Originally published as Genetics Published Articles Ahead of Print on May 5, 2008.

Genetics, Vol. 179, 637-650, May 2008, Copyright © 2008
doi:10.1534/genetics.107.082370

Gene-Centric Genomewide Association Study via Entropy

Yuehua Cui^*,1, Guolian Kang^*, Kelian Sun, Minping Qian, Roberto Romero and Wenjiang Fu

^* Department of Statistics and Probability and Department of Epidemiology, Michigan State University, East Lansing, Michigan 48824 and The Perinatology Research Branch, National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, Maryland 48201

¹ Corresponding author: A432 Wells Hall, Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824.
E-mail: cui{at}stt.msu.edu

Genes are the functional units in most organisms. Compared to genetic variants located outside genes, genic variants are more likely to affect disease risk. The development of the human HapMap project provides an unprecedented opportunity for genetic association studies at the genomewide level for elucidating disease etiology. Currently, most association studies at the single-nucleotide polymorphism (SNP) or the haplotype level rely on the linkage information between SNP markers and disease variants, with which association findings are difficult to replicate. Moreover, variants in genes might not be sufficiently covered by currently available methods. In this article, we present a gene-centric approach via entropy statistics for a genomewide association study to identify disease genes. The new entropy-based approach considers genic variants within one gene simultaneously and is developed on the basis of a joint genotype distribution among genetic variants for an association test. A grouping algorithm based on a penalized entropy measure is proposed to reduce the dimension of the test statistic. Type I error rates and power of the entropy test are evaluated through extensive simulation studies. The results indicate that the entropy test has stable power under different disease models with a reasonable sample size. Compared to single SNP-based analysis, the gene-centric approach has greater power, especially when there is more than one disease variant in a gene. As the genomewide genic SNPs become available, our entropy-based gene-centric approach would provide a robust and computationally efficient way for gene-based genomewide association study.

International Access Link

Online ISS 1091-6490
Copyright 2008 by the Genetics Society of America
phone: 412-268-1812   fax: 412-268-1813   email: genetics-gsa{at}andrew.cmu.edu