Region-Based Association Analysis of Human Quantitative Traits in Related Individuals

被引：25

作者：

Belonogova, Nadezhda M. ^{[1
]}

Svishcheva, Gulnara R. ^{[1
]}

van Duijn, Cornelia M. ^{[2
]}

Aulchenko, Yurii S. ^{[1
]}

Axenovich, Tatiana I. ^{[1
]}

机构：

[1] Russian Acad Sci, Inst Cytol & Genet, Siberian Branch, Novosibirsk 630090, Russia

[2] Erasmus MC, Dept Epidemiol, Rotterdam, Netherlands

来源：

PLOS ONE | 2013年 / 8卷 / 06期

基金：

俄罗斯基础研究基金会;

关键词：

MISSING HERITABILITY; HUMAN HEIGHT; KERNEL; REGRESSION; VARIANTS; TESTS; LOCI;

D O I：

10.1371/journal.pone.0065395

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Regional-based association analysis instead of individual testing of each SNP was introduced in genome-wide association studies to increase the power of gene mapping, especially for rare genetic variants. For regional association tests, the kernel machine-based regression approach was recently proposed as a more powerful alternative to collapsing-based methods. However, the vast majority of existing algorithms and software for the kernel machine-based regression are applicable only to unrelated samples. In this paper, we present a new method for the kernel machine-based regression association analysis of quantitative traits in samples of related individuals. The method is based on the GRAMMAR+ transformation of phenotypes of related individuals, followed by use of existing kernel machine-based regression software for unrelated samples. We compared the performance of kernel-based association analysis on the material of the Genetic Analysis Workshop 17 family sample and real human data by using our transformation, the original untransformed trait, and environmental residuals. We demonstrated that only the GRAMMAR+ transformation produced type I errors close to the nominal value and that this method had the highest empirical power. The new method can be applied to analysis of related samples by using existing software for kernel-based association analysis developed for unrelated samples.

引用

页数：7

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