Multi-resolution localization of causal variants across the genome

Abstract

In the statistical analysis of genome-wide association data, it is challenging to precisely localize the variants that affect complex traits, due to linkage disequilibrium, and to maximize power while limiting spurious findings. Here we report on KnockoffZoom: a flexible method that localizes causal variants at multiple resolutions by testing the conditional associations of genetic segments of decreasing width, while provably controlling the false discovery rate. Our method utilizes artificial genotypes as negative controls and is equally valid for quantitative and binary phenotypes, without requiring any assumptions about their genetic architectures. Instead, we rely on well-established genetic models of linkage disequilibrium. We demonstrate that our method can detect more associations than mixed effects models and achieve fine-mapping precision, at comparable computational cost. Lastly, we apply KnockoffZoom to data from 350k subjects in the UK Biobank and report many new findings.

A Simple New Approach to Variable Selection in Regression, with Application to Genetic Fine Mapping

“…While our work here was under peer review and available as a preprint (Wang et al ., 2019), we became aware of new related work in Sesia et al . (2020). Similarly to hierinf this new method tests groups of variables at multiple resolutions in a hierarchy; but it improves on hierinf by controlling the FDR of selected groups (rather than type I error), and with statistical guarantees that hold even in the presence of highly correlated variables.…”

“…Comparisons with our method find that their significant groups are typically larger than ours (Sesia et al . (2020), Fig. 4), presumably in part because of the fundamental limitation with the hierarchical approach (discussed above).…”

Causal inference in genetic trio studies

“…been successfully deployed to analyze GWAS data (33)(34)(35), although the connection with causal discovery was not previously developed.…”

“…To further increase power, the external GWAS data should be used to prioritize the most promising regions; ref. 43 (33,35). With this end in mind, independent P values for different regions are desirable for at least two reasons: First, they can be used with powerful error-controlling procedures, such as SeqStep (32) and accumulation tests (45); and second, with algorithms such as the Benjamini-Hochberg procedure (47), it is well known that dependent P values can lead to a high number of false positives for a given dataset (48).…”

“…This work also uses conditional independence testing as the foundation for causal discovery but builds upon the conditional randomization test ( 31 ) to give finite-sample statistical guarantees. Our approach is also related to that of knockoffs ( 31 , 32 ), which provides finite-sample statistical guarantees and has been successfully deployed to analyze GWAS data ( 33 – 35 ), although the connection with causal discovery was not previously developed.…”