Collapsing-based and kernel-based single-gene analyses applied to Genetic Analysis Workshop 17 mini-exome data

BMC Proceedings

Proceedings

Xiting Yan¹  Hongyu Zhao¹  Joon Sang Lee¹  John Ferguson¹  Xianghua Zhang²  Lun Li³  Wei Zheng⁴ 

[1] Division of Biostatistics, Yale School of Public Health, Yale University, PO Box 208034, 60 College St, 06520-8034, New Haven, CT, USA;Division of Biostatistics, Yale School of Public Health, Yale University, PO Box 208034, 60 College St, 06520-8034, New Haven, CT, USA;Department of Electronic Science and Technology, University of Science and Technology of China, 230027, Hefei, Anhui, China;Division of Biostatistics, Yale School of Public Health, Yale University, PO Box 208034, 60 College St, 06520-8034, New Haven, CT, USA;Hubei Bioinformatics and Molecular Imaging Key Laboratory, Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China;Keck Biotechnology Resource Laboratory, Yale University, 300 George St, 06511, New Haven, CT, USA;

关键词: Rare Variant;    Causal Variant;    Genetic Analysis Workshop;    Nonsynonymous SNPs;    Lower Minor Allele Frequency;   

DOI  :  10.1186/1753-6561-5-S9-S117

来源: Springer

【 摘 要 】

Recently there has been great interest in identifying rare variants associated with common diseases. We apply several collapsing-based and kernel-based single-gene association tests to Genetic Analysis Workshop 17 (GAW17) rare variant association data with unrelated individuals without knowledge of the simulation model. We also implement modified versions of these methods using additional information, such as minor allele frequency (MAF) and functional annotation. For each of four given traits provided in GAW17, we use the Bayesian mixed-effects model to estimate the phenotypic variance explained by the given environmental and genotypic data and to infer an individual-specific genetic effect to use directly in single-gene association tests. After obtaining information on the GAW17 simulation model, we compare the performance of all methods and examine the top genes identified by those methods. We find that collapsing-based methods with weights based on MAFs are sensitive to the “lower MAF, larger effect size” assumption, whereas kernel-based methods are more robust when this assumption is violated. In addition, many false-positive genes identified by multiple methods often contain variants with exactly the same genotype distribution as the causal variants used in the simulation model. When the sample size is much smaller than the number of rare variants, it is more likely that causal and noncausal variants will share the same or similar genotype distribution. This likely contributes to the low power and large number of false-positive results of all methods in detecting causal variants associated with disease in the GAW17 data set.

【 授权许可】

Unknown   
© Li et al; licensee BioMed Central Ltd. 2011. 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.