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This article is part of the supplement: Genetic Analysis Workshop 17: Unraveling Human Exome Data

Open Access Proceedings

Old lessons learned anew: family-based methods for detecting genes responsible for quantitative and qualitative traits in the Genetic Analysis Workshop 17 mini-exome sequence data

Claire L Simpson1, Cristina M Justice2, Mera Krishnan2, Robert Wojciechowski1, Heejong Sung2, Jerry Cai2, Tiffany Green1, Deyana Lewis1, Dana Behneman2, Alexander F Wilson2 and Joan E Bailey-Wilson1*

  • * Corresponding author: Joan E Bailey-Wilson jebw@mail.nih.gov

  • † Equal contributors

Author Affiliations

1 Statistical Genetics Section, Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, 31 Center Drive, 333 Cassell Drive Suite 1200, Baltimore, MD 21224, USA

2 Genometrics Section, Inherited Disease Research Branch, National Human Genome Research Institute, National Institutes of Health, 31 Center Drive, 333 Cassell Drive Suite 1200, Baltimore, MD 21224, USA

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BMC Proceedings 2011, 5(Suppl 9):S83  doi:10.1186/1753-6561-5-S9-S83

Published: 29 November 2011

Abstract

Family-based study designs are again becoming popular as new next-generation sequencing technologies make whole-exome and whole-genome sequencing projects economically and temporally feasible. Here we evaluate the statistical properties of linkage analyses and family-based tests of association for the Genetic Analysis Workshop 17 mini-exome sequence data. Based on our results, the linkage methods using relative pairs or nuclear families had low power, with the best results coming from variance components linkage analysis in nuclear families and Elston-Stewart model-based linkage analysis in extended pedigrees. For family-based tests of association, both ASSOC and ROMP performed well for genes with large effects, but ROMP had the advantage of not requiring parental genotypes in the analysis. For the linkage analyses we conclude that genome-wide significance levels appear to control type I error well but that “suggestive” significance levels do not. Methods that make use of the extended pedigrees are well powered to detect major loci segregating in the families even when there is substantial genetic heterogeneity and the trait is mainly polygenic. However, large numbers of such pedigrees will be necessary to detect all major loci. The family-based tests of association found the same major loci as the linkage analyses and detected low-frequency loci with moderate effect sizes, but control of type I error was not as stringent.