Abstract
Genome-wide association studies (GWAS) evaluate associations between genetic variants and a trait or disease of interest free of prior biological hypotheses. GWAS require stringent correction for multiple testing, with genome-wide significance typically defined as association p-value <5*10−8. This study presents a new tool that uses external information about genes to prioritize SNP associations (GenToS). For a given list of candidate genes, GenToS calculates an appropriate statistical significance threshold and then searches for trait-associated variants in summary statistics from human GWAS. It thereby allows for identifying trait-associated genetic variants that do not meet genome-wide significance. The program additionally tests for enrichment of significant candidate gene associations in the human GWAS data compared to the number expected by chance. As proof of principle, this report used external information from a comprehensive resource of genetically manipulated and systematically phenotyped mice. Based on selected murine phenotypes for which human GWAS data for corresponding traits were publicly available, several candidate gene input lists were derived. Using GenToS for the investigation of candidate genes underlying murine skeletal phenotypes in data from a large human discovery GWAS meta-analysis of bone mineral density resulted in the identification of significantly associated variants in 29 genes. Index variants in 28 of these loci were subsequently replicated in an independent GWAS replication step, highlighting that they are true positive associations. One signal, COL11A1, has not been discovered through GWAS so far and represents a novel human candidate gene for altered bone mineral density. The number of observed genes that contained significant SNP associations in human GWAS based on murine candidate gene input lists was much greater than the number expected by chance across several complex human traits (enrichment p-value as low as 10−10). GenToS can be used with any candidate gene list, any GWAS summary file, runs on a desktop computer and is freely available.
Highlights
Genome-wide association studies (GWAS) are an unbiased approach to identify genomic risk loci for complex diseases and to gain insight into underlying pathogenic mechanisms
It requires a candidate gene input list that contains gene identifiers of human orthologs of genes causing a specific phenotype in genetically manipulated mice
The number of independent common single nucleotide polymorphisms (SNPs) within each candidate gene region is determined based on a reference population, to subsequently calculate a statistical significance threshold based on the number of independent SNPs across all genes on a list (Fig 1B)
Summary
Genome-wide association studies (GWAS) are an unbiased approach to identify genomic risk loci for complex diseases and to gain insight into underlying pathogenic mechanisms. To reduce the type I error and account for association testing of an estimated one million common independent single nucleotide polymorphisms (SNPs) in the human genome [3], a multiple testing corrected significance level (alpha of 5Ã10−8 [0.05/ 1,000,000]) has been adopted in the GWAS community. This rather conservative Bonferroni correction results in an increased type II error: increasingly larger GWAS meta-analyses of the same phenotype have demonstrated that results for a given GWAS meta-analysis contain multiple true positive findings that do not achieve genome-wide significant association p-values. Approaches to identify additional candidate genes among these suggestive but not genome-wide significantly associated loci are needed
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