Filtering genetic variants and placing informative priors based on putative biological function

BMC Genetics

Proceedings

Elizabeth W. Pugh¹  Julia N. Bailey²  Dörthe Malzahn³  Stefanie Friedrichs³  Xiao Qing Liu⁴  Marcio Almeida⁵ 

[1] Center for Inherited Disease Research, Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA;Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, USA;Epilepsy Genetics/Genomics Laboratory, West Los Angeles Veterans Administration, Los Angeles, CA, USA;Department of Genetic Epidemiology, University Medical Center, Georg-August University Göttingen, Göttingen, Germany;Department of Obstetrics, Gynecology, and Reproductive Sciences, Department of Biochemistry and Medical Genetics, Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada;Children’s Hospital Research Institute of Manitoba, Winnipeg, MB, Canada;South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Brownsville, TX, USA;

关键词: Genetic Analysis Workshop;    Aggregation Test;    Sequence Kernel Association Test;    Burden Test;    Sequential Oligogenic Linkage Analysis Routine;   

DOI  :  10.1186/s12863-015-0313-x

来源: Springer

【 摘 要 】

High-density genetic marker data, especially sequence data, imply an immense multiple testing burden. This can be ameliorated by filtering genetic variants, exploiting or accounting for correlations between variants, jointly testing variants, and by incorporating informative priors. Priors can be based on biological knowledge or predicted variant function, or even be used to integrate gene expression or other omics data. Based on Genetic Analysis Workshop (GAW) 19 data, this article discusses diversity and usefulness of functional variant scores provided, for example, by PolyPhen2, SIFT, or RegulomeDB annotations. Incorporating functional scores into variant filters or weights and adjusting the significance level for correlations between variants yielded significant associations with blood pressure traits in a large family study of Mexican Americans (GAW19 data set). Marker rs218966 in gene PHF14 and rs9836027 in MAP4 significantly associated with hypertension; additionally, rare variants in SNUPN significantly associated with systolic blood pressure. Variant weights strongly influenced the power of kernel methods and burden tests. Apart from variant weights in test statistics, prior weights may also be used when combining test statistics or to informatively weight p values while controlling false discovery rate (FDR). Indeed, power improved when gene expression data for FDR-controlled informative weighting of association test p values of genes was used. Finally, approaches exploiting variant correlations included identity-by-descent mapping and the optimal strategy for joint testing rare and common variants, which was observed to depend on linkage disequilibrium structure.

【 授权许可】

Unknown   
© Friedrichs et al. 2016. This article is published under license to BioMed Central Ltd. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.