A comparative analysis of family-based and population-based association tests using whole genome sequence data

BMC Proceedings

Proceedings

Nan M Laird¹  Dandi Qiao¹  Wai-Ki Yip¹  Jin J Zhou²  Merry-Lynn N McDonald³  Michael H Cho⁴ 

[1] Biostatistics Department, Harvard School of Public Health, 02115, Boston, MA, USA;Biostatistics Department, Harvard School of Public Health, 02115, Boston, MA, USA;Division of Epidemiology and Biostatistics, College of Public Health, University of Arizona, 85724, Tucson, AZ, USA;Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, 02115, Boston, MA, USA;Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, 02115, Boston, MA, USA;Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 02115, Boston, MA, USA;

关键词: Genetic Analysis Workshop;    Semiparametric Regression;    Expression Quantitative Trait Locus;    Sequence Kernel Association Test;    GAW18 Data;   

DOI  :  10.1186/1753-6561-8-S1-S33

来源: Springer

【 摘 要 】

The revolution in next-generation sequencing has made obtaining both common and rare high-quality sequence variants across the entire genome feasible. Because researchers are now faced with the analytical challenges of handling a massive amount of genetic variant information from sequencing studies, numerous methods have been developed to assess the impact of both common and rare variants on disease traits. In this report, whole genome sequencing data from Genetic Analysis Workshop 18 was used to compare the power of several methods, considering both family-based and population-based designs, to detect association with variants in the MAP4 gene region and on chromosome 3 with blood pressure. To prioritize variants across the genome for testing, variants were first functionally assessed using prediction algorithms and expression quantitative trait loci (eQTLs) data. Four set-based tests in the family-based association tests (FBAT) framework--FBAT-v, FBAT-lmm, FBAT-m, and FBAT-l--were used to analyze 20 pedigrees, and 2 variance component tests, sequence kernel association test (SKAT) and genome-wide complex trait analysis (GCTA), were used with 142 unrelated individuals in the sample. Both set-based and variance-component-based tests had high power and an adequate type I error rate. Of the various FBATs, FBAT-l demonstrated superior performance, indicating the potential for it to be used in rare-variant analysis. The updated FBAT package is available at: http://www.hsph.harvard.edu/fbat/.

【 授权许可】

Unknown   
© Zhou 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.