U-statistics-based tests for multiple genes in genetic association studies

Document Type

Article

Publication Date

10-22-2008

Abstract

As our understanding of biological pathways and the genes that regulate these pathways increases, consideration of these biological pathways has become an increasingly important part of genetic and molecular epidemiology. Pathway-based genetic association studies often involve genotyping of variants in genes acting in certain biological pathways. Such pathway-based genetic association studies can potentially capture the highly heterogeneous nature of many complex traits, with multiple causative loci and multiple alleles at some of the causative loci. In this paper, we develop two nonparametric test statistics that consider simultaneously the effects of multiple markers. Our approach, which is based on data-adaptive U-statistics, can handle both qualitative data such as case-control data and quantitative continuous phenotype data. Simulations demonstrate that our proposed methods are more powerful than standard methods, especially when there are multiple risk loci each with small genetic effects. When the number of disease-predisposing genes is small, the data-adaptive weighting of the U-statistics over all the markers produces similar power to commonly used single marker tests. We further illustrate the potential merits of our proposed tests in the analysis of a data set from a pathway-based candidate gene association study of breast cancer and hormone metabolism pathways. Finally, potential applications of the proposed tests to genome-wide association studies are also discussed. © Journal compilation © 2008 Blackwell Publishing Ltd/University College London.

Identifier

54049114308 (Scopus)

Publication Title

Annals of Human Genetics

External Full Text Location

https://doi.org/10.1111/j.1469-1809.2008.00473.x

e-ISSN

14691809

ISSN

00034800

PubMed ID

18691161

First Page

821

Last Page

833

Issue

6

Volume

72

Grant

P01CA077596

Fund Ref

National Cancer Institute

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