A goodness-of-fit association test for whole genome sequencing data

BMC Proceedings

Proceedings

Li Yang¹  Zheyang Wu¹  Jing Xuan¹ 

[1] Department of Mathematical Sciences, Worcester Polytechnic Institute, 100 Institute Road, 01609-2280, Worcester, MA, USA;

关键词: Rare Variant;    Whole Genome Sequencing;    Sparse Signal;    Genetic Analysis Workshop;    Whole Genome Sequencing Data;   

DOI  :  10.1186/1753-6561-8-S1-S51

来源: Springer

【 摘 要 】

Although many genetic factors have been successfully identified for human diseases in genome-wide association studies, genes discovered to date only account for a small proportion of overall genetic contributions to many complex traits. Association studies have difficulty in detecting the remaining true genetic variants that are either common variants with weak allelic effects, or rare variants that have strong allelic effects but are weakly associated at the population level. In this work, we applied a goodness-of-fit test for detecting sets of common and rare variants associated with quantitative or binary traits by using whole genome sequencing data. This test has been proved optimal for detecting weak and sparse signals in the literature, which fits the requirements for targeting the genetic components of missing heritability. Furthermore, this p value-combining method allows one to incorporate different data and/or research results for meta-analysis. The method was used to simultaneously analyse the whole genome sequencing and genome-wide association studies data of Genetic Analysis Workshop 18 for detecting true genetic variants. The results show that goodness-of-fit test is comparable or better than the influential sequence kernel association test in many cases.

【 授权许可】

Unknown   
© Yang et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.