Gene-Based Tests of Association

Abstract

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.

RETRACTED: Identification of gene ontologies linked to prefrontal–hippocampal functional coupling in the human brain

“…The LDsnpR package was used to assign SNPs to genes using chromosomal position and linkage disequilibrium information from Human Emsembl 66 release and the CEU (Utah residents with ancestry from northern and western Europe) sample from HapMap Phase II. SNPs were mapped to gene bins if they were (i) located within the specified gene boundary, (ii) within an additional 20-kb window to account for proximal regulatory elements, and (iii) if they were in linkage disequilibrium with another SNP that fell within these boundaries (pairwise linkage disequilibrium threshold <0.8) (21,25,91). Once SNPs were mapped to gene bins the minimum SNP association P value per gene was extracted, and a gene score was produced using a modified Sidak test as implemented in LDsnpR (92).…”

Genetic Association–Guided Analysis of Gene Networks for the Study of Complex Traits

“…3 Recent work has demonstrated that GWAS discoveries provide a fruitful means to reposition drugs 4 and that the presence of a GWAS hit in a drug target is positively associated with the chance that a drug will progress through clinical trials and ultimately be approved. 5 Each GWAS produces a list of phenotype association P values for each variant, or gene for gene-based tests, [6][7][8][9][10] and variants or genes with P values below a certain threshold are considered associated with the phenotype.…”

Building Up a Robust Risk Mathematical Platform to Predict Colorectal Cancer

“…For low dimensional data, Wu et al [19] and Huang et al [20] propose the logistic regression and the greedy Bayesian model. To process high dimensional dichotomous data, Hahn and his colleagues [21][22][23] propose to use multifactor dimensionality reduction (MDR) method for mapping them into the low dimensional space and Li et al [24] propose a novel forward U test to estimate the possibility of the risk of CRC.…”