Gene-based tests of association

• Published in: PLoS genetics 2011-07, Vol.7 (7), p.e1002177-e1002177
• Main Authors: Huang, Hailiang, Chanda, Pritam, Alonso, Alvaro, Bader, Joel S, Arking, Dan E
• Format: Article
• Language: English
• Subjects: Atherosclerosis; Biology; Computer Science; Computer Simulation; Electrocardiogram; Electrocardiography; Genes; Genetic Predisposition to Disease; Genetic screening; Genetics; Genome-Wide Association Study; Genomes; Genotype; Humans; Mathematics; Methods; Models, Genetic; Parameter estimation; Phenotype; Polymorphism, Single Nucleotide; Principal components analysis; Single nucleotide polymorphisms; Studies
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1002177

Genome-wide association studies (GWAS) are now used routinely to identify SNPs associated with complex human phenotypes. In several cases, multiple variants within a gene contribute independently to disease risk. Here we introduce a novel Gene-Wide Significance (GWiS) test that uses greedy Bayesian model selection to identify the independent effects within a gene, which are combined to generate a stronger statistical signal. Permutation tests provide p-values that correct for the number of independent tests genome-wide and within each genetic locus. When applied to a dataset comprising 2.5 million SNPs in up to 8,000 individuals measured for various electrocardiography (ECG) parameters, this method identifies more validated associations than conventional GWAS approaches. The method also provides, for the first time, systematic assessments of the number of independent effects within a gene and the fraction of disease-associated genes housing multiple independent effects, observed at 35%-50% of loci in our study. This method can be generalized to other study designs, retains power for low-frequency alleles, and provides gene-based p-values that are directly compatible for pathway-based meta-analysis.