Haplotype Kernel Association Test as a Powerful Method to Identify Chromosomal Regions Harboring Uncommon Causal Variants. Issue 6 (5th June 2013)

• Record Type: Journal Article
• Authors: Lin, Wan‐Yu; Yi, Nengjun; Lou, Xiang‐Yang; Zhi, Degui; Zhang, Kui; Gao, Guimin; Tiwari, Hemant K.; Liu, Nianjun
• Is Part Of: Genetic epidemiology. Volume 37:Issue 6(2013)
• Page Start: 560
• Page End: 570
• Publication Date: 2013-06-05
• Subjects: Genetic epidemiology -- Periodicals; Heredity -- Periodicals; Medical geography -- Periodicals; 614
• DOI: 10.1002/gepi.21740
• Languages: English
• ISSNs: 0741-0395

Abstract

<abstract abstract-type="main"> <title>ABSTRACT</title> <p>For most complex diseases, the fraction of heritability that can be explained by the variants discovered from genome‐wide association studies is minor. Although the so‐called "rare variants" (minor allele frequency [MAF] &lt; 1%) have attracted increasing attention, they are unlikely to account for much of the "missing heritability" because very few people may carry these rare variants. The genetic variants that are likely to fill in the "missing heritability" include uncommon causal variants (MAF &lt; 5%), which are generally untyped in association studies using tagging single‐nucleotide polymorphisms (SNPs) or commercial SNP arrays. Developing powerful statistical methods can help to identify chromosomal regions harboring uncommon causal variants, while bypassing the genome‐wide or exome‐wide next‐generation sequencing. In this work, we propose a haplotype kernel association test (<italic>HKAT</italic>) that is equivalent to testing the variance component of random effects for distinct haplotypes. With an appropriate weighting scheme given to haplotypes, we can further enhance the ability of <italic>HKAT</italic> to detect uncommon causal variants. With scenarios simulated according to the population genetics theory, <italic>HKAT</italic> is shown to be a powerful method for detecting chromosomal regions harboring uncommon causal variants.</p> </abstract>