MSI Seminar: Selective inference approaches for augmenting genetic association studies with multi-omics metadata

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Published on September 30, 2021 Updated on October 4, 2021

on the November 24, 2021

On November 24, 2021 at 2:00 p.m.


100% online

Held online via Zoom

Online seminar held by Ronald Yurko, PhD student in Statistics & Data Science (Carnegie Mellon University, Pittsburgh)


Ronald Yurko, PhD student in Statistics & Data Science – Carnegie Mellon University, Roeder Lab – Pittsburgh


To correct for a large number of hypothesis tests, most researchers rely on simple multiple testing corrections. Yet, new selective inference methodologies could improve power by enabling exploration of test statistics with covariates for informative weights while retaining desired statistical guarantees. We explore one such framework, adaptive p-value thresholding (AdaPT), in the context of genome-wide association studies under two types of regimes: (1) testing individual single nucleotide polymorphisms (SNPs) for schizophrenia and (2) the aggregation of SNPs into gene-based test statistics for autism spectrum disorder. We address the practical challenges of implementing AdaPT in these high-dimensional -omics settings, such as incorporating metadata with gradient boosted trees as well as adjusting for dependence induced from linkage disequilibrium (LD). To address the latter concern, we introduce an agglomerative algorithm with a linkage function determined by the LD-induced correlation structure of the gene-based test statistics. The advantages of our approaches are twofold: increased power and increased interpretability, with the latter expediting our understanding of the etiology of human diseases, disorders, and other phenotypes.


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