Assessing genomewide statistical significance in linkage studies

Genetic Epidemiology
D Y Lin, Fei Zou

Abstract

Assessment of genomewide statistical significance in multipoint linkage analysis is a thorny problem. The existing analytical solutions rely on strong assumptions (i.e., infinitely dense or equally spaced genetic markers that are fully informative and completely observed, and a single type of relative pair) which are rarely satisfied in real human studies, while simulation-based methods are computationally intensive and may not be applicable to complex data structures and sophisticated genetic models. Here, we propose a conceptually simple and numerically efficient Monte Carlo procedure for determining genomewide significance levels that is applicable to all linkage studies. The pedigree structure is completely general; the marker data are totally arbitrary in respect to number, spacing, informativeness, and missingness; the trait can be qualitative, quantitative, or multivariate; the alternative hypothesis can be two-sided or one-sided; and the statistic can be parametric or nonparametric. The usefulness of the proposed approach is demonstrated through extensive simulation studies and an application to the nuclear family data from the Tenth Genetic Analysis Workshop.

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Citations

Mar 10, 2007·Biological Psychiatry·Patrick F Sullivan
Mar 8, 2006·Immunology and Cell Biology·Suran L Fernando, Warwick J Britton
Apr 6, 2006·Genetics·Jian LiZhao-Bang Zeng
Oct 20, 2009·Journal of the Royal Statistical Society. Series B, Statistical Methodology·Arnab MaityNilanjan Chatterjee
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Nov 26, 2010·Genetic Epidemiology·Chao XingGuan Xing
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Dec 23, 2006·American Journal of Human Genetics·Nilanjan ChatterjeeSholom Wacholder
Sep 29, 2005·American Journal of Human Genetics·D Y Lin
Dec 11, 2019·Genetic Epidemiology·Yeonil KimFei Zou

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