Identification of putative causal loci in whole-genome sequencing data via knockoff statistics

Abstract

The analysis of whole-genome sequencing studies is challenging due to the large number of rare variants in noncoding regions and the lack of natural units for testing. We propose a statistical method to detect and localize rare and common risk variants in whole-genome sequencing studies based on a recently developed knockoff framework. It can (1) prioritize causal variants over associations due to linkage disequilibrium thereby improving interpretability; (2) help distinguish the signal due to rare variants from shadow effects of significant common variants nearby; (3) integrate multiple knockoffs for improved power, stability, and reproducibility; and (4) flexibly incorporate state-of-the-art and future association tests to achieve the benefits proposed here. In applications to whole-genome sequencing data from the Alzheimer’s Disease Sequencing Project (ADSP) and COPDGene samples from NHLBI Trans-Omics for Precision Medicine (TOPMed) Program we show that our method compared with conventional association tests can lead to substantially more discoveries.

Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes

“…The framework allows for the incorporation of a variety of functional genomics annotations as weights for individual variants included in the tests. Furthermore, an aspect of our testing framework is the derivation of knockoff statistics based on the generation of knockoff (synthetic) genetic data that resemble the original genotypes in terms of correlation structure, but are conditionally independent of the outcome variable given the true genotypes ( 8 , 26 , 30 ). The knockoff genotypes are essentially noisy copies of the original genotypes and serve as negative controls for the original genotype data; they help to select important genes while controlling the FDR.…”

“…We compare with the nearest competitor gene-based tests in the literature, namely, MAGMA/H-MAGMA ( 10 , 11 ), TWAS/FUSION ( 14 ), and STAAR-O ( 12 ). We also show comparisons with the recently proposed window-based test KnockoffScreen ( 8 ).…”

“…Moreover, how to best analyze the noncoding part of the genome to increase power remains unclear. Recently, several sliding window approaches have been proposed to scan the genome with flexible window sizes and appropriate adjustments for multiple testing, while accounting for correlations among test statistics ( 7 , 8 ). However, these approaches are essentially scanning the genome in a one-dimensional (1D) fashion and fail to take into account the three-dimensional (3D) structure of the genome ( 9 ).…”

“…Furthermore, unlike the knockoff framework implemented here, the conventional methods do not naturally account for correlations due to LD. The proposed gene-based test is related to a recently proposed window-based test, KnockoffScreen ( 8 ). Specifically, we employ the knockoff generation algorithm for genotype data that we have introduced in KnockoffScreen ( 8 ) and develop knockoff-based inference for gene-based tests.…”

“…The proposed gene-based test is related to a recently proposed window-based test, KnockoffScreen ( 8 ). Specifically, we employ the knockoff generation algorithm for genotype data that we have introduced in KnockoffScreen ( 8 ) and develop knockoff-based inference for gene-based tests. We demonstrate below that the proposed test has important advantages compared with the window-based test KnockoffScreen in terms of controlling the FDR at gene level.…”