docs/sample_queries/gnomad/gnomad.ipynb

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            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
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          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KiFAYj0Jhm8_"
      },
      "source": [
        "# Sample Notebook for exploring gnomAD in BigQuery\n",
        "This notebook contains sample queries to explore the gnomAD dataset which is hosted through the Google Cloud Public Datasets Program."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pW09tTZBh9zq"
      },
      "source": [
        "## Setup and Authentication\n",
        "\n",
        "If you just want to look at sample results, you can scroll down to see the output of the existing queries without having to run anything. If you would like to re-run the queries or make changes, you will need to authenticate as your user and set the Google Cloud project in which to run the analysis."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QG8bvNuTgRji"
      },
      "source": [
        "# Import libraries\n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "# Imports for using and authenticating BigQuery\n",
        "from google.colab import auth"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54BmRpw3imuP"
      },
      "source": [
        "### User Authentication\n",
        "Before running any queries using BigQuery, you need to first authenticate yourself by running the following cell. If you are running it for the first time, it will ask you to follow a link to log in using your Google identity account, and accept the data access requests to your profile. Once this is done, it will generate a string of verification code, which you should paste back to the cell below and press enter. This should be a Google account which you can login to and which has access to run BigQuery jobs in the Google Cloud project specified in the next step."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJGkLDCDgnuc"
      },
      "source": [
        "auth.authenticate_user()"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FoYinYtci5Iv"
      },
      "source": [
        "### Set Google Cloud Project\n",
        "To run queries in BigQuery, you need to specify the Google Cloud project that will be used. The queries below report the number of bytes billed by each query. The first 1 TB of query data processed in a project per month is free. For more details, see the [BigQuery Pricing](https://cloud.google.com/bigquery/pricing) page.\n",
        "\n",
        "To find your Project ID, go to the [Project Settings page](https://console.cloud.google.com/iam-admin/settings) in the Google [Cloud Console](https://console.cloud.google.com/). You can select the project you want using the drop-down menu at the top of the page."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nkUF1y9IhDla"
      },
      "source": [
        "# Replace project_id with your Google Cloud Project ID. \n",
        "os.environ[\"GOOGLE_CLOUD_PROJECT\"]='project-id'"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sS2NHDSDmqDJ"
      },
      "source": [
        "# gnomAD Queries Type1: Explore a particular genomic region\n",
        "This category include queries that extract information from a region of the genome, for example a gene. Because gnomAD BigQuery tables utilize [integer range partitioning](https://cloud.google.com/bigquery/docs/creating-integer-range-partitions) they are optimized for this type of query.\n",
        "\n",
        "The main requirement to use this feature is to limit queries to a particular region by adding these conditions to the `WHERE` clause:\n",
        "\n",
        "`WHERE start_position >= X AND start_position <= Y`\n",
        "\n",
        "Where `[X, Y]` is the region of interest.\n",
        "\n",
        "You can find values of `X` and `Y` by refering to an external databses. For example the following table sumarizes the start and end positions for 4 genes on chromosome 17 extracted from an external resource:\n",
        "\n",
        "| Gene \t| X \t| Y \t| Source \t|\n",
        "|:-:\t|-\t|-\t|-\t|\n",
        "| BRCA1 \t| 43044295 \t| 43125364 \t| [link](https://ghr.nlm.nih.gov/gene/BRCA1#location) \t|\n",
        "| COL1A1 \t| 50184096 \t| 50201649 \t| [link](https://ghr.nlm.nih.gov/gene/COL1A1#location) \t|\n",
        "| TP53 \t| 31094927 \t| 31377677 \t| [link](https://ghr.nlm.nih.gov/gene/TP53#location) \t|\n",
        "| NF1 \t| 56593699 \t| 56595611 \t| [link](https://ghr.nlm.nih.gov/gene/NF1#location) \t|\n",
        "\n",
        "Alternatively you could use the following query that extract the same infomration directly from gnomAD tables.\n",
        "\n",
        "In the following example we are using `BRCA1` on `chr17` as an example. You can enter your gene of interest and chromosome to modify all the following queries. If your query returns `NaN` this might be because you specified the wrong chromosome, which will query the wrong table.\n",
        "\n",
        "Also you can choose which version of the gnomAD dataset you'd like to use for all the queries:\n",
        " * `v2_1_1_exomes`\n",
        " * `v2_1_1_genomes`\n",
        " * `v3_genomes`\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9kq6xHqSmqDJ",
        "outputId": "40da15b2-3381-4cfe-bace-45544e523143",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126,
          "referenced_widgets": [
            "26b58b97065c4af79b678110ee555bae",
            "088f2f8177524786aff8fc38fdcee367",
            "9df30d679d52486f81c68f4397329e47",
            "51ad6267e2d94b57a28839887f35e17a",
            "4ad10dc62bfc4b1697479cdccf9b87cb",
            "1e02eefc989f46ba9a87342d31a4e708",
            "89ca1892d51d410aa99e4618bb9d5224",
            "27ed9d68da4641fd90b9718b3e8c3f53",
            "23db269e1ee74e2e998c3dba52105105"
          ]
        }
      },
      "source": [
        "import ipywidgets as widgets\n",
        "\n",
        "print(\"Variables for Region (Type 1) Queries\")\n",
        "\n",
        "gnomad_version_widget_region = widgets.Dropdown(\n",
        "    options=['v2_1_1_exomes', 'v2_1_1_genomes', 'v3_genomes'],\n",
        "    value='v3_genomes',\n",
        "    description='gnomAD version:',\n",
        "    disabled=False,\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "display(gnomad_version_widget_region)\n",
        "\n",
        "chromosome_widget_region = widgets.Dropdown(\n",
        "    options=['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8',\n",
        "             'chr9', 'chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15',\n",
        "             'chr16', 'chr17', 'chr18', 'chr19', 'chr20', 'chr21', 'chr22',\n",
        "             'chrX', 'chrY'],\n",
        "    value='chr17',\n",
        "    description='Chromosome:',\n",
        "    disabled=False,\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "display(chromosome_widget_region)\n",
        "\n",
        "gene_symbol_widget_region= widgets.Text(\n",
        "    value='BRCA1',\n",
        "    placeholder='gene_symbol',\n",
        "    description='Gene Symbol:',\n",
        "    disabled=False,\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "display(gene_symbol_widget_region)\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Variables for Region (Type 1) Queries\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "26b58b97065c4af79b678110ee555bae",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Dropdown(description='gnomAD version:', index=2, options=('v2_1_1_exomes', 'v2_1_1_genomes', 'v3_genomes'), st…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "51ad6267e2d94b57a28839887f35e17a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Dropdown(description='Chromosome:', index=16, options=('chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7',…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "89ca1892d51d410aa99e4618bb9d5224",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Text(value='BRCA1', description='Gene Symbol:', placeholder='gene_symbol', style=DescriptionStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C7FEUTunI-tP",
        "outputId": "e4067f70-b46c-4dbd-ca61-fb4776f87fe8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Set the variables for the rest of the Type 1 queries based on the values above.\n",
        "gnomad_version_region=gnomad_version_widget_region.value\n",
        "chromosome_region=chromosome_widget_region.value\n",
        "gene_symbol_region=gene_symbol_widget_region.value\n",
        "\n",
        "\n",
        "print('Running Region (Type 1) queries on gnomAD version: {}, chromosome: {}, gene symbol: {}'.format(\n",
        "    gnomad_version_region,\n",
        "    chromosome_region,\n",
        "    gene_symbol_region\n",
        "))\n",
        "\n",
        "if gnomad_version_region.startswith('v3'):\n",
        "  # Variant type (snv, indel, multi-snv, multi-indel, or mixed) is stored under difference columns in V2 and V3\n",
        "  variant_type_col = 'variant_type'\n",
        "  extra_columns = ''\n",
        "else:\n",
        "  variant_type_col = 'alternate_bases. allele_type'\n",
        "  # These vep columns only exist in V2\n",
        "  extra_columns = 'vep.STRAND AS STRAND, vep.Protein_position AS Protein_pos,'"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running Region (Type 1) queries on gnomAD version: v3_genomes, chromosome: chr17, gene symbol: BRCA1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJkNyocBmqDQ"
      },
      "source": [
        "from google.cloud import bigquery\n",
        "\n",
        "client = bigquery.Client()\n",
        "\n",
        "def run_query(query):\n",
        "    query_job = client.query(query)\n",
        "    result = query_job.to_dataframe(progress_bar_type='tqdm_notebook')\n",
        "    gb_processed = (query_job.total_bytes_billed / 1024 ** 3)\n",
        "    print('This query processed {} GB of data which is {}% of your 1 TB monthly free quota.'.format(gb_processed, round(gb_processed / 1024 * 100, 4)))\n",
        "    return result"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQOgAs_imqDS",
        "outputId": "0cc97110-064a-439e-e084-22f460853abc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
            "e71ca7a92d7c48cc854063dbfc53d139",
            "9d1d9481ff134f5f940fb8fd78d803ed",
            "d9aa3104a0cc45bca995407067d22985",
            "33a5b77b89464358a55e3f1cfe96290a",
            "cc851b862b454d5090360812e4bb0fbb",
            "d593dac7d2a04899a3d548960a7d0c82",
            "8574ae994d9940fda4a1b7ac94d3083a",
            "fed0ea3093454fe98fd2503340bc1c89"
          ]
        }
      },
      "source": [
        "query_template = \"\"\"\n",
        "SELECT MIN(start_position) AS X, MAX(end_position) AS Y\n",
        "FROM `bigquery-public-data.gnomAD.{GNOMAD_VER}__{CHROM}` AS main_table\n",
        "WHERE EXISTS\n",
        "  (SELECT 1 FROM UNNEST(main_table.alternate_bases) AS alternate_bases\n",
        "   WHERE EXISTS (SELECT 1 from alternate_bases.vep WHERE SYMBOL = '{GENE}'))\n",
        "\"\"\"\n",
        "query = query_template.format(GNOMAD_VER=gnomad_version_region,\n",
        "                              CHROM=chromosome_region,\n",
        "                              GENE=gene_symbol_region)\n",
        "\n",
        "limits = run_query(query)\n",
        "\n",
        "print(limits)\n",
        "x = limits.at[0, 'X']\n",
        "y = limits.at[0, 'Y']"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e71ca7a92d7c48cc854063dbfc53d139",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1.0, style=ProgressStyle(description_wi…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "This query processed 1.0771484375 GB of data which is 0.1052% of your 1 TB monthly free quota.\n",
            "          X         Y\n",
            "0  43039296  43175241\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmC0cJLpmqDV"
      },
      "source": [
        "After you found the `[X, Y]` range for your gene of interst you can run *Type1* queries efficiently. Here are a couple of examples:\n",
        "\n",
        "### Query 1.1a - Variant Type (BigQuery)\n",
        "Find the number of INDELs and SNVs in the region of interest using BigQuery"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lv2IrzzZmqDW",
        "outputId": "c3588e1c-927a-406d-ec91-c5a6bf4fbd9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177,
          "referenced_widgets": [
            "56b5ce1740d34102bf15c4e837c3b325",
            "df1af7ad0ff5476caf8afc8444d0e8b5",
            "36fda5bc518147bc9ad3be3f64bd4dbf",
            "e3e2e4821ba7432dba80d0cb6d1a0290",
            "c22e79a78b7a4e969b08a326f497379c",
            "3e4fd1c8b5d44d1ba9586e3ba42ceb5e",
            "a0178c219a104901acdfdeef0dff686b",
            "4703d62f20574bf493f7594343b5f182"
          ]
        }
      },
      "source": [
        "# NOTE: For v2_1_1 the \"variant_type\" column must be replaced with \"alternate_bases.allele_type AS variant_type\"\n",
        "query_template = \"\"\"\n",
        "SELECT COUNT(1) AS num, variant_type\n",
        "FROM (\n",
        "SELECT DISTINCT \n",
        "       start_position,\n",
        "       reference_bases,\n",
        "       alternate_bases.alt,\n",
        "       {VAR_TYPE_COL} AS variant_type,\n",
        "FROM `bigquery-public-data.gnomAD.{GNOMAD_VER}__{CHROM}` AS main_table,\n",
        "     main_table.alternate_bases AS alternate_bases\n",
        "WHERE start_position >= {X} AND start_position <= {Y}\n",
        ")\n",
        "GROUP BY 2\n",
        "ORDER BY 1 DESC\n",
        "\"\"\"\n",
        "query = query_template.format(GNOMAD_VER=gnomad_version_region,\n",
        "                              CHROM=chromosome_region,\n",
        "                              VAR_TYPE_COL=variant_type_col, X=x, Y=y)\n",
        "summary = run_query(query)\n",
        "summary.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "56b5ce1740d34102bf15c4e837c3b325",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=2.0, style=ProgressStyle(description_wi…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "This query processed 0.009765625 GB of data which is 0.001% of your 1 TB monthly free quota.\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>num</th>\n",
              "      <th>variant_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28016</td>\n",
              "      <td>snv</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6238</td>\n",
              "      <td>indel</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     num variant_type\n",
              "0  28016          snv\n",
              "1   6238        indel"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EEdQo6lHUKWK"
      },
      "source": [
        "### Query 1.1b - Variant Type (Python)\n",
        "You can also find the number of INDELs and SNVs in the region of interest by doing the aggregation and count in Python using the dataframe."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4zQxbRJcUKWQ",
        "outputId": "225e9084-5581-4b6c-a8fc-cd24d780afe6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134,
          "referenced_widgets": [
            "c5cca218c3844c14831bf0f120572e4e",
            "e9c58e4ba3ae40c2bf09cbcd1491d4dd",
            "8ae17bf022484709aee18e2979003cdf",
            "099f53f0565d457484f4e4e1b48da99d",
            "90c8a207bfa84555a4a4279779a9a4f7",
            "9173aa645729438caf94ee8ab69ba813",
            "f6e33f2870c44b4198484e53731f148b",
            "ba9603a0904e4a0fa2a511a5c0e6b6e2"
          ]
        }
      },
      "source": [
        "# NOTE: For v2_1_1 the \"variant_type\" column must be replaced with \"alternate_bases.allele_type AS variant_type\"\n",
        "query_template = \"\"\"\n",
        "SELECT DISTINCT \n",
        "       start_position,\n",
        "       reference_bases,\n",
        "       alternate_bases.alt,\n",
        "       {VAR_TYPE_COL} AS variant_type,\n",
        "FROM `bigquery-public-data.gnomAD.{GNOMAD_VER}__{CHROM}` AS main_table,\n",
        "     main_table.alternate_bases AS alternate_bases\n",
        "WHERE start_position >= {X} AND start_position <= {Y}\n",
        "ORDER BY 1,2\n",
        "\"\"\"\n",
        "query = query_template.format(GNOMAD_VER=gnomad_version_region,\n",
        "                              CHROM=chromosome_region,\n",
        "                              VAR_TYPE_COL=variant_type_col, X=x, Y=y)\n",
        "summary_dataframe = run_query(query)\n",
        "\n",
        "# Count the number of each variant type in Python instead of in BigQuery\n",
        "print('Number of variants by type:')\n",
        "for v in summary_dataframe.variant_type.unique():\n",
        "  print('{}: {}'.format(v,\n",
        "                        np.count_nonzero(summary_dataframe['variant_type'] == v)))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c5cca218c3844c14831bf0f120572e4e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=34254.0, style=ProgressStyle(descriptio…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "This query processed 0.009765625 GB of data which is 0.001% of your 1 TB monthly free quota.\n",
            "Number of variants by type:\n",
            "snv: 28016\n",
            "indel: 6238\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QR9XdsnfmqDY"
      },
      "source": [
        "Instead of aggregating the results in BigQuery to count the number of each variant type, we could return all rows and process them here. The following query adds a few more columns to the previous query. \n",
        "\n",
        "### Query 1.2 - Allele Count by Sex\n",
        "A query to retrieve all variants in the region of interest along with `AN` and `AC` values split by sex.\n",
        "\n",
        " * `AN`: Total number of alleles in samples\n",
        " * `AC`: Alternate allele count for samples\n",
        " * `nhomalt`: The number of individuals that are called homozygous for the alternate allele."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4JYyaxegmqDZ",
        "outputId": "2d5eca11-c0f2-4fff-9f9e-2ed5ed6b212b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270,
          "referenced_widgets": [
            "6624f43903b245b8a9b721ab2a6c4ea2",
            "0310b7c1bb6c45828ce11b9431812eec",
            "a8aa53d84b9e4a3ebb0ca3854e90b4e6",
            "93df602b9b9c486ba2e9124126147b36",
            "5418f53240c74e5ba97a63c5848fd1d9",
            "8453673113fa483bbd31213bc31d017b",
            "ef547fcb1588451484f3b72faac6a158",
            "e2a5dc9cedc84ae7bcf7539818f7f2f3"
          ]
        }
      },
      "source": [
        "# NOTE: For v2_1_1 the \"variant_type\" column must be replaced with \"alternate_bases.allele_type AS variant_type\"\n",
        "query_template = \"\"\"\n",
        "SELECT reference_name AS CHROM, \n",
        "       start_position AS POS,\n",
        "       names AS ID,\n",
        "       reference_bases AS REF,\n",
        "       alternate_bases.alt AS ALT,\n",
        "       AN,\n",
        "       AN_male,\n",
        "       AN_female,\n",
        "       alternate_bases.AC AS AC,\n",
        "       alternate_bases.AC_male AS AC_male,\n",
        "       alternate_bases.AC_female AS AC_female,\n",
        "       alternate_bases.nhomalt AS nhomalt,\n",
        "       alternate_bases.nhomalt_male AS nhomalt_male,\n",
        "       alternate_bases.nhomalt_female AS nhomalt_female,\n",
        "FROM `bigquery-public-data.gnomAD.{GNOMAD_VER}__{CHROM}` AS main_table,\n",
        "     main_table.alternate_bases AS alternate_bases\n",
        "WHERE start_position >= {X} AND start_position <= {Y}\n",
        "ORDER BY 1,2\n",
        "\"\"\"\n",
        "query = query_template.format(GNOMAD_VER=gnomad_version_region,\n",
        "                              CHROM=chromosome_region, X=x, Y=y)\n",
        "\n",
        "stats_sex = run_query(query)\n",
        "stats_sex.head()\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6624f43903b245b8a9b721ab2a6c4ea2",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=34254.0, style=ProgressStyle(descriptio…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "This query processed 0.009765625 GB of data which is 0.001% of your 1 TB monthly free quota.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CHROM</th>\n",
              "      <th>POS</th>\n",
              "      <th>ID</th>\n",
              "      <th>REF</th>\n",
              "      <th>ALT</th>\n",
              "      <th>AN</th>\n",
              "      <th>AN_male</th>\n",
              "      <th>AN_female</th>\n",
              "      <th>AC</th>\n",
              "      <th>AC_male</th>\n",
              "      <th>AC_female</th>\n",
              "      <th>nhomalt</th>\n",
              "      <th>nhomalt_male</th>\n",
              "      <th>nhomalt_female</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43039296</td>\n",
              "      <td>[rs909752554]</td>\n",
              "      <td>A</td>\n",
              "      <td>T</td>\n",
              "      <td>143340</td>\n",
              "      <td>69472</td>\n",
              "      <td>73868</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43039301</td>\n",
              "      <td>[rs1016951561]</td>\n",
              "      <td>T</td>\n",
              "      <td>C</td>\n",
              "      <td>143316</td>\n",
              "      <td>69456</td>\n",
              "      <td>73860</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43039307</td>\n",
              "      <td>[]</td>\n",
              "      <td>T</td>\n",
              "      <td>C</td>\n",
              "      <td>143302</td>\n",
              "      <td>69434</td>\n",
              "      <td>73868</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43039316</td>\n",
              "      <td>[]</td>\n",
              "      <td>A</td>\n",
              "      <td>G</td>\n",
              "      <td>143326</td>\n",
              "      <td>69468</td>\n",
              "      <td>73858</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43039336</td>\n",
              "      <td>[]</td>\n",
              "      <td>TAG</td>\n",
              "      <td>T</td>\n",
              "      <td>143244</td>\n",
              "      <td>69424</td>\n",
              "      <td>73820</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CHROM       POS              ID  ... nhomalt nhomalt_male  nhomalt_female\n",
              "0  chr17  43039296   [rs909752554]  ...       0            0               0\n",
              "1  chr17  43039301  [rs1016951561]  ...       0            0               0\n",
              "2  chr17  43039307              []  ...       0            0               0\n",
              "3  chr17  43039316              []  ...       0            0               0\n",
              "4  chr17  43039336              []  ...       0            0               0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IcaAuthSWG0i"
      },
      "source": [
        "We can then perform further analysis on the dataframe such as filtering out variants with a low allele count (AC)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eIMQgCF_WX67",
        "outputId": "8fa0f7a1-22b3-4e34-8906-a4a1a8dc7e57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "stats_sex_filtered_ac=stats_sex.loc[stats_sex['AC'] > 10]\n",
        "stats_sex_filtered_ac.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CHROM</th>\n",
              "      <th>POS</th>\n",
              "      <th>ID</th>\n",
              "      <th>REF</th>\n",
              "      <th>ALT</th>\n",
              "      <th>AN</th>\n",
              "      <th>AN_male</th>\n",
              "      <th>AN_female</th>\n",
              "      <th>AC</th>\n",
              "      <th>AC_male</th>\n",
              "      <th>AC_female</th>\n",
              "      <th>nhomalt</th>\n",
              "      <th>nhomalt_male</th>\n",
              "      <th>nhomalt_female</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43039470</td>\n",
              "      <td>[rs78603756]</td>\n",
              "      <td>G</td>\n",
              "      <td>A</td>\n",
              "      <td>143292</td>\n",
              "      <td>69446</td>\n",
              "      <td>73846</td>\n",
              "      <td>194</td>\n",
              "      <td>97</td>\n",
              "      <td>97</td>\n",
              "      <td>3</td>\n",
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              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43039598</td>\n",
              "      <td>[rs144306902]</td>\n",
              "      <td>TG</td>\n",
              "      <td>T</td>\n",
              "      <td>143292</td>\n",
              "      <td>69442</td>\n",
              "      <td>73850</td>\n",
              "      <td>144</td>\n",
              "      <td>68</td>\n",
              "      <td>76</td>\n",
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              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43039687</td>\n",
              "      <td>[rs577085330]</td>\n",
              "      <td>T</td>\n",
              "      <td>G</td>\n",
              "      <td>143308</td>\n",
              "      <td>69474</td>\n",
              "      <td>73834</td>\n",
              "      <td>46</td>\n",
              "      <td>17</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43039719</td>\n",
              "      <td>[rs567107803]</td>\n",
              "      <td>A</td>\n",
              "      <td>G</td>\n",
              "      <td>143270</td>\n",
              "      <td>69436</td>\n",
              "      <td>73834</td>\n",
              "      <td>21</td>\n",
              "      <td>12</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43039742</td>\n",
              "      <td>[rs537848117]</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>143320</td>\n",
              "      <td>69466</td>\n",
              "      <td>73854</td>\n",
              "      <td>14</td>\n",
              "      <td>5</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    CHROM       POS             ID  ... nhomalt nhomalt_male  nhomalt_female\n",
              "23  chr17  43039470   [rs78603756]  ...       3            1               2\n",
              "39  chr17  43039598  [rs144306902]  ...       0            0               0\n",
              "54  chr17  43039687  [rs577085330]  ...       0            0               0\n",
              "59  chr17  43039719  [rs567107803]  ...       0            0               0\n",
              "61  chr17  43039742  [rs537848117]  ...       0            0               0\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KSBLLmkGWpkS"
      },
      "source": [
        "Or we could filter to find variants that were most common in females that were not found in any male samples."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oPsQ4xQqW0oe",
        "outputId": "105ea395-3572-4141-d007-731b60295ef8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "stats_sex_no_male=stats_sex.loc[stats_sex['AC_male'] == 0].sort_values(by=('AC_female'),\n",
        "                                                                       ascending = False)\n",
        "stats_sex_no_male.head(10)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>43089177</td>\n",
              "      <td>[rs956067034]</td>\n",
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              "    <tr>\n",
              "      <th>9579</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43080462</td>\n",
              "      <td>[rs1044512678]</td>\n",
              "      <td>C</td>\n",
              "      <td>T</td>\n",
              "      <td>143124</td>\n",
              "      <td>69360</td>\n",
              "      <td>73764</td>\n",
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              "    <tr>\n",
              "      <th>5978</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43066019</td>\n",
              "      <td>[rs754006900]</td>\n",
              "      <td>C</td>\n",
              "      <td>T</td>\n",
              "      <td>143274</td>\n",
              "      <td>69430</td>\n",
              "      <td>73844</td>\n",
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              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25685</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43139040</td>\n",
              "      <td>[rs1259203084]</td>\n",
              "      <td>G</td>\n",
              "      <td>C</td>\n",
              "      <td>143142</td>\n",
              "      <td>69356</td>\n",
              "      <td>73786</td>\n",
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              "      <td>7</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>14642</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43100663</td>\n",
              "      <td>[rs1470061722]</td>\n",
              "      <td>A</td>\n",
              "      <td>ATGT</td>\n",
              "      <td>18456</td>\n",
              "      <td>7494</td>\n",
              "      <td>10962</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24640</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43135141</td>\n",
              "      <td>[rs375148945]</td>\n",
              "      <td>C</td>\n",
              "      <td>T</td>\n",
              "      <td>143364</td>\n",
              "      <td>69482</td>\n",
              "      <td>73882</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30813</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43160144</td>\n",
              "      <td>[rs1024173306]</td>\n",
              "      <td>C</td>\n",
              "      <td>T</td>\n",
              "      <td>142904</td>\n",
              "      <td>69214</td>\n",
              "      <td>73690</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       CHROM       POS              ID  ... nhomalt nhomalt_male  nhomalt_female\n",
              "18860  chr17  43114391              []  ...       2            0               2\n",
              "11212  chr17  43088452   [rs936519818]  ...       2            0               2\n",
              "33872  chr17  43173828   [rs923058414]  ...       0            0               0\n",
              "11343  chr17  43089177   [rs956067034]  ...       0            0               0\n",
              "9579   chr17  43080462  [rs1044512678]  ...       0            0               0\n",
              "5978   chr17  43066019   [rs754006900]  ...       0            0               0\n",
              "25685  chr17  43139040  [rs1259203084]  ...       0            0               0\n",
              "14642  chr17  43100663  [rs1470061722]  ...       0            0               0\n",
              "24640  chr17  43135141   [rs375148945]  ...       0            0               0\n",
              "30813  chr17  43160144  [rs1024173306]  ...       0            0               0\n",
              "\n",
              "[10 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "crtWu-hNmqDc"
      },
      "source": [
        "Instead of splitting `AN` and `AC` values by sex we can analyze ancestry.\n",
        "\n",
        "### Query 1.3 - Allele Count by Ancestry\n",
        "A query to retrieve all variants in the region of interest along with `AN` and `AC` values for the following ancestries:\n",
        "* `afr`: African-American/African ancestry\n",
        "* `amr`: Latino ancestry\n",
        "* `eas`: East Asian ancestry\n",
        "* `nfe`: Non-Finnish European ancestry"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NV8yUwNcmqDc",
        "outputId": "d85946d7-2f62-49c8-ff19-d68bd9257131",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270,
          "referenced_widgets": [
            "fb40868d06374c94be3c8bfc870530a4",
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            "a4223bdbc21f42dab207d065c7c8dbbd",
            "9ee3dcf5c4aa419c90db855797aed7f7",
            "b00a1e70fc324d25828bb4229b8cb557",
            "22763183698a4729af0ec1b9a3d32814",
            "721e388866b04ea58d9cd9f9ad7291ed",
            "2b0eeac57d014ee29acd0ecac77c12c0"
          ]
        }
      },
      "source": [
        "# NOTE: For v2_1_1 the \"variant_type\" column must be replaced with \"alternate_bases.allele_type AS variant_type\"\n",
        "query_template = \"\"\"\n",
        "SELECT reference_name AS CHROM, \n",
        "       start_position AS POS,\n",
        "       names AS ID,\n",
        "       reference_bases AS REF,\n",
        "       alternate_bases.alt AS ALT,\n",
        "       AN_afr,\n",
        "       AN_amr,\n",
        "       AN_eas,\n",
        "       AN_nfe,\n",
        "       alternate_bases.AC_afr AS AC_afr,\n",
        "       alternate_bases.AC_amr AS AC_amr,\n",
        "       alternate_bases.AC_eas AS AC_eas,\n",
        "       alternate_bases.AC_nfe AS AC_nfe,\n",
        "FROM `bigquery-public-data.gnomAD.{GNOMAD_VER}__{CHROM}` AS main_table,\n",
        "     main_table.alternate_bases AS alternate_bases\n",
        "WHERE start_position >= {X} AND start_position <= {Y}\n",
        "ORDER BY 1,2\n",
        "\"\"\"\n",
        "query = query_template.format(GNOMAD_VER=gnomad_version_region,\n",
        "                              CHROM=chromosome_region, X=x, Y=y)\n",
        "\n",
        "stats_ancestry = run_query(query)\n",
        "stats_ancestry.head()\n"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fb40868d06374c94be3c8bfc870530a4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=34254.0, style=ProgressStyle(descriptio…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "This query processed 0.009765625 GB of data which is 0.001% of your 1 TB monthly free quota.\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>CHROM</th>\n",
              "      <th>POS</th>\n",
              "      <th>ID</th>\n",
              "      <th>REF</th>\n",
              "      <th>ALT</th>\n",
              "      <th>AN_afr</th>\n",
              "      <th>AN_amr</th>\n",
              "      <th>AN_eas</th>\n",
              "      <th>AN_nfe</th>\n",
              "      <th>AC_afr</th>\n",
              "      <th>AC_amr</th>\n",
              "      <th>AC_eas</th>\n",
              "      <th>AC_nfe</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43039296</td>\n",
              "      <td>[rs909752554]</td>\n",
              "      <td>A</td>\n",
              "      <td>T</td>\n",
              "      <td>42066</td>\n",
              "      <td>13650</td>\n",
              "      <td>3132</td>\n",
              "      <td>64586</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43039301</td>\n",
              "      <td>[rs1016951561]</td>\n",
              "      <td>T</td>\n",
              "      <td>C</td>\n",
              "      <td>42054</td>\n",
              "      <td>13660</td>\n",
              "      <td>3130</td>\n",
              "      <td>64578</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43039307</td>\n",
              "      <td>[]</td>\n",
              "      <td>T</td>\n",
              "      <td>C</td>\n",
              "      <td>42064</td>\n",
              "      <td>13652</td>\n",
              "      <td>3134</td>\n",
              "      <td>64572</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43039316</td>\n",
              "      <td>[]</td>\n",
              "      <td>A</td>\n",
              "      <td>G</td>\n",
              "      <td>42054</td>\n",
              "      <td>13660</td>\n",
              "      <td>3132</td>\n",
              "      <td>64576</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43039336</td>\n",
              "      <td>[]</td>\n",
              "      <td>TAG</td>\n",
              "      <td>T</td>\n",
              "      <td>42028</td>\n",
              "      <td>13650</td>\n",
              "      <td>3134</td>\n",
              "      <td>64566</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CHROM       POS              ID  REF  ... AC_afr  AC_amr  AC_eas  AC_nfe\n",
              "0  chr17  43039296   [rs909752554]    A  ...      1       0       0       0\n",
              "1  chr17  43039301  [rs1016951561]    T  ...      4       0       0       3\n",
              "2  chr17  43039307              []    T  ...      0       0       0       1\n",
              "3  chr17  43039316              []    A  ...      0       0       0       1\n",
              "4  chr17  43039336              []  TAG  ...      0       0       0       0\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kzIoUN7V9m_P"
      },
      "source": [
        "An example here would be to report the most common variant for each ancestry that was not present in any of the others."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OrA0IVH39nT-",
        "outputId": "fa3ace13-8940-4729-8d52-fd2db85dc9a1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        }
      },
      "source": [
        "stats_ancestry_amr=stats_ancestry.loc[\n",
        "                                      (stats_ancestry['AC_amr'] > 0) &\n",
        "                                      (stats_ancestry['AC_afr'] == 0) &\n",
        "                                      (stats_ancestry['AC_eas'] == 0) &\n",
        "                                      (stats_ancestry['AC_nfe'] == 0)].sort_values(by=('AC_amr'),\n",
        "                                                                                   ascending = False)\n",
        "stats_ancestry_amr.head(10)\n"
      ],
      "execution_count": 14,
      "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>CHROM</th>\n",
              "      <th>POS</th>\n",
              "      <th>ID</th>\n",
              "      <th>REF</th>\n",
              "      <th>ALT</th>\n",
              "      <th>AN_afr</th>\n",
              "      <th>AN_amr</th>\n",
              "      <th>AN_eas</th>\n",
              "      <th>AN_nfe</th>\n",
              "      <th>AC_afr</th>\n",
              "      <th>AC_amr</th>\n",
              "      <th>AC_eas</th>\n",
              "      <th>AC_nfe</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>19552</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43117514</td>\n",
              "      <td>[rs959656795]</td>\n",
              "      <td>C</td>\n",
              "      <td>T</td>\n",
              "      <td>41940</td>\n",
              "      <td>13624</td>\n",
              "      <td>3130</td>\n",
              "      <td>64538</td>\n",
              "      <td>0</td>\n",
              "      <td>116</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25105</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43136769</td>\n",
              "      <td>[rs184110348]</td>\n",
              "      <td>A</td>\n",
              "      <td>T</td>\n",
              "      <td>42036</td>\n",
              "      <td>13646</td>\n",
              "      <td>3130</td>\n",
              "      <td>64548</td>\n",
              "      <td>0</td>\n",
              "      <td>115</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2534</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43051138</td>\n",
              "      <td>[]</td>\n",
              "      <td>G</td>\n",
              "      <td>A</td>\n",
              "      <td>42044</td>\n",
              "      <td>13646</td>\n",
              "      <td>3134</td>\n",
              "      <td>64578</td>\n",
              "      <td>0</td>\n",
              "      <td>109</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27664</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43146696</td>\n",
              "      <td>[rs1465731657]</td>\n",
              "      <td>A</td>\n",
              "      <td>G</td>\n",
              "      <td>42050</td>\n",
              "      <td>13652</td>\n",
              "      <td>3132</td>\n",
              "      <td>64556</td>\n",
              "      <td>0</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22413</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43127095</td>\n",
              "      <td>[]</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>42054</td>\n",
              "      <td>13664</td>\n",
              "      <td>3126</td>\n",
              "      <td>64560</td>\n",
              "      <td>0</td>\n",
              "      <td>58</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17425</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43109147</td>\n",
              "      <td>[rs903868257]</td>\n",
              "      <td>T</td>\n",
              "      <td>G</td>\n",
              "      <td>41976</td>\n",
              "      <td>13610</td>\n",
              "      <td>3132</td>\n",
              "      <td>64480</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29698</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43155652</td>\n",
              "      <td>[rs1479788758]</td>\n",
              "      <td>C</td>\n",
              "      <td>A</td>\n",
              "      <td>42026</td>\n",
              "      <td>13646</td>\n",
              "      <td>3128</td>\n",
              "      <td>64566</td>\n",
              "      <td>0</td>\n",
              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21767</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43124833</td>\n",
              "      <td>[rs1325199812]</td>\n",
              "      <td>G</td>\n",
              "      <td>A</td>\n",
              "      <td>41994</td>\n",
              "      <td>13626</td>\n",
              "      <td>3130</td>\n",
              "      <td>64542</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8878</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43077467</td>\n",
              "      <td>[rs1197599545]</td>\n",
              "      <td>T</td>\n",
              "      <td>A</td>\n",
              "      <td>42000</td>\n",
              "      <td>13630</td>\n",
              "      <td>3128</td>\n",
              "      <td>64522</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33789</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43173619</td>\n",
              "      <td>[rs186203260]</td>\n",
              "      <td>T</td>\n",
              "      <td>G</td>\n",
              "      <td>42024</td>\n",
              "      <td>13646</td>\n",
              "      <td>3134</td>\n",
              "      <td>64550</td>\n",
              "      <td>0</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       CHROM       POS              ID REF  ... AC_afr  AC_amr  AC_eas  AC_nfe\n",
              "19552  chr17  43117514   [rs959656795]   C  ...      0     116       0       0\n",
              "25105  chr17  43136769   [rs184110348]   A  ...      0     115       0       0\n",
              "2534   chr17  43051138              []   G  ...      0     109       0       0\n",
              "27664  chr17  43146696  [rs1465731657]   A  ...      0      64       0       0\n",
              "22413  chr17  43127095              []   C  ...      0      58       0       0\n",
              "17425  chr17  43109147   [rs903868257]   T  ...      0      55       0       0\n",
              "29698  chr17  43155652  [rs1479788758]   C  ...      0      36       0       0\n",
              "21767  chr17  43124833  [rs1325199812]   G  ...      0      35       0       0\n",
              "8878   chr17  43077467  [rs1197599545]   T  ...      0      32       0       0\n",
              "33789  chr17  43173619   [rs186203260]   T  ...      0      32       0       0\n",
              "\n",
              "[10 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NxhS-7_fPhtx"
      },
      "source": [
        "### Query 1.4 - gnomAD Columns\n",
        "gnomAD tables have many more columns, you can find the full list of columns along with their description using the following query.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ecNDTKKwPlnG",
        "outputId": "e69f36b3-229e-47c1-98e7-5186837ead6b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349,
          "referenced_widgets": [
            "fcaf933853bb43d88ce5e48961e78a94",
            "9d11ef09131a4b4290414d5eeb1786b5",
            "bb8b8fdab53b4b03982ed60e9d3c4f06",
            "607e958e770f445388ce3b7c3d0a22f1",
            "9d8c3948165d4a899f9094a71149b8d3",
            "2d3cbde9cb7f48b7b33f6f4b3789f6a9",
            "3b3c82832cf54d5e818e9ab88153430d",
            "d2499a774f304189b7ffe805264684bf"
          ]
        }
      },
      "source": [
        "query_template = \"\"\"\n",
        "SELECT column_name, field_path, description\n",
        "FROM `bigquery-public-data`.gnomAD.INFORMATION_SCHEMA.COLUMN_FIELD_PATHS\n",
        "WHERE table_name = \"{GNOMAD_VER}__{CHROM}\"\n",
        "      AND column_name IN (\n",
        "          SELECT COLUMN_NAME\n",
        "          FROM `bigquery-public-data`.gnomAD.INFORMATION_SCHEMA.COLUMNS\n",
        "          WHERE table_name = \"{GNOMAD_VER}__{CHROM}\")\n",
        "\"\"\"\n",
        "query = query_template.format(GNOMAD_VER=gnomad_version_region,\n",
        "                              CHROM=chromosome_region)\n",
        "\n",
        "column_info = run_query(query)\n",
        "print('There are {} columns in `bigquery-public-data.gnomAD.{}__{}` table'.format(len(column_info.index),\n",
        "                                                                                  gnomad_version_region,\n",
        "                                                                                  chromosome_region))\n",
        "column_info.head(7)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "fcaf933853bb43d88ce5e48961e78a94",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=181.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "This query processed 0.01953125 GB of data which is 0.0019% of your 1 TB monthly free quota.\n",
            "There are 181 columns in `bigquery-public-data.gnomAD.v3_genomes__chr17` table\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>column_name</th>\n",
              "      <th>field_path</th>\n",
              "      <th>description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>reference_name</td>\n",
              "      <td>reference_name</td>\n",
              "      <td>Reference name.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>start_position</td>\n",
              "      <td>start_position</td>\n",
              "      <td>Start position (0-based). Corresponds to the f...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>end_position</td>\n",
              "      <td>end_position</td>\n",
              "      <td>End position (0-based). Corresponds to the fir...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>reference_bases</td>\n",
              "      <td>reference_bases</td>\n",
              "      <td>Reference bases.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>alternate_bases</td>\n",
              "      <td>alternate_bases</td>\n",
              "      <td>One record for each alternate base (if any).</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>alternate_bases</td>\n",
              "      <td>alternate_bases.alt</td>\n",
              "      <td>Alternate base.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>alternate_bases</td>\n",
              "      <td>alternate_bases.AC</td>\n",
              "      <td>Alternate allele count for samples</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       column_name  ...                                        description\n",
              "0   reference_name  ...                                    Reference name.\n",
              "1   start_position  ...  Start position (0-based). Corresponds to the f...\n",
              "2     end_position  ...  End position (0-based). Corresponds to the fir...\n",
              "3  reference_bases  ...                                   Reference bases.\n",
              "4  alternate_bases  ...       One record for each alternate base (if any).\n",
              "5  alternate_bases  ...                                    Alternate base.\n",
              "6  alternate_bases  ...                 Alternate allele count for samples\n",
              "\n",
              "[7 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yeOYzQ-KOosz"
      },
      "source": [
        "Using `column_info` dataframe you can find other available values for the ancestry slice:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h97Pwy6OOosz",
        "outputId": "ff697d7e-74ae-4eb5-f32f-b268f3354e7f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "AN_columns = column_info[column_info['column_name'].str.startswith('AN')] # Retain only rows that column_name starts with \"AN\"\n",
        "AN_columns = AN_columns[['column_name', 'description']] # Drop extra column (field_path)\n",
        "AN_columns = AN_columns.sort_values(by=['column_name']) # Sort by column_name\n",
        "AN_columns.head(11)"
      ],
      "execution_count": 16,
      "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>column_name</th>\n",
              "      <th>description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>129</th>\n",
              "      <td>AN</td>\n",
              "      <td>Total number of alleles in samples</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>AN_afr</td>\n",
              "      <td>Total number of alleles in samples of African-...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>AN_afr_female</td>\n",
              "      <td>Total number of alleles in female samples of A...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>151</th>\n",
              "      <td>AN_afr_male</td>\n",
              "      <td>Total number of alleles in male samples of Afr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>AN_ami</td>\n",
              "      <td>Total number of alleles in samples of Amish an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>AN_ami_female</td>\n",
              "      <td>Total number of alleles in female samples of A...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>AN_ami_male</td>\n",
              "      <td>Total number of alleles in male samples of Ami...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>AN_amr</td>\n",
              "      <td>Total number of alleles in samples of Latino a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>AN_amr_female</td>\n",
              "      <td>Total number of alleles in female samples of L...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>AN_amr_male</td>\n",
              "      <td>Total number of alleles in male samples of Lat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>AN_asj</td>\n",
              "      <td>Total number of alleles in samples of Ashkenaz...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       column_name                                        description\n",
              "129             AN                 Total number of alleles in samples\n",
              "159         AN_afr  Total number of alleles in samples of African-...\n",
              "172  AN_afr_female  Total number of alleles in female samples of A...\n",
              "151    AN_afr_male  Total number of alleles in male samples of Afr...\n",
              "155         AN_ami  Total number of alleles in samples of Amish an...\n",
              "158  AN_ami_female  Total number of alleles in female samples of A...\n",
              "177    AN_ami_male  Total number of alleles in male samples of Ami...\n",
              "174         AN_amr  Total number of alleles in samples of Latino a...\n",
              "169  AN_amr_female  Total number of alleles in female samples of L...\n",
              "168    AN_amr_male  Total number of alleles in male samples of Lat...\n",
              "167         AN_asj  Total number of alleles in samples of Ashkenaz..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6MJ03MHfOos2"
      },
      "source": [
        "Note that the corresponding values for `AC` and `AF` (Alternate allele frequency) exist under the `alternate_bases` column."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ilYK8FdHOos3",
        "outputId": "cc0dfd07-4028-488e-a7d3-d2b6995aa5c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "AC_columns = column_info[column_info['field_path'].str.startswith('alternate_bases.AC')] # Retain only rows that field_path starts with \"alternate_bases.AC\"\n",
        "AC_columns = AC_columns[['field_path', 'description']] # Drop extra column (column_name)\n",
        "AC_columns = AC_columns.sort_values(by=['field_path']) # Sort by field_path\n",
        "AC_columns.head(11)"
      ],
      "execution_count": 17,
      "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>field_path</th>\n",
              "      <th>description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>alternate_bases.AC</td>\n",
              "      <td>Alternate allele count for samples</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>alternate_bases.AC_afr</td>\n",
              "      <td>Alternate allele count for samples of African-...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>alternate_bases.AC_afr_female</td>\n",
              "      <td>Alternate allele count for female samples of A...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>alternate_bases.AC_afr_male</td>\n",
              "      <td>Alternate allele count for male samples of Afr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>alternate_bases.AC_ami</td>\n",
              "      <td>Alternate allele count for samples of Amish an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>alternate_bases.AC_ami_female</td>\n",
              "      <td>Alternate allele count for female samples of A...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>alternate_bases.AC_ami_male</td>\n",
              "      <td>Alternate allele count for male samples of Ami...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>alternate_bases.AC_amr</td>\n",
              "      <td>Alternate allele count for samples of Latino a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>alternate_bases.AC_amr_female</td>\n",
              "      <td>Alternate allele count for female samples of L...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>alternate_bases.AC_amr_male</td>\n",
              "      <td>Alternate allele count for male samples of Lat...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>alternate_bases.AC_asj</td>\n",
              "      <td>Alternate allele count for samples of Ashkenaz...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       field_path                                        description\n",
              "6              alternate_bases.AC                 Alternate allele count for samples\n",
              "39         alternate_bases.AC_afr  Alternate allele count for samples of African-...\n",
              "79  alternate_bases.AC_afr_female  Alternate allele count for female samples of A...\n",
              "15    alternate_bases.AC_afr_male  Alternate allele count for male samples of Afr...\n",
              "27         alternate_bases.AC_ami  Alternate allele count for samples of Amish an...\n",
              "36  alternate_bases.AC_ami_female  Alternate allele count for female samples of A...\n",
              "94    alternate_bases.AC_ami_male  Alternate allele count for male samples of Ami...\n",
              "85         alternate_bases.AC_amr  Alternate allele count for samples of Latino a...\n",
              "70  alternate_bases.AC_amr_female  Alternate allele count for female samples of L...\n",
              "66    alternate_bases.AC_amr_male  Alternate allele count for male samples of Lat...\n",
              "63         alternate_bases.AC_asj  Alternate allele count for samples of Ashkenaz..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CvjPXfN3Oos5"
      },
      "source": [
        "Please refer to gnomAD release announcements ([v2.1](https://gnomad.broadinstitute.org/blog/2018-10-gnomad-v2-1/) and [v3.0](https://gnomad.broadinstitute.org/blog/2019-10-gnomad-v3-0/)) for more details about demographics and annotation slices.\n",
        "\n",
        "The next query showcases how to use `AN` and `AC` values.\n",
        "\n",
        "### Query 1.5 - Burden of Mutation\n",
        "Given a region of interest, compute the burden of mutation for the gene along with other summary statistics."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9aQNKh2wOos6",
        "outputId": "9022b209-b784-4ac4-99ad-bf34f21be163",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163,
          "referenced_widgets": [
            "cc52c3395ffa42dfb14e5e8bd4489106",
            "1deadc320f094ac58e564cb934284b6e",
            "8c2191e8bc664e1487ec847fb6d3d33f",
            "6000ae1b37e9420bab6cf8311937795a",
            "d68e66fab6084cc28d3a09824e935f1b",
            "897378a97ae84b10b092a24e5da5be42",
            "48e02cc90b4c4de1ad0d8ef350550527",
            "2aa0143a9cbe4d578a4e9f1f7896637c"
          ]
        }
      },
      "source": [
        "query_template = \"\"\"\n",
        "WITH summary_stats AS (\n",
        "SELECT\n",
        "  COUNT(1) AS num_variants,\n",
        "  SUM(ARRAY_LENGTH(alternate_bases)) AS num_alts,  # This data appears to be bi-allelic.\n",
        "  SUM((SELECT alt.AC FROM UNNEST(alternate_bases) AS alt)) AS sum_AC,\n",
        "  APPROX_QUANTILES((SELECT alt.AC FROM UNNEST(alternate_bases) AS alt), 10) AS quantiles_AC,\n",
        "  SUM(AN) AS sum_AN,\n",
        "  APPROX_QUANTILES(AN, 10) AS quantiles_AN,\n",
        "  -- Also include some information from Variant Effect Predictor (VEP).\n",
        "  STRING_AGG(DISTINCT (SELECT annot.symbol FROM UNNEST(alternate_bases) AS alt,\n",
        "                                                UNNEST(vep) AS annot LIMIT 1), ', ') AS genes\n",
        "FROM `bigquery-public-data.gnomAD.{GNOMAD_VER}__{CHROM}` AS main_table\n",
        "WHERE start_position >= {X} AND start_position <= {Y})\n",
        "---\n",
        "--- The resulting quantiles and burden_of_mutation score give a very rough idea of the mutation\n",
        "--- rate within these particular regions of the genome. This query could be further refined to\n",
        "--- compute over smaller windows within the regions of interest and/or over different groupings\n",
        "--- of AC and AN by population.\n",
        "---\n",
        "SELECT\n",
        "  ROUND(({Y} - {X}) / num_variants, 3) AS burden_of_mutation,\n",
        "  *,\n",
        "FROM summary_stats\n",
        "\"\"\"\n",
        "query = query_template.format(GNOMAD_VER=gnomad_version_region,\n",
        "                              CHROM=chromosome_region, X=x, Y=y)\n",
        "\n",
        "burden_of_mu = run_query(query)\n",
        "burden_of_mu.head()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cc52c3395ffa42dfb14e5e8bd4489106",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1.0, style=ProgressStyle(description_wi…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "This query processed 0.009765625 GB of data which is 0.001% of your 1 TB monthly free quota.\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>burden_of_mutation</th>\n",
              "      <th>num_variants</th>\n",
              "      <th>num_alts</th>\n",
              "      <th>sum_AC</th>\n",
              "      <th>quantiles_AC</th>\n",
              "      <th>sum_AN</th>\n",
              "      <th>quantiles_AN</th>\n",
              "      <th>genes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.969</td>\n",
              "      <td>34254</td>\n",
              "      <td>34254</td>\n",
              "      <td>16394200</td>\n",
              "      <td>[0, 0, 1, 1, 1, 1, 2, 3, 5, 19, 143046]</td>\n",
              "      <td>4625933720</td>\n",
              "      <td>[274, 124506, 140022, 142428, 142944, 143120, ...</td>\n",
              "      <td>BRCA1, NBR1, AC060780.1, AC060780.2, AC060780....</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   burden_of_mutation  ...                                              genes\n",
              "0               3.969  ...  BRCA1, NBR1, AC060780.1, AC060780.2, AC060780....\n",
              "\n",
              "[1 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rteMw6VYOos8"
      },
      "source": [
        "The other column to use is `alternate_bases.vep` which contains the [VEP annotaions](https://uswest.ensembl.org/info/docs/tools/vep/index.html) for each variant."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHkpgp6kOos9",
        "outputId": "84dc2ac1-ff8d-4c92-c0dd-2b6b1d2fe51f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        }
      },
      "source": [
        "vep_columns = column_info[column_info['field_path'].str.startswith('alternate_bases.vep')] # Retain only rows that field_path starts with \"alternate_bases.vep\"\n",
        "vep_columns = vep_columns[['field_path', 'description']] # Drop extra column (column_name)\n",
        "vep_columns.head(22)"
      ],
      "execution_count": 19,
      "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>field_path</th>\n",
              "      <th>description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>110</th>\n",
              "      <td>alternate_bases.vep</td>\n",
              "      <td>List of vep annotations for this alternate.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>111</th>\n",
              "      <td>alternate_bases.vep.allele</td>\n",
              "      <td>The ALT part of the annotation field.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>112</th>\n",
              "      <td>alternate_bases.vep.Consequence</td>\n",
              "      <td>Consequence type of this variant</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>113</th>\n",
              "      <td>alternate_bases.vep.IMPACT</td>\n",
              "      <td>The impact modifier for the consequence type</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>114</th>\n",
              "      <td>alternate_bases.vep.SYMBOL</td>\n",
              "      <td>The gene symbol</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>115</th>\n",
              "      <td>alternate_bases.vep.Gene</td>\n",
              "      <td>Ensembl stable ID of affected gene</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>116</th>\n",
              "      <td>alternate_bases.vep.Feature_type</td>\n",
              "      <td>Type of feature. Currently one of Transcript, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>alternate_bases.vep.Feature</td>\n",
              "      <td>Ensembl stable ID of feature</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>118</th>\n",
              "      <td>alternate_bases.vep.BIOTYPE</td>\n",
              "      <td>Biotype of transcript or regulatory feature</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>alternate_bases.vep.EXON</td>\n",
              "      <td>The exon number (out of total number)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>alternate_bases.vep.INTRON</td>\n",
              "      <td>The intron number (out of total number)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>alternate_bases.vep.empty</td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           field_path                                        description\n",
              "110               alternate_bases.vep        List of vep annotations for this alternate.\n",
              "111        alternate_bases.vep.allele              The ALT part of the annotation field.\n",
              "112   alternate_bases.vep.Consequence                   Consequence type of this variant\n",
              "113        alternate_bases.vep.IMPACT       The impact modifier for the consequence type\n",
              "114        alternate_bases.vep.SYMBOL                                    The gene symbol\n",
              "115          alternate_bases.vep.Gene                 Ensembl stable ID of affected gene\n",
              "116  alternate_bases.vep.Feature_type  Type of feature. Currently one of Transcript, ...\n",
              "117       alternate_bases.vep.Feature                       Ensembl stable ID of feature\n",
              "118       alternate_bases.vep.BIOTYPE        Biotype of transcript or regulatory feature\n",
              "119          alternate_bases.vep.EXON              The exon number (out of total number)\n",
              "120        alternate_bases.vep.INTRON            The intron number (out of total number)\n",
              "121         alternate_bases.vep.empty                                                   "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NIc8J5NEOos_"
      },
      "source": [
        "The next query showcases how to use some of the `vep` annotation values.\n",
        "\n",
        "### Query 1.6 - VEP Annotations\n",
        "Given a region of interest, examine `vep` annotations to pull out [missense variants](https://en.wikipedia.org/wiki/Missense_mutation).\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQsJc6MXOos_",
        "outputId": "84df7da3-5348-47a6-9a54-198305291005",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270,
          "referenced_widgets": [
            "e47f70c5d839401d8c354891008501cb",
            "376019ce8a104c248a03bda9c708f055",
            "d57cc2815b204e33b728520f4a0b7214",
            "dc134b16d6de48148b7f76debce4686e",
            "234edd320613450c9dc00cd5c4bd42eb",
            "32997a3978e34d77932871befe9bee71",
            "31f5241372c24f4cbc4242c58ab6f0a7",
            "b96568addc7b436bab0603eecd6816fc"
          ]
        }
      },
      "source": [
        "query_template = \"\"\"\n",
        "SELECT reference_name AS CHROM, \n",
        "       start_position AS POS,\n",
        "       names AS ID,\n",
        "       reference_bases AS REF,\n",
        "       alternate_bases.alt AS ALT,\n",
        "       vep.Consequence AS Consequence,\n",
        "       vep.IMPACT AS Impact,\n",
        "       vep.SYMBOL AS Symbol,\n",
        "       vep.Gene AS Gene,\n",
        "       vep.EXON AS EXON,\n",
        "       vep.INTRON AS INTRON,\n",
        "       {EXTRA_COLS}\n",
        "FROM `bigquery-public-data.gnomAD.{GNOMAD_VER}__{CHROM}` AS main_table,\n",
        "     main_table.alternate_bases AS alternate_bases,\n",
        "     alternate_bases.vep AS vep\n",
        "WHERE start_position >= {X} AND start_position <= {Y} AND\n",
        "      REGEXP_CONTAINS(vep.Consequence, r\"missense_variant\")\n",
        "ORDER BY start_position, reference_bases\n",
        "\"\"\"\n",
        "query = query_template.format(GNOMAD_VER=gnomad_version_region,\n",
        "                              CHROM=chromosome_region,\n",
        "                              EXTRA_COLS=extra_columns, X=x, Y=y)\n",
        "\n",
        "neg_variants = run_query(query)\n",
        "neg_variants.head()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e47f70c5d839401d8c354891008501cb",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=5162.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "This query processed 0.0341796875 GB of data which is 0.0033% of your 1 TB monthly free quota.\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>CHROM</th>\n",
              "      <th>POS</th>\n",
              "      <th>ID</th>\n",
              "      <th>REF</th>\n",
              "      <th>ALT</th>\n",
              "      <th>Consequence</th>\n",
              "      <th>Impact</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>Gene</th>\n",
              "      <th>EXON</th>\n",
              "      <th>INTRON</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43045684</td>\n",
              "      <td>[rs80357183]</td>\n",
              "      <td>T</td>\n",
              "      <td>A</td>\n",
              "      <td>missense_variant</td>\n",
              "      <td>MODERATE</td>\n",
              "      <td>BRCA1</td>\n",
              "      <td>ENSG00000012048</td>\n",
              "      <td>22/22</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43045684</td>\n",
              "      <td>[rs80357183]</td>\n",
              "      <td>T</td>\n",
              "      <td>A</td>\n",
              "      <td>missense_variant</td>\n",
              "      <td>MODERATE</td>\n",
              "      <td>BRCA1</td>\n",
              "      <td>ENSG00000012048</td>\n",
              "      <td>23/23</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43045684</td>\n",
              "      <td>[rs80357183]</td>\n",
              "      <td>T</td>\n",
              "      <td>A</td>\n",
              "      <td>missense_variant</td>\n",
              "      <td>MODERATE</td>\n",
              "      <td>BRCA1</td>\n",
              "      <td>ENSG00000012048</td>\n",
              "      <td>24/24</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43045684</td>\n",
              "      <td>[rs80357183]</td>\n",
              "      <td>T</td>\n",
              "      <td>A</td>\n",
              "      <td>missense_variant</td>\n",
              "      <td>MODERATE</td>\n",
              "      <td>BRCA1</td>\n",
              "      <td>ENSG00000012048</td>\n",
              "      <td>23/23</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>chr17</td>\n",
              "      <td>43045684</td>\n",
              "      <td>[rs80357183]</td>\n",
              "      <td>T</td>\n",
              "      <td>A</td>\n",
              "      <td>missense_variant</td>\n",
              "      <td>MODERATE</td>\n",
              "      <td>BRCA1</td>\n",
              "      <td>ENSG00000012048</td>\n",
              "      <td>22/22</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   CHROM       POS            ID REF  ... Symbol             Gene   EXON INTRON\n",
              "0  chr17  43045684  [rs80357183]   T  ...  BRCA1  ENSG00000012048  22/22   None\n",
              "1  chr17  43045684  [rs80357183]   T  ...  BRCA1  ENSG00000012048  23/23   None\n",
              "2  chr17  43045684  [rs80357183]   T  ...  BRCA1  ENSG00000012048  24/24   None\n",
              "3  chr17  43045684  [rs80357183]   T  ...  BRCA1  ENSG00000012048  23/23   None\n",
              "4  chr17  43045684  [rs80357183]   T  ...  BRCA1  ENSG00000012048  22/22   None\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Agb4xFx2mqDi"
      },
      "source": [
        "# gnomAD Queries Type2: Explore an entire chromosome\n",
        "\n",
        "This section queries across an entire chromosome.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M-eJUtlOEIgY",
        "outputId": "f60736b4-47f9-41d7-a527-c3a6ec2d8e7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 96,
          "referenced_widgets": [
            "04b04101f4d7402ca2f7b0ff550f883a",
            "6056f94e9ca14f2cbee0481cb40a7c4f",
            "62abe0d1f96d4744b1922d169bf337f3",
            "6763c27c30ab42abb88d23062c891bf7",
            "3d9d11ed08794c43a866b415672a1ea2",
            "3882ffc1042e4e2caeffc3c55a8bc2ea"
          ]
        }
      },
      "source": [
        "import ipywidgets as widgets\n",
        "\n",
        "print(\"Variables for Chromosome (Type 2) queries\")\n",
        "\n",
        "gnomad_version_widget_chr = widgets.Dropdown(\n",
        "    options=['v2_1_1_exomes', 'v2_1_1_genomes', 'v3_genomes'],\n",
        "    value='v2_1_1_exomes',\n",
        "    description='gnomAD version:',\n",
        "    disabled=False,\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "display(gnomad_version_widget_chr)\n",
        "\n",
        "chromosome_widget_chr = widgets.Dropdown(\n",
        "    options=['chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7', 'chr8',\n",
        "             'chr9', 'chr10', 'chr11', 'chr12', 'chr13', 'chr14', 'chr15',\n",
        "             'chr16', 'chr17', 'chr18', 'chr19', 'chr20', 'chr21', 'chr22',\n",
        "             'chrX', 'chrY'],\n",
        "    value='chr17',\n",
        "    description='Chromosome:',\n",
        "    disabled=False,\n",
        "    style={'description_width': 'initial'}\n",
        ")\n",
        "\n",
        "display(chromosome_widget_chr)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Variables for Chromosome (Type 2) queries\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "04b04101f4d7402ca2f7b0ff550f883a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Dropdown(description='gnomAD version:', options=('v2_1_1_exomes', 'v2_1_1_genomes', 'v3_genomes'), style=Descr…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6763c27c30ab42abb88d23062c891bf7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Dropdown(description='Chromosome:', index=16, options=('chr1', 'chr2', 'chr3', 'chr4', 'chr5', 'chr6', 'chr7',…"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "etWAza7lEpz6",
        "outputId": "f93eb191-13b8-4ed2-ff4f-a744b17fd74b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "# Set the variables for the rest of the Chromosome (Type 2) queries based on the values above.\n",
        "gnomad_version_chr=gnomad_version_widget_chr.value\n",
        "chromosome_chr=chromosome_widget_chr.value\n",
        "print('Running chromosome (Type 2) queries on gnomAD version: {}, chromosome: {}'.format(\n",
        "    gnomad_version_chr,\n",
        "    chromosome_chr\n",
        "))\n",
        "\n",
        "if gnomad_version_chr.startswith('v3'):\n",
        "  # Variant type (snv, indel, multi-snv, multi-indel, or mixed) is stored under difference columns in V2 and V3\n",
        "  variant_type_col = 'variant_type'\n",
        "  extra_columns = ''\n",
        "else:\n",
        "  variant_type_col = 'alternate_bases. allele_type'\n",
        "  # These vep columns only exist in V2\n",
        "  extra_columns = 'vep.STRAND AS STRAND, vep.Protein_position AS Protein_pos,'"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Running chromosome (Type 2) queries on gnomAD version: v2_1_1_exomes, chromosome: chr17\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "17GsksosA7hb"
      },
      "source": [
        "## Query 2.1 - Find alleles that occur at least in 90% of samples\n",
        "Find all variants on the selected chromosome that were observed in at least 90% of samples. In other words, this query finds variants where allele frequency is very high for non-REF alleles."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n69cLjhfmqDi",
        "outputId": "f2d5f94f-509a-4151-ac70-9161466efec6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270,
          "referenced_widgets": [
            "341c28def461400cbff409fa8e46528a",
            "55b9f8c1c5ab417391ba9db32b6049f6",
            "9e03d5c4a3f1434aafac076ccf45ddf6",
            "3db540ad2f184c5b90b6e28e8b3da3c1",
            "2cd8457577c549c39fae098845315cb3",
            "cb687c033e874ee0a8a915964701424e",
            "8d4a615cebd845f1bb7c84b8cc43adb1",
            "d0dd08b93b8d48f4bbb2f36764c53e59"
          ]
        }
      },
      "source": [
        "query_template = \"\"\"\n",
        "SELECT reference_name AS CHROM, \n",
        "       start_position AS POS,\n",
        "       names AS ID,\n",
        "       reference_bases AS REF,\n",
        "       alternate_bases.alt AS ALT,\n",
        "       vep.SYMBOL AS Symbol,\n",
        "       vep.Gene AS Gene,\n",
        "       AN,\n",
        "       alternate_bases.AC AS AC,\n",
        "       alternate_bases.AF AS AF,\n",
        "       vep.EXON AS EXON,\n",
        "       vep.INTRON AS INTRON,\n",
        "       {EXTRA_COLS}\n",
        "FROM `bigquery-public-data.gnomAD.{GNOMAD_VER}__{CHROM}` AS main_table,\n",
        "     main_table.alternate_bases AS alternate_bases,\n",
        "     alternate_bases.vep AS vep\n",
        "WHERE AN > 0 AND AF > 0.9 \n",
        "ORDER BY AN DESC\n",
        "\"\"\"\n",
        "query = query_template.format(GNOMAD_VER=gnomad_version_chr,\n",
        "                              CHROM=chromosome_chr,\n",
        "                              EXTRA_COLS=extra_columns)\n",
        "\n",
        "high_af = run_query(query)\n",
        "high_af.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "341c28def461400cbff409fa8e46528a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=3973.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "This query processed 0.412109375 GB of data which is 0.0402% of your 1 TB monthly free quota.\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>CHROM</th>\n",
              "      <th>POS</th>\n",
              "      <th>ID</th>\n",
              "      <th>REF</th>\n",
              "      <th>ALT</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>Gene</th>\n",
              "      <th>AN</th>\n",
              "      <th>AC</th>\n",
              "      <th>AF</th>\n",
              "      <th>EXON</th>\n",
              "      <th>INTRON</th>\n",
              "      <th>STRAND</th>\n",
              "      <th>Protein_pos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17</td>\n",
              "      <td>75190845</td>\n",
              "      <td>[rs673918]</td>\n",
              "      <td>A</td>\n",
              "      <td>C</td>\n",
              "      <td>SEC14L1</td>\n",
              "      <td>ENSG00000129657</td>\n",
              "      <td>251494</td>\n",
              "      <td>250755</td>\n",
              "      <td>0.997062</td>\n",
              "      <td>9/20</td>\n",
              "      <td>None</td>\n",
              "      <td>1.0</td>\n",
              "      <td>188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>17</td>\n",
              "      <td>75190845</td>\n",
              "      <td>[rs673918]</td>\n",
              "      <td>A</td>\n",
              "      <td>C</td>\n",
              "      <td>SEC14L1</td>\n",
              "      <td>ENSG00000129657</td>\n",
              "      <td>251494</td>\n",
              "      <td>250755</td>\n",
              "      <td>0.997062</td>\n",
              "      <td>7/18</td>\n",
              "      <td>None</td>\n",
              "      <td>1.0</td>\n",
              "      <td>188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17</td>\n",
              "      <td>75190845</td>\n",
              "      <td>[rs673918]</td>\n",
              "      <td>A</td>\n",
              "      <td>C</td>\n",
              "      <td>SEC14L1</td>\n",
              "      <td>ENSG00000129657</td>\n",
              "      <td>251494</td>\n",
              "      <td>250755</td>\n",
              "      <td>0.997062</td>\n",
              "      <td>8/18</td>\n",
              "      <td>None</td>\n",
              "      <td>1.0</td>\n",
              "      <td>188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>17</td>\n",
              "      <td>75190845</td>\n",
              "      <td>[rs673918]</td>\n",
              "      <td>A</td>\n",
              "      <td>C</td>\n",
              "      <td>SEC14L1</td>\n",
              "      <td>ENSG00000129657</td>\n",
              "      <td>251494</td>\n",
              "      <td>250755</td>\n",
              "      <td>0.997062</td>\n",
              "      <td>5/15</td>\n",
              "      <td>None</td>\n",
              "      <td>1.0</td>\n",
              "      <td>154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17</td>\n",
              "      <td>75190845</td>\n",
              "      <td>[rs673918]</td>\n",
              "      <td>A</td>\n",
              "      <td>C</td>\n",
              "      <td>SEC14L1</td>\n",
              "      <td>ENSG00000129657</td>\n",
              "      <td>251494</td>\n",
              "      <td>250755</td>\n",
              "      <td>0.997062</td>\n",
              "      <td>7/17</td>\n",
              "      <td>None</td>\n",
              "      <td>1.0</td>\n",
              "      <td>188</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CHROM       POS          ID REF  ...  EXON INTRON STRAND  Protein_pos\n",
              "0    17  75190845  [rs673918]   A  ...  9/20   None    1.0          188\n",
              "1    17  75190845  [rs673918]   A  ...  7/18   None    1.0          188\n",
              "2    17  75190845  [rs673918]   A  ...  8/18   None    1.0          188\n",
              "3    17  75190845  [rs673918]   A  ...  5/15   None    1.0          154\n",
              "4    17  75190845  [rs673918]   A  ...  7/17   None    1.0          188\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GxTI7n_OmqDn"
      },
      "source": [
        "We can condense the result and only list gene symbols and the number of variants found in the previous query:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BnoQ8MGKmqDo",
        "outputId": "2d0e5886-3c2d-468d-9511-09e79c494ec7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "high_af.groupby('Symbol').count()[['POS']].sort_values(by=['POS'],\n",
        "                                                               ascending=False).head(10)"
      ],
      "execution_count": 25,
      "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>POS</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Symbol</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MYO15B</th>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GPS1</th>\n",
              "      <td>74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TMEM199</th>\n",
              "      <td>69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RECQL5</th>\n",
              "      <td>56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NUP85</th>\n",
              "      <td>54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MFSD11</th>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PELP1</th>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KIAA0195</th>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SARM1</th>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AARSD1</th>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          POS\n",
              "Symbol       \n",
              "MYO15B    100\n",
              "GPS1       74\n",
              "TMEM199    69\n",
              "RECQL5     56\n",
              "NUP85      54\n",
              "MFSD11     51\n",
              "PELP1      51\n",
              "KIAA0195   46\n",
              "SARM1      45\n",
              "AARSD1     42"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w7A9jlrHmqDq"
      },
      "source": [
        "## Query 2.2 - Top variants by ancenstry difference\n",
        "Find top 1,000 variants on the selected chromosome that show the most significant differences between male samples of African-American ancestry versus Finnish ancestry"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ggChrvDlmqDq",
        "outputId": "63873e41-b44c-44eb-80c7-05600c3d2861",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290,
          "referenced_widgets": [
            "4a18a41841084092890f420bd7f4a869",
            "5023a1389ddd4be8b8c011affa62b126",
            "dfe398fb484b4747b3caeb402c6bfa52",
            "c166823727a94ee280bf8444f6ecfab7",
            "f4e8ac8169e94c8c88cca77ffe452e18",
            "d42b5c8b1c47443a8f9b9e5bccfb6f8f",
            "4c5daa97a62f4ba5a0a57cb97679159b",
            "6313ca4356834e5a982ce240112454cb"
          ]
        }
      },
      "source": [
        "query_template = \"\"\"\n",
        "SELECT reference_name AS CHROM, \n",
        "       start_position AS POS,\n",
        "       names AS ID,\n",
        "       reference_bases AS REF,\n",
        "       alternate_bases.alt AS ALT,\n",
        "       vep.SYMBOL AS Symbol,\n",
        "       vep.Gene AS Gene,\n",
        "       AN,\n",
        "       alternate_bases.AC_fin_male AS AC_fin_m,\n",
        "       alternate_bases.AC_afr_male AS AC_afr_m,\n",
        "       ROUND(ABS(alternate_bases.AC_fin_male - alternate_bases.AC_afr_male) / alternate_bases.AC_male, 3) AS fin_afr_diff,\n",
        "       vep.EXON AS EXON,\n",
        "       vep.INTRON AS INTRON,\n",
        "       {EXTRA_COLS} \n",
        "FROM `bigquery-public-data.gnomAD.{GNOMAD_VER}__{CHROM}` AS main_table,\n",
        "     main_table.alternate_bases AS alternate_bases,\n",
        "     alternate_bases.vep AS vep\n",
        "WHERE vep.SYMBOL IS NOT NULL AND\n",
        "      alternate_bases.AC_male > 20 AND alternate_bases.AC_fin_male > 0 AND alternate_bases.AC_afr_male > 0\n",
        "order by fin_afr_diff DESC\n",
        "LIMIT 1000\n",
        "\"\"\"\n",
        "\n",
        "query = query_template.format(GNOMAD_VER=gnomad_version_chr,\n",
        "                              CHROM=chromosome_chr,\n",
        "                              EXTRA_COLS=extra_columns)                              \n",
        "\n",
        "stats_chr_ancestry = run_query(query)\n",
        "stats_chr_ancestry.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4a18a41841084092890f420bd7f4a869",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1000.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "This query processed 0.4189453125 GB of data which is 0.0409% of your 1 TB monthly free quota.\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>CHROM</th>\n",
              "      <th>POS</th>\n",
              "      <th>ID</th>\n",
              "      <th>REF</th>\n",
              "      <th>ALT</th>\n",
              "      <th>Symbol</th>\n",
              "      <th>Gene</th>\n",
              "      <th>AN</th>\n",
              "      <th>AC_fin_m</th>\n",
              "      <th>AC_afr_m</th>\n",
              "      <th>fin_afr_diff</th>\n",
              "      <th>EXON</th>\n",
              "      <th>INTRON</th>\n",
              "      <th>STRAND</th>\n",
              "      <th>Protein_pos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17</td>\n",
              "      <td>76557038</td>\n",
              "      <td>[rs193275811]</td>\n",
              "      <td>G</td>\n",
              "      <td>A</td>\n",
              "      <td>DNAH17</td>\n",
              "      <td>ENSG00000187775</td>\n",
              "      <td>150282</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>0.929</td>\n",
              "      <td>None</td>\n",
              "      <td>8/12</td>\n",
              "      <td>-1</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>17</td>\n",
              "      <td>76557038</td>\n",
              "      <td>[rs193275811]</td>\n",
              "      <td>G</td>\n",
              "      <td>A</td>\n",
              "      <td>DNAH17</td>\n",
              "      <td>ENSG00000187775</td>\n",
              "      <td>150282</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>0.929</td>\n",
              "      <td>None</td>\n",
              "      <td>12/80</td>\n",
              "      <td>-1</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17</td>\n",
              "      <td>76557038</td>\n",
              "      <td>[rs193275811]</td>\n",
              "      <td>G</td>\n",
              "      <td>A</td>\n",
              "      <td>DNAH17</td>\n",
              "      <td>ENSG00000187775</td>\n",
              "      <td>150282</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>0.929</td>\n",
              "      <td>None</td>\n",
              "      <td>12/80</td>\n",
              "      <td>-1</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>17</td>\n",
              "      <td>76116856</td>\n",
              "      <td>[rs146998467]</td>\n",
              "      <td>C</td>\n",
              "      <td>T</td>\n",
              "      <td>TMC6</td>\n",
              "      <td>ENSG00000141524</td>\n",
              "      <td>250306</td>\n",
              "      <td>228</td>\n",
              "      <td>1</td>\n",
              "      <td>0.919</td>\n",
              "      <td>13/13</td>\n",
              "      <td>None</td>\n",
              "      <td>-1</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17</td>\n",
              "      <td>76116856</td>\n",
              "      <td>[rs146998467]</td>\n",
              "      <td>C</td>\n",
              "      <td>T</td>\n",
              "      <td>TMC6</td>\n",
              "      <td>ENSG00000141524</td>\n",
              "      <td>250306</td>\n",
              "      <td>228</td>\n",
              "      <td>1</td>\n",
              "      <td>0.919</td>\n",
              "      <td>None</td>\n",
              "      <td>10/11</td>\n",
              "      <td>-1</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CHROM       POS             ID REF  ...   EXON INTRON STRAND  Protein_pos\n",
              "0    17  76557038  [rs193275811]   G  ...   None   8/12     -1         None\n",
              "1    17  76557038  [rs193275811]   G  ...   None  12/80     -1         None\n",
              "2    17  76557038  [rs193275811]   G  ...   None  12/80     -1         None\n",
              "3    17  76116856  [rs146998467]   C  ...  13/13   None     -1         None\n",
              "4    17  76116856  [rs146998467]   C  ...   None  10/11     -1         None\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5xkN7ewQmqDw"
      },
      "source": [
        "## Query 2.3 - Find genes with high number of INDELs\n",
        "Find top 1000 genes with the highest number of INDELs on the selected chromosome."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4FvIPEhlmqDw",
        "outputId": "20241587-8b13-45ea-de82-106803df9af3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425,
          "referenced_widgets": [
            "6ce1c3fc9fc4417e92dc32d44711fab7",
            "32c06db11e794c9b812d573994616eec",
            "475d1fb2eb754e90ac243023d5e24b47",
            "5f6f6262cdc045f78129880809618f0c",
            "758fa652c43145628fc343bc30e1bee8",
            "49a2bc37ff6d49688dde2eca2d0a3442",
            "62810141dbb5435fa5dd9e7ed2daf352",
            "36250839218246b5a28819c8b2a93f8d"
          ]
        }
      },
      "source": [
        "query_template = \"\"\"\n",
        "SELECT Symbol, count(1) AS num_indels\n",
        "FROM\n",
        "    (\n",
        "    SELECT DISTINCT \n",
        "        start_position AS str_pos,\n",
        "        alternate_bases.alt AS alt,\n",
        "        vep.SYMBOL AS Symbol,\n",
        "        {VAR_TYPE_COL} AS variant_type,\n",
        "    FROM `bigquery-public-data.gnomAD.{GNOMAD_VER}__{CHROM}` AS main_table,\n",
        "        main_table.alternate_bases AS alternate_bases,\n",
        "        alternate_bases.vep AS vep\n",
        "    WHERE vep.SYMBOL IS NOT NULL AND variant_type IN ('ins', 'del', 'indel')\n",
        "    )\n",
        "GROUP BY 1\n",
        "ORDER BY 2 DESC\n",
        "LIMIT 1000\n",
        "\"\"\"\n",
        "query = query_template.format(GNOMAD_VER=gnomad_version_chr,\n",
        "                              CHROM=chromosome_chr,\n",
        "                              VAR_TYPE_COL=variant_type_col)\n",
        "\n",
        "indel_stats = run_query(query)\n",
        "indel_stats.head(10)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6ce1c3fc9fc4417e92dc32d44711fab7",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1000.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "This query processed 0.09765625 GB of data which is 0.0095% of your 1 TB monthly free quota.\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>Symbol</th>\n",
              "      <th>num_indels</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>CTC-297N7.11</td>\n",
              "      <td>363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RP11-799N11.1</td>\n",
              "      <td>344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>DNAH17</td>\n",
              "      <td>278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RP11-192H23.4</td>\n",
              "      <td>240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RNF213</td>\n",
              "      <td>221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>MYO15A</td>\n",
              "      <td>211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>NCOR1</td>\n",
              "      <td>208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>DNAH9</td>\n",
              "      <td>207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>RP11-68I3.2</td>\n",
              "      <td>202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>MYO15B</td>\n",
              "      <td>198</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Symbol  num_indels\n",
              "0   CTC-297N7.11         363\n",
              "1  RP11-799N11.1         344\n",
              "2         DNAH17         278\n",
              "3  RP11-192H23.4         240\n",
              "4         RNF213         221\n",
              "5         MYO15A         211\n",
              "6          NCOR1         208\n",
              "7          DNAH9         207\n",
              "8    RP11-68I3.2         202\n",
              "9         MYO15B         198"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DteicRVbBDfu"
      },
      "source": [
        "## Query 2.4 - Find distribution of SNVs across a chromosome\n",
        "Find the distribution of SNVs across the selected chromosome. In order to be able to plot the result we group base pairs into buckets of size 10,000.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FojNXJY6DIw0",
        "outputId": "5942ecb9-7b61-45df-98ea-5e767af0c5fb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270,
          "referenced_widgets": [
            "b2406dd554ef42cb88464b7aed20845f",
            "f302bde312794b5286c9b04ae14f3495",
            "8280608dde5d4b6c9fdb32cdf8037278",
            "21fb707bd3bc4c19846b1195b5f3d578",
            "91812a525cc444589b38637fa8e73472",
            "12c68617a243438eac83cd31aea0ce87",
            "6a44ed1391ed4c04b4d36ee11fd7548d",
            "86971951838342ff8a44c2ebddc64510"
          ]
        }
      },
      "source": [
        "bucket_size = 10000\n",
        "query_template = \"\"\"\n",
        "SELECT CAST(FLOOR(DIV(start_position, {BUCKET})) AS INT64) AS start_pos_bucket ,\n",
        "       count(1) AS num_snv\n",
        "FROM \n",
        "    (\n",
        "    SELECT DISTINCT \n",
        "          start_position,\n",
        "          alternate_bases.alt AS alt,\n",
        "          {VAR_TYPE_COL} AS variant_type,\n",
        "    FROM `bigquery-public-data.gnomAD.{GNOMAD_VER}__{CHROM}` AS main_table,\n",
        "        main_table.alternate_bases AS alternate_bases\n",
        "    WHERE variant_type = 'snv'\n",
        "    )\n",
        "GROUP BY 1\n",
        "ORDER BY 1\n",
        "\"\"\"\n",
        "query = query_template.format(GNOMAD_VER=gnomad_version_chr,\n",
        "                              CHROM=chromosome_chr,\n",
        "                              VAR_TYPE_COL=variant_type_col,\n",
        "                              BUCKET=bucket_size)\n",
        "\n",
        "snv_dist = run_query(query)\n",
        "snv_dist.head()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b2406dd554ef42cb88464b7aed20845f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=3451.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "This query processed 0.0224609375 GB of data which is 0.0022% of your 1 TB monthly free quota.\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>start_pos_bucket</th>\n",
              "      <th>num_snv</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>139</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6</td>\n",
              "      <td>161</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   start_pos_bucket  num_snv\n",
              "0                 0       66\n",
              "1                 1      139\n",
              "2                 2       40\n",
              "3                 3       52\n",
              "4                 6      161"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FxFpLITCec0J",
        "outputId": "db7b8347-0886-4bb8-deb2-0015f6dce7a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(dpi=150)\n",
        "plt.bar(snv_dist.start_pos_bucket, snv_dist.num_snv)\n",
        "plt.xlabel(\"Bucket number of start_pos\")\n",
        "plt.ylabel(\"No of SNVs in each bucket\")\n",
        "plt.title(\"Distribution of SNVs on {} for buckets of {} base pairs\".format(chromosome_chr, bucket_size))\n",
        "plt.show()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 900x600 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}