "HG00103\tGBR\tEUR\tmale\n",
        "\n",
        "HG00105\tGBR\tEUR\tmale\n",
        "\n",
        "HG00106\tGBR\tEUR\tfemale\n",
        "\n",
        "This is a small file, so we can read it in memory. The pandas library contains many useful functions to read and parse files in table format. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MopWRSDZFHip",
        "colab_type": "code",
        "colab": {},
        "outputId": "9675d6df-ef6e-42c4-a631-50ecccb37b2e"
      },
      "source": [
        "### 50\n",
        "population_file_name = 'integrated_call_samples_v3.20130502.ALL.panel'\n",
        "population_file = os.path.join(os.sep, 'Users', 'aa25desh' , 'data1000gen', population_file_name)\n",
        "samples = pandas.read_csv(population_file, sep='\\t')\n",
        "samples.head()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sample</th>\n",
              "      <th>pop</th>\n",
              "      <th>super_pop</th>\n",
              "      <th>gender</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>0</td>\n",
              "      <td>HG00096</td>\n",
              "      <td>GBR</td>\n",
              "      <td>EUR</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>HG00097</td>\n",
              "      <td>GBR</td>\n",
              "      <td>EUR</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>HG00099</td>\n",
              "      <td>GBR</td>\n",
              "      <td>EUR</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>HG00100</td>\n",
              "      <td>GBR</td>\n",
              "      <td>EUR</td>\n",
              "      <td>female</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>HG00101</td>\n",
              "      <td>GBR</td>\n",
              "      <td>EUR</td>\n",
              "      <td>male</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    sample  pop super_pop  gender\n",
              "0  HG00096  GBR       EUR    male\n",
              "1  HG00097  GBR       EUR  female\n",
              "2  HG00099  GBR       EUR  female\n",
              "3  HG00100  GBR       EUR  female\n",
              "4  HG00101  GBR       EUR    male"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M0-CMijaFHit",
        "colab_type": "text"
      },
      "source": [
        "Count number of samples per population"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqklAJ7_FHiv",
        "colab_type": "code",
        "colab": {},
        "outputId": "fad3ef6a-4d53-4f1a-bfbc-c5a4322262a4"
      },
      "source": [
        "### 51\n",
        "samples['pop'].value_counts()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GWD    113\n",
              "YRI    108\n",
              "IBS    107\n",
              "TSI    107\n",
              "CHS    105\n",
              "JPT    104\n",
              "PUR    104\n",
              "GIH    103\n",
              "CHB    103\n",
              "ITU    102\n",
              "STU    102\n",
              "ESN     99\n",
              "LWK     99\n",
              "FIN     99\n",
              "CEU     99\n",
              "KHV     99\n",
              "PJL     96\n",
              "ACB     96\n",
              "CLM     94\n",
              "CDX     93\n",
              "GBR     91\n",
              "BEB     86\n",
              "MSL     85\n",
              "PEL     85\n",
              "MXL     64\n",
              "ASW     61\n",
              "Name: pop, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bqc9HCOgFHix",
        "colab_type": "code",
        "colab": {},
        "outputId": "99552900-58e9-4446-9292-a6700de4edbd"
      },
      "source": [
        "### 52\n",
        "populations = np.array(samples['pop'].value_counts().keys())\n",
        "populations"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['GWD', 'YRI', 'IBS', 'TSI', 'CHS', 'JPT', 'PUR', 'GIH', 'CHB',\n",
              "       'ITU', 'STU', 'ESN', 'LWK', 'FIN', 'CEU', 'KHV', 'PJL', 'ACB',\n",
              "       'CLM', 'CDX', 'GBR', 'BEB', 'MSL', 'PEL', 'MXL', 'ASW'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rbMizkRjFHiz",
        "colab_type": "text"
      },
      "source": [
        "Find positions of samples within each population"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UHxZErS-FHi0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 53\n",
        "# index samples using positions\n",
        "samples.reset_index(drop=True, inplace=True)\n",
        "samples.head()\n",
        "\n",
        "subpops = {\n",
        "    # for each population, get the list of samples that belong to the population\n",
        "    pop_iter: samples[samples['pop'] == pop_iter].index.tolist() for pop_iter in populations\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q9id_1kBFHi3",
        "colab_type": "code",
        "colab": {},
        "outputId": "ee1cbeed-443a-4f02-b3a1-9a06d38a70b2"
      },
      "source": [
        "### 54\n",
        "subpops"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'GWD': [945,\n",
              "  946,\n",
              "  947,\n",
              "  948,\n",
              "  983,\n",
              "  984,\n",
              "  985,\n",
              "  986,\n",
              "  987,\n",
              "  988,\n",
              "  989,\n",
              "  992,\n",
              "  993,\n",
              "  994,\n",
              "  995,\n",
              "  996,\n",
              "  997,\n",
              "  998,\n",
              "  999,\n",
              "  1005,\n",
              "  1006,\n",
              "  1007,\n",
              "  1008,\n",
              "  1009,\n",
              "  1010,\n",
              "  1011,\n",
              "  1012,\n",
              "  1013,\n",
              "  1014,\n",
              "  1015,\n",
              "  1016,\n",
              "  1017,\n",
              "  1018,\n",
              "  1019,\n",
              "  1020,\n",
              "  1031,\n",
              "  1032,\n",
              "  1033,\n",
              "  1034,\n",
              "  1035,\n",
              "  1036,\n",
              "  1050,\n",
              "  1051,\n",
              "  1052,\n",
              "  1053,\n",
              "  1054,\n",
              "  1055,\n",
              "  1065,\n",
              "  1066,\n",
              "  1067,\n",
              "  1068,\n",
              "  1069,\n",
              "  1070,\n",
              "  1071,\n",
              "  1072,\n",
              "  1073,\n",
              "  1086,\n",
              "  1087,\n",
              "  1088,\n",
              "  1089,\n",
              "  1090,\n",
              "  1091,\n",
              "  1092,\n",
              "  1093,\n",
              "  1094,\n",
              "  1095,\n",
              "  1096,\n",
              "  1097,\n",
              "  1098,\n",
              "  1099,\n",
              "  1100,\n",
              "  1101,\n",
              "  1102,\n",
              "  1103,\n",
              "  1104,\n",
              "  1105,\n",
              "  1106,\n",
              "  1107,\n",
              "  1108,\n",
              "  1109,\n",
              "  1110,\n",
              "  1111,\n",
              "  1112,\n",
              "  1113,\n",
              "  1114,\n",
              "  1115,\n",
              "  1116,\n",
              "  1117,\n",
              "  1118,\n",
              "  1119,\n",
              "  1120,\n",
              "  1121,\n",
              "  1122,\n",
              "  1142,\n",
              "  1143,\n",
              "  1154,\n",
              "  1155,\n",
              "  1156,\n",
              "  1157,\n",
              "  1158,\n",
              "  1159,\n",
              "  1160,\n",
              "  1161,\n",
              "  1162,\n",
              "  1163,\n",
              "  1244,\n",
              "  1245,\n",
              "  1246,\n",
              "  1247,\n",
              "  1248,\n",
              "  1249,\n",
              "  1329,\n",
              "  1330],\n",
              " 'YRI': [1755,\n",
              "  1756,\n",
              "  1757,\n",
              "  1758,\n",
              "  1759,\n",
              "  1760,\n",
              "  1761,\n",
              "  1762,\n",
              "  1763,\n",
              "  1764,\n",
              "  1765,\n",
              "  1766,\n",
              "  1767,\n",
              "  1768,\n",
              "  1769,\n",
              "  1770,\n",
              "  1771,\n",
              "  1772,\n",
              "  1773,\n",
              "  1877,\n",
              "  1878,\n",
              "  1879,\n",
              "  1880,\n",
              "  1881,\n",
              "  1882,\n",
              "  1883,\n",
              "  1884,\n",
              "  1885,\n",
              "  1886,\n",
              "  1887,\n",
              "  1888,\n",
              "  1889,\n",
              "  1890,\n",
              "  1891,\n",
              "  1892,\n",
              "  1893,\n",
              "  1894,\n",
              "  1895,\n",
              "  1896,\n",
              "  1897,\n",
              "  1898,\n",
              "  1899,\n",
              "  1900,\n",
              "  1901,\n",
              "  1902,\n",
              "  1903,\n",
              "  1904,\n",
              "  1905,\n",
              "  2028,\n",
              "  2029,\n",
              "  2030,\n",
              "  2031,\n",
              "  2032,\n",
              "  2033,\n",
              "  2034,\n",
              "  2035,\n",
              "  2036,\n",
              "  2037,\n",
              "  2038,\n",
              "  2039,\n",
              "  2040,\n",
              "  2041,\n",
              "  2042,\n",
              "  2043,\n",
              "  2044,\n",
              "  2045,\n",
              "  2046,\n",
              "  2047,\n",
              "  2048,\n",
              "  2049,\n",
              "  2050,\n",
              "  2051,\n",
              "  2052,\n",
              "  2053,\n",
              "  2054,\n",
              "  2055,\n",
              "  2056,\n",
              "  2057,\n",
              "  2058,\n",
              "  2059,\n",
              "  2060,\n",
              "  2061,\n",
              "  2062,\n",
              "  2063,\n",
              "  2064,\n",
              "  2065,\n",
              "  2066,\n",
              "  2067,\n",
              "  2068,\n",
              "  2069,\n",
              "  2070,\n",
              "  2071,\n",
              "  2072,\n",
              "  2073,\n",
              "  2074,\n",
              "  2075,\n",
              "  2076,\n",
              "  2077,\n",
              "  2078,\n",
              "  2079,\n",
              "  2080,\n",
              "  2081,\n",
              "  2082,\n",
              "  2083,\n",
              "  2084,\n",
              "  2085,\n",
              "  2086,\n",
              "  2087],\n",
              " 'IBS': [501,\n",
              "  502,\n",
              "  503,\n",
              "  504,\n",
              "  505,\n",
              "  506,\n",
              "  507,\n",
              "  508,\n",
              "  509,\n",
              "  510,\n",
              "  511,\n",
              "  512,\n",
              "  513,\n",
              "  514,\n",
              "  515,\n",
              "  516,\n",
              "  517,\n",
              "  518,\n",
              "  519,\n",
              "  520,\n",
              "  521,\n",
              "  522,\n",
              "  523,\n",
              "  524,\n",
              "  544,\n",
              "  545,\n",
              "  546,\n",
              "  547,\n",
              "  548,\n",
              "  549,\n",
              "  550,\n",
              "  551,\n",
              "  552,\n",
              "  553,\n",
              "  554,\n",
              "  555,\n",
              "  556,\n",
              "  557,\n",
              "  558,\n",
              "  559,\n",
              "  560,\n",
              "  561,\n",
              "  562,\n",
              "  563,\n",
              "  564,\n",
              "  565,\n",
              "  566,\n",
              "  567,\n",
              "  568,\n",
              "  569,\n",
              "  570,\n",
              "  571,\n",
              "  572,\n",
              "  573,\n",
              "  574,\n",
              "  575,\n",
              "  576,\n",
              "  577,\n",
              "  578,\n",
              "  579,\n",
              "  580,\n",
              "  581,\n",
              "  582,\n",
              "  583,\n",
              "  584,\n",
              "  585,\n",
              "  586,\n",
              "  587,\n",
              "  588,\n",
              "  589,\n",
              "  590,\n",
              "  591,\n",
              "  592,\n",
              "  593,\n",
              "  594,\n",
              "  595,\n",
              "  596,\n",
              "  597,\n",
              "  598,\n",
              "  599,\n",
              "  600,\n",
              "  601,\n",
              "  602,\n",
              "  603,\n",
              "  604,\n",
              "  605,\n",
              "  606,\n",
              "  607,\n",
              "  608,\n",
              "  609,\n",
              "  610,\n",
              "  611,\n",
              "  612,\n",
              "  613,\n",
              "  838,\n",
              "  839,\n",
              "  840,\n",
              "  841,\n",
              "  842,\n",
              "  843,\n",
              "  844,\n",
              "  845,\n",
              "  846,\n",
              "  847,\n",
              "  848,\n",
              "  849,\n",
              "  850],\n",
              " 'TSI': [2294,\n",
              "  2295,\n",
              "  2296,\n",
              "  2297,\n",
              "  2298,\n",
              "  2299,\n",
              "  2300,\n",
              "  2301,\n",
              "  2302,\n",
              "  2303,\n",
              "  2304,\n",
              "  2305,\n",
              "  2306,\n",
              "  2307,\n",
              "  2308,\n",
              "  2309,\n",
              "  2310,\n",
              "  2311,\n",
              "  2312,\n",
              "  2313,\n",
              "  2314,\n",
              "  2315,\n",
              "  2316,\n",
              "  2317,\n",
              "  2318,\n",
              "  2319,\n",
              "  2320,\n",
              "  2321,\n",
              "  2322,\n",
              "  2323,\n",
              "  2324,\n",
              "  2325,\n",
              "  2326,\n",
              "  2327,\n",
              "  2328,\n",
              "  2329,\n",
              "  2330,\n",
              "  2331,\n",
              "  2332,\n",
              "  2333,\n",
              "  2334,\n",
              "  2335,\n",
              "  2336,\n",
              "  2337,\n",
              "  2338,\n",
              "  2339,\n",
              "  2340,\n",
              "  2341,\n",
              "  2342,\n",
              "  2343,\n",
              "  2344,\n",
              "  2345,\n",
              "  2346,\n",
              "  2347,\n",
              "  2348,\n",
              "  2349,\n",
              "  2350,\n",
              "  2351,\n",
              "  2352,\n",
              "  2353,\n",
              "  2354,\n",
              "  2355,\n",
              "  2356,\n",
              "  2357,\n",
              "  2358,\n",
              "  2359,\n",
              "  2360,\n",
              "  2361,\n",
              "  2362,\n",
              "  2363,\n",
              "  2364,\n",
              "  2365,\n",
              "  2366,\n",
              "  2367,\n",
              "  2368,\n",
              "  2369,\n",
              "  2370,\n",
              "  2371,\n",
              "  2372,\n",
              "  2373,\n",
              "  2374,\n",
              "  2375,\n",
              "  2376,\n",
              "  2377,\n",
              "  2378,\n",
              "  2379,\n",
              "  2380,\n",
              "  2381,\n",
              "  2382,\n",
              "  2383,\n",
              "  2384,\n",
              "  2385,\n",
              "  2386,\n",
              "  2387,\n",
              "  2388,\n",
              "  2389,\n",
              "  2390,\n",
              "  2391,\n",
              "  2392,\n",
              "  2393,\n",
              "  2394,\n",
              "  2395,\n",
              "  2396,\n",
              "  2397,\n",
              "  2398,\n",
              "  2399,\n",
              "  2400],\n",
              " 'CHS': [185,\n",
              "  186,\n",
              "  187,\n",
              "  188,\n",
              "  189,\n",
              "  190,\n",
              "  191,\n",
              "  192,\n",
              "  193,\n",
              "  194,\n",
              "  195,\n",
              "  196,\n",
              "  197,\n",
              "  198,\n",
              "  199,\n",
              "  200,\n",
              "  201,\n",
              "  202,\n",
              "  203,\n",
              "  204,\n",
              "  205,\n",
              "  206,\n",
              "  207,\n",
              "  208,\n",
              "  209,\n",
              "  210,\n",
              "  211,\n",
              "  212,\n",
              "  213,\n",
              "  214,\n",
              "  215,\n",
              "  216,\n",
              "  217,\n",
              "  218,\n",
              "  219,\n",
              "  220,\n",
              "  221,\n",
              "  222,\n",
              "  223,\n",
              "  224,\n",
              "  225,\n",
              "  226,\n",
              "  230,\n",
              "  231,\n",
              "  232,\n",
              "  233,\n",
              "  234,\n",
              "  235,\n",
              "  236,\n",
              "  237,\n",
              "  238,\n",
              "  239,\n",
              "  240,\n",
              "  241,\n",
              "  242,\n",
              "  243,\n",
              "  244,\n",
              "  245,\n",
              "  246,\n",
              "  247,\n",
              "  248,\n",
              "  249,\n",
              "  250,\n",
              "  251,\n",
              "  252,\n",
              "  253,\n",
              "  254,\n",
              "  255,\n",
              "  256,\n",
              "  257,\n",
              "  258,\n",
              "  259,\n",
              "  260,\n",
              "  261,\n",
              "  262,\n",
              "  263,\n",
              "  264,\n",
              "  269,\n",
              "  270,\n",
              "  271,\n",
              "  272,\n",
              "  273,\n",
              "  274,\n",
              "  275,\n",
              "  276,\n",
              "  277,\n",
              "  278,\n",
              "  279,\n",
              "  280,\n",
              "  281,\n",
              "  282,\n",
              "  283,\n",
              "  284,\n",
              "  285,\n",
              "  286,\n",
              "  287,\n",
              "  288,\n",
              "  289,\n",
              "  290,\n",
              "  291,\n",
              "  292,\n",
              "  293,\n",
              "  294,\n",
              "  295,\n",
              "  296],\n",
              " 'JPT': [1906,\n",
              "  1907,\n",
              "  1908,\n",
              "  1909,\n",
              "  1910,\n",
              "  1911,\n",
              "  1912,\n",
              "  1913,\n",
              "  1914,\n",
              "  1915,\n",
              "  1916,\n",
              "  1917,\n",
              "  1918,\n",
              "  1919,\n",
              "  1920,\n",
              "  1921,\n",
              "  1922,\n",
              "  1923,\n",
              "  1924,\n",
              "  1925,\n",
              "  1926,\n",
              "  1927,\n",
              "  1928,\n",
              "  1929,\n",
              "  1930,\n",
              "  1931,\n",
              "  1932,\n",
              "  1933,\n",
              "  1934,\n",
              "  1935,\n",
              "  1936,\n",
              "  1937,\n",
              "  1938,\n",
              "  1939,\n",
              "  1940,\n",
              "  1941,\n",
              "  1942,\n",
              "  1943,\n",
              "  1944,\n",
              "  1945,\n",
              "  1946,\n",
              "  1947,\n",
              "  1948,\n",
              "  1949,\n",
              "  1950,\n",
              "  1951,\n",
              "  1952,\n",
              "  1953,\n",
              "  1954,\n",
              "  1955,\n",
              "  1956,\n",
              "  1957,\n",
              "  1958,\n",
              "  1959,\n",
              "  1960,\n",
              "  1961,\n",
              "  1962,\n",
              "  1963,\n",
              "  1964,\n",
              "  1965,\n",
              "  1966,\n",
              "  1967,\n",
              "  1968,\n",
              "  1969,\n",
              "  1970,\n",
              "  1971,\n",
              "  1972,\n",
              "  1973,\n",
              "  1974,\n",
              "  1975,\n",
              "  1994,\n",
              "  1995,\n",
              "  1996,\n",
              "  1997,\n",
              "  1998,\n",
              "  1999,\n",
              "  2000,\n",
              "  2001,\n",
              "  2002,\n",
              "  2003,\n",
              "  2004,\n",
              "  2005,\n",
              "  2006,\n",
              "  2007,\n",
              "  2008,\n",
              "  2009,\n",
              "  2010,\n",
              "  2011,\n",
              "  2012,\n",
              "  2013,\n",
              "  2014,\n",
              "  2015,\n",
              "  2016,\n",
              "  2017,\n",
              "  2018,\n",
              "  2019,\n",
              "  2020,\n",
              "  2021,\n",
              "  2022,\n",
              "  2023,\n",
              "  2024,\n",
              "  2025,\n",
              "  2026,\n",
              "  2027],\n",
              " 'PUR': [227,\n",
              "  228,\n",
              "  229,\n",
              "  265,\n",
              "  266,\n",
              "  267,\n",
              "  268,\n",
              "  297,\n",
              "  298,\n",
              "  299,\n",
              "  300,\n",
              "  301,\n",
              "  302,\n",
              "  303,\n",
              "  304,\n",
              "  305,\n",
              "  321,\n",
              "  322,\n",
              "  323,\n",
              "  324,\n",
              "  325,\n",
              "  326,\n",
              "  327,\n",
              "  328,\n",
              "  329,\n",
              "  330,\n",
              "  331,\n",
              "  332,\n",
              "  333,\n",
              "  334,\n",
              "  335,\n",
              "  336,\n",
              "  337,\n",
              "  338,\n",
              "  339,\n",
              "  340,\n",
              "  341,\n",
              "  342,\n",
              "  343,\n",
              "  344,\n",
              "  345,\n",
              "  346,\n",
              "  347,\n",
              "  348,\n",
              "  349,\n",
              "  350,\n",
              "  351,\n",
              "  352,\n",
              "  353,\n",
              "  354,\n",
              "  355,\n",
              "  356,\n",
              "  357,\n",
              "  358,\n",
              "  359,\n",
              "  360,\n",
              "  361,\n",
              "  380,\n",
              "  381,\n",
              "  382,\n",
              "  383,\n",
              "  384,\n",
              "  385,\n",
              "  386,\n",
              "  387,\n",
              "  388,\n",
              "  389,\n",
              "  390,\n",
              "  391,\n",
              "  392,\n",
              "  393,\n",
              "  394,\n",
              "  395,\n",
              "  396,\n",
              "  397,\n",
              "  398,\n",
              "  399,\n",
              "  400,\n",
              "  401,\n",
              "  402,\n",
              "  403,\n",
              "  404,\n",
              "  405,\n",
              "  422,\n",
              "  423,\n",
              "  424,\n",
              "  425,\n",
              "  426,\n",
              "  427,\n",
              "  428,\n",
              "  429,\n",
              "  430,\n",
              "  431,\n",
              "  460,\n",
              "  461,\n",
              "  462,\n",
              "  463,\n",
              "  464,\n",
              "  465,\n",
              "  466,\n",
              "  467,\n",
              "  468,\n",
              "  469,\n",
              "  470],\n",
              " 'GIH': [2401,\n",
              "  2402,\n",
              "  2403,\n",
              "  2404,\n",
              "  2405,\n",
              "  2406,\n",
              "  2407,\n",
              "  2408,\n",
              "  2409,\n",
              "  2410,\n",
              "  2411,\n",
              "  2412,\n",
              "  2413,\n",
              "  2414,\n",
              "  2415,\n",
              "  2416,\n",
              "  2417,\n",
              "  2418,\n",
              "  2419,\n",
              "  2420,\n",
              "  2421,\n",
              "  2422,\n",
              "  2423,\n",
              "  2424,\n",
              "  2425,\n",
              "  2426,\n",
              "  2427,\n",
              "  2428,\n",
              "  2429,\n",
              "  2430,\n",
              "  2431,\n",
              "  2432,\n",
              "  2433,\n",
              "  2434,\n",
              "  2435,\n",
              "  2436,\n",
              "  2437,\n",
              "  2438,\n",
              "  2439,\n",
              "  2440,\n",
              "  2441,\n",
              "  2442,\n",
              "  2443,\n",
              "  2444,\n",
              "  2445,\n",
              "  2446,\n",
              "  2447,\n",
              "  2448,\n",
              "  2449,\n",
              "  2450,\n",
              "  2451,\n",
              "  2452,\n",
              "  2453,\n",
              "  2454,\n",
              "  2455,\n",
              "  2456,\n",
              "  2457,\n",
              "  2458,\n",
              "  2459,\n",
              "  2460,\n",
              "  2461,\n",
              "  2462,\n",
              "  2463,\n",
              "  2464,\n",
              "  2465,\n",
              "  2466,\n",
              "  2467,\n",
              "  2468,\n",
              "  2469,\n",
              "  2470,\n",
              "  2471,\n",
              "  2472,\n",
              "  2473,\n",
              "  2474,\n",
              "  2475,\n",
              "  2476,\n",
              "  2477,\n",
              "  2478,\n",
              "  2479,\n",
              "  2480,\n",
              "  2481,\n",
              "  2482,\n",
              "  2483,\n",
              "  2484,\n",
              "  2485,\n",
              "  2486,\n",
              "  2487,\n",
              "  2488,\n",
              "  2489,\n",
              "  2490,\n",
              "  2491,\n",
              "  2492,\n",
              "  2493,\n",
              "  2494,\n",
              "  2495,\n",
              "  2496,\n",
              "  2497,\n",
              "  2498,\n",
              "  2499,\n",
              "  2500,\n",
              "  2501,\n",
              "  2502,\n",
              "  2503],\n",
              " 'CHB': [1774,\n",
              "  1775,\n",
              "  1776,\n",
              "  1777,\n",
              "  1778,\n",
              "  1779,\n",
              "  1780,\n",
              "  1781,\n",
              "  1782,\n",
              "  1783,\n",
              "  1784,\n",
              "  1785,\n",
              "  1786,\n",
              "  1787,\n",
              "  1788,\n",
              "  1789,\n",
              "  1790,\n",
              "  1791,\n",
              "  1792,\n",
              "  1793,\n",
              "  1794,\n",
              "  1795,\n",
              "  1796,\n",
              "  1797,\n",
              "  1798,\n",
              "  1799,\n",
              "  1800,\n",
              "  1801,\n",
              "  1802,\n",
              "  1803,\n",
              "  1804,\n",
              "  1805,\n",
              "  1806,\n",
              "  1807,\n",
              "  1808,\n",
              "  1809,\n",
              "  1810,\n",
              "  1811,\n",
              "  1812,\n",
              "  1813,\n",
              "  1814,\n",
              "  1815,\n",
              "  1816,\n",
              "  1817,\n",
              "  1818,\n",
              "  1819,\n",
              "  1820,\n",
              "  1821,\n",
              "  1822,\n",
              "  1823,\n",
              "  1824,\n",
              "  1825,\n",
              "  1826,\n",
              "  1827,\n",
              "  1828,\n",
              "  1829,\n",
              "  1830,\n",
              "  1831,\n",
              "  1832,\n",
              "  1833,\n",
              "  1834,\n",
              "  1835,\n",
              "  1836,\n",
              "  1837,\n",
              "  1838,\n",
              "  1839,\n",
              "  1840,\n",
              "  1841,\n",
              "  1842,\n",
              "  1843,\n",
              "  1844,\n",
              "  1845,\n",
              "  1846,\n",
              "  1847,\n",
              "  1848,\n",
              "  1849,\n",
              "  1850,\n",
              "  1851,\n",
              "  1852,\n",
              "  1853,\n",
              "  1854,\n",
              "  1855,\n",
              "  1856,\n",
              "  1857,\n",
              "  1858,\n",
              "  1859,\n",
              "  1860,\n",
              "  1861,\n",
              "  1862,\n",
              "  1863,\n",
              "  1864,\n",
              "  1865,\n",
              "  1866,\n",
              "  1867,\n",
              "  1868,\n",
              "  1869,\n",
              "  1870,\n",
              "  1871,\n",
              "  1872,\n",
              "  1873,\n",
              "  1874,\n",
              "  1875,\n",
              "  1876],\n",
              " 'ITU': [1405,\n",
              "  1406,\n",
              "  1407,\n",
              "  1408,\n",
              "  1409,\n",
              "  1410,\n",
              "  1411,\n",
              "  1412,\n",
              "  1413,\n",
              "  1414,\n",
              "  1415,\n",
              "  1421,\n",
              "  1437,\n",
              "  1438,\n",
              "  1439,\n",
              "  1440,\n",
              "  1441,\n",
              "  1442,\n",
              "  1443,\n",
              "  1444,\n",
              "  1445,\n",
              "  1446,\n",
              "  1447,\n",
              "  1448,\n",
              "  1449,\n",
              "  1450,\n",
              "  1451,\n",
              "  1452,\n",
              "  1453,\n",
              "  1454,\n",
              "  1455,\n",
              "  1456,\n",
              "  1490,\n",
              "  1491,\n",
              "  1492,\n",
              "  1493,\n",
              "  1494,\n",
              "  1495,\n",
              "  1496,\n",
              "  1497,\n",
              "  1498,\n",
              "  1499,\n",
              "  1500,\n",
              "  1501,\n",
              "  1502,\n",
              "  1503,\n",
              "  1504,\n",
              "  1548,\n",
              "  1549,\n",
              "  1550,\n",
              "  1551,\n",
              "  1552,\n",
              "  1553,\n",
              "  1554,\n",
              "  1555,\n",
              "  1556,\n",
              "  1557,\n",
              "  1558,\n",
              "  1559,\n",
              "  1568,\n",
              "  1569,\n",
              "  1572,\n",
              "  1573,\n",
              "  1574,\n",
              "  1575,\n",
              "  1576,\n",
              "  1577,\n",
              "  1578,\n",
              "  1579,\n",
              "  1580,\n",
              "  1581,\n",
              "  1589,\n",
              "  1590,\n",
              "  1591,\n",
              "  1592,\n",
              "  1593,\n",
              "  1594,\n",
              "  1595,\n",
              "  1596,\n",
              "  1598,\n",
              "  1599,\n",
              "  1600,\n",
              "  1601,\n",
              "  1602,\n",
              "  1603,\n",
              "  1604,\n",
              "  1609,\n",
              "  1638,\n",
              "  1639,\n",
              "  1640,\n",
              "  1641,\n",
              "  1642,\n",
              "  1644,\n",
              "  1645,\n",
              "  1646,\n",
              "  1647,\n",
              "  1648,\n",
              "  1649,\n",
              "  1650,\n",
              "  1653,\n",
              "  1654,\n",
              "  1655],\n",
              " 'STU': [1367,\n",
              "  1368,\n",
              "  1369,\n",
              "  1370,\n",
              "  1371,\n",
              "  1379,\n",
              "  1380,\n",
              "  1381,\n",
              "  1382,\n",
              "  1383,\n",
              "  1384,\n",
              "  1385,\n",
              "  1386,\n",
              "  1387,\n",
              "  1388,\n",
              "  1389,\n",
              "  1390,\n",
              "  1391,\n",
              "  1392,\n",
              "  1393,\n",
              "  1394,\n",
              "  1395,\n",
              "  1396,\n",
              "  1397,\n",
              "  1404,\n",
              "  1416,\n",
              "  1417,\n",
              "  1418,\n",
              "  1419,\n",
              "  1420,\n",
              "  1422,\n",
              "  1423,\n",
              "  1424,\n",
              "  1425,\n",
              "  1426,\n",
              "  1427,\n",
              "  1428,\n",
              "  1429,\n",
              "  1430,\n",
              "  1431,\n",
              "  1432,\n",
              "  1433,\n",
              "  1477,\n",
              "  1478,\n",
              "  1479,\n",
              "  1480,\n",
              "  1481,\n",
              "  1482,\n",
              "  1483,\n",
              "  1484,\n",
              "  1485,\n",
              "  1486,\n",
              "  1487,\n",
              "  1488,\n",
              "  1489,\n",
              "  1505,\n",
              "  1506,\n",
              "  1507,\n",
              "  1508,\n",
              "  1509,\n",
              "  1510,\n",
              "  1511,\n",
              "  1512,\n",
              "  1513,\n",
              "  1514,\n",
              "  1515,\n",
              "  1516,\n",
              "  1517,\n",
              "  1539,\n",
              "  1540,\n",
              "  1541,\n",
              "  1542,\n",
              "  1543,\n",
              "  1544,\n",
              "  1545,\n",
              "  1546,\n",
              "  1547,\n",
              "  1560,\n",
              "  1561,\n",
              "  1562,\n",
              "  1563,\n",
              "  1564,\n",
              "  1565,\n",
              "  1566,\n",
              "  1567,\n",
              "  1570,\n",
              "  1571,\n",
              "  1582,\n",
              "  1583,\n",
              "  1584,\n",
              "  1585,\n",
              "  1586,\n",
              "  1587,\n",
              "  1588,\n",
              "  1597,\n",
              "  1605,\n",
              "  1606,\n",
              "  1607,\n",
              "  1608,\n",
              "  1643,\n",
              "  1651,\n",
              "  1652],\n",
              " 'ESN': [1123,\n",
              "  1124,\n",
              "  1125,\n",
              "  1126,\n",
              "  1127,\n",
              "  1128,\n",
              "  1129,\n",
              "  1130,\n",
              "  1131,\n",
              "  1132,\n",
              "  1133,\n",
              "  1134,\n",
              "  1135,\n",
              "  1136,\n",
              "  1137,\n",
              "  1138,\n",
              "  1139,\n",
              "  1140,\n",
              "  1141,\n",
              "  1191,\n",
              "  1192,\n",
              "  1193,\n",
              "  1194,\n",
              "  1195,\n",
              "  1196,\n",
              "  1197,\n",
              "  1198,\n",
              "  1199,\n",
              "  1200,\n",
              "  1201,\n",
              "  1202,\n",
              "  1203,\n",
              "  1204,\n",
              "  1205,\n",
              "  1206,\n",
              "  1207,\n",
              "  1208,\n",
              "  1209,\n",
              "  1210,\n",
              "  1211,\n",
              "  1212,\n",
              "  1213,\n",
              "  1214,\n",
              "  1215,\n",
              "  1216,\n",
              "  1217,\n",
              "  1218,\n",
              "  1219,\n",
              "  1220,\n",
              "  1221,\n",
              "  1222,\n",
              "  1223,\n",
              "  1224,\n",
              "  1225,\n",
              "  1226,\n",
              "  1227,\n",
              "  1228,\n",
              "  1229,\n",
              "  1230,\n",
              "  1231,\n",
              "  1232,\n",
              "  1233,\n",
              "  1250,\n",
              "  1251,\n",
              "  1252,\n",
              "  1253,\n",
              "  1254,\n",
              "  1255,\n",
              "  1256,\n",
              "  1257,\n",
              "  1258,\n",
              "  1259,\n",
              "  1260,\n",
              "  1261,\n",
              "  1262,\n",
              "  1263,\n",
              "  1264,\n",
              "  1265,\n",
              "  1266,\n",
              "  1267,\n",
              "  1268,\n",
              "  1269,\n",
              "  1270,\n",
              "  1271,\n",
              "  1272,\n",
              "  1273,\n",
              "  1274,\n",
              "  1275,\n",
              "  1276,\n",
              "  1277,\n",
              "  1278,\n",
              "  1321,\n",
              "  1322,\n",
              "  1323,\n",
              "  1324,\n",
              "  1325,\n",
              "  1326,\n",
              "  1327,\n",
              "  1328],\n",
              " 'LWK': [1976,\n",
              "  1977,\n",
              "  1978,\n",
              "  1979,\n",
              "  1980,\n",
              "  1981,\n",
              "  1982,\n",
              "  1983,\n",
              "  1984,\n",
              "  1985,\n",
              "  1986,\n",
              "  1987,\n",
              "  1988,\n",
              "  1989,\n",
              "  1990,\n",
              "  1991,\n",
              "  1992,\n",
              "  1993,\n",
              "  2088,\n",
              "  2089,\n",
              "  2090,\n",
              "  2091,\n",
              "  2092,\n",
              "  2093,\n",
              "  2094,\n",
              "  2095,\n",
              "  2096,\n",
              "  2097,\n",
              "  2098,\n",
              "  2099,\n",
              "  2100,\n",
              "  2101,\n",
              "  2102,\n",
              "  2103,\n",
              "  2104,\n",
              "  2105,\n",
              "  2106,\n",
              "  2107,\n",
              "  2108,\n",
              "  2109,\n",
              "  2110,\n",
              "  2111,\n",
              "  2112,\n",
              "  2113,\n",
              "  2114,\n",
              "  2115,\n",
              "  2116,\n",
              "  2117,\n",
              "  2118,\n",
              "  2119,\n",
              "  2120,\n",
              "  2121,\n",
              "  2122,\n",
              "  2123,\n",
              "  2124,\n",
              "  2125,\n",
              "  2126,\n",
              "  2127,\n",
              "  2128,\n",
              "  2129,\n",
              "  2130,\n",
              "  2131,\n",
              "  2132,\n",
              "  2133,\n",
              "  2134,\n",
              "  2135,\n",
              "  2136,\n",
              "  2137,\n",
              "  2138,\n",
              "  2139,\n",
              "  2140,\n",
              "  2141,\n",
              "  2142,\n",
              "  2143,\n",
              "  2144,\n",
              "  2145,\n",
              "  2146,\n",
              "  2147,\n",
              "  2148,\n",
              "  2149,\n",
              "  2150,\n",
              "  2151,\n",
              "  2152,\n",
              "  2153,\n",
              "  2154,\n",
              "  2155,\n",
              "  2156,\n",
              "  2157,\n",
              "  2158,\n",
              "  2159,\n",
              "  2160,\n",
              "  2161,\n",
              "  2162,\n",
              "  2163,\n",
              "  2164,\n",
              "  2165,\n",
              "  2166,\n",
              "  2167,\n",
              "  2168],\n",
              " 'FIN': [55,\n",
              "  56,\n",
              "  57,\n",
              "  58,\n",
              "  59,\n",
              "  60,\n",
              "  61,\n",
              "  62,\n",
              "  63,\n",
              "  64,\n",
              "  65,\n",
              "  66,\n",
              "  67,\n",
              "  68,\n",
              "  69,\n",
              "  70,\n",
              "  71,\n",
              "  103,\n",
              "  104,\n",
              "  105,\n",
              "  106,\n",
              "  107,\n",
              "  108,\n",
              "  109,\n",
              "  110,\n",
              "  111,\n",
              "  112,\n",
              "  113,\n",
              "  114,\n",
              "  115,\n",
              "  116,\n",
              "  117,\n",
              "  118,\n",
              "  119,\n",
              "  120,\n",
              "  121,\n",
              "  122,\n",
              "  123,\n",
              "  124,\n",
              "  125,\n",
              "  126,\n",
              "  127,\n",
              "  128,\n",
              "  129,\n",
              "  130,\n",
              "  131,\n",
              "  132,\n",
              "  133,\n",
              "  134,\n",
              "  135,\n",
              "  136,\n",
              "  137,\n",
              "  138,\n",
              "  139,\n",
              "  140,\n",
              "  141,\n",
              "  142,\n",
              "  143,\n",
              "  144,\n",
              "  145,\n",
              "  146,\n",
              "  147,\n",
              "  148,\n",
              "  149,\n",
              "  150,\n",
              "  151,\n",
              "  152,\n",
              "  153,\n",
              "  154,\n",
              "  155,\n",
              "  156,\n",
              "  157,\n",
              "  158,\n",
              "  159,\n",
              "  160,\n",
              "  161,\n",
              "  162,\n",
              "  163,\n",
              "  164,\n",
              "  165,\n",
              "  166,\n",
              "  167,\n",
              "  168,\n",
              "  169,\n",
              "  170,\n",
              "  171,\n",
              "  172,\n",
              "  173,\n",
              "  174,\n",
              "  175,\n",
              "  176,\n",
              "  177,\n",
              "  178,\n",
              "  179,\n",
              "  180,\n",
              "  181,\n",
              "  182,\n",
              "  183,\n",
              "  184],\n",
              " 'CEU': [1656,\n",
              "  1657,\n",
              "  1658,\n",
              "  1659,\n",
              "  1660,\n",
              "  1661,\n",
              "  1662,\n",
              "  1663,\n",
              "  1664,\n",
              "  1665,\n",
              "  1666,\n",
              "  1667,\n",
              "  1668,\n",
              "  1669,\n",
              "  1670,\n",
              "  1671,\n",
              "  1672,\n",
              "  1673,\n",
              "  1674,\n",
              "  1675,\n",
              "  1676,\n",
              "  1677,\n",
              "  1678,\n",
              "  1679,\n",
              "  1680,\n",
              "  1681,\n",
              "  1682,\n",
              "  1683,\n",
              "  1684,\n",
              "  1685,\n",
              "  1686,\n",
              "  1687,\n",
              "  1688,\n",
              "  1689,\n",
              "  1690,\n",
              "  1691,\n",
              "  1692,\n",
              "  1693,\n",
              "  1694,\n",
              "  1695,\n",
              "  1696,\n",
              "  1697,\n",
              "  1698,\n",
              "  1699,\n",
              "  1700,\n",
              "  1701,\n",
              "  1702,\n",
              "  1703,\n",
              "  1704,\n",
              "  1705,\n",
              "  1706,\n",
              "  1707,\n",
              "  1708,\n",
              "  1709,\n",
              "  1710,\n",
              "  1711,\n",
              "  1712,\n",
              "  1713,\n",
              "  1714,\n",
              "  1715,\n",
              "  1716,\n",
              "  1717,\n",
              "  1718,\n",
              "  1719,\n",
              "  1720,\n",
              "  1721,\n",
              "  1722,\n",
              "  1723,\n",
              "  1724,\n",
              "  1725,\n",
              "  1726,\n",
              "  1727,\n",
              "  1728,\n",
              "  1729,\n",
              "  1730,\n",
              "  1731,\n",
              "  1732,\n",
              "  1733,\n",
              "  1734,\n",
              "  1735,\n",
              "  1736,\n",
              "  1737,\n",
              "  1738,\n",
              "  1739,\n",
              "  1740,\n",
              "  1741,\n",
              "  1742,\n",
              "  1743,\n",
              "  1744,\n",
              "  1745,\n",
              "  1746,\n",
              "  1747,\n",
              "  1748,\n",
              "  1749,\n",
              "  1750,\n",
              "  1751,\n",
              "  1752,\n",
              "  1753,\n",
              "  1754],\n",
              " 'KHV': [538,\n",
              "  539,\n",
              "  540,\n",
              "  541,\n",
              "  542,\n",
              "  543,\n",
              "  639,\n",
              "  640,\n",
              "  641,\n",
              "  642,\n",
              "  643,\n",
              "  644,\n",
              "  645,\n",
              "  646,\n",
              "  647,\n",
              "  648,\n",
              "  649,\n",
              "  650,\n",
              "  651,\n",
              "  652,\n",
              "  653,\n",
              "  654,\n",
              "  655,\n",
              "  656,\n",
              "  657,\n",
              "  658,\n",
              "  659,\n",
              "  660,\n",
              "  661,\n",
              "  662,\n",
              "  663,\n",
              "  664,\n",
              "  665,\n",
              "  666,\n",
              "  667,\n",
              "  668,\n",
              "  669,\n",
              "  670,\n",
              "  671,\n",
              "  672,\n",
              "  744,\n",
              "  745,\n",
              "  746,\n",
              "  747,\n",
              "  748,\n",
              "  749,\n",
              "  750,\n",
              "  751,\n",
              "  752,\n",
              "  753,\n",
              "  754,\n",
              "  755,\n",
              "  756,\n",
              "  757,\n",
              "  758,\n",
              "  759,\n",
              "  760,\n",
              "  765,\n",
              "  766,\n",
              "  767,\n",
              "  768,\n",
              "  769,\n",
              "  770,\n",
              "  771,\n",
              "  772,\n",
              "  773,\n",
              "  774,\n",
              "  775,\n",
              "  776,\n",
              "  777,\n",
              "  778,\n",
              "  779,\n",
              "  780,\n",
              "  781,\n",
              "  782,\n",
              "  783,\n",
              "  784,\n",
              "  785,\n",
              "  795,\n",
              "  796,\n",
              "  797,\n",
              "  798,\n",
              "  799,\n",
              "  800,\n",
              "  801,\n",
              "  802,\n",
              "  803,\n",
              "  804,\n",
              "  805,\n",
              "  806,\n",
              "  807,\n",
              "  808,\n",
              "  809,\n",
              "  810,\n",
              "  811,\n",
              "  969,\n",
              "  970,\n",
              "  971,\n",
              "  972],\n",
              " 'PJL': [534,\n",
              "  535,\n",
              "  536,\n",
              "  537,\n",
              "  958,\n",
              "  959,\n",
              "  960,\n",
              "  961,\n",
              "  1000,\n",
              "  1001,\n",
              "  1002,\n",
              "  1003,\n",
              "  1004,\n",
              "  1021,\n",
              "  1022,\n",
              "  1023,\n",
              "  1024,\n",
              "  1025,\n",
              "  1026,\n",
              "  1027,\n",
              "  1028,\n",
              "  1029,\n",
              "  1030,\n",
              "  1037,\n",
              "  1038,\n",
              "  1039,\n",
              "  1040,\n",
              "  1041,\n",
              "  1042,\n",
              "  1043,\n",
              "  1044,\n",
              "  1045,\n",
              "  1046,\n",
              "  1047,\n",
              "  1048,\n",
              "  1049,\n",
              "  1056,\n",
              "  1057,\n",
              "  1058,\n",
              "  1059,\n",
              "  1060,\n",
              "  1061,\n",
              "  1062,\n",
              "  1063,\n",
              "  1064,\n",
              "  1074,\n",
              "  1075,\n",
              "  1076,\n",
              "  1077,\n",
              "  1078,\n",
              "  1079,\n",
              "  1080,\n",
              "  1081,\n",
              "  1082,\n",
              "  1083,\n",
              "  1084,\n",
              "  1085,\n",
              "  1148,\n",
              "  1149,\n",
              "  1150,\n",
              "  1151,\n",
              "  1152,\n",
              "  1153,\n",
              "  1238,\n",
              "  1239,\n",
              "  1240,\n",
              "  1241,\n",
              "  1242,\n",
              "  1243,\n",
              "  1318,\n",
              "  1319,\n",
              "  1320,\n",
              "  1359,\n",
              "  1360,\n",
              "  1361,\n",
              "  1362,\n",
              "  1363,\n",
              "  1364,\n",
              "  1365,\n",
              "  1366,\n",
              "  1372,\n",
              "  1373,\n",
              "  1374,\n",
              "  1375,\n",
              "  1376,\n",
              "  1377,\n",
              "  1378,\n",
              "  1398,\n",
              "  1399,\n",
              "  1400,\n",
              "  1401,\n",
              "  1402,\n",
              "  1403,\n",
              "  1434,\n",
              "  1435,\n",
              "  1436],\n",
              " 'ACB': [673,\n",
              "  674,\n",
              "  675,\n",
              "  676,\n",
              "  677,\n",
              "  678,\n",
              "  679,\n",
              "  680,\n",
              "  683,\n",
              "  684,\n",
              "  685,\n",
              "  686,\n",
              "  687,\n",
              "  712,\n",
              "  713,\n",
              "  727,\n",
              "  728,\n",
              "  729,\n",
              "  730,\n",
              "  731,\n",
              "  739,\n",
              "  740,\n",
              "  741,\n",
              "  742,\n",
              "  743,\n",
              "  761,\n",
              "  762,\n",
              "  763,\n",
              "  764,\n",
              "  788,\n",
              "  792,\n",
              "  793,\n",
              "  794,\n",
              "  812,\n",
              "  813,\n",
              "  854,\n",
              "  855,\n",
              "  867,\n",
              "  868,\n",
              "  869,\n",
              "  870,\n",
              "  879,\n",
              "  880,\n",
              "  881,\n",
              "  883,\n",
              "  884,\n",
              "  885,\n",
              "  886,\n",
              "  887,\n",
              "  888,\n",
              "  889,\n",
              "  890,\n",
              "  891,\n",
              "  892,\n",
              "  893,\n",
              "  894,\n",
              "  895,\n",
              "  933,\n",
              "  934,\n",
              "  936,\n",
              "  937,\n",
              "  938,\n",
              "  939,\n",
              "  940,\n",
              "  941,\n",
              "  942,\n",
              "  943,\n",
              "  944,\n",
              "  949,\n",
              "  950,\n",
              "  951,\n",
              "  952,\n",
              "  953,\n",
              "  954,\n",
              "  955,\n",
              "  956,\n",
              "  957,\n",
              "  962,\n",
              "  963,\n",
              "  964,\n",
              "  965,\n",
              "  966,\n",
              "  967,\n",
              "  968,\n",
              "  973,\n",
              "  974,\n",
              "  975,\n",
              "  976,\n",
              "  977,\n",
              "  978,\n",
              "  979,\n",
              "  980,\n",
              "  981,\n",
              "  982,\n",
              "  990,\n",
              "  991],\n",
              " 'CLM': [362,\n",
              "  363,\n",
              "  364,\n",
              "  365,\n",
              "  366,\n",
              "  367,\n",
              "  368,\n",
              "  369,\n",
              "  370,\n",
              "  371,\n",
              "  372,\n",
              "  373,\n",
              "  374,\n",
              "  375,\n",
              "  376,\n",
              "  377,\n",
              "  378,\n",
              "  379,\n",
              "  406,\n",
              "  407,\n",
              "  408,\n",
              "  409,\n",
              "  410,\n",
              "  411,\n",
              "  412,\n",
              "  413,\n",
              "  414,\n",
              "  415,\n",
              "  416,\n",
              "  417,\n",
              "  418,\n",
              "  419,\n",
              "  420,\n",
              "  421,\n",
              "  433,\n",
              "  434,\n",
              "  435,\n",
              "  436,\n",
              "  437,\n",
              "  438,\n",
              "  439,\n",
              "  440,\n",
              "  441,\n",
              "  442,\n",
              "  443,\n",
              "  444,\n",
              "  445,\n",
              "  446,\n",
              "  447,\n",
              "  448,\n",
              "  449,\n",
              "  450,\n",
              "  451,\n",
              "  452,\n",
              "  453,\n",
              "  454,\n",
              "  455,\n",
              "  456,\n",
              "  457,\n",
              "  458,\n",
              "  459,\n",
              "  471,\n",
              "  472,\n",
              "  473,\n",
              "  474,\n",
              "  475,\n",
              "  476,\n",
              "  477,\n",
              "  478,\n",
              "  479,\n",
              "  480,\n",
              "  481,\n",
              "  482,\n",
              "  483,\n",
              "  484,\n",
              "  485,\n",
              "  486,\n",
              "  487,\n",
              "  488,\n",
              "  489,\n",
              "  490,\n",
              "  491,\n",
              "  492,\n",
              "  493,\n",
              "  494,\n",
              "  495,\n",
              "  496,\n",
              "  497,\n",
              "  498,\n",
              "  499,\n",
              "  500,\n",
              "  525,\n",
              "  526,\n",
              "  527],\n",
              " 'CDX': [306,\n",
              "  307,\n",
              "  308,\n",
              "  309,\n",
              "  310,\n",
              "  311,\n",
              "  312,\n",
              "  313,\n",
              "  314,\n",
              "  315,\n",
              "  316,\n",
              "  317,\n",
              "  318,\n",
              "  319,\n",
              "  320,\n",
              "  617,\n",
              "  618,\n",
              "  619,\n",
              "  620,\n",
              "  621,\n",
              "  622,\n",
              "  623,\n",
              "  624,\n",
              "  625,\n",
              "  626,\n",
              "  627,\n",
              "  628,\n",
              "  629,\n",
              "  630,\n",
              "  631,\n",
              "  632,\n",
              "  633,\n",
              "  634,\n",
              "  635,\n",
              "  636,\n",
              "  637,\n",
              "  638,\n",
              "  817,\n",
              "  818,\n",
              "  819,\n",
              "  820,\n",
              "  821,\n",
              "  822,\n",
              "  823,\n",
              "  824,\n",
              "  825,\n",
              "  826,\n",
              "  827,\n",
              "  828,\n",
              "  829,\n",
              "  830,\n",
              "  831,\n",
              "  832,\n",
              "  833,\n",
              "  834,\n",
              "  835,\n",
              "  836,\n",
              "  851,\n",
              "  898,\n",
              "  899,\n",
              "  900,\n",
              "  901,\n",
              "  902,\n",
              "  903,\n",
              "  904,\n",
              "  905,\n",
              "  906,\n",
              "  907,\n",
              "  908,\n",
              "  909,\n",
              "  910,\n",
              "  911,\n",
              "  912,\n",
              "  913,\n",
              "  914,\n",
              "  915,\n",
              "  916,\n",
              "  917,\n",
              "  918,\n",
              "  919,\n",
              "  920,\n",
              "  921,\n",
              "  922,\n",
              "  923,\n",
              "  924,\n",
              "  925,\n",
              "  926,\n",
              "  927,\n",
              "  928,\n",
              "  929,\n",
              "  930,\n",
              "  931,\n",
              "  932],\n",
              " 'GBR': [0,\n",
              "  1,\n",
              "  2,\n",
              "  3,\n",
              "  4,\n",
              "  5,\n",
              "  6,\n",
              "  7,\n",
              "  8,\n",
              "  9,\n",
              "  10,\n",
              "  11,\n",
              "  12,\n",
              "  13,\n",
              "  14,\n",
              "  15,\n",
              "  16,\n",
              "  17,\n",
              "  18,\n",
              "  19,\n",
              "  20,\n",
              "  21,\n",
              "  22,\n",
              "  23,\n",
              "  24,\n",
              "  25,\n",
              "  26,\n",
              "  27,\n",
              "  28,\n",
              "  29,\n",
              "  30,\n",
              "  31,\n",
              "  32,\n",
              "  33,\n",
              "  34,\n",
              "  35,\n",
              "  36,\n",
              "  37,\n",
              "  38,\n",
              "  39,\n",
              "  40,\n",
              "  41,\n",
              "  42,\n",
              "  43,\n",
              "  44,\n",
              "  45,\n",
              "  46,\n",
              "  47,\n",
              "  48,\n",
              "  49,\n",
              "  50,\n",
              "  51,\n",
              "  52,\n",
              "  53,\n",
              "  54,\n",
              "  72,\n",
              "  73,\n",
              "  74,\n",
              "  75,\n",
              "  76,\n",
              "  77,\n",
              "  78,\n",
              "  79,\n",
              "  80,\n",
              "  81,\n",
              "  82,\n",
              "  83,\n",
              "  84,\n",
              "  85,\n",
              "  86,\n",
              "  87,\n",
              "  88,\n",
              "  89,\n",
              "  90,\n",
              "  91,\n",
              "  92,\n",
              "  93,\n",
              "  94,\n",
              "  95,\n",
              "  96,\n",
              "  97,\n",
              "  98,\n",
              "  99,\n",
              "  100,\n",
              "  101,\n",
              "  102,\n",
              "  432,\n",
              "  614,\n",
              "  615,\n",
              "  616,\n",
              "  837],\n",
              " 'BEB': [1144,\n",
              "  1145,\n",
              "  1146,\n",
              "  1147,\n",
              "  1346,\n",
              "  1347,\n",
              "  1348,\n",
              "  1349,\n",
              "  1350,\n",
              "  1351,\n",
              "  1352,\n",
              "  1353,\n",
              "  1354,\n",
              "  1355,\n",
              "  1356,\n",
              "  1357,\n",
              "  1358,\n",
              "  1457,\n",
              "  1458,\n",
              "  1459,\n",
              "  1460,\n",
              "  1461,\n",
              "  1462,\n",
              "  1463,\n",
              "  1464,\n",
              "  1465,\n",
              "  1466,\n",
              "  1467,\n",
              "  1468,\n",
              "  1469,\n",
              "  1470,\n",
              "  1471,\n",
              "  1472,\n",
              "  1473,\n",
              "  1474,\n",
              "  1475,\n",
              "  1476,\n",
              "  1518,\n",
              "  1519,\n",
              "  1520,\n",
              "  1521,\n",
              "  1522,\n",
              "  1523,\n",
              "  1524,\n",
              "  1525,\n",
              "  1526,\n",
              "  1527,\n",
              "  1528,\n",
              "  1529,\n",
              "  1530,\n",
              "  1531,\n",
              "  1532,\n",
              "  1533,\n",
              "  1534,\n",
              "  1535,\n",
              "  1536,\n",
              "  1537,\n",
              "  1538,\n",
              "  1610,\n",
              "  1611,\n",
              "  1612,\n",
              "  1613,\n",
              "  1614,\n",
              "  1615,\n",
              "  1616,\n",
              "  1617,\n",
              "  1618,\n",
              "  1619,\n",
              "  1620,\n",
              "  1621,\n",
              "  1622,\n",
              "  1623,\n",
              "  1624,\n",
              "  1625,\n",
              "  1626,\n",
              "  1627,\n",
              "  1628,\n",
              "  1629,\n",
              "  1630,\n",
              "  1631,\n",
              "  1632,\n",
              "  1633,\n",
              "  1634,\n",
              "  1635,\n",
              "  1636,\n",
              "  1637],\n",
              " 'MSL': [1164,\n",
              "  1165,\n",
              "  1166,\n",
              "  1167,\n",
              "  1168,\n",
              "  1169,\n",
              "  1170,\n",
              "  1171,\n",
              "  1172,\n",
              "  1173,\n",
              "  1174,\n",
              "  1175,\n",
              "  1176,\n",
              "  1177,\n",
              "  1178,\n",
              "  1179,\n",
              "  1180,\n",
              "  1181,\n",
              "  1182,\n",
              "  1183,\n",
              "  1184,\n",
              "  1185,\n",
              "  1186,\n",
              "  1187,\n",
              "  1188,\n",
              "  1189,\n",
              "  1190,\n",
              "  1234,\n",
              "  1235,\n",
              "  1236,\n",
              "  1237,\n",
              "  1279,\n",
              "  1280,\n",
              "  1281,\n",
              "  1282,\n",
              "  1283,\n",
              "  1284,\n",
              "  1285,\n",
              "  1286,\n",
              "  1287,\n",
              "  1288,\n",
              "  1289,\n",
              "  1290,\n",
              "  1291,\n",
              "  1292,\n",
              "  1293,\n",
              "  1294,\n",
              "  1295,\n",
              "  1296,\n",
              "  1297,\n",
              "  1298,\n",
              "  1299,\n",
              "  1300,\n",
              "  1301,\n",
              "  1302,\n",
              "  1303,\n",
              "  1304,\n",
              "  1305,\n",
              "  1306,\n",
              "  1307,\n",
              "  1308,\n",
              "  1309,\n",
              "  1310,\n",
              "  1311,\n",
              "  1312,\n",
              "  1313,\n",
              "  1314,\n",
              "  1315,\n",
              "  1316,\n",
              "  1317,\n",
              "  1331,\n",
              "  1332,\n",
              "  1333,\n",
              "  1334,\n",
              "  1335,\n",
              "  1336,\n",
              "  1337,\n",
              "  1338,\n",
              "  1339,\n",
              "  1340,\n",
              "  1341,\n",
              "  1342,\n",
              "  1343,\n",
              "  1344,\n",
              "  1345],\n",
              " 'PEL': [528,\n",
              "  529,\n",
              "  530,\n",
              "  531,\n",
              "  532,\n",
              "  533,\n",
              "  681,\n",
              "  682,\n",
              "  688,\n",
              "  689,\n",
              "  690,\n",
              "  691,\n",
              "  692,\n",
              "  693,\n",
              "  694,\n",
              "  695,\n",
              "  696,\n",
              "  697,\n",
              "  698,\n",
              "  699,\n",
              "  700,\n",
              "  701,\n",
              "  702,\n",
              "  703,\n",
              "  704,\n",
              "  705,\n",
              "  706,\n",
              "  707,\n",
              "  708,\n",
              "  709,\n",
              "  710,\n",
              "  711,\n",
              "  714,\n",
              "  715,\n",
              "  716,\n",
              "  717,\n",
              "  718,\n",
              "  719,\n",
              "  720,\n",
              "  721,\n",
              "  722,\n",
              "  723,\n",
              "  724,\n",
              "  725,\n",
              "  726,\n",
              "  732,\n",
              "  733,\n",
              "  734,\n",
              "  735,\n",
              "  736,\n",
              "  737,\n",
              "  738,\n",
              "  786,\n",
              "  787,\n",
              "  789,\n",
              "  790,\n",
              "  791,\n",
              "  814,\n",
              "  815,\n",
              "  816,\n",
              "  852,\n",
              "  853,\n",
              "  856,\n",
              "  857,\n",
              "  858,\n",
              "  859,\n",
              "  860,\n",
              "  861,\n",
              "  862,\n",
              "  863,\n",
              "  864,\n",
              "  865,\n",
              "  866,\n",
              "  871,\n",
              "  872,\n",
              "  873,\n",
              "  874,\n",
              "  875,\n",
              "  876,\n",
              "  877,\n",
              "  878,\n",
              "  882,\n",
              "  896,\n",
              "  897,\n",
              "  935],\n",
              " 'MXL': [2170,\n",
              "  2171,\n",
              "  2172,\n",
              "  2173,\n",
              "  2174,\n",
              "  2175,\n",
              "  2176,\n",
              "  2177,\n",
              "  2178,\n",
              "  2179,\n",
              "  2180,\n",
              "  2181,\n",
              "  2182,\n",
              "  2183,\n",
              "  2184,\n",
              "  2185,\n",
              "  2186,\n",
              "  2187,\n",
              "  2188,\n",
              "  2197,\n",
              "  2198,\n",
              "  2199,\n",
              "  2200,\n",
              "  2201,\n",
              "  2202,\n",
              "  2203,\n",
              "  2204,\n",
              "  2205,\n",
              "  2206,\n",
              "  2207,\n",
              "  2208,\n",
              "  2209,\n",
              "  2210,\n",
              "  2211,\n",
              "  2212,\n",
              "  2213,\n",
              "  2214,\n",
              "  2215,\n",
              "  2216,\n",
              "  2217,\n",
              "  2218,\n",
              "  2219,\n",
              "  2220,\n",
              "  2221,\n",
              "  2222,\n",
              "  2223,\n",
              "  2224,\n",
              "  2225,\n",
              "  2226,\n",
              "  2227,\n",
              "  2228,\n",
              "  2229,\n",
              "  2230,\n",
              "  2231,\n",
              "  2232,\n",
              "  2233,\n",
              "  2234,\n",
              "  2235,\n",
              "  2236,\n",
              "  2237,\n",
              "  2238,\n",
              "  2239,\n",
              "  2240,\n",
              "  2241],\n",
              " 'ASW': [2169,\n",
              "  2189,\n",
              "  2190,\n",
              "  2191,\n",
              "  2192,\n",
              "  2193,\n",
              "  2194,\n",
              "  2195,\n",
              "  2196,\n",
              "  2242,\n",
              "  2243,\n",
              "  2244,\n",
              "  2245,\n",
              "  2246,\n",
              "  2247,\n",
              "  2248,\n",
              "  2249,\n",
              "  2250,\n",
              "  2251,\n",
              "  2252,\n",
              "  2253,\n",
              "  2254,\n",
              "  2255,\n",
              "  2256,\n",
              "  2257,\n",
              "  2258,\n",
              "  2259,\n",
              "  2260,\n",
              "  2261,\n",
              "  2262,\n",
              "  2263,\n",
              "  2264,\n",
              "  2265,\n",
              "  2266,\n",
              "  2267,\n",
              "  2268,\n",
              "  2269,\n",
              "  2270,\n",
              "  2271,\n",
              "  2272,\n",
              "  2273,\n",
              "  2274,\n",
              "  2275,\n",
              "  2276,\n",
              "  2277,\n",
              "  2278,\n",
              "  2279,\n",
              "  2280,\n",
              "  2281,\n",
              "  2282,\n",
              "  2283,\n",
              "  2284,\n",
              "  2285,\n",
              "  2286,\n",
              "  2287,\n",
              "  2288,\n",
              "  2289,\n",
              "  2290,\n",
              "  2291,\n",
              "  2292,\n",
              "  2293]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y2NzgXayFHi5",
        "colab_type": "text"
      },
      "source": [
        "We'll now extract genotypes within the stric mask so that we can compute Hardy-Weinberg properties within each population. The following step takes a few minutes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "upkeQpe3FHi6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 55\n",
        "strict_genotypes = genotypes_biallelic.compress(is_common).compress(in_mask)\n",
        "strict_genotypes_012 = genotypes_012.compress(is_common).compress(in_mask)\n",
        "ac_subpops = strict_genotypes.count_alleles_subpops(subpops, max_allele=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TsFtkgzgFHi9",
        "colab_type": "text"
      },
      "source": [
        "The next step is a bit of a hack. I wanted to use the .count_alleles_subpops function to count the number of homozygotes and heterozygotes within each sub-population. This was not supported, so I cheat by pretending that the genotypes 0,1, and 2 are actually three different alleles in a haploid individual. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RqwO-dzqFHi_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 56\n",
        "genotype_012_as_triallelic_haploid = allel.GenotypeArray(strict_genotypes_012[:].reshape(\n",
        "    [strict_genotypes_012.shape[0], strict_genotypes_012.shape[1], 1]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w1SMbsrOFHjB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 57\n",
        "observed_counts = genotype_012_as_triallelic_haploid.count_alleles_subpops(subpops, max_allele=2)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "td3PcARYFHjE",
        "colab_type": "text"
      },
      "source": [
        "Expected heterozygosity within a population"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2PasanKuFHjF",
        "colab_type": "code",
        "colab": {},
        "outputId": "416fd963-f802-491d-e8d3-6322edca943e"
      },
      "source": [
        "### 58\n",
        "\n",
        "for i,pop in enumerate(populations):\n",
        "    #to reduce the plotting burden, don't plot unless freqency is above 5%\n",
        "    \n",
        "    freq = ac_subpops[pop][:,1]/ac_subpops[pop].sum(axis = 1)\n",
        "    iscommon = np.logical_and((freq>0.05), (freq<0.95))\n",
        "    freq_common = freq.compress(iscommon)\n",
        "    counts = observed_counts[pop].compress(iscommon)\n",
        "    tot_counts = counts.sum(axis = 1)\n",
        "    \n",
        "    \n",
        "    \n",
        "    plt.plot(freq_common, counts[:,0]/ tot_counts, 'x', \n",
        "             color = 'r', markersize = 0.5 )\n",
        "    plt.plot(freq_common[:], counts[:,1]/ tot_counts[:], 'x', \n",
        "             color = 'orange', markersize = 0.5)\n",
        "    plt.plot(freq_common, counts[:,2]/ tot_counts,'x', \n",
        "             color = 'b', markersize = 0.5 )\n",
        "    plt.ylim([0,1])\n",
        "    plt.xlabel(\"alternate allele frequency\")\n",
        "    plt.ylabel(\"proportion of genotypes\")\n",
        "    \n",
        "    ####\n",
        "    # Now plot the theory values\n",
        "\n",
        "x_range = np.arange(0,1,.01) # A list of sorted values to help in plotting\n",
        "\n",
        "plt.plot(x_range, proportion_hom_ref(x_range), 'r', \n",
        "         label = \"homozygote reference\" ) \n",
        "plt.plot(x_range,  proportion_het(x_range),  color = 'orange', label = \"heterozygote\" ) \n",
        "plt.plot(x_range,  proportion_hom_alt(x_range), 'b',label = \"homozygote alternate\" ) \n",
        "plt.legend(loc = \"upper center\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x1c5d1a9d10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2jmCX81VFHjI",
        "colab_type": "text"
      },
      "source": [
        "Compute the heterozygosity vs expectations within each population."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aGTgDWXzFHjJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 59\n",
        "het_expectations = {}\n",
        "excess_hets = {}\n",
        "excess_hets_ratio = {}\n",
        "het_count_cutoff = 0\n",
        "for population in populations:\n",
        "    allele_counts_subpop = ac_subpops[population]\n",
        "    number_of_genotypes_subpop = observed_counts[population].sum(axis=1)\n",
        "    het_expectation = allele_counts_subpop[:,0] * allele_counts_subpop[:,1] / (2.*np.array(\n",
        "        number_of_genotypes_subpop)) \n",
        "    filter_expectations = het_expectation > het_count_cutoff\n",
        "\n",
        "    het_expectations[population] = np.compress(filter_expectations, het_expectation)\n",
        "    excess_hets[population] = np.compress(filter_expectations, observed_counts[population][:,1]-het_expectation)\n",
        "    excess_hets_ratio[population] = np.compress(filter_expectations, observed_counts[population][:,1]) \\\n",
        "                                /np.compress(filter_expectations, het_expectation)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GPZaTxjtFHjL",
        "colab_type": "code",
        "colab": {},
        "outputId": "b06919a3-b013-4128-ce2c-bba87b67789a"
      },
      "source": [
        "### 60\n",
        "plt.boxplot([list(excess_hets[pop])[:] for pop in populations], showmeans = True);\n",
        "plt.plot([0,len(populations)],[0,0],'k--')\n",
        "plt.xticks(np.arange(len(populations))+1.2,populations, rotation=90);\n",
        "plt.xlabel(\"population\")\n",
        "plt.ylabel(\"excess hets (absolute difference)\")\n",
        "plt.ylim([-5,5])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(-5, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n9C9mfusFHjP",
        "colab_type": "text"
      },
      "source": [
        "The overall counts of heterozygotes vs the number predicted by Hardy-Weinberg. We have a bit too many observed heterozygotes. I'm not sure why. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fQdDyBZIFHjQ",
        "colab_type": "code",
        "colab": {},
        "outputId": "f61a80fb-9d95-4ca9-d1f4-ab9c0977072d"
      },
      "source": [
        "### 61\n",
        "print(\"expected\", np.sum([het_expectations[pop][:].sum() for pop in populations]))\n",
        "print(\"observed\", np.sum([excess_hets[pop].sum() + het_expectations[pop][:].sum()for pop in populations]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "expected 45729602.48760315\n",
            "observed 45751994.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eQL3dLcXFHjS",
        "colab_type": "code",
        "colab": {},
        "outputId": "581cf6a6-eb8f-4d45-b2d1-013fe84f3acf"
      },
      "source": [
        "### 62\n",
        "plt.boxplot([list(excess_hets_ratio[pop])[:] for pop in populations], showmeans = True);\n",
        "plt.plot([0,len(populations)],[1,1],'k--')\n",
        "plt.xticks(np.arange(len(populations))+1.2,populations, rotation=90);\n",
        "plt.xlabel(\"population\")\n",
        "plt.ylabel(\"excess hets ratio\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'excess hets ratio')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNDNmo8RFHjU",
        "colab_type": "text"
      },
      "source": [
        "Finally, we compute the excess heterozygosity per individual rather than per per SNP."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJjrD5yaFHjV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 63\n",
        "het_per_sample = (genotype_012_as_triallelic_haploid==1).sum(axis = 0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7OBHC5_vFHjW",
        "colab_type": "text"
      },
      "source": [
        "For each population, compute the expected heterozygosity per individual (this is the same for all individuals in the population)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dsMiOoCMFHjW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 64\n",
        "het_per_sample\n",
        "excess_het_per_sample_per_pop = {} \n",
        "total_observed_hets = 0\n",
        "total_expected_hets = 0\n",
        "for pop in populations:\n",
        "    expected_hets = het_expectations[pop].sum()/samples['pop'].value_counts()[pop]\n",
        "    observed_hets = het_per_sample[subpops[pop]]\n",
        "    excess_het_per_sample_per_pop[pop] = observed_hets - expected_hets\n",
        "    total_observed_hets += np.sum(het_per_sample[subpops[pop]])\n",
        "    total_expected_hets += het_expectations[pop].sum()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KWjMJuWqFHjY",
        "colab_type": "code",
        "colab": {},
        "outputId": "f6cd7db6-d13a-45ff-e986-c22f92f204c2"
      },
      "source": [
        "### 65\n",
        "_=plt.boxplot([excess_het_per_sample_per_pop[pop].reshape([samples['pop'].value_counts()[pop]]) \n",
        "             for pop in populations],showmeans = True)\n",
        "plt.xticks(np.arange(len(populations))+1.2,populations, rotation=90);\n",
        "plt.plot([0,len(populations)],[0,0],'k--')\n",
        "\n",
        "plt.xlabel(\"population\")\n",
        "plt.ylabel(\"excess hets\")\n",
        "plt.title(\"distribution of excess hets per individual\")\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'distribution of excess hets per individual')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zu4FUs-RFHjZ",
        "colab_type": "text"
      },
      "source": [
        "By contrast to the full population case, there doesn't appear to be a strong excess of heterozygotes once populations have been split."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KH8SkimjFHja",
        "colab_type": "text"
      },
      "source": [
        "When we considered the entire population, there was clearly an excess of homozygotes, but here it's more subtle. The Q-Q plots should also look better:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a3njKSxYFHja",
        "colab_type": "text"
      },
      "source": [
        "Here's a few questions to think about: \n",
        "\n",
        "* Are the 25 populations in the 1000 Genomes project truly \"populations\"? I.e., are they in Hardy-weinberg equilibrium?\n",
        "\n",
        "* The code we wrote is not very efficient--we were only able to analyze a tiny subset of one genome. How would you make it more efficient? It doesn't need to be a fancy programming trick. Try it out. \n",
        "\n",
        "* There are still regions that fail the HW test. What do you think is going on there? How would you go about figuring it out?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3EMeFZxZFHjb",
        "colab_type": "text"
      },
      "source": [
        "# Transitions and transversions\n",
        "\n",
        "Elevated Hardy-Weiberg disequilibrium for some variants can be an indicator of poor quality of the sequencing data. Here we discuss another measure of variant quality: the ratio of trnasitions to transversions. This should have been discussed in class -- otherwise, you can read explanations from [this scikit tutorial](http://alimanfoo.github.io/2016/06/10/scikit-allel-tour.html), which I used extensively in preparing these notes. \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8zEzoC9rFHjb",
        "colab_type": "text"
      },
      "source": [
        "We can only compute transitions/transversions for biallelic sites, so we first restrict to biallelic:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W5LowOj3FHjc",
        "colab_type": "code",
        "colab": {},
        "outputId": "8747f062-b73d-4a7b-add7-d96077389bd0"
      },
      "source": [
        "### 66\n",
        "refs = np.array(callset['variants']['REF']).compress(is_biallelic, axis=0).astype(str)\n",
        "alts = np.array(callset['variants']['ALT'])[:,0].compress(is_biallelic, axis=0).astype(str)\n",
        "mutations = np.char.add(refs, alts)\n",
        "mutations"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['AG', 'GA', 'CT', ..., 'AG', 'CT', 'CT'], dtype='<U511')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bj5sdJKGFHje",
        "colab_type": "code",
        "colab": {},
        "outputId": "ef562d30-b7e0-4149-8792-9917066bdc85"
      },
      "source": [
        "### 67\n",
        "# This function is modified from http://alimanfoo.github.io/2016/06/10/scikit-allel-tour.html\n",
        "#\n",
        "def locate_transitions(x):\n",
        "    x = np.asarray(x)\n",
        "    return np.logical_or.reduce(((x == 'AG'), (x == 'GA') , (x == 'CT') , (x == 'TC')))\n",
        "is_ti = locate_transitions(mutations)\n",
        "is_ti"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True,  True, ...,  True,  True,  True])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q5OtaTI7FHjg",
        "colab_type": "text"
      },
      "source": [
        "We can now define a function that takes the vector of mutations and computes the proportion of transitions to transversions:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Lncw01N2FHjg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "### 68\n",
        "# This function is copied from http://alimanfoo.github.io/2016/06/10/scikit-allel-tour.html\n",
        "\n",
        "def ti_tv(x):\n",
        "    if len(x) == 0:\n",
        "        return np.nan\n",
        "    is_ti = locate_transitions(x)\n",
        "    n_ti = np.count_nonzero(is_ti)\n",
        "    n_tv = np.count_nonzero(~is_ti)\n",
        "    if n_tv > 0:\n",
        "        return n_ti / n_tv\n",
        "    else:\n",
        "        return np.nan"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1yKPlZCbFHji",
        "colab_type": "code",
        "colab": {},
        "outputId": "71d863cc-677e-40ff-f5d0-2c9a395569e3"
      },
      "source": [
        "### 69\n",
        "print(\"ti_tv for all mutations\", ti_tv(mutations))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ti_tv for all mutations 2.1271147651659486\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z2vwUIirFHjk",
        "colab_type": "code",
        "colab": {},
        "outputId": "17919bf2-1b0d-4c30-8a25-36d8b3c38c58"
      },
      "source": [
        "### 70\n",
        "common_mutations = mutations.compress(is_common)\n",
        "print(\"ti_tv for all mutations\", ti_tv(common_mutations))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ti_tv for all mutations 1.6545823532124901\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4hlW83lFHjm",
        "colab_type": "code",
        "colab": {},
        "outputId": "cbaf5e31-a61b-4ffd-bcc1-361e8d4f1b8d"
      },
      "source": [
        "### 71\n",
        "strict_mutations = common_mutations.compress(in_mask)\n",
        "print(\"ti_tv for all mutations\", ti_tv(strict_mutations))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ti_tv for all mutations 1.9614309426050494\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYTu5xA_FHjo",
        "colab_type": "code",
        "colab": {},
        "outputId": "e3b07980-4ba6-4ace-e1dc-697a2d5c392c"
      },
      "source": [
        "### 72\n",
        "ti_tv(strict_mutations)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.9614309426050494"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S17nStznFHjq",
        "colab_type": "code",
        "colab": {},
        "outputId": "e2b85005-4136-42ef-c737-89b5ca2fa987"
      },
      "source": [
        "### 73\n",
        "rare_mutations = mutations.compress(1-is_common)\n",
        "print(\"ti_tv for rare mutations\", ti_tv(rare_mutations))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ti_tv for rare mutations 2.194030776867709\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U8vRzKvXFHjs",
        "colab_type": "text"
      },
      "source": [
        "As expected, restricting the analysis to the strict mask increases the ti/tv ratio. This is consistent with the much higher rate of false positives in the low-quality regions. \n",
        "\n",
        "By contrast, the lower rate of transitions among common mutations was a surprise to me.  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1_fCQXrqFHjs",
        "colab_type": "text"
      },
      "source": [
        "© Simon Gravel, McGill University. "
      ]
    }
  ]
}