A new permutation strategy of pathway-based approach for genome-wide association study

BackgroundRecently introduced pathway-based approach is promising and advantageous to improve the efficiency of analyzing genome-wide association scan (GWAS) data to identify disease variants by jointly considering variants of the genes that belong to the same biological pathway. However, the current available pathway-based approaches for analyzing GWAS have limited power and efficiency.ResultsWe proposed a new and efficient permutation strategy based on SNP randomization for determining significance in pathway analysis of GWAS. The developed permutation strategy was evaluated and compared to two previously available methods, i.e. sample permutation and gene permutation, through simulation studies and a study on a real dataset. Results showed that the proposed permutation strategy is more powerful and efficient with greatly reducing the computational complexity.ConclusionOur findings indicate the improved performance of SNP permutation and thus render pathway-based analysis of GWAS more applicable and attractive.

[1]  Judy H. Cho,et al.  A Genome-Wide Association Study Identifies IL23R as an Inflammatory Bowel Disease Gene , 2006, Science.

[2]  B. Mendonca,et al.  Height and bone mineral density in androgen insensitivity syndrome with mutations in the androgen receptor gene , 2007, Osteoporosis International.

[3]  K. Roeder,et al.  Genomic Control for Association Studies , 1999, Biometrics.

[4]  Peter Bühlmann,et al.  Analyzing gene expression data in terms of gene sets: methodological issues , 2007, Bioinform..

[5]  Yan Guo,et al.  Genome-wide association and follow-up replication studies identified ADAMTS18 and TGFBR3 as bone mass candidate genes in different ethnic groups. , 2009, American journal of human genetics.

[6]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.

[7]  P. Rosenberg,et al.  Pathway analysis by adaptive combination of P‐values , 2009, Genetic epidemiology.

[8]  M. McCarthy,et al.  Genome-wide association studies for complex traits: consensus, uncertainty and challenges , 2008, Nature Reviews Genetics.

[9]  Marcia M. Nizzari,et al.  Genome-Wide Association Analysis Identifies Loci for Type 2 Diabetes and Triglyceride Levels , 2007, Science.

[10]  Seon-Young Kim,et al.  Gene-set approach for expression pattern analysis , 2008, Briefings Bioinform..

[11]  Ali Torkamani,et al.  Pathway and network analysis with high-density allelic association data. , 2009, Methods in molecular biology.

[12]  T J Chambers,et al.  Stimulation of bone nodule formation in vitro by prostaglandins E1 and E2. , 1992, Endocrinology.

[13]  Elizabeth A. Heron,et al.  The SNP ratio test: pathway analysis of genome-wide association datasets , 2009, Bioinform..

[14]  Momiao Xiong,et al.  Gene and pathway-based second-wave analysis of genome-wide association studies , 2010, European Journal of Human Genetics.

[15]  U Settmacher,et al.  Prevention of early loss of bone mineral density after liver transplantation by prostaglandin E1. , 2007, Transplantation proceedings.

[16]  Sang-Bae Kim,et al.  GAzer: gene set analyzer , 2007, Bioinform..

[17]  C. Wijmenga,et al.  Using genome‐wide pathway analysis to unravel the etiology of complex diseases , 2009, Genetic epidemiology.

[18]  N. Schork,et al.  Pathway analysis of seven common diseases assessed by genome-wide association. , 2008, Genomics.

[19]  Mark M Iles,et al.  What Can Genome-Wide Association Studies Tell Us about the Genetics of Common Disease , 2008, PLoS genetics.

[20]  Carl D Langefeld,et al.  Association methods in human genetics. , 2007, Methods in molecular biology.

[21]  D. Balding.  A tutorial on statistical methods for population association studies , 2006, Nature Reviews Genetics.

[22]  Simona Toti,et al.  Eu.Gene Analyzer a tool for integrating gene expression data with pathway databases , 2007, Bioinform..

[23]  Yoshiji Yamada,et al.  Association of polymorphisms of the androgen receptor and klotho genes with bone mineral density in Japanese women , 2004, Journal of Molecular Medicine.

[24]  Kai Wang,et al.  Pathway-based approaches for analysis of genomewide association studies. , 2007, American journal of human genetics.

[25]  Pablo Tamayo,et al.  Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[26]  Fuu-Jen Tsai,et al.  Androgen receptor (AR) gene microsatellite polymorphism in postmenopausal women: correlation to bone mineral density and susceptibility to osteoporosis. , 2003, European journal of obstetrics, gynecology, and reproductive biology.

[27]  K. Jakobs,et al.  A new phospholipase-C–calcium signalling pathway mediated by cyclic AMP and a Rap GTPase , 2001, Nature Cell Biology.

[28]  Risto Honkanen,et al.  Relation of Androgen Receptor Gene Polymorphism to Bone Mineral Density and Fracture Risk in Early Postmenopausal Women During a 5‐Year Randomized Hormone Replacement Therapy Trial , 2003, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[29]  Yoav Benjamini,et al.  Identifying differentially expressed genes using false discovery rate controlling procedures , 2003, Bioinform..

[30]  Minping Qian,et al.  Gene-Centric Genomewide Association Study via Entropy , 2008, Genetics.