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| Original file line number | Diff line number | Diff line change |
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| import hail as hl | ||
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| # hailctl dataproc start guhancluster --vep GRCh38 --zone us-west1-b --num-preemptible-workers 100 --worker-machine-type n1-standard-16 --master-machine-type n1-highmem-32 | ||
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| hl.init(log="/home/gwas.log") | ||
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| def compute_pcs(mt, pc_num): | ||
| # Run PCA on synonymous, NFE <AF > 0.01, LD Pruned variants | ||
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| print("Performing variant QC and filtering on call rate...") | ||
| mt = hl.variant_qc(mt, name="variant_qc") | ||
| mt = mt.filter_rows(mt.variant_qc.call_rate > 0.95) | ||
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| # Lifted over version, generated with above code | ||
| nfe_ht = hl.read_table("gs://ibd-exomes/v36meta/fin_enriched_exomes_38.ht") | ||
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| mt = mt.annotate_rows(nfe_ht=nfe_ht[mt.row_key]) | ||
| print("Filtering gnomAD NFE AF > 0.01...") | ||
| mt = mt.filter_rows(mt.nfe_ht.nfe.AF > 0.01) | ||
| print(mt.count()) | ||
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| print("Filtering for synonymous variants...") | ||
| mt = mt.filter_rows(mt.vep.most_severe_consequence == "synonymous_variant") | ||
| print(mt.count()) | ||
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| # LD PRUNE | ||
| pruned_variants = hl.ld_prune(mt.GT) | ||
| print("Pruning...") | ||
| mt = mt.filter_rows(hl.is_defined(pruned_variants[mt.row_key])) | ||
| print(mt.count()) | ||
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| print("Computing and annotating PCs...") | ||
| eigenvalues, pcs, _ = hl.hwe_normalized_pca(mt.GT, k=pc_num) | ||
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| return eigenvalues, pcs | ||
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| def run_logistic_regression(test, mt, pop, disease): | ||
| result = hl.logistic_regression_rows( | ||
| test=test, | ||
| y=mt.is_case, | ||
| x=mt.GT.n_alt_alleles(), | ||
| covariates=[ | ||
| 1, | ||
| mt.scores[0], | ||
| mt.scores[1], | ||
| mt.scores[2], | ||
| mt.scores[3], | ||
| mt.scores[4], | ||
| mt.scores[5], | ||
| mt.scores[6], | ||
| mt.scores[7], | ||
| mt.scores[8], | ||
| mt.scores[9], | ||
| ], | ||
| ) | ||
| result = result.checkpoint( | ||
| "gs://ibd-exomes/v36meta/" + pop + "_" + disease + "_" + test + "_results.ht", | ||
| overwrite=True, | ||
| ) | ||
| return result | ||
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| def export_tsv(mt, pop, disease): | ||
| rows = mt.rows() | ||
| rows = rows.key_by() | ||
| print("Annotating specific fields...") | ||
| rows = rows.annotate( | ||
| maf=rows.variant_qc.AF, | ||
| call_rate=rows.variant_qc.call_rate, | ||
| HGVSp=rows.vep.transcript_consequences.map(lambda x: x.hgvsp), | ||
| HGVSc=rows.vep.transcript_consequences.map(lambda x: x.hgvsc), | ||
| consequence=rows.vep.most_severe_consequence, | ||
| mean_dp=rows.variant_qc.dp_stats.mean, | ||
| gene_symbol=rows.vep.transcript_consequences.map(lambda x: x.gene_symbol), | ||
| ) | ||
| rows.select( | ||
| V=rows.V, | ||
| P=hl.cond(rows.wald_fit.converged == True, rows.wald_p, rows.firth_p), | ||
| OR=hl.cond( | ||
| rows.wald_fit.converged == True, | ||
| hl.exp(rows.wald_beta), | ||
| hl.exp(rows.firth_beta), | ||
| ), | ||
| SE=hl.cond( | ||
| rows.wald_fit.converged == True, | ||
| rows.wald_se, | ||
| (rows.firth_beta / (1 - hl.qnorm(rows.firth_p))), | ||
| ), | ||
| CaAC=rows.caac, | ||
| CaNAC=rows.canac, | ||
| CoAC=rows.coac, | ||
| CoNAC=rows.conac, | ||
| maf=rows.maf, | ||
| call_rate=rows.call_rate, | ||
| mean_dp=rows.mean_dp, | ||
| case_phwe=rows.case_phwe, | ||
| control_phwe=rows.control_phwe, | ||
| TEST=hl.cond(rows.wald_fit.converged == True, "WALD", "FIRTH"), | ||
| HGVSp=rows.HGVSp, | ||
| HGVSc=rows.HGVSc, | ||
| consequence=rows.consequence, | ||
| gene_symbol=rows.gene_symbol, | ||
| ).export( | ||
| "gs://ibd-exomes/v36meta/" | ||
| + pop | ||
| + "_" | ||
| + disease | ||
| + "_logreg_results.tsv.gz" | ||
| ) | ||
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| def main(): | ||
| # for pop in ["AJ", "FIN", "LIT", "NFE"]: | ||
| for pop in ["NFE"]: | ||
| print("Running " + pop + " Wald...") | ||
| for disease in ["cd", "ibd", "uc"]: | ||
| # for disease in ["cd"]: | ||
| # Read in MT | ||
| pop_ca_co_mt = hl.read_matrix_table( | ||
| "gs://ibd-exomes/v36meta/" + pop + "_" + disease + ".mt" | ||
| ) | ||
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| # Coalesce | ||
| pop_ca_co_mt = pop_ca_co_mt.naive_coalesce(500) | ||
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| # Prep for GWAS with C/C status and PCs | ||
| print("Annotating case/control status...") | ||
| pop_ca_co_mt = pop_ca_co_mt.annotate_cols( | ||
| is_case=(~(pop_ca_co_mt.control.contains(pop_ca_co_mt.DIAGNOSIS))) | ||
| ) | ||
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| # Checkpoint | ||
| pop_ca_co_mt = pop_ca_co_mt.checkpoint( | ||
| "gs://ibd-exomes/v36meta/" + pop + "_" + disease + "_diagnosis.mt", overwrite=True | ||
| ) | ||
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| # print("Performing VEP...") | ||
| # pop_ca_co_mt = hl.vep(pop_ca_co_mt, "gs://hail-common/vep/vep/vep95-GRCh38-loftee-gcloud.json") | ||
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| eigenvalues, pcs = compute_pcs(pop_ca_co_mt, 10) | ||
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| pop_ca_co_mt = pop_ca_co_mt.annotate_cols(scores=pcs[pop_ca_co_mt.s].scores) | ||
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| # Checkpoint | ||
| pop_ca_co_mt = pop_ca_co_mt.checkpoint( | ||
| "gs://ibd-exomes/v36meta/" + pop + "_" + disease + "_pcs.mt", overwrite=True | ||
| ) | ||
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| # Read in MT | ||
| pop_ca_co_mt = hl.read_matrix_table( | ||
| "gs://ibd-exomes/v36meta/" + pop + "_" + disease + "_pcs.mt" | ||
| ) | ||
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| # print(pop_ca_co_mt.count()) | ||
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| print( | ||
| "Conducting Wald test for " | ||
| + pop | ||
| + " population, " | ||
| + disease | ||
| + "..." | ||
| ) | ||
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| # Run, annotate with results | ||
| wald_result = run_logistic_regression("wald", pop_ca_co_mt, pop, disease) | ||
| print(wald_result.count()) | ||
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| wald_result = hl.read_table( | ||
| "gs://ibd-exomes/v36meta/" + pop + "_" + disease + "_wald_results.ht" | ||
| ) | ||
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| pop_ca_co_mt = pop_ca_co_mt.annotate_rows( | ||
| wald_beta=wald_result[pop_ca_co_mt.row_key].beta, | ||
| wald_se=wald_result[pop_ca_co_mt.row_key].standard_error, | ||
| wald_z_stat=wald_result[pop_ca_co_mt.row_key].z_stat, | ||
| wald_p=wald_result[pop_ca_co_mt.row_key].p_value, | ||
| wald_fit=wald_result[pop_ca_co_mt.row_key].fit, | ||
| ) | ||
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| # # Rerun with Firth | ||
| # pop_ca_co_firth_mt = pop_ca_co_mt.filter_rows( | ||
| # pop_ca_co_mt.wald_fit.converged == False | ||
| # ) | ||
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| # print( | ||
| # "Conducting Firth test for " | ||
| # + pop | ||
| # + " population, " | ||
| # + disease | ||
| # + "..." | ||
| # ) | ||
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| # print(pop_ca_co_firth_mt.count()) | ||
| # firth_result = run_logistic_regression( | ||
| # "firth", pop_ca_co_firth_mt, pop, disease | ||
| # ) | ||
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| firth_result = hl.read_table( | ||
| "gs://ibd-exomes/v36meta/" + pop + "_" + disease + "_firth_results.ht" | ||
| ) | ||
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| # Merge | ||
| pop_ca_co_mt = pop_ca_co_mt.annotate_rows( | ||
| firth_beta=firth_result[pop_ca_co_mt.row_key].beta, | ||
| firth_chi_sq_stat=firth_result[pop_ca_co_mt.row_key].chi_sq_stat, | ||
| firth_p=firth_result[pop_ca_co_mt.row_key].p_value, | ||
| firth_fit=firth_result[pop_ca_co_mt.row_key].fit, | ||
| ) | ||
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| print("Exporting logreg results to table...") | ||
| export_tsv(pop_ca_co_mt, pop, disease) | ||
|
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| if __name__ == "__main__": | ||
| main() |
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