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Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole-Genome Sequencing Studies.
- Source :
-
American Journal of Human Genetics . May2019, Vol. 104 Issue 5, p802-814. 13p. - Publication Year :
- 2019
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *FALSE positive error
*ERROR rates
*LINKAGE disequilibrium
Subjects
Details
- Language :
- English
- ISSN :
- 00029297
- Volume :
- 104
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- American Journal of Human Genetics
- Publication Type :
- Academic Journal
- Accession number :
- 136152184
- Full Text :
- https://doi.org/10.1016/j.ajhg.2019.03.002