BACKGROUND: Genome-wide association studies (GWASs) have identified hundreds of genetic variants associated with complex diseases, but these variants appear to explain very little of the disease heritability. The typical single-locus association analysis in a GWAS fails to detect variants with small effect sizes and to capture higher-order interaction among these variants. Multilocus association analysis provides a powerful alternative by jointly modeling the variants within a gene or a pathway and by reducing the burden of multiple hypothesis testing in a GWAS. METHODS: Here, we propose a powerful and flexible dimension reduction approach to model multilocus association. We use a Bayesian partitioning model which clusters SNPs according to their direction of association, models higher-order interactions using a flexible scoring scheme and uses posterior marginal probabilities to detect association between the SNP set and the disease. RESULTS: We illustrate our method using extensive simulation studies and applying it to detect multilocus interaction in Atherosclerosis Risk in Communities (ARIC) GWAS with type 2 diabetes. CONCLUSION: We demonstrate that our approach has better power to detect multilocus interactions than several existing approaches. When applied to the ARIC study dataset with 9,328 individuals to study gene-based associations for type 2 diabetes, our method identified some novel variants not detected by conventional single-locus association analyses.
BACKGROUND: Genome-wide association studies (GWASs) have identified hundreds of genetic variants associated with complex diseases, but these variants appear to explain very little of the disease heritability. The typical single-locus association analysis in a GWAS fails to detect variants with small effect sizes and to capture higher-order interaction among these variants. Multilocus association analysis provides a powerful alternative by jointly modeling the variants within a gene or a pathway and by reducing the burden of multiple hypothesis testing in a GWAS. METHODS: Here, we propose a powerful and flexible dimension reduction approach to model multilocus association. We use a Bayesian partitioning model which clusters SNPs according to their direction of association, models higher-order interactions using a flexible scoring scheme and uses posterior marginal probabilities to detect association between the SNP set and the disease. RESULTS: We illustrate our method using extensive simulation studies and applying it to detect multilocus interaction in Atherosclerosis Risk in Communities (ARIC) GWAS with type 2 diabetes. CONCLUSION: We demonstrate that our approach has better power to detect multilocus interactions than several existing approaches. When applied to the ARIC study dataset with 9,328 individuals to study gene-based associations for type 2 diabetes, our method identified some novel variants not detected by conventional single-locus association analyses.
Authors: Michael C Wu; Peter Kraft; Michael P Epstein; Deanne M Taylor; Stephen J Chanock; David J Hunter; Xihong Lin Journal: Am J Hum Genet Date: 2010-06-11 Impact factor: 11.025
Authors: Benjamin F Voight; Laura J Scott; Valgerdur Steinthorsdottir; Andrew P Morris; Christian Dina; Ryan P Welch; Eleftheria Zeggini; Cornelia Huth; Yurii S Aulchenko; Gudmar Thorleifsson; Laura J McCulloch; Teresa Ferreira; Harald Grallert; Najaf Amin; Guanming Wu; Cristen J Willer; Soumya Raychaudhuri; Steve A McCarroll; Claudia Langenberg; Oliver M Hofmann; Josée Dupuis; Lu Qi; Ayellet V Segrè; Mandy van Hoek; Pau Navarro; Kristin Ardlie; Beverley Balkau; Rafn Benediktsson; Amanda J Bennett; Roza Blagieva; Eric Boerwinkle; Lori L Bonnycastle; Kristina Bengtsson Boström; Bert Bravenboer; Suzannah Bumpstead; Noisël P Burtt; Guillaume Charpentier; Peter S Chines; Marilyn Cornelis; David J Couper; Gabe Crawford; Alex S F Doney; Katherine S Elliott; Amanda L Elliott; Michael R Erdos; Caroline S Fox; Christopher S Franklin; Martha Ganser; Christian Gieger; Niels Grarup; Todd Green; Simon Griffin; Christopher J Groves; Candace Guiducci; Samy Hadjadj; Neelam Hassanali; Christian Herder; Bo Isomaa; Anne U Jackson; Paul R V Johnson; Torben Jørgensen; Wen H L Kao; Norman Klopp; Augustine Kong; Peter Kraft; Johanna Kuusisto; Torsten Lauritzen; Man Li; Aloysius Lieverse; Cecilia M Lindgren; Valeriya Lyssenko; Michel Marre; Thomas Meitinger; Kristian Midthjell; Mario A Morken; Narisu Narisu; Peter Nilsson; Katharine R Owen; Felicity Payne; John R B Perry; Ann-Kristin Petersen; Carl Platou; Christine Proença; Inga Prokopenko; Wolfgang Rathmann; N William Rayner; Neil R Robertson; Ghislain Rocheleau; Michael Roden; Michael J Sampson; Richa Saxena; Beverley M Shields; Peter Shrader; Gunnar Sigurdsson; Thomas Sparsø; Klaus Strassburger; Heather M Stringham; Qi Sun; Amy J Swift; Barbara Thorand; Jean Tichet; Tiinamaija Tuomi; Rob M van Dam; Timon W van Haeften; Thijs van Herpt; Jana V van Vliet-Ostaptchouk; G Bragi Walters; Michael N Weedon; Cisca Wijmenga; Jacqueline Witteman; Richard N Bergman; Stephane Cauchi; Francis S Collins; Anna L Gloyn; Ulf Gyllensten; Torben Hansen; Winston A Hide; Graham A Hitman; Albert Hofman; David J Hunter; Kristian Hveem; Markku Laakso; Karen L Mohlke; Andrew D Morris; Colin N A Palmer; Peter P Pramstaller; Igor Rudan; Eric Sijbrands; Lincoln D Stein; Jaakko Tuomilehto; Andre Uitterlinden; Mark Walker; Nicholas J Wareham; Richard M Watanabe; Gonçalo R Abecasis; Bernhard O Boehm; Harry Campbell; Mark J Daly; Andrew T Hattersley; Frank B Hu; James B Meigs; James S Pankow; Oluf Pedersen; H-Erich Wichmann; Inês Barroso; Jose C Florez; Timothy M Frayling; Leif Groop; Rob Sladek; Unnur Thorsteinsdottir; James F Wilson; Thomas Illig; Philippe Froguel; Cornelia M van Duijn; Kari Stefansson; David Altshuler; Michael Boehnke; Mark I McCarthy Journal: Nat Genet Date: 2010-07 Impact factor: 38.330