Adjustment of familial relatedness in association test for rare variants

BMC Proceedings

Proceedings

Can Yang¹  Lin Hou¹  Hongyu Zhao¹  Cong Li²  Xiaowei Chen²  Mengjie Chen² 

[1] Department of Biostatistics, Yale School of Public Health, 60 College Street, 06520, New Haven, CT, USA;Program in Computational Biology and Bioinformatics, Yale University, 06520, New Haven, CT, USA;

关键词: Linear Mixed Model;    Rare Variant;    Genetic Analysis Workshop;    Familial Relatedness;    Genetic Similarity Matrix;   

DOI  :  10.1186/1753-6561-8-S1-S39

来源: Springer

【 摘 要 】

High-throughput sequencing technology allows researchers to test associations between phenotypes and all the variants identified throughout the genome, and is especially useful for analyzing rare variants. However, the statistical power to identify phenotype-associated rare variants is very low with typical genome-wide association studies because of their low allele frequencies among unrelated individuals. In contrast, a family-based design may have more power because rare variants are more likely to be enriched in families than among unrelated individuals. Regardless, an analysis of family-based association studies needs to account appropriately for relatedness between family members. We analyzed the observed quantitative trait systolic blood pressure as well as the simulated Q1 data in the Genetic Analysis Workshop 18 data set using 4 tests: (a) a single-variant test, (b) a collapsing test, (c) a single-variant test where familial relatedness was accounted for, and (d) a collapsing test where familial relatedness was accounted for. We then compared the results of the 4 methods and observed that adjusting for familial relatedness could appropriately control the false-positive rate while maintaining reasonable power to detect several strongly associated variants/genes.

【 授权许可】

Unknown   
© Li 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.