Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole-Genome Sequencing Studies

Abstract

Whole-genome sequencing (WGS) studies are being widely conducted in order to identify rare variants associated with human diseases and disease-related traits. Classical single-marker association analyses for rare variants have limited power, and variant-set-based analyses are commonly used by researchers for analyzing rare variants. However, existing variant-set-based approaches need to pre-specify genetic regions for analysis; hence, they are not directly applicable to WGS data because of the large number of intergenic and intron regions that consist of a massive number of non-coding variants. The commonly used sliding-window method requires the pre-specification of fixed window sizes, which are often unknown as a priori, are difficult to specify in practice, and are subject to limitations given that the sizes of genetic-association regions are likely to vary across the genome and phenotypes. We propose a computationally efficient and dynamic scan-statistic method (Scan the Genome [SCANG]) for analyzing WGS data; this method flexibly detects the sizes and the locations of rare-variant association regions without the need to specify a prior, fixed window size. The proposed method controls for the genome-wise type I error rate and accounts for the linkage disequilibrium among genetic variants. It allows the detected sizes of rare-variant association regions to vary across the genome. Through extensive simulated studies that consider a wide variety of scenarios, we show that SCANG substantially outperforms several alternative methods for detecting rare-variant-associations while controlling for the genome-wise type I error rates. We illustrate SCANG by analyzing the WGS lipids data from the Atherosclerosis Risk in Communities (ARIC) study.

Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale

“…The STAAR procedure is fast and scalable for very large WGS studies and biobanks of hundreds of thousands to millions of samples for both quantitative and dichotomous phenotypes as it uses estimated sparse GRMs 38 to fit the null GLMM and to scan the genome. Besides using sliding windows of a pre-specified fixed window length, STAAR could be extended to flexibly detect the sizes and locations of coding and non-coding rare variant association regions using the dynamic window analysis method SCANG 83 . In addition, STAAR could be extended to settings with survival, unbalanced case-control, and multiple phenotypes, and hence could provide a comprehensive framework for WGS RVAS.…”

Leveraging Methylation Alterations to Discover Potential Causal Genes Associated With the Survival Risk of Cervical Cancer in TCGA Through a Two-Stage Inference Approach

“…where ω k represents the nonnegative weight for each p k with and K = 5; in the absence of prior knowledge, the equal weights are adapted, and assume that ω k is not related to p k . It has been theoretically demonstrated that the dependency among p -values imposes little influence on the final pooled p -values in ACAT, especially on exceedingly small p -values which are of particular interest for practitioners ( Li et al, 2019 ; Liu et al, 2019 ). Therefore, ACAT renders the potential to allow us to aggregate correlated p -values obtained from multiple tests into a single well-calibrated p -value that can maintain the type I error control correctly.…”

Detecting Local Genetic Correlations with Scan Statistics

“…We use a scan statistics approach to identify regions showing correlated effects between different traits. This type of approach has been used for burden test in a single-trait setting 77 . Suppose ( , * are the sample sizes for two GWASs, respectively, and we first consider the simpler case that there is no sample overlap between two GWASs.…”