Association tests for rare and common variants based on genotypic and phenotypic measures of similarity between individuals

BMC Proceedings

Proceedings

Indranil Mukhopadhyay¹  Jingyuan Zhao²  Garrett Teoh Hor Keong²  Anbupalam Thalamuthu²  Venkateswarlu Kondragunta³ 

[1] Human Genetics Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, 700108, Kolkata, India;Human Genetics, Genome Institute of Singapore, 60 Biopolis Street 02-01, 138672, Singapore;Internal Medicine, Eli-Lilly and Company, Lilly Corporate Center, 46285, Indianapolis, IN, USA;

关键词: Quantitative Trait;    Common Variant;    Rare Variant;    Association Test;    Phenotypic Similarity;   

DOI  :  10.1186/1753-6561-5-S9-S89

来源: Springer

【 摘 要 】

Genome-wide association studies have helped us identify thousands of common variants associated with several widespread complex diseases. However, for most traits, these variants account for only a small fraction of phenotypic variance or heritability. Next-generation sequencing technologies are being used to identify additional rare variants hypothesized to have higher effect sizes than the already identified common variants, and to contribute significantly to the fraction of heritability that is still unexplained. Several pooling strategies have been proposed to test the joint association of multiple rare variants, because testing them individually may not be optimal. Within a gene or genomic region, if there are both rare and common variants, testing their joint association may be desirable to determine their synergistic effects. We propose new methods to test the joint association of several rare and common variants with binary and quantitative traits. Our association test for quantitative traits is based on genotypic and phenotypic measures of similarity between pairs of individuals. For the binary trait or case-control samples, we recently proposed an association test based on the genotypic similarity between individuals. Here, we develop a modified version of this test for rare variants. Our tests can be used for samples taken from multiple subpopulations. The power of our test statistics for case-control samples and quantitative traits was evaluated using the GAW17 simulated data sets. Type I error rates for the proposed tests are well controlled. Our tests are able to identify some of the important causal genes in the GAW17 simulated data sets.

【 授权许可】

CC BY   
© Thalamuthu et al; licensee BioMed Central Ltd. 2011