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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run MAGMA on KGWAS Sumstats to get gene-based p-values\n",
"Given the local bfile where it stores the genotype data, you can run MAGMA to get gene-based p-values. Here is an example code to run it assuming you have (1) saved a trained model under `./data/model/test` (2) downloaded magma executable from [here](https://cncr.nl/research/magma/) (3) have a genotype file for your cohort"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"All required data files are present.\n",
"--loading KG---\n",
"--using enformer SNP embedding--\n",
"--using random go embedding--\n",
"--using ESM gene embedding--\n",
"Loading example GWAS file...\n",
"Example file already exists locally.\n",
"Loading GWAS file from ./data/biochemistry_Creatinine_fastgwa_full_10000_1.fastGWA...\n",
"Using ldsc weight...\n",
"ldsc_weight mean: 0.9999999999999993\n",
"Annotation file already exists locally.\n",
"MAGMA command executed successfully.\n",
"Output: Welcome to MAGMA v1.10 (linux)\n",
"Using flags:\n",
"\t--bfile /dfs/project/datasets/20220524-ukbiobank/data/genetics/ukb_sample_0.1\n",
"\t--gene-annot ./data/gene_annotation.genes.annot\n",
"\t--pval ./data//model_pred/new_experiments/test_magma_format.csv N=9988\n",
"\t--out ./data//model_pred/new_experiments/test_magma_out\n",
"\n",
"Start time is 21:07:31, Monday 25 Nov 2024\n",
"\n",
"Loading PLINK-format data...\n",
"Reading file /dfs/project/datasets/20220524-ukbiobank/data/genetics/ukb_sample_0.1.fam... 48769 individuals read\n",
"Reading file /dfs/project/datasets/20220524-ukbiobank/data/genetics/ukb_sample_0.1.bim... 784256 SNPs read\n",
"Preparing file /dfs/project/datasets/20220524-ukbiobank/data/genetics/ukb_sample_0.1.bed... \n",
"\n",
"Reading SNP p-values from file ./data//model_pred/new_experiments/test_magma_format.csv... \n",
"\tdetected 2 variables in file\n",
"\tusing variable: SNP (SNP id)\n",
"\tusing variable: P (p-value)\n",
"\tread 542759 lines from file, containing valid SNP p-values for 542758 SNPs in data (100% of lines, 69.21% of SNPs in data)\n",
"Loading gene annotation from file ./data/gene_annotation.genes.annot... \n",
"\t17919 gene definitions read from file\n",
"\tfound 16637 genes containing valid SNPs in genotype data\n",
"\n",
"\n",
"Starting gene analysis... \n",
"\tusing model: SNPwise-mean\n",
"\tengaging automatic 'big-data' mode\n",
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"\tprocessed genes: 16600 (99.8%) \n",
"\tprocessed genes: 16632 (100%) \n",
" \n",
"\twriting gene analysis results to file ./data//model_pred/new_experiments/test_magma_out.genes.out\n",
"\twriting intermediate output to file ./data//model_pred/new_experiments/test_magma_out.genes.raw\n",
"\n",
"\n",
"End time is 21:34:07, Monday 25 Nov 2024 (elapsed: 00:26:36)\n",
"\n"
]
}
],
"source": [
"import sys\n",
"sys.path.append('../')\n",
"from kgwas import KGWAS, KGWAS_Data\n",
"data = KGWAS_Data(data_path = './data/')\n",
"data.load_kg()\n",
"\n",
"data.load_external_gwas(example_file = True)\n",
"data.process_gwas_file()\n",
"data.prepare_split()\n",
"\n",
"run = KGWAS(data, device = 'cuda:7', exp_name = 'test')\n",
"run.load_pretrained('./data/model/test')\n",
"run.run_magma(path_to_magma = \"/dfs/user/kexinh/ggwas/magma\", bfile = \"/dfs/project/datasets/20220524-ukbiobank/data/genetics/ukb_sample_0.1\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is an example output of the MAGMA run:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"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>GENE</th>\n",
" <th>CHR</th>\n",
" <th>START</th>\n",
" <th>STOP</th>\n",
" <th>NSNPS</th>\n",
" <th>NPARAM</th>\n",
" <th>N</th>\n",
" <th>ZSTAT</th>\n",
" <th>P</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>148398</td>\n",
" <td>1</td>\n",
" <td>859993</td>\n",
" <td>879961</td>\n",
" <td>6</td>\n",
" <td>4</td>\n",
" <td>9988</td>\n",
" <td>-1.10670</td>\n",
" <td>0.865800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>26155</td>\n",
" <td>1</td>\n",
" <td>879583</td>\n",
" <td>894679</td>\n",
" <td>9</td>\n",
" <td>4</td>\n",
" <td>9988</td>\n",
" <td>0.36490</td>\n",
" <td>0.357590</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>339451</td>\n",
" <td>1</td>\n",
" <td>895967</td>\n",
" <td>901099</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>9988</td>\n",
" <td>1.40540</td>\n",
" <td>0.079955</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>84069</td>\n",
" <td>1</td>\n",
" <td>901872</td>\n",
" <td>910488</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>9988</td>\n",
" <td>1.56410</td>\n",
" <td>0.058896</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>84808</td>\n",
" <td>1</td>\n",
" <td>910579</td>\n",
" <td>917473</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>9988</td>\n",
" <td>1.25460</td>\n",
" <td>0.104820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16632</th>\n",
" <td>23542</td>\n",
" <td>22</td>\n",
" <td>51039131</td>\n",
" <td>51049979</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>9988</td>\n",
" <td>1.15830</td>\n",
" <td>0.123360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16633</th>\n",
" <td>410</td>\n",
" <td>22</td>\n",
" <td>51061182</td>\n",
" <td>51066601</td>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" <td>9988</td>\n",
" <td>-0.65834</td>\n",
" <td>0.744840</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16634</th>\n",
" <td>85358</td>\n",
" <td>22</td>\n",
" <td>51113070</td>\n",
" <td>51171640</td>\n",
" <td>41</td>\n",
" <td>15</td>\n",
" <td>9988</td>\n",
" <td>-1.18500</td>\n",
" <td>0.881990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16635</th>\n",
" <td>101928892</td>\n",
" <td>22</td>\n",
" <td>51123086</td>\n",
" <td>51125473</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>9988</td>\n",
" <td>-0.14590</td>\n",
" <td>0.558000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16636</th>\n",
" <td>49</td>\n",
" <td>22</td>\n",
" <td>51176652</td>\n",
" <td>51183727</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>9988</td>\n",
" <td>-0.42425</td>\n",
" <td>0.664310</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>16637 rows × 9 columns</p>\n",
"</div>"
],
"text/plain": [
" GENE CHR START STOP NSNPS NPARAM N ZSTAT \\\n",
"0 148398 1 859993 879961 6 4 9988 -1.10670 \n",
"1 26155 1 879583 894679 9 4 9988 0.36490 \n",
"2 339451 1 895967 901099 1 1 9988 1.40540 \n",
"3 84069 1 901872 910488 3 2 9988 1.56410 \n",
"4 84808 1 910579 917473 2 1 9988 1.25460 \n",
"... ... ... ... ... ... ... ... ... \n",
"16632 23542 22 51039131 51049979 3 2 9988 1.15830 \n",
"16633 410 22 51061182 51066601 9 5 9988 -0.65834 \n",
"16634 85358 22 51113070 51171640 41 15 9988 -1.18500 \n",
"16635 101928892 22 51123086 51125473 2 1 9988 -0.14590 \n",
"16636 49 22 51176652 51183727 1 1 9988 -0.42425 \n",
"\n",
" P \n",
"0 0.865800 \n",
"1 0.357590 \n",
"2 0.079955 \n",
"3 0.058896 \n",
"4 0.104820 \n",
"... ... \n",
"16632 0.123360 \n",
"16633 0.744840 \n",
"16634 0.881990 \n",
"16635 0.558000 \n",
"16636 0.664310 \n",
"\n",
"[16637 rows x 9 columns]"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"pd.read_csv('./data/model_pred/new_experiments/test_magma_out.genes.out', sep = '\\s+')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To know which gene this gene id maps to, you can use the following file:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>79501</td>\n",
" <td>1</td>\n",
" <td>69091</td>\n",
" <td>70008</td>\n",
" <td>+</td>\n",
" <td>OR4F5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>100996442</td>\n",
" <td>1</td>\n",
" <td>142447</td>\n",
" <td>174392</td>\n",
" <td>-</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>729759</td>\n",
" <td>1</td>\n",
" <td>367659</td>\n",
" <td>368597</td>\n",
" <td>+</td>\n",
" <td>OR4F29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>81399</td>\n",
" <td>1</td>\n",
" <td>621096</td>\n",
" <td>622034</td>\n",
" <td>-</td>\n",
" <td>OR4F16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>148398</td>\n",
" <td>1</td>\n",
" <td>859993</td>\n",
" <td>879961</td>\n",
" <td>+</td>\n",
" <td>SAMD11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19422</th>\n",
" <td>442867</td>\n",
" <td>Y</td>\n",
" <td>26764151</td>\n",
" <td>26785354</td>\n",
" <td>+</td>\n",
" <td>BPY2B</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19423</th>\n",
" <td>57054</td>\n",
" <td>Y</td>\n",
" <td>26909216</td>\n",
" <td>26959639</td>\n",
" <td>-</td>\n",
" <td>DAZ3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19424</th>\n",
" <td>57135</td>\n",
" <td>Y</td>\n",
" <td>26979967</td>\n",
" <td>27053187</td>\n",
" <td>+</td>\n",
" <td>DAZ4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19425</th>\n",
" <td>442868</td>\n",
" <td>Y</td>\n",
" <td>27177048</td>\n",
" <td>27198251</td>\n",
" <td>-</td>\n",
" <td>BPY2C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19426</th>\n",
" <td>9085</td>\n",
" <td>Y</td>\n",
" <td>27768264</td>\n",
" <td>27771049</td>\n",
" <td>+</td>\n",
" <td>CDY1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>19427 rows × 6 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2 3 4 5\n",
"0 79501 1 69091 70008 + OR4F5\n",
"1 100996442 1 142447 174392 - LOC100996442\n",
"2 729759 1 367659 368597 + OR4F29\n",
"3 81399 1 621096 622034 - OR4F16\n",
"4 148398 1 859993 879961 + SAMD11\n",
"... ... .. ... ... .. ...\n",
"19422 442867 Y 26764151 26785354 + BPY2B\n",
"19423 57054 Y 26909216 26959639 - DAZ3\n",
"19424 57135 Y 26979967 27053187 + DAZ4\n",
"19425 442868 Y 27177048 27198251 - BPY2C\n",
"19426 9085 Y 27768264 27771049 + CDY1\n",
"\n",
"[19427 rows x 6 columns]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pd.read_csv('./data/misc_data/NCBI37.3.gene.loc', sep = '\\t', header = None)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "a100_env",
"language": "python",
"name": "python3"
},
"language_info": {
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"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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