Gene-based multiple trait analysis for exome sequencing data

BMC Proceedings

Proceedings

Jingyuan Zhao¹  Anbupalam Thalamuthu¹ 

[1] Human Genetics, Genome Institute of Singapore, 60 Biopolis Street 02-01, 138672, Singapore;

关键词: Rare Variant;    Association Test;    Central Corneal Thickness;    Principal Component Score;    Multiple Trait;   

DOI  :  10.1186/1753-6561-5-S9-S75

来源: Springer

【 摘 要 】

The common genetic variants identified through genome-wide association studies explain only a small proportion of the genetic risk for complex diseases. The advancement of next-generation sequencing technologies has enabled the detection of rare variants that are expected to contribute significantly to the missing heritability. Some genetic association studies provide multiple correlated traits for analysis. Multiple trait analysis has the potential to improve the power to detect pleiotropic genetic variants that influence multiple traits. We propose a gene-level association test for multiple traits that accounts for correlation among the traits. Gene- or region-level testing for association involves both common and rare variants. Statistical tests for common variants may have limited power for individual rare variants because of their low frequency and multiple testing issues. To address these concerns, we use the weighted-sum pooling method to test the joint association of multiple rare and common variants within a gene. The proposed method is applied to the Genetic Association Workshop 17 (GAW17) simulated mini-exome data to analyze multiple traits. Because of the nature of the GAW17 simulation model, increased power was not observed for multiple-trait analysis compared to single-trait analysis. However, multiple-trait analysis did not result in a substantial loss of power because of the testing of multiple traits. We conclude that this method would be useful for identifying pleiotropic genes.

【 授权许可】

CC BY   
© Zhao and Thalamuthu; licensee BioMed Central Ltd. 2011