{ "cells": [ { "cell_type": "markdown", "id": "accurate-breakdown", "metadata": {}, "source": [ "# GWAS Tutorial" ] }, { "cell_type": "markdown", "id": "written-product", "metadata": {}, "source": [ "This notebook is an [sgkit](https://pystatgen.github.io/sgkit) port of Hail's [GWAS Tutorial](https://nbviewer.jupyter.org/github/tomwhite/sgkit/blob/86753e814c6d56982b6950ec3de727f3b1bfad7d/docs/examples/01-genome-wide-association-study.ipynb), which demonstrates how to run a genome-wide SNP association test. Readers are encouraged to read the Hail tutorial alongside this one for more background, and to see the motivation behind some of the steps.\n", "\n", "_Note that some of the results do not exactly match the output from Hail. Also, since sgkit is still a 0.x release, its API is still subject to non-backwards compatible changes._" ] }, { "cell_type": "code", "execution_count": 1, "id": "valid-pride", "metadata": {}, "outputs": [], "source": [ "import sgkit as sg\n", "from sgkit.io.vcf import vcf_to_zarr" ] }, { "cell_type": "markdown", "id": "scheduled-spelling", "metadata": {}, "source": [ "Before using sgkit, we import some standard Python libraries and set the Xarray display options to not show all the attributes in a dataset by default." ] }, { "cell_type": "code", "execution_count": 2, "id": "compound-activity", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import xarray as xr\n", "xr.set_options(display_expand_attrs=False, display_expand_data_vars=True);" ] }, { "cell_type": "markdown", "id": "color-trinity", "metadata": {}, "source": [ "## Download public 1000 Genomes data" ] }, { "cell_type": "markdown", "id": "useful-imperial", "metadata": {}, "source": [ "We use the same small (20MB) portion of the public 1000 Genomes data that Hail uses.\n", "\n", "First, download the file locally:" ] }, { "cell_type": "code", "execution_count": 3, "id": "taken-banana", "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "import requests\n", "\n", "if not Path(\"1kg.vcf.bgz\").exists():\n", " response = requests.get(\"https://storage.googleapis.com/sgkit-gwas-tutorial/1kg.vcf.bgz\")\n", " with open(\"1kg.vcf.bgz\", \"wb\") as f:\n", " f.write(response.content)" ] }, { "cell_type": "markdown", "id": "headed-restoration", "metadata": {}, "source": [ "## Importing data from VCF" ] }, { "cell_type": "markdown", "id": "elementary-college", "metadata": {}, "source": [ "Next, [convert it to Zarr](https://pystatgen.github.io/sgkit/latest/user_guide.html#converting-genetic-data-to-zarr), stored on the local filesystem in a directory called _1kg.zarr_." ] }, { "cell_type": "code", "execution_count": 4, "id": "composed-injury", "metadata": {}, "outputs": [], "source": [ "vcf_to_zarr(\"1kg.vcf.bgz\", \"1kg.zarr\", max_alt_alleles=1,\n", " fields=[\"FORMAT/GT\", \"FORMAT/DP\", \"FORMAT/GQ\", \"FORMAT/AD\"],\n", " field_defs={\"FORMAT/AD\": {\"Number\": \"R\"}})" ] }, { "cell_type": "markdown", "id": "plastic-running", "metadata": {}, "source": [ "We passed a few arguments to the `vcf_to_zarr` conversion function, so it only converts the first alternate allele (`max_alt_alleles=1`), and to load extra VCF fields we are interested in (`GT`, `DP`, `GQ`, and `AD`). Also, `AD` needed defining as having a `Number` definition of `R` (one value for each allele, including the reference), since the dataset we are using defines it as `.` which means \"unknown\".\n", "\n", "Now the data has been written as Zarr, all downstream operations on will be much faster. Note that sgkit uses an [Xarray](http://xarray.pydata.org/en/stable/) dataset to represent the VCF data, where Hail uses MatrixTable." ] }, { "cell_type": "code", "execution_count": 5, "id": "blind-sunset", "metadata": {}, "outputs": [], "source": [ "ds = sg.load_dataset(\"1kg.zarr\")" ] }, { "cell_type": "markdown", "id": "imperial-california", "metadata": {}, "source": [ "## Getting to know our data" ] }, { "cell_type": "markdown", "id": "otherwise-constant", "metadata": {}, "source": [ "To start with we'll look at some summary data from the dataset.\n", "\n", "The simplest thing is to look at the dimensions and data variables in the Xarray dataset." ] }, { "cell_type": "code", "execution_count": 6, "id": "hundred-manitoba", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:               (alleles: 2, ploidy: 2, samples: 284, variants: 10879)\n",
       "Dimensions without coordinates: alleles, ploidy, samples, variants\n",
       "Data variables:\n",
       "    call_AD               (variants, samples, alleles) int32 dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_DP               (variants, samples) int32 dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    call_GQ               (variants, samples) int32 dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    call_genotype         (variants, samples, ploidy) int8 dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_genotype_mask    (variants, samples, ploidy) bool dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_genotype_phased  (variants, samples) bool dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    sample_id             (samples) <U7 dask.array<chunksize=(284,), meta=np.ndarray>\n",
       "    variant_allele        (variants, alleles) object dask.array<chunksize=(10000, 2), meta=np.ndarray>\n",
       "    variant_contig        (variants) int8 dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_id            (variants) object dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_id_mask       (variants) bool dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_position      (variants) int32 dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "Attributes: (1)
" ], "text/plain": [ "\n", "Dimensions: (alleles: 2, ploidy: 2, samples: 284, variants: 10879)\n", "Dimensions without coordinates: alleles, ploidy, samples, variants\n", "Data variables:\n", " call_AD (variants, samples, alleles) int32 dask.array\n", " call_DP (variants, samples) int32 dask.array\n", " call_GQ (variants, samples) int32 dask.array\n", " call_genotype (variants, samples, ploidy) int8 dask.array\n", " call_genotype_mask (variants, samples, ploidy) bool dask.array\n", " call_genotype_phased (variants, samples) bool dask.array\n", " sample_id (samples) \n", " variant_allele (variants, alleles) object dask.array\n", " variant_contig (variants) int8 dask.array\n", " variant_id (variants) object dask.array\n", " variant_id_mask (variants) bool dask.array\n", " variant_position (variants) int32 dask.array\n", "Attributes: (1)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds" ] }, { "cell_type": "markdown", "id": "available-officer", "metadata": {}, "source": [ "Next we'll use `display_genotypes` to show the the first and last few variants and samples.\n", "\n", "_Note: sgkit does not store the contig names in an easily accessible form, so we compute a variable `variant_contig_name` in the same dataset storing them for later use, and set an index so we can see the variant name, position, and ID._" ] }, { "cell_type": "code", "execution_count": 7, "id": "friendly-nation", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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samplesHG00096HG00099...NA21133NA21143
variants
(1, 904165, .)0/00/0...0/00/0
(1, 909917, .)0/00/0...0/00/0
(1, 986963, .)0/00/0...0/00/0
(1, 1563691, .)./.0/0...0/00/0
(1, 1707740, .)0/10/1...0/10/0
..................
(X, 152660491, .)./.0/0...1/10/0
(X, 153031688, .)0/00/0...0/00/0
(X, 153674876, .)0/00/0...0/00/0
(X, 153706320, .)./.0/0...0/00/0
(X, 154087368, .)0/01/1...1/11/1
\n", "

10879 rows x 284 columns

" ], "text/plain": [ "samples HG00096 HG00099 ... NA21133 NA21143\n", "variants ... \n", "(1, 904165, .) 0/0 0/0 ... 0/0 0/0\n", "(1, 909917, .) 0/0 0/0 ... 0/0 0/0\n", "(1, 986963, .) 0/0 0/0 ... 0/0 0/0\n", "(1, 1563691, .) ./. 0/0 ... 0/0 0/0\n", "(1, 1707740, .) 0/1 0/1 ... 0/1 0/0\n", "... ... ... ... ... ...\n", "(X, 152660491, .) ./. 0/0 ... 1/1 0/0\n", "(X, 153031688, .) 0/0 0/0 ... 0/0 0/0\n", "(X, 153674876, .) 0/0 0/0 ... 0/0 0/0\n", "(X, 153706320, .) ./. 0/0 ... 0/0 0/0\n", "(X, 154087368, .) 0/0 1/1 ... 1/1 1/1\n", "\n", "[10879 rows x 284 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds[\"variant_contig_name\"] = ([\"variants\"], [ds.contigs[contig] for contig in ds[\"variant_contig\"].values])\n", "ds2 = ds.set_index({\"variants\": (\"variant_contig_name\", \"variant_position\", \"variant_id\")})\n", "sg.display_genotypes(ds2, max_variants=10, max_samples=5)" ] }, { "cell_type": "markdown", "id": "durable-insured", "metadata": {}, "source": [ "We can show the alleles too.\n", "\n", "_Note: this needs work to make it easier to do_" ] }, { "cell_type": "code", "execution_count": 8, "id": "forward-testimony", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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variant_allele
variant_contig_namevariant_positionvariant_id
1904165.[G, A]
909917.[G, A]
986963.[C, T]
1563691.[T, G]
1707740.[T, G]
\n", "
" ], "text/plain": [ " variant_allele\n", "variant_contig_name variant_position variant_id \n", "1 904165 . [G, A]\n", " 909917 . [G, A]\n", " 986963 . [C, T]\n", " 1563691 . [T, G]\n", " 1707740 . [T, G]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_variant = ds[[v for v in ds.data_vars if v.startswith(\"variant_\")]].to_dataframe()\n", "df_variant.groupby([\"variant_contig_name\", \"variant_position\", \"variant_id\"]).agg({\"variant_allele\": lambda x: list(x)}).head(5)" ] }, { "cell_type": "markdown", "id": "systematic-bailey", "metadata": {}, "source": [ "Show the first five sample IDs by referencing the dataset variable directly:" ] }, { "cell_type": "code", "execution_count": 9, "id": "thick-newark", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['HG00096', 'HG00099', 'HG00105', 'HG00118', 'HG00129'], dtype='\n", "Index: 3500 entries, HG00096 to NA21144\n", "Data columns (total 5 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Population 3500 non-null object\n", " 1 SuperPopulation 3500 non-null object\n", " 2 isFemale 3500 non-null bool \n", " 3 PurpleHair 3500 non-null bool \n", " 4 CaffeineConsumption 3500 non-null int64 \n", "dtypes: bool(2), int64(1), object(2)\n", "memory usage: 116.2+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 12, "id": "ideal-typing", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PopulationSuperPopulationisFemalePurpleHairCaffeineConsumption
Sample
HG00096GBREURFalseFalse4
HG00097GBREURTrueTrue4
HG00098GBREURFalseFalse5
HG00099GBREURTrueFalse4
HG00100GBREURTrueFalse5
..................
NA21137GIHSASTrueFalse1
NA21141GIHSASTrueTrue2
NA21142GIHSASTrueTrue2
NA21143GIHSASTrueTrue5
NA21144GIHSASTrueFalse3
\n", "

3500 rows × 5 columns

\n", "
" ], "text/plain": [ " Population SuperPopulation isFemale PurpleHair CaffeineConsumption\n", "Sample \n", "HG00096 GBR EUR False False 4\n", "HG00097 GBR EUR True True 4\n", "HG00098 GBR EUR False False 5\n", "HG00099 GBR EUR True False 4\n", "HG00100 GBR EUR True False 5\n", "... ... ... ... ... ...\n", "NA21137 GIH SAS True False 1\n", "NA21141 GIH SAS True True 2\n", "NA21142 GIH SAS True True 2\n", "NA21143 GIH SAS True True 5\n", "NA21144 GIH SAS True False 3\n", "\n", "[3500 rows x 5 columns]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "id": "suited-split", "metadata": {}, "source": [ "To join the annotation data with the genetic data, we convert it to Xarray, then do a join." ] }, { "cell_type": "code", "execution_count": 13, "id": "hidden-custom", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:               (alleles: 2, ploidy: 2, samples: 284, variants: 10879)\n",
       "Dimensions without coordinates: alleles, ploidy, samples, variants\n",
       "Data variables: (12/17)\n",
       "    call_AD               (variants, samples, alleles) int32 dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_DP               (variants, samples) int32 dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    call_GQ               (variants, samples) int32 dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    call_genotype         (variants, samples, ploidy) int8 dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_genotype_mask    (variants, samples, ploidy) bool dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_genotype_phased  (variants, samples) bool dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    variant_contig_name   (variants) <U2 '1' '1' '1' '1' '1' ... 'X' 'X' 'X' 'X'\n",
       "    Population            (samples) object 'GBR' 'GBR' 'GBR' ... 'GIH' 'GIH'\n",
       "    SuperPopulation       (samples) object 'EUR' 'EUR' 'EUR' ... 'SAS' 'SAS'\n",
       "    isFemale              (samples) bool False True False ... False False True\n",
       "    PurpleHair            (samples) bool False False False ... False True True\n",
       "    CaffeineConsumption   (samples) int64 4 4 4 3 6 2 4 2 1 ... 5 6 4 6 4 6 5 5\n",
       "Attributes: (1)
" ], "text/plain": [ "\n", "Dimensions: (alleles: 2, ploidy: 2, samples: 284, variants: 10879)\n", "Dimensions without coordinates: alleles, ploidy, samples, variants\n", "Data variables: (12/17)\n", " call_AD (variants, samples, alleles) int32 dask.array\n", " call_DP (variants, samples) int32 dask.array\n", " call_GQ (variants, samples) int32 dask.array\n", " call_genotype (variants, samples, ploidy) int8 dask.array\n", " call_genotype_mask (variants, samples, ploidy) bool dask.array\n", " call_genotype_phased (variants, samples) bool dask.array\n", " ... ...\n", " variant_contig_name (variants) " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dp = ds.call_DP.where(ds.call_DP >= 0) # filter out missing\n", "dp.attrs[\"long_name\"] = \"DP\"\n", "xr.plot.hist(dp, range=(0, 30), bins=30, size=8, edgecolor=\"black\");" ] }, { "cell_type": "markdown", "id": "capital-roller", "metadata": {}, "source": [ "## Quality control" ] }, { "cell_type": "markdown", "id": "quarterly-surfing", "metadata": {}, "source": [ "QC is the process of filtering out poor quality data before running an analysis. This is usually an iterative process.\n", "\n", "The [`sample_stats`](https://pystatgen.github.io/sgkit/latest/generated/sgkit.sample_stats.html) function in sgkit computes a collection of useful metrics for each sample and stores them in new variables. (The Hail equivalent is `sample_qc`.)\n", "\n", "Here's the dataset _before_ running `sample_stats`." ] }, { "cell_type": "code", "execution_count": 22, "id": "becoming-colon", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:               (alleles: 2, ploidy: 2, samples: 284, variants: 10879)\n",
       "Dimensions without coordinates: alleles, ploidy, samples, variants\n",
       "Data variables: (12/17)\n",
       "    call_AD               (variants, samples, alleles) int32 dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_DP               (variants, samples) int32 dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    call_GQ               (variants, samples) int32 dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    call_genotype         (variants, samples, ploidy) int8 dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_genotype_mask    (variants, samples, ploidy) bool dask.array<chunksize=(10000, 284, 2), meta=np.ndarray>\n",
       "    call_genotype_phased  (variants, samples) bool dask.array<chunksize=(10000, 284), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    variant_contig_name   (variants) <U2 '1' '1' '1' '1' '1' ... 'X' 'X' 'X' 'X'\n",
       "    Population            (samples) object 'GBR' 'GBR' 'GBR' ... 'GIH' 'GIH'\n",
       "    SuperPopulation       (samples) object 'EUR' 'EUR' 'EUR' ... 'SAS' 'SAS'\n",
       "    isFemale              (samples) bool False True False ... False False True\n",
       "    PurpleHair            (samples) bool False False False ... False True True\n",
       "    CaffeineConsumption   (samples) int64 4 4 4 3 6 2 4 2 1 ... 5 6 4 6 4 6 5 5\n",
       "Attributes: (1)
" ], "text/plain": [ "\n", "Dimensions: (alleles: 2, ploidy: 2, samples: 284, variants: 10879)\n", "Dimensions without coordinates: alleles, ploidy, samples, variants\n", "Data variables: (12/17)\n", " call_AD (variants, samples, alleles) int32 dask.array\n", " call_DP (variants, samples) int32 dask.array\n", " call_GQ (variants, samples) int32 dask.array\n", " call_genotype (variants, samples, ploidy) int8 dask.array\n", " call_genotype_mask (variants, samples, ploidy) bool dask.array\n", " call_genotype_phased (variants, samples) bool dask.array\n", " ... ...\n", " variant_contig_name (variants) \n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:               (alleles: 2, ploidy: 2, samples: 284, variants: 10879)\n",
       "Dimensions without coordinates: alleles, ploidy, samples, variants\n",
       "Data variables: (12/23)\n",
       "    sample_n_called       (samples) int64 dask.array<chunksize=(284,), meta=np.ndarray>\n",
       "    sample_call_rate      (samples) float64 dask.array<chunksize=(284,), meta=np.ndarray>\n",
       "    sample_n_het          (samples) int64 dask.array<chunksize=(284,), meta=np.ndarray>\n",
       "    sample_n_hom_ref      (samples) int64 dask.array<chunksize=(284,), meta=np.ndarray>\n",
       "    sample_n_hom_alt      (samples) int64 dask.array<chunksize=(284,), meta=np.ndarray>\n",
       "    sample_n_non_ref      (samples) int64 dask.array<chunksize=(284,), meta=np.ndarray>\n",
       "    ...                    ...\n",
       "    variant_contig_name   (variants) <U2 '1' '1' '1' '1' '1' ... 'X' 'X' 'X' 'X'\n",
       "    Population            (samples) object 'GBR' 'GBR' 'GBR' ... 'GIH' 'GIH'\n",
       "    SuperPopulation       (samples) object 'EUR' 'EUR' 'EUR' ... 'SAS' 'SAS'\n",
       "    isFemale              (samples) bool False True False ... False False True\n",
       "    PurpleHair            (samples) bool False False False ... False True True\n",
       "    CaffeineConsumption   (samples) int64 4 4 4 3 6 2 4 2 1 ... 5 6 4 6 4 6 5 5\n",
       "Attributes: (1)
" ], "text/plain": [ "\n", "Dimensions: (alleles: 2, ploidy: 2, samples: 284, variants: 10879)\n", "Dimensions without coordinates: alleles, ploidy, samples, variants\n", "Data variables: (12/23)\n", " sample_n_called (samples) int64 dask.array\n", " sample_call_rate (samples) float64 dask.array\n", " sample_n_het (samples) int64 dask.array\n", " sample_n_hom_ref (samples) int64 dask.array\n", " sample_n_hom_alt (samples) int64 dask.array\n", " sample_n_non_ref (samples) int64 dask.array\n", " ... ...\n", " variant_contig_name (variants) " ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds.sample_call_rate.attrs[\"long_name\"] = \"Sample call rate\"\n", "xr.plot.hist(ds.sample_call_rate, range=(.88,1), bins=50, size=8, edgecolor=\"black\");" ] }, { "cell_type": "code", "execution_count": 25, "id": "certain-needle", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "gq = ds.call_GQ.where(ds.call_GQ >= 0) # filter out missing\n", "sample_gq_mean = gq.mean(dim=\"variants\")\n", "sample_gq_mean.attrs[\"long_name\"] = \"Mean Sample GQ\"\n", "xr.plot.hist(sample_gq_mean, range=(10,70), bins=60, size=8, edgecolor=\"black\");" ] }, { "cell_type": "code", "execution_count": 26, "id": "north-bryan", "metadata": {}, "outputs": [ { "data": { "image/png": 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oiHy9qpltGU0mdbzryEnnh4PhNRecobX3PavUmI8/V09ySBu3DwY9yxOXy87Mc31oNKVf7n9+Qk93MQO4woMeH3puf1V/ZJ4vGDFQDQCAeBCuURHZcytngnJ4W/KF1/XgwKt6ct+vtWLTzgnhODsYrv30+8fPC4fqjEOjKd20eZekwrXIxdQph4N9rX5kXk8zfgAAUE2YLQQV89ie13RPckj/cN+z4zNaSNKqpWfqhosXav6xbx8PyOHSkXzBsKu9VeseeF5bdw9PCNYZ+14/rGs37NDc2c0TZruYO7t5fLaRqWbCyJ6BI9PecgTT6c6KUggzfgAAEA96rlERt27ZrTVbByZsy+5BzVc6km97OHRnNEkKx+yR1JgOHB7NuSJhphe8UG90pXp8oy7jaKSBatSWAwDKiXCNirg7+dKkbdk9qPkCYL7t4dDdkmjSB884Th3vOkrfuf+58Z7slkTThGXKV27eNSksF+qJrtTUZHGE+kYYqEZtOQCg3AjXKJtwD+JFHcdr99BbPddnnniU/uLD7XkHD2bLtT1f6D5r/tHauH1QkrR8yYIJ5003LJerxze7t5X5hmeG2nIAQLmZu099VA3o7Oz0vr6+SjcDefQkh3Tthh0aSY2pJdGkNZcv1mN7Xss7HV6521ZNZQPh3tY5zYkJAzmrqZ21IN+9BACgFGb2qLt35txHuEY5fO67D4/PCS1JFy5s1Xc/d7ak6gu3lbZy8y6t3zY4/viKcxdMmm87Co1y3xvl5wQAlE+hcE1ZCCoqiprYegtP5SgBaaRa5EaoLQcAVA/CNWKXmTpuVpPpzTFXS6JJy5cskFR6TWw4JN7+8B598IzjJtVV15py1HXnu+/5/lCptz9gAACIC+EaRZlpuAqH35ZEky5cOG9C+C21lzYcEkdSY9q6e1gPPbe/5nti4+xt7UkOac/+N9SSaNJIamzCqpa5erMbqZcbAIBSsYgMppS9cMp0FjHJDr/zj337hGCW6aW94twFMwpt4cVQMsILzlRKlAu+RHmtzO8yU/9+4cLW8fueqzdbyt3LDQAAciNcY0qlhKtiVgLs7mib0SqHmd70q847VRcubFVLoqngdcqllD9G4r5WoT92wr+rhElzZzdP2l7pewsAQLWjLARTKqV0o5T64UKlKLmmWFu+ZEFV1AWXc27l6V6r0O+yu6NNV513qtbe96xSY651Dzyvs+Yf3VCrOQIAUCrCNaZUariaSf3wVHW+uULlTHq/w9eLKjyWc8GXqBfBOXB4VKmx9PSc4bDOjBsAABSHcI2iTCdczSSoZp8zVY9sVAG2JzmkjdsH9eDAqxpJjUUyYK+cPb0zuVah3yUrQQIAUBoWkUGkZrIiXq5zJE35PJlAPnd2sw4cHi1pJpOwuBZtqRVMuwcAQGEsIoOyKaYGuJhe6lVLz5yyRzazbabTxIWvm0FvLYuuAABQCmYLQUmyp4GbamaJXLNb5DunmFlEoprJJD0HdytzONeAck5zCADAdNFzjRnLN+iwUI/zTHup86nUTCaoDBa0AQBUO8I1ZixfCUixA+ZaEk3as/8N9SSHZlyKUImZTCqtkWuiyznNIQAAM0FZCGZsJouLZMLwhQvTx27dPVzyQiszXYSmFpVzgZpqxII2AIBqR881ipKrt3Q6vca5BjGOpNL10fRAFq/Re24p5QEAVDvCNaZUqM41u6wiVwi/dcvu8VX/Muczn/LMcN9qs5QHANA4CNeYUr7e0uwgnSuES9LaeweUCqZTL3UQY6n1xrVer0zPLQAA1Y1FZDClYhd56e0f1vptg+PnXbiwVS+8dki7hw6Ob0s0mdZ++v0zDsbTXaAmyvMBAACkwovIMKARU8r0ll5x7oLxQJqrNzt73ugHB16dGKxNuub802ccaEuZ0zqK8wEAAKZCuEZRMjNySNLKzbs0d3bzpFkbwiH8g2ccp5HU2Pj5C9uO0NrPdOqGixfOuA2lzhTRCDNNsMAKAACVRVkIipZdVnHVeafqwOHRnLW/cZVgNHrNdSGUvQAAUB6FykIY0IiiZZdVHDg8Ot6bnS2ugXelzhRRzzNNNPo0fQAAVAPKQlC06ZZVNNLiLtWgEcpeAACodpSFYFriLKuo55KNcuEeAgAQv0JlIYRrlCyKQEe9MOoVf/AAQP2h5hqx6EkOaeP2QT048KpGUmOTVm+cjmLrhQkqqCWFVjcFANQnaq6RV6Fp3TKhYevu4fEp90qZO7qYeuHMNddvG9SKTTuZbg5Vj7nVAaDxEK6RU09ySNdu2KH12wZ17YYdk4JsODRklDKILtdCNdlmElSY9xkzEdXrhkGmANB4Yg3XZnaJme02swEz+1KO/QvM7B4ze8LM7jWzk0L7vmZmT5nZ02a22swszrZiYqDYuH1wvEd6JDWmjdsHJxybvRrjhQtbS/7Ie6rZRaYbVOjprl7V/EdPlK+bYv5oBADUl9hqrs0sIWmNpG5JeyU9YmZ3unsydNhtkta7+/fM7Pcl3SLpM2b2e5I+KOl9wXEPSDpf0r1xtbfRZdeGnvHOdxQ8Pq55rKO8JvM+V6dqr0OO+nVTz3OrAwAmi7Pn+mxJA+7+nLuPSLpd0tKsYzok/Sz4fmtov0uaLalF0tskNUuqvi6uOpIdKOYd8Ta1JNIvj5ZEk5YvWTDpnHBPc7l6IqczdzYfyVenaq9D5nUDAChFnLOFnChpT+jxXklLso55XNJlkr4u6ROS5prZce6+zcy2SnpRkkn6hrs/HWNbG15Xe6vu6Ns7PhXe8iULtHzJgqJ6iau1J7ISveuYWvZrrdrCK68bAEApKj0V3/WSvmFmV0q6X9I+SSkzO0PS70jK1GD3mFmXu/eGTzazqyVdLUknn3xy2Rpdi6aawq5QoNi4fVAbtw9q+ZIFRQ80rJZA0sgfyVfrtIW1EF4b+XUDAChNbIvImNm5km5y94uDxzdKkrvfkuf4IyQ94+4nmdkNkma7+83BvpWSDrv71/Jdj0Vk8pvpAi2ZGUMyAxtbEk1ac/niSedW2wIw1Roqy6naficAANSTQovIxFlz/YikdjM71cxaJH1K0p1ZDZtnZpk23ChpXfD9LyWdb2azzKxZ6cGMlIXM0ExrXHv735rDWkrPGpLr3GqaEYEZQtKqva4ZAIB6FVu4dvc3JV0naYvSwfgH7v6Uma0ys0uDwy6QtNvMfi6pTdJXg+0/lPSspCeVrst+3N3/V1xtrXczHaDV1d46PqhRkmY1Wd5zpzPQME6EyjQG5QEAUBmx1ly7+12S7sratjL0/Q+VDtLZ56Uk/VmcbWskM61x7e5o0+c/dJq+tXVAY5KapjnVeLHlGVGWcVT7YLlyqYW6ZgAA6lFsNdflRs11NLKD7srNu7R+21sLyFxx7gKtWnpmUc9TTM1vHLXB1FwDAIA4Faq5rvRsIagCmTA6d3az1j3w/IQp9WbaE1zsDCJxzDTCTA8AAKBSCNcNLtxznDApFXyQkQm6q5aeOaPygmJDOWUcAACgnhCuG1y45zjlUqLJlBrzCUF3Jj3Bxdb8UhsMAADqCTXXDS675vmq807VgcOjBYMuNc0AAKCRUXONvKbbc1ytS50DAABUA8I1plX2Uc1LnQMAAFRanCs0osb0JIe0cvOugqsasjgJAABAfvRcN6BcNdPFlnsUW0ZCXTYAAGhEhOsG05Mc0rUbdmgkNabbH96jNZcvVndH26Ryj43bBwvO8DHViouZoL5h+y91zfmn64aLF8by89QD/hABAKB+UBZShwqVd2zcPqiR1JgkaSQ1po3b06svdrW3qiXx1svhwYFXC5aHFDJher8x19p7B2b8XPUu84fI+m2DWrFpJ/cJAIAaR7iuMzMNa90dbfrgGceNPx5Jjam3f3hGbehqb1WiycYfp1wzfq56l2uAKAAAqF2E6zozVVhbvmTBeA91S6JJy5csmLAvisGK3R1tuub805UI8jUDH/NjgCgAAPWFmus6M9Vy4t0dbVpz+eKcNb5RrpaYqbG+O/mSLuo4ftJzUWecxgqVAADUF1ZorEPVEFyzV34Mzz4SHlTZkmgaH1QJAABQCwqt0EhZSB3q7mjTqqVnlhRYi5nzupBC5Sn5BlUCAADUOsI1Jrl1y25d8/2+kmawoJYYAAA0IsI1JuhJDmntfc8qFVQLzXQGi0wt8RXnLpi0IE2hQZUAAAC1jAGNDWA6Ndi9/cNKjb1Vh58wlTRrSL5VHvMNqgQAAKhlhOs6kS9AF7useUZ4tpFEk+ma80+PJfxOtcojAABALSJc14FCATrXwMJCoZap4QAAAGaOmus6UGhmjpkMLIxithEAAIBGRM91HSi0cAw90QAAAOXDIjJ1ohoWjgEAAGgEhRaRoee6TjBAEAAAoPII1ygaveMAAACFMaARRcnMSFLKqo0AAAD1jnDdYHqSQ1q5ede0w3GhGUkAAACQRrhuIKX0Ps9kSj8AAIBGQ811Aym0oMxU9dRM6QcAADA1wnUDyTcfdrFLpDMjCQAAQGGE6waSr/d5ukukAwAAIDfCdYPJ1ftcaIXHcmKqPwAAUOsI16iKeupiS1MAAACqGeEakipfT01pCgAAqAdMxYeqwFR/yDbTOdkBAKgkeq4xpXLUQldDaQqqB2VCAIBaRbhGQeUMOZUuTUH1oEwIAFCrKAtBQSx7jkqgTAgAUKvoua4Duco2oirliGuaPqbdQyGUCQEAapW5e6XbEInOzk7v6+urdDPKLly2Mac5odXLFknSpG2lhJOog3CuNhOeAABArTCzR929M9c+eq5rXL6yjSjrVaOuhaaeFgAA1CtqrmtcrtrUaq9Xrfb2AQAAzBRlIXUgzprruFR7+wAAAPIpVBZCuAYAAACmoVC4piwEAAAAiAjhGgAAAIgI4RoAAACICOEaAAAAiAjhGgAAAIgI4RoAAACICCs0oqYwPzYAAKhm9FyjZvQkh7Ri006t3zaoFZt2qic5VOkmAQAATEC4Rs3o7R/WodGUJOnQaEq9/cMVbhEAAMBEhGvk1ZMc0srNu6qmh7irvVVzmhOSpDnNCXW1t1a4RQAAABNRc42cMiUYh0ZTuqNvr1YvW1TxGufujjatXraImmsAAFC1Yu25NrNLzGy3mQ2Y2Zdy7F9gZveY2RNmdq+ZnRTad7KZ/dTMnjazpJmdEmdbMVG1lmB0d7Rp1dIzCdYAAKAqxRauzSwhaY2kj0rqkLTMzDqyDrtN0np3f5+kVZJuCe1bL+lWd/8dSWdLejmutmIySjAAAACmL86ykLMlDbj7c5JkZrdLWiopGTqmQ9K/D77fKunHwbEdkma5e48kufvBGNuJHCjBAAAAmL44w/WJkvaEHu+VtCTrmMclXSbp65I+IWmumR0n6d2SXjOz/ynpVEl3S/qSu6dibG9NinPe5+6ONkI1AADANFR6tpDrJZ1vZjslnS9pn6SU0qG/K9j/AUmnSboy+2Qzu9rM+sysb3i4OmqCy4l5nwEAAKpLnOF6n6T5occnBdvGufsL7n6Zuy+S9OVg22tK93I/5u7PufubSpeLLM6+gLt/29073b2ztbXxaoKrddAhAABAo4ozXD8iqd3MTjWzFkmfknRn+AAzm2dmmTbcKGld6NyjzSyTmH9fE2u1IQYdAgAAVJvYaq7d/U0zu07SFkkJSevc/SkzWyWpz93vlHSBpFvMzCXdL+na4NyUmV0v6R4zM0mPSvpOXG2tVQw6BAAAqC7m7pVuQyQ6Ozu9r6+v0s0AAABAnTOzR929M9e+Sg9oBAAAAOoGy58DindKQwAA0DjouUbDY0pDAAAQFcI1Gh5TGgIAgKgQrtHwmNIQAABEhZrrGkNtcPSY0hAAAESFcF1DMrXBh0ZTuqNvr1YvW0QQjEh3Rxv3EgAAlIyykBpCbTAAAEB1I1zXEGqDAQAAqhtlITWE2mAAAIDqRriuMdOtDWYAJAAAQPlQFlLHWBwFAACgvAjXdYwBkAAAAOVFuK5jDIAEAAAoL2qu6xgDIAEAAMqLcF3nWBwFAACgfCgLAQAAACJCuAYAAAAiQrgGAAAAIkK4BgAAACJCuAYAAAAiQrgGAAAAIkK4BgAAACIyZbi2tE+b2crg8clmdnb8TatvPckhrdy8Sz3JoUo3BQAAABEppuf6m5LOlbQseHxA0prYWtQAepJDWrFpp9ZvG9Q13+/TrVt2V7pJAAAAiEAx4XqJu18r6bAkufuvJLXE2qo619s/rEOjKUlSyqW19z1LDzYAAEAdKCZcj5pZQpJLkpm1ShqLtVV1rqu9VQl763FqzNXbP1y5BgEAACASxYTr1ZJ+JOmdZvZVSQ9IuiXWVtW57o42XXPBGUo0pRP2nOaEutpbK9wqAAAAlGrWVAe4+wYze1TShyWZpD9096djb1mdu+HihTpr/tHq7R9WV3urujvaKt0kAAAAlGjKcG1m33f3z0h6Jsc2lKC7o41QDQAAUEeKKQt5b/hBUH/9/niaAwAAANSuvOHazG40swOS3mdmvzazA8HjlyVtLlsLGwTzXgMAANS+vOHa3W9x97mSbnX3I919bvB1nLvfWMY21r3wvNcrNu0kYAMAANSoYgY03mhmx0hqlzQ7tP3+OBvWSMLzXh8aTam3f5habAAAgBpUzPLn/5ek+yVtkfTXwX9virdZjaWrvVVzmhOSmJYPAACglk3Zcy3pLyR9QNJD7n6hmb1H0t/E26zG0t3RptXLFjEtHwAAQI0rJlwfdvfDZiYze5u7P2NmC2NvWYNhWj4AAIDaV0y43mtmR0v6saQeM/uVpME4GwUAAADUomIGNH4i+PYmM9sq6ShJ/xZrqwAAAIAaVDBcBwvGPOXu75Ekd7+vLK0CAAAAalDB2ULcPSVpt5mdXKb2NCwWkQEAAKh9xdRcHyPpKTN7WNJvMhvd/dLYWtVgMovIHBpN6Y6+vVq9bBGDGwEAAGpQMeH6r2JvRYNjERkAAID6UMyARuqsY9STHNKe/W+oJdGkkdQYi8gAAADUsGJ6rhGTcDlIS6JJFy5s1fIlC+i1BgAAqFFTLn+O+ITLQUZSY5p/7NsJ1gAAADWMcF1BXe2tmtOckCTKQQAAAOpA3rIQM3tSkufaJcnd/X2xtapBdHe0afWyRertH1ZXeyu91gAAADWuUM31x8vWigbW3dFGqAYAAKgTecO1uw+WsyEorCc5RA83AABAlStUFnJAhctCjoytVZiARWYAAABqQ94Bje4+192PzPE1l2BdXrkWmQEAAED1KXq2EDN7p5mdnPmKs1GYiFlFAAAAasOUi8iY2aWS/k7SuyS9LGmBpKclvTfepiGDWUWiRw07AACIQzErNN4s6RxJd7v7IjO7UNKn420WsjGrSHSoYQcAAHEppixk1N1fldRkZk3uvlVSZ8ztAmJDDTsAAIhLMeH6NTM7QtL9kjaY2dcl/SbeZgHxyVXD3pMc0srNu9STHKpw6wAAQC0z91yz7YUOMHuHpENKB/HLJR0laUPQmz3VuZdI+rqkhKR/dPe/zdq/QNI6Sa2S9kv6tLvvDe0/UlJS0o/d/bpC1+rs7PS+vr6pmgRImlhzLWm8TGROc4IyEQAAUJCZPeruOSs5ium5fqekFnd/092/J+k7kuYWcdGEpDWSPiqpQ9IyM+vIOuw2SeuDpdRXSbola//NSveYA5Hq7mjTqqVnqrujjTIRAAAQmWLC9R2SxkKPU8G2qZwtacDdn3P3EUm3S1qadUyHpJ8F328N7zez90tqk/TTIq6FiDRieQRTHQIAgKgUE65nBeFYkhR831LEeSdK2hN6vDfYFva4pMuC7z8haa6ZHWdmTUpP/3d9oQuY2dVm1mdmfcPD9DaWKjOLxvptg1qxaWfDBOzMVIdXnLuAkhAAAFCSYsL1cDDXtSTJzJZKeiWi618v6Xwz2ynpfEn7lO4Z/4Kku8L117m4+7fdvdPdO1tb6W0sVSOXR4TLRAAAAGaqmHmur1F6lpBvBI/3SvpMEeftkzQ/9PikYNs4d39BQc91MCPJH7n7a2Z2rqQuM/uCpCMktZjZQXf/UhHXxQx1tbfqjr694wP7KI8AAACYninDtbs/K+mcIPzK3Q8W+dyPSGo3s1OVDtWfkrQ8fICZzZO0393HJN2o9MwhcvfLQ8dcKamTYB0/VoIEAAAoTTE915KmFaozx79pZtdJ2qL0VHzr3P0pM1slqc/d75R0gaRbzMyVnhXk2ulcA9FjJUgAAICZm3Ke61rBPNcAAAAoh1LnuQYAAABQhCnDtZm93cz+ysy+EzxuN7OPx980AAAAoLYU03P9XUm/lXRu8HifpP8aW4sAAACAGlVMuD7d3b8maVSS3P0NSRZrqwAAAIAaVMxsISNmNkeSS5KZna50TzZi1pMcYlo8AACAGlJMuP6KpH+TNN/MNkj6oKQr42wU3lqK/NBoSnf07WVZbgAAgBpQzCIyPWa2Q9I5SpeD/IW7R7X8OfLItRQ54br28WkEAAD1LW/NtZktznxJWiDpRUkvSDo52IYYdbW3ak5zQpJYirxOZD6NWL9tUCs27VRPcqjSTQIAABEr1HP9dwX2uaTfj7gtCGEp8vrDpxEAANS/vOHa3S8sZ0MwGUuR15eu9lbd0bdXh0ZTfBoBAECdmrLm2sxmS/qCpPOU7rHulbTW3Q/H3DagrvBpBAAA9a+Y2ULWSzog6b8Hj5dL+r6kP46rUUC94tMIAADqWzHh+kx37wg93mpmybgahPgxYwUAAEA8ilmhcYeZnZN5YGZLJPXF1yTEiRkrAAAA4lNMuH6/pP9tZr8ws19I2ibpA2b2pJk9EWvrELlcM1YAAAAgGsWUhVwSeytQNsxYAQAAEJ9iVmgcNLNjJM0PH+/uO+JsGOLBjBUAAADxKWYqvpslXSnpWaWn4pNYRKamMWMFAABAPIopC/kTSae7+0jcjQEAAABqWTEDGndJOjrmdgAAAAA1r5ie61sk7TSzXZJ+m9no7pfG1ioAAACgBhUTrr8n6b9JelLSWLzNAQAAAGpXMeH6DXdfHXtLAAAAgBpXTLjuNbNbJN2piWUhTMUHAAAAhBQTrhcF/z0ntI2p+AAAAIAsxSwic2E5GgIAAADUumJ6rmVmfyDpvZJmZ7a5+6q4GgUAAADUoinnuTaztZI+KemLkkzSH0taEHO7AAAAgJpTzCIyv+fuV0j6lbv/taRzJb073mYBAAAAtaeYcH0o+O8bZvYuSaOSToivSQAAAEBtKqbm+idmdrSkWyXtUHqmkO/E2SgAAACgFhUzW8jNwbf/YmY/kTTb3V+Pt1kAAABA7clbFmJmHzCz40OPr5D0A0k3m9mx5WgcAAAAUEsK1Vz/g6QRSTKzD0n6W0nrJb0u6dvxNw0AAACoLYXKQhLuvj/4/pOSvu3u/6J0echjsbcMAAAAqDGFeq4TZpYJ3x+W9LPQvqIWnwEAAAAaSaGQvEnSfWb2itLT8fVKkpmdoXRpCAAAAICQvOHa3b9qZvcoPaf1T93dg11NSq/WCAAAACCkYHmHuz+UY9vP42sOAAAAULuKWaERAAAAQBEI1wAAAEBECNcAAABARAjXAAAAQEQI1wAAAEBECNcAAABARAjXAAAAQEQI1wAAAEBECNcAAABARAjXAAAAQEQI1wAAAEBECNcAAABARAjXAAAAQEQI1wAAAEBECNcAAABARAjXAAAAQEQI1wAAAEBEYg3XZnaJme02swEz+1KO/QvM7B4ze8LM7jWzk4LtZ5nZNjN7Ktj3yTjbCQAAAEQhtnBtZglJayR9VFKHpGVm1pF12G2S1rv7+yStknRLsP0NSVe4+3slXSLp/zWzo+NqKwAAABCFOHuuz5Y04O7PufuIpNslLc06pkPSz4Lvt2b2u/vP3b0/+P4FSS9Lao2xrQAAAEDJ4gzXJ0raE3q8N9gW9riky4LvPyFprpkdFz7AzM6W1CLp2ZjaCQAAAESi0gMar5d0vpntlHS+pH2SUpmdZnaCpO9L+py7j2WfbGZXm1mfmfUNDw+Xq80AAABATnGG632S5ocenxRsG+fuL7j7Ze6+SNKXg22vSZKZHSnp/5f0ZXd/KNcF3P3b7t7p7p2trVSNAAAAoLLiDNePSGo3s1PNrEXSpyTdGT7AzOaZWaYNN0paF2xvkfQjpQc7/jDGNgIAAACRiS1cu/ubkq6TtEXS05J+4O5PmdkqM7s0OOwCSbvN7OeS2iR9Ndj+J5I+JOlKM3ss+DorrrYCAAAAUTB3r3QbItHZ2el9fX2VbgYAAADqnJk96u6dufbNKndj6llPcki9/cPqam9Vd0dbpZsDAACAMqv0bCF1oyc5pBWbdmr9tkGt2LRTPcmhSjcJAAAAZUa4jkhv/7AOjaZnETw0mlJvP1MDAgAANBrCdUS62ls1pzkhSZrTnFBXO1MDAgAANBpqriPS3dGm1csWUXMNAADQwAjXEeruaCNUAwAANDDKQgAAAICIEK4BAACAiBCuAQAAgIgQrgEAAICIEK4BAACAiBCuAQAAgIgQrgEAAICIEK4BAACAiBCuAQAAgIgQrgEAAICIsPx5iXqSQ+rtH1ZXeytLnwMAADQ4eq5L0JMc0opNO7V+26BWbNqpnuRQpZsEAACACiJcl6C3f1iHRlOSpEOjKfX2D1e4RQAAAKgkwnUJutpbNac5IUma05xQV3trhVsEAACASqLmugTdHW1avWwRNdcAAACQRLguWSZQZ0pCCNgAAACNi7KQEjGoEQAAABmE6xIxqBEAAAAZhOsSMagRAAAAGdRcl4hBjQAAAMggXEegu6ONUA0AAADKQgAAAICoEK4BAACAiBCuAQAAgIgQrgEAAICIEK4BAACAiBCuAQAAgIgQrgEAAICIMM91lelJDrEgDQAAQI2i57qK9CSHtGLTTq3fNqgVm3aqJzlU6SYBAABgGgjXVaS3f1iHRlOSpEOjKfX2D1e4RQAAAJgOwnUV6Wpv1ZzmhCRpTnNCXe2tFW4RAAAApoOa6yrS3dGm1csWUXMNAABQowjXVaa7o41QDQAAUKMoCwEAAAAiQrgGAAAAIkK4BgAAACJCuAYAAAAiQrgGAAAAIkK4BgAAACJCuAYAAAAiQrgGAAAAIkK4BgAAACJCuAYAAAAiQrgGAAAAIkK4BgAAACJCuAYAAAAiQrgGAAAAIkK4BgAAACJCuAYAAAAiEmu4NrNLzGy3mQ2Y2Zdy7F9gZveY2RNmdq+ZnRTa91kz6w++PhtnOwEAAIAoxBauzSwhaY2kj0rqkLTMzDqyDrtN0np3f5+kVZJuCc49VtJXJC2RdLakr5jZMXG1FQAAAIhCnD3XZ0sacPfn3H1E0u2SlmYd0yHpZ8H3W0P7L5bU4+773f1XknokXRJjWwEAAICSxRmuT5S0J/R4b7At7HFJlwXff0LSXDM7rshzAQAAgKpS6QGN10s638x2Sjpf0j5JqWJPNrOrzazPzPqGh4fjaiMAAABQlDjD9T5J80OPTwq2jXP3F9z9MndfJOnLwbbXijk3OPbb7t7p7p2tra0RNx8AAACYnjjD9SOS2s3sVDNrkfQpSXeGDzCzeWaWacONktYF32+R9BEzOyYYyPiRYBsAAABQtWIL1+7+pqTrlA7FT0v6gbs/ZWarzOzS4LALJO02s59LapP01eDc/ZJuVjqgPyJpVbANAAAAqFrm7pVuQyQ6Ozu9r6+v0s0AAABAnTOzR929M9e+Sg9oBAAAAOoG4RoAAACICOEaAAAAiAjhGgAAAIgI4RoAAACICOEaAAAAiAjhGgAAAIgI4RoAAACICOEaAAAAiAjhGgAAAIgI4RoAAACICOEaAAAAiAjhGgAAAIgI4RoAAACICOEaAAAAiAjhGgAAAIgI4RoAAACICOEaAAAAiAjhGgAAAIgI4RoAAACICOEaAAAAiAjhGgAAAIgI4RoAAACICOEaAAAAiAjhGgAAAIgI4RoAAACICOEaAAAAiMisSjcAUk9ySL39w+pqb1V3R1ulmwMAAIAZIlzHbKrg3JMc0opNO3VoNKU7+vZq9bJFBGwAAIAaRVlIjDLBef22Qa3YtFM9yaFJx/T2D+vQaEqSdGg0pd7+4XI3EwAAABEhXMeomODc1d6qOc0JSdKc5oS62lvL2kYAAABEh7KQGHW1t+qOvr06NJrKG5y7O9q0etkiaq4BAADqgLl7pdsQic7OTu/r66t0MyZhsCIAAEB9MbNH3b0z1z56rmPW3dFGqAYAAGgQ1FwDAAAAESFcAwAAABEhXAMAAAARIVwDAAAAESFcAwAAABEhXAMAAAARIVwDAAAAESFcAwAAABEhXAMAAAARIVwDAAAAESFcAwAAABEhXAMAAAARIVwDAAAAESFcAwAAABEhXAMAAAARIVwDAAAAESFcAwAAABEhXAMAAAARIVwDAAAAESFcAwAAABEhXAMAAAARIVwDAAAAEYk1XJvZJWa228wGzOxLOfafbGZbzWynmT1hZh8Ltjeb2ffM7Ekze9rMboyznQAAAEAUYgvXZpaQtEbSRyV1SFpmZh1Zh/0XST9w90WSPiXpm8H2P5b0Nnf/XUnvl/RnZnZKXG0FAAAAohBnz/XZkgbc/Tl3H5F0u6SlWce4pCOD74+S9EJo+zvMbJakOZJGJP06xrYCAAAAJYszXJ8oaU/o8d5gW9hNkj5tZnsl3SXpi8H2H0r6jaQXJf1S0m3uvj/GtgIAAAAlq/SAxmWS/sndT5L0MUnfN7MmpXu9U5LeJelUSf/BzE7LPtnMrjazPjPrGx4eLme7AQAAgEniDNf7JM0PPT4p2Bb2p5J+IEnuvk3SbEnzJC2X9G/uPuruL0t6UFJn9gXc/dvu3ununa2trTH8CAAAAEDx4gzXj0hqN7NTzaxF6QGLd2Yd80tJH5YkM/sdpcP1cLD994Pt75B0jqRnYmxrJHqSQ1q5eZd6kkOVbgoAAAAqILZw7e5vSrpO0hZJTys9K8hTZrbKzC4NDvsPkj5vZo9L2iTpSnd3pWcZOcLMnlI6pH/X3Z+Iq61R6EkOacWmnVq/bVArNu0kYAMAADSgWXE+ubvfpfRAxfC2laHvk5I+mOO8g0pPx1czevuHdWg0JUk6NJpSb/+wujvaKtwqAAAAlFOlBzTWja72Vs1pTkiS5jQn1NVODTgAAECjibXnupF0d7Rp9bJF6u0fVld7K73WAAAADYhwHaHujjZCNQAAQAOjLAQAAACICOEaAAAAiAjhGgAAAIgI4RoAAACICOEaAAAAiAjhGgAAAIgI4RoAAACICOEaAAAAiAjhGgAAAIgI4RoAAACICOEaAAAAiAjhGgAAAIgI4RoAAACICOEaAAAAiAjhGgAAAIgI4RoAAACIiLl7pdsQCTMbljQ4w9PnSXolwuZgIu5vvLi/8eHexov7Gy/ub7y4v/Gq9vu7wN1bc+2om3BdCjPrc/fOSrejXnF/48X9jQ/3Nl7c33hxf+PF/Y1XLd9fykIAAACAiBCuAQAAgIgQrtO+XekG1Dnub7y4v/Hh3saL+xsv7m+8uL/xqtn7S801AAAAEBF6rgEAAICINEy4NrP5ZrbVzJJm9pSZ/UWOYy4ws9fN7LHga2Ul2lqrzOwXZvZkcO/6cuw3M1ttZgNm9oSZLa5EO2uNmS0MvSYfM7Nfm9lfZh3Da3eazGydmb1sZrtC2441sx4z6w/+e0yecz8bHNNvZp8tX6trQ557e6uZPRP8v/8jMzs6z7kF30eQ9/7eZGb7Qu8BH8tz7iVmtjt4H/5S+VpdO/Lc3/8Rure/MLPH8pzL67eAfFms3t57G6YsxMxOkHSCu+8ws7mSHpX0h+6eDB1zgaTr3f3jlWllbTOzX0jqdPec81IGb/ZflPQxSUskfd3dl5SvhbXPzBKS9kla4u6Doe0XiNfutJjZhyQdlLTe3c8Mtn1N0n53/9sgeBzj7v8p67xjJfVJ6pTkSr+XvN/df1XWH6CK5bm3H5H0M3d/08z+myRl39vguF+owPsI8t7fmyQddPfbCpyXkPRzSd2S9kp6RNKy8L+DyH1/s/b/naTX3X1Vjn2/EK/fvPJlMUlXqo7eexum59rdX3T3HcH3ByQ9LenEyraq4SxV+s3K3f0hSUcH/6OheB+W9Gw4WGNm3P1+SfuzNi+V9L3g++8p/aaf7WJJPe6+P3hT75F0SVztrEW57q27/9Td3wwePiTppLI3rE7kee0W42xJA+7+nLuPSLpd6dc8QgrdXzMzSX8iaVNZG1UnCmSxunrvbZhwHWZmp0haJGl7jt3nmtnjZvavZvbe8ras5rmkn5rZo2Z2dY79J0raE3q8V/yBM12fUv43dV67pWtz9xeD71+S1JbjGF7HpbtK0r/m2TfV+wjyuy4ou1mX52N1Xrul65I05O79efbz+i1SVharq/fehgvXZnaEpH+R9Jfu/uus3TuUXs7y30n675J+XObm1brz3H2xpI9Kujb4aA0RMbMWSZdKuiPHbl67EfN0zVxj1M2VkZl9WdKbkjbkOYT3kZn5lqTTJZ0l6UVJf1fR1tSvZSrca83rtwiFslg9vPc2VLg2s2alf5kb3P1/Zu9391+7+8Hg+7skNZvZvDI3s2a5+77gvy9L+pHSH0GG7ZM0P/T4pGAbivNRSTvcfSh7B6/dyAxlSpWC/76c4xhexzNkZldK+rikyz3PgJ8i3keQg7sPuXvK3cckfUe57xuv3RKY2SxJl0n6H/mO4fU7tTxZrK7eexsmXAd1Uv+fpKfd/f/Jc8zxwXEys7OVvj+vlq+VtcvM3hEMTpCZvUPSRyTtyjrsTklXWNo5Sg8IeVEoVt4eE167kblTUmYE+mclbc5xzBZJHzGzY4KP3j8SbEMBZnaJpP8o6VJ3fyPPMcW8jyCHrPErn1Du+/aIpHYzOzX4JOxTSr/mUZyLJD3j7ntz7eT1O7UCWay+3nvdvSG+JJ2n9McMT0h6LPj6mKRrJF0THHOdpKckPa70gJvfq3S7a+VL0mnBfXs8uIdfDraH769JWiPpWUlPKj2iuuJtr4UvSe9QOiwfFdrGa7e0e7pJ6Y/PR5Wu3ftTScdJukd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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dp = ds.call_DP.where(ds.call_DP >= 0) # filter out missing\n", "sample_dp_mean = dp.mean(dim=\"variants\")\n", "sample_dp_mean.attrs[\"long_name\"] = \"Mean Sample DP\"\n", "ds[\"sample_dp_mean\"] = sample_dp_mean # add new data array to dataset\n", "ds.plot.scatter(x=\"sample_dp_mean\", y=\"sample_call_rate\", size=8, s=10);" ] }, { "cell_type": "markdown", "id": "voluntary-dietary", "metadata": {}, "source": [ "The following removes outliers using an arbitrary cutoff." ] }, { "cell_type": "code", "execution_count": 27, "id": "handed-reducing", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "After filter, 250/284 samples remain.\n" ] } ], "source": [ "ds = ds.sel(samples=((ds.sample_dp_mean >= 4) & (ds.sample_call_rate >= 0.97)))\n", "print(f\"After filter, {len(ds.samples)}/284 samples remain.\")" ] }, { "cell_type": "markdown", "id": "wicked-threshold", "metadata": {}, "source": [ "Genotype QC is more complicated. First we calculate a variable `ab`, which is the fraction of reads that were the alternate allele." ] }, { "cell_type": "code", "execution_count": 28, "id": "selective-interval", "metadata": {}, "outputs": [], "source": [ "# fill rows with nan where no alternate alleles were read or where sum of reads is 0\n", "ad1 = ds.call_AD.sel(dict(alleles=1)).pipe(lambda v: v.where(v >= 0))\n", "adsum = ds.call_AD.sum(dim=\"alleles\").pipe(lambda v: v.where(v != 0))\n", "# compute alternate allele read fraction\n", "ab = ad1 / adsum" ] }, { "cell_type": "markdown", "id": "transsexual-macro", "metadata": {}, "source": [ "Then we can use the `ab` variable in the filter condition, to filter homozygous reference calls with >10% alternate reads, homozygous alternate calls with >10% reference reads, or heterozygote calls without a ref/alt balance near 50%." ] }, { "cell_type": "code", "execution_count": 29, "id": "bearing-jenny", "metadata": {}, "outputs": [], "source": [ "GT = ds.call_genotype\n", "hom_ref = (GT == 0).all(dim=\"ploidy\")\n", "het = GT[..., 0] != GT[..., 1]\n", "hom_alt = ((GT > 0) & (GT[..., 0] == GT)).all(dim=\"ploidy\")\n", "filter_condition_ab = ((hom_ref & (ab <= 0.1)) |\n", " (het & (ab >= 0.25) & (ab <= 0.75)) |\n", " (hom_alt & (ab >= 0.9)))" ] }, { "cell_type": "code", "execution_count": 30, "id": "painted-terminal", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filtering 3.65% entries out of downstream analysis.\n" ] } ], "source": [ "filter_mask = xr.where(ds.call_genotype_mask, True, filter_condition_ab)\n", "fraction_filtered = GT.where(~filter_mask).count().values / GT.size\n", "print(f\"Filtering {fraction_filtered * 100:.2f}% entries out of downstream analysis.\")" ] }, { "cell_type": "markdown", "id": "israeli-harris", "metadata": {}, "source": [ "_Note: genotype QC is filtering out slightly different numbers of entries compared to the Hail tutorial._" ] }, { "cell_type": "markdown", "id": "banner-structure", "metadata": {}, "source": [ "Variant QC is similar. This time we use the [`variant_stats`](https://pystatgen.github.io/sgkit/latest/generated/sgkit.variant_stats.html) function, but we won't do any filtering on these variables." ] }, { "cell_type": "code", "execution_count": 31, "id": "specialized-train", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.Dataset>\n",
       "Dimensions:                   (alleles: 2, ploidy: 2, samples: 250, variants: 10879)\n",
       "Dimensions without coordinates: alleles, ploidy, samples, variants\n",
       "Data variables: (12/33)\n",
       "    variant_n_called          (variants) int64 dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_call_rate         (variants) float64 dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_n_het             (variants) int64 dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_n_hom_ref         (variants) int64 dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_n_hom_alt         (variants) int64 dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_n_non_ref         (variants) int64 dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    ...                        ...\n",
       "    Population                (samples) object 'GBR' 'GBR' 'GBR' ... 'GIH' 'GIH'\n",
       "    SuperPopulation           (samples) object 'EUR' 'EUR' 'EUR' ... 'SAS' 'SAS'\n",
       "    isFemale                  (samples) bool False True False ... False True\n",
       "    PurpleHair                (samples) bool False False False ... True True\n",
       "    CaffeineConsumption       (samples) int64 4 4 4 3 6 2 2 5 ... 6 4 6 4 6 5 5\n",
       "    sample_dp_mean            (samples) float64 dask.array<chunksize=(250,), meta=np.ndarray>\n",
       "Attributes: (1)
" ], "text/plain": [ "\n", "Dimensions: (alleles: 2, ploidy: 2, samples: 250, variants: 10879)\n", "Dimensions without coordinates: alleles, ploidy, samples, variants\n", "Data variables: (12/33)\n", " variant_n_called (variants) int64 dask.array\n", " variant_call_rate (variants) float64 dask.array\n", " variant_n_het (variants) int64 dask.array\n", " variant_n_hom_ref (variants) int64 dask.array\n", " variant_n_hom_alt (variants) int64 dask.array\n", " variant_n_non_ref (variants) int64 dask.array\n", " ... ...\n", " Population (samples) object 'GBR' 'GBR' 'GBR' ... 'GIH' 'GIH'\n", " SuperPopulation (samples) object 'EUR' 'EUR' 'EUR' ... 'SAS' 'SAS'\n", " isFemale (samples) bool False True False ... False True\n", " PurpleHair (samples) bool False False False ... True True\n", " CaffeineConsumption (samples) int64 4 4 4 3 6 2 2 5 ... 6 4 6 4 6 5 5\n", " sample_dp_mean (samples) float64 dask.array\n", "Attributes: (1)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds = sg.variant_stats(ds)\n", "ds" ] }, { "cell_type": "markdown", "id": "jewish-people", "metadata": {}, "source": [ "## Let's do a GWAS!" ] }, { "cell_type": "markdown", "id": "determined-population", "metadata": {}, "source": [ "First we need to restrict to common variants (1% cutoff), and not far from Hardy-Weinberg equilibrium." ] }, { "cell_type": "code", "execution_count": 32, "id": "delayed-destiny", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/tom/workspace/sgkit/sgkit/utils.py:139: MergeWarning: The following variables in the input dataset will be replaced in the output: variant_n_het, variant_n_hom_alt, variant_n_hom_ref, variant_n_non_ref\n", " MergeWarning,\n" ] } ], "source": [ "ds = sg.hardy_weinberg_test(ds)" ] }, { "cell_type": "markdown", "id": "color-detection", "metadata": {}, "source": [ "(The warning is telling us that some variables are being regenerated, and can be safely ignored.)" ] }, { "cell_type": "code", "execution_count": 33, "id": "optical-television", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "Dimensions:                   (alleles: 2, ploidy: 2, samples: 250, variants: 10879)\n",
       "Dimensions without coordinates: alleles, ploidy, samples, variants\n",
       "Data variables: (12/34)\n",
       "    variant_hwe_p_value       (variants) float64 dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_n_het             (variants) int64 dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_n_hom_ref         (variants) int64 dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_n_hom_alt         (variants) int64 dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_n_non_ref         (variants) int64 dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    variant_n_called          (variants) int64 dask.array<chunksize=(10000,), meta=np.ndarray>\n",
       "    ...                        ...\n",
       "    Population                (samples) object 'GBR' 'GBR' 'GBR' ... 'GIH' 'GIH'\n",
       "    SuperPopulation           (samples) object 'EUR' 'EUR' 'EUR' ... 'SAS' 'SAS'\n",
       "    isFemale                  (samples) bool False True False ... False True\n",
       "    PurpleHair                (samples) bool False False False ... True True\n",
       "    CaffeineConsumption       (samples) int64 4 4 4 3 6 2 2 5 ... 6 4 6 4 6 5 5\n",
       "    sample_dp_mean            (samples) float64 dask.array<chunksize=(250,), meta=np.ndarray>\n",
       "Attributes: (1)
" ], "text/plain": [ "\n", "Dimensions: (alleles: 2, ploidy: 2, samples: 250, variants: 10879)\n", "Dimensions without coordinates: alleles, ploidy, samples, variants\n", "Data variables: (12/34)\n", " variant_hwe_p_value (variants) float64 dask.array\n", " variant_n_het (variants) int64 dask.array\n", " variant_n_hom_ref (variants) int64 dask.array\n", " variant_n_hom_alt (variants) int64 dask.array\n", " variant_n_non_ref (variants) int64 dask.array\n", " variant_n_called (variants) int64 dask.array\n", " ... ...\n", " Population (samples) object 'GBR' 'GBR' 'GBR' ... 'GIH' 'GIH'\n", " SuperPopulation (samples) object 'EUR' 'EUR' 'EUR' ... 'SAS' 'SAS'\n", " isFemale (samples) bool False True False ... False True\n", " PurpleHair (samples) bool False False False ... True True\n", " CaffeineConsumption (samples) int64 4 4 4 3 6 2 2 5 ... 6 4 6 4 6 5 5\n", " sample_dp_mean (samples) float64 dask.array\n", "Attributes: (1)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds" ] }, { "cell_type": "code", "execution_count": 34, "id": "minus-airline", "metadata": {}, "outputs": [], "source": [ "ds = ds.sel(variants=((ds.variant_allele_frequency[:,1] > 0.01) & (ds.variant_hwe_p_value > 1e-6)))" ] }, { "cell_type": "markdown", "id": "wireless-italian", "metadata": {}, "source": [ "_Note: again, the number of variants is different to the Hail tutorial, but the final results work out to be very similar._" ] }, { "cell_type": "code", "execution_count": 35, "id": "sexual-charles", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Samples: 250 Variants: 8394\n" ] } ], "source": [ "print(f\"Samples: {len(ds.samples)} Variants: {len(ds.variants)}\")" ] }, { "cell_type": "markdown", "id": "lovely-scratch", "metadata": {}, "source": [ "Run a linear regression of dosage (number of alt alleles) against the `CaffeineConsumption` trait." ] }, { "cell_type": "code", "execution_count": 36, "id": "forty-richardson", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:                   (alleles: 2, ploidy: 2, samples: 250, traits: 1, variants: 8394)\n",
       "Dimensions without coordinates: alleles, ploidy, samples, traits, variants\n",
       "Data variables: (12/38)\n",
       "    variant_linreg_beta       (variants, traits) float64 dask.array<chunksize=(7918, 1), meta=np.ndarray>\n",
       "    variant_linreg_t_value    (variants, traits) float64 dask.array<chunksize=(7918, 1), meta=np.ndarray>\n",
       "    variant_linreg_p_value    (variants, traits) float64 dask.array<chunksize=(7918, 1), meta=np.ndarray>\n",
       "    variant_hwe_p_value       (variants) float64 dask.array<chunksize=(7918,), meta=np.ndarray>\n",
       "    variant_n_het             (variants) int64 dask.array<chunksize=(7918,), meta=np.ndarray>\n",
       "    variant_n_hom_ref         (variants) int64 dask.array<chunksize=(7918,), meta=np.ndarray>\n",
       "    ...                        ...\n",
       "    SuperPopulation           (samples) object 'EUR' 'EUR' 'EUR' ... 'SAS' 'SAS'\n",
       "    isFemale                  (samples) bool False True False ... False True\n",
       "    PurpleHair                (samples) bool False False False ... True True\n",
       "    CaffeineConsumption       (samples) int64 4 4 4 3 6 2 2 5 ... 6 4 6 4 6 5 5\n",
       "    sample_dp_mean            (samples) float64 dask.array<chunksize=(250,), meta=np.ndarray>\n",
       "    call_dosage               (variants, samples) int64 dask.array<chunksize=(7918, 250), meta=np.ndarray>\n",
       "Attributes: (1)
" ], "text/plain": [ "\n", "Dimensions: (alleles: 2, ploidy: 2, samples: 250, traits: 1, variants: 8394)\n", "Dimensions without coordinates: alleles, ploidy, samples, traits, variants\n", "Data variables: (12/38)\n", " variant_linreg_beta (variants, traits) float64 dask.array\n", " variant_linreg_t_value (variants, traits) float64 dask.array\n", " variant_linreg_p_value (variants, traits) float64 dask.array\n", " variant_hwe_p_value (variants) float64 dask.array\n", " variant_n_het (variants) int64 dask.array\n", " variant_n_hom_ref (variants) int64 dask.array\n", " ... ...\n", " SuperPopulation (samples) object 'EUR' 'EUR' 'EUR' ... 'SAS' 'SAS'\n", " isFemale (samples) bool False True False ... False True\n", " PurpleHair (samples) bool False False False ... True True\n", " CaffeineConsumption (samples) int64 4 4 4 3 6 2 2 5 ... 6 4 6 4 6 5 5\n", " sample_dp_mean (samples) float64 dask.array\n", " call_dosage (variants, samples) int64 dask.array\n", "Attributes: (1)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds[\"call_dosage\"] = ds.call_genotype.sum(dim=\"ploidy\")\n", "ds_lr = sg.gwas_linear_regression(ds, dosage=\"call_dosage\",\n", " add_intercept=True, covariates=[], traits=[\"CaffeineConsumption\"])\n", "ds_lr" ] }, { "cell_type": "markdown", "id": "caring-bikini", "metadata": {}, "source": [ "You can see that new variables have been added for `variant_linreg_p_value`, `variant_linreg_t_value`, and `variant_linreg_beta`.\n", "\n", "Since sgkit doesn't have any plotting utilities, we implement Manhattan plots and QQ plots here using Seaborn." ] }, { "cell_type": "code", "execution_count": 37, "id": "cubic-assessment", "metadata": {}, "outputs": [], "source": [ "import seaborn as sns\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 38, "id": "radio-gnome", "metadata": {}, "outputs": [], "source": [ "def manhattan_plot(ds):\n", " df = ds[[\"variant_contig_name\", \"variant_contig\", \"variant_position\", \"variant_linreg_p_value\"]].to_dataframe()\n", " df[\"variant_linreg_log_p_value\"] = -np.log10(df[\"variant_linreg_p_value\"])\n", " df = df.astype({\"variant_position\": np.int64}) # to avoid overflow in cumulative_pos\n", " \n", " # from https://github.com/mojones/video_notebooks/blob/master/Manhattan%20plots%20in%20Python.ipynb, cell 20\n", " running_pos = 0\n", " cumulative_pos = []\n", " for chrom, group_df in df.groupby(\"variant_contig\"): \n", " cumulative_pos.append(group_df[\"variant_position\"] + running_pos)\n", " running_pos += group_df[\"variant_position\"].max()\n", " df[\"cumulative_pos\"] = pd.concat(cumulative_pos)\n", " \n", " df[\"color group\"] = df[\"variant_contig\"].apply(lambda x : \"A\" if x % 2 == 0 else \"B\")\n", " \n", " g = sns.relplot(\n", " data = df,\n", " x = \"cumulative_pos\",\n", " y = \"variant_linreg_log_p_value\",\n", " hue = \"color group\",\n", " palette = [\"blue\", \"orange\"],\n", " linewidth=0,\n", " s=10,\n", " legend=None,\n", " aspect=3\n", " )\n", " g.ax.set_xlabel(\"Chromosome\")\n", " g.ax.set_xticks(df.groupby(\"variant_contig\")[\"cumulative_pos\"].median())\n", " g.ax.set_xticklabels(df[\"variant_contig_name\"].unique())" ] }, { "cell_type": "code", "execution_count": 39, "id": "inner-egypt", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "manhattan_plot(ds_lr)" ] }, { "cell_type": "code", "execution_count": 40, "id": "static-grove", "metadata": {}, "outputs": [], "source": [ "import math\n", "import matplotlib.pyplot as plt\n", "\n", "def qq_plot(ds):\n", " p = ds[\"variant_linreg_p_value\"].squeeze().values\n", " p.sort()\n", " n = len(p)\n", " expected_p = -np.log10(np.arange(1, n + 1) / n)\n", " observed_p = -np.log10(p)\n", " max_val = math.ceil(max(np.max(expected_p), np.max(observed_p)))\n", "\n", " df = pd.DataFrame({\"Expected -log10(p)\": expected_p, \"Observed -log10(p)\": observed_p})\n", "\n", " fig, ax = plt.subplots(figsize=(12, 12));\n", " g = sns.scatterplot(data=df, x=\"Expected -log10(p)\", y=\"Observed -log10(p)\", ax=ax, linewidth=0)\n", "\n", " x_pred = np.linspace(0, max_val, 50)\n", " sns.lineplot(x=x_pred, y=x_pred, ax=ax)\n", " \n", " g.set(xlim=(0, max_val), ylim=(0, max_val))" ] }, { "cell_type": "code", "execution_count": 41, "id": "brutal-pharmaceutical", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "qq_plot(ds_lr)" ] }, { "cell_type": "markdown", "id": "timely-tsunami", "metadata": {}, "source": [ "## Confounded!" ] }, { "cell_type": "markdown", "id": "young-parallel", "metadata": {}, "source": [ "As explained in the Hail tutorial, the data contains a confounder, so it is necessary to include ancestry as a covariate in the linear regression.\n", "\n", "Rather than just use the reported ancestry, it's better to use principal components from running a PCA on the data." ] }, { "cell_type": "code", "execution_count": 42, "id": "virgin-creativity", "metadata": {}, "outputs": [], "source": [ "ds_pca = sg.stats.pca.count_call_alternate_alleles(ds)" ] }, { "cell_type": "code", "execution_count": 43, "id": "humanitarian-starter", "metadata": {}, "outputs": [], "source": [ "# To run PCA we need to filter out variants with any missing alt allele counts\n", "# Or where the counts are zero for all samples\n", "variant_mask = ((ds_pca.call_alternate_allele_count < 0).any(dim=\"samples\")) | \\\n", " (ds_pca.call_alternate_allele_count.std(dim=\"samples\") <= 0.0)\n", "ds_pca = ds_pca.sel(variants=~variant_mask)" ] }, { "cell_type": "code", "execution_count": 44, "id": "statistical-bolivia", "metadata": {}, "outputs": [], "source": [ "ds_pca = sg.pca(ds_pca)" ] }, { "cell_type": "code", "execution_count": 45, "id": "furnished-modem", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ -8.453594 , -26.128803 , -11.008169 , ..., -14.80122 ,\n", " 24.5373 , -1.0794903],\n", " [ -9.496218 , -26.319607 , -10.1165 , ..., 1.682763 ,\n", " 7.681702 , -5.9572787],\n", " [ -7.8734045, -25.404314 , -9.859191 , ..., 4.3828683,\n", " -9.368458 , 6.3843384],\n", " ...,\n", " [-10.974407 , -11.576628 , 20.124643 , ..., -4.421063 ,\n", " -0.5393093, 1.0124549],\n", " [-10.754401 , -11.414771 , 15.358786 , ..., 1.7951618,\n", " 3.426365 , -7.985674 ],\n", " [-13.062883 , -11.688104 , 16.351276 , ..., -7.205048 ,\n", " -1.7339791, 5.1750174]], dtype=float32)" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_pca.sample_pca_projection.values" ] }, { "cell_type": "code", "execution_count": 46, "id": "faced-dutch", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "
<xarray.Dataset>\n",
       "Dimensions:                              (alleles: 2, components: 10, ploidy: 2, samples: 250, variants: 3491)\n",
       "Dimensions without coordinates: alleles, components, ploidy, samples, variants\n",
       "Data variables: (12/41)\n",
       "    sample_pca_projection                (samples, components) float32 dask.array<chunksize=(250, 10), meta=np.ndarray>\n",
       "    sample_pca_component                 (variants, components) float32 dask.array<chunksize=(3325, 10), meta=np.ndarray>\n",
       "    sample_pca_explained_variance        (components) float32 dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "    sample_pca_explained_variance_ratio  (components) float32 dask.array<chunksize=(10,), meta=np.ndarray>\n",
       "    sample_pca_loading                   (variants, components) float32 dask.array<chunksize=(3325, 10), meta=np.ndarray>\n",
       "    call_alternate_allele_count          (variants, samples) int16 dask.array<chunksize=(3325, 250), meta=np.ndarray>\n",
       "    ...                                   ...\n",
       "    SuperPopulation                      (samples) object 'EUR' 'EUR' ... 'SAS'\n",
       "    isFemale                             (samples) bool False True ... True\n",
       "    PurpleHair                           (samples) bool False False ... True\n",
       "    CaffeineConsumption                  (samples) int64 4 4 4 3 6 ... 6 4 6 5 5\n",
       "    sample_dp_mean                       (samples) float64 dask.array<chunksize=(250,), meta=np.ndarray>\n",
       "    call_dosage                          (variants, samples) int64 dask.array<chunksize=(3325, 250), meta=np.ndarray>\n",
       "Attributes: (1)
" ], "text/plain": [ "\n", "Dimensions: (alleles: 2, components: 10, ploidy: 2, samples: 250, variants: 3491)\n", "Dimensions without coordinates: alleles, components, ploidy, samples, variants\n", "Data variables: (12/41)\n", " sample_pca_projection (samples, components) float32 dask.array\n", " sample_pca_component (variants, components) float32 dask.array\n", " sample_pca_explained_variance (components) float32 dask.array\n", " sample_pca_explained_variance_ratio (components) float32 dask.array\n", " sample_pca_loading (variants, components) float32 dask.array\n", " call_alternate_allele_count (variants, samples) int16 dask.array\n", " ... ...\n", " SuperPopulation (samples) object 'EUR' 'EUR' ... 'SAS'\n", " isFemale (samples) bool False True ... True\n", " PurpleHair (samples) bool False False ... True\n", " CaffeineConsumption (samples) int64 4 4 4 3 6 ... 6 4 6 5 5\n", " sample_dp_mean (samples) float64 dask.array\n", " call_dosage (variants, samples) int64 dask.array\n", "Attributes: (1)" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ds_pca" ] }, { "cell_type": "markdown", "id": "interior-puzzle", "metadata": {}, "source": [ "Let's plot the first two components. Notice how they cluster by ancestry." ] }, { "cell_type": "code", "execution_count": 47, "id": "returning-champagne", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ds_pca[\"sample_pca_projection_0\"] = ds_pca.sample_pca_projection[:,0]\n", "ds_pca[\"sample_pca_projection_1\"] = ds_pca.sample_pca_projection[:,1]\n", "ds_pca[\"sample_pca_projection_2\"] = ds_pca.sample_pca_projection[:,2]\n", "ds_pca.plot.scatter(x=\"sample_pca_projection_0\", y=\"sample_pca_projection_1\", hue=\"SuperPopulation\", size=8, s=10);" ] }, { "cell_type": "markdown", "id": "abroad-pilot", "metadata": {}, "source": [ "Now we can rerun our linear regression, controlling for sample sex and the first few principal components." ] }, { "cell_type": "code", "execution_count": 48, "id": "armed-newspaper", "metadata": {}, "outputs": [], "source": [ "# copy pca components back to dataset with full set of variants to run linear regression again\n", "ds[\"sample_pca_projection_0\"] = ds_pca.sample_pca_projection[:,0]\n", "ds[\"sample_pca_projection_1\"] = ds_pca.sample_pca_projection[:,1]\n", "ds[\"sample_pca_projection_2\"] = ds_pca.sample_pca_projection[:,2]\n", "ds_lr = sg.gwas_linear_regression(ds, dosage=\"call_dosage\",\n", " add_intercept=True,\n", " covariates=[\"isFemale\", \"sample_pca_projection_0\", \"sample_pca_projection_1\", \"sample_pca_projection_2\"],\n", " traits=[\"CaffeineConsumption\"])" ] }, { "cell_type": "code", "execution_count": 49, "id": "excellent-buffer", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "manhattan_plot(ds_lr)" ] }, { "cell_type": "markdown", "id": "incorrect-mystery", "metadata": {}, "source": [ "The \"caffeine consumption\" locus in chromosome 8 is clearly apparent, just like in the Hail tutorial." ] }, { "cell_type": "code", "execution_count": null, "id": "western-selection", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 5 }