A selective inference approach for false discovery rate control using multiomics covariates yields insights into disease risk

To correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using aux...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2020-06, Vol.117 (26), p.15028-15035
Main Authors: Yurko, Ronald, G’Sell, Max, Roeder, Kathryn, Devlin, Bernie
Format: Article
Language:English
Subjects:
Biological Sciences
Bipolar disorder
Gene expression
Gene mapping
Genome-wide association studies
Genomes
Genotypes
Health risks
Hypotheses
Mental disorders
Nucleotides
Polygenic inheritance
Prefrontal cortex
Quantitative trait loci
Schizophrenia
Single-nucleotide polymorphism
Statistical analysis
Statistical inference
Statistical tests
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Summary:To correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new methodologies of selective inference could potentially improve power while retaining statistical guarantees, especially those that enable exploration of test statistics using auxiliary information (covariates) to weight hypothesis tests for association. We explore one such method, adaptive P-value thresholding (AdaPT), in the framework of genome-wide association studies (GWAS) and gene expression/coexpression studies, with particular emphasis on schizophrenia (SCZ). Selected SCZ GWAS association P values play the role of the primary data for AdaPT; single-nucleotide polymorphisms (SNPs) are selected because they are gene expression quantitative trait loci (eQTLs). This natural pairing of SNPs and genes allow us to map the following covariate values to these pairs: GWAS statistics from genetically correlated bipolar disorder, the effect size of SNP genotypes on gene expression, and gene– gene coexpression, captured by subnetwork (module) membership. In all, 24 covariates per SNP/gene pair were included in the AdaPT analysis using flexible gradient boosted trees. We demonstrate a substantial increase in power to detect SCZ associations using gene expression information from the developing human prefrontal cortex. We interpret these results in light of recent theories about the polygenic nature of SCZ. Importantly, our entire process for identifying enrichment and creating features with independent complementary data sources can be implemented in many different high-throughput settings to ultimately improve power.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1918862117