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, a novel 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 8,28,32 . The knockoff genotypes are essentially noisy copies of the original genotypes and serve as negative controls for the original genotype data; they help select important genes while controlling the FDR.…”

“…Note that the well-known permutation procedure that permutes the samples does not guarantee these exchangeability properties between the original and knockoff genotypes. To generate valid knockoff genotypes we can use a sequential model for knockoff generation that leverages the local patterns of linkage disequilibrium, as previously proposed based on the Hidden Markov Models (HMMs) 32,84 or an auto-regressive model 8 , in such a way that the knockoff genotypes are exchangeable with the original (true) genotypes G but are independent of the phenotype conditional on the original genotypes. The knockoff genotypes serve as negative controls and are designed to mimic the correlation or LD structure found within the original genotypes.…”

“…We calculate the residualˆ j = G j −Ĝ j and its permutationˆ j * , and then we define the knockoff feature asG j =Ĝ j +ˆ j * . More details on this knockoff generation procedure, and its theoretical and empirical properties can be found in 8 .…”

“…Multiple knockoffs. To improve the power and stability of the knockoff procedure we implement a multiple knockoff procedure 8,85 where the inference is based on generating multiple, independent knockoff datasets. Gimenez and Zou 85 proposed an extension of the sequential 18 model for knockoff generation to multiple knockoffs and showed the validity of the multiple knockoff generation procedure in controlling the FDR.…”

“…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 9,10 .…”