A Versatile Gene-Based Test for Genome-wide Association Studies

Abstract

We have derived a versatile gene-based test for genome-wide association studies (GWAS). Our approach, called VEGAS (versatile gene-based association study), is applicable to all GWAS designs, including family-based GWAS, meta-analyses of GWAS on the basis of summary data, and DNA-pooling-based GWAS, where existing approaches based on permutation are not possible, as well as singleton data, where they are. The test incorporates information from a full set of markers (or a defined subset) within a gene and accounts for linkage disequilibrium between markers by using simulations from the multivariate normal distribution. We show that for an association study using singletons, our approach produces results equivalent to those obtained via permutation in a fraction of the computation time. We demonstrate proof-of-principle by using the gene-based test to replicate several genes known to be associated on the basis of results from a family-based GWAS for height in 11,536 individuals and a DNA-pooling-based GWAS for melanoma in approximately 1300 cases and controls. Our method has the potential to identify novel associated genes; provide a basis for selecting SNPs for replication; and be directly used in network (pathway) approaches that require per-gene association test statistics. We have implemented the approach in both an easy-to-use web interface, which only requires the uploading of markers with their association p-values, and a separate downloadable application.

Genomewide meta‐analysis identifies loci associated with IGF ‐I and IGFBP ‐3 levels with impact on age‐related traits

“…Genomewide gene‐based tests which account for both gene length and LD between SNPs were performed by vegas 0.8.27 (Versatile Gene‐Based Association Study) (Liu et al ., 2010) using SNP P ‐value results from the overall meta‐analyses. SNPs were allocated to one or more autosomal genes using gene boundaries ±50 kb.…”

Shared genetic contribution to ischemic stroke and Alzheimer's disease

“…We used a second pathway analysis approach (GSEA‐VEGAS) to verify any associated sets as follows. We calculated gene‐level association statistics for all genes using VEGAS56 and then calculated a gene‐set enrichment statistic for each of the pathways. We calculated an empirical p value by sampling random gene sets with the same number of genes as the tested pathway and compared GSEA‐enrichment statistics between the simulated and tested pathways.…”

10 Years of GWAS Discovery: Biology, Function, and Translation

“…New Analysis Methodology Underpinning New Discovery GWAS data have led to new analysis methods that fall into a number of categories depending on their purpose: (1) methods of better modeling population structure and relatedness between individuals in a sample during association analyses, [28][29][30][31][32][33][34] (2) methods of detecting novel variants and gene loci on the basis of GWAS summary statistics, [35][36][37] (3) methods of estimating and partitioning genetic (co)variance, 38,39 and (4) methods of inferring causality. [40][41][42] In addition, GWAS discoveries and interpretation have benefited substantially from improved algorithms in statistical imputation of unobserved genotypes and statistical imputation of human leukocyte antigen (HLA) genes and amino acid polymorphisms.…”