GenToS: Use of Orthologous Gene Information to Prioritize Signals from Human GWAS

Anselm S Hoppmann; Pascal Schlosser; Rolf Backofen; Ekkehart Lausch; Anna Köttgen

doi:10.1371/journal.pone.0162466

GenToS: Use of Orthologous Gene Information to Prioritize Signals from Human GWAS

PLoS One. 2016 Sep 9;11(9):e0162466. doi: 10.1371/journal.pone.0162466. eCollection 2016.

Authors

Anselm S Hoppmann^{1

2}, Pascal Schlosser³, Rolf Backofen⁴, Ekkehart Lausch¹, Anna Köttgen²

Affiliations

¹ Dept. of Pediatric Genetics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
² Division of Genetic Epidemiology, Institute for Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
³ Institute for Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
⁴ Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.

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.

MeSH terms

Animals
Bone and Bones / metabolism
Collagen Type XI / genetics
Genetic Variation / genetics
Genome-Wide Association Study / methods*
Humans
Mice
Phenotype
Polymorphism, Single Nucleotide / genetics

Substances

COL11A1 protein, human
Collagen Type XI

Grants and funding

This work was supported by Deutsche Forschungsgemeinschaft (www.dfg.de) through grants SFB 1140, KO 3598/3-1 and SFB 992, by the Bundesministerium für Bildung und Forschung (www.bmbf.de) through grants 031 A538A (German Network for Bioinformatics) and FACE, and by the EU Seventh Framework Programme (http://cordis.europa.eu/fp7/home_en.html) through grant 602300. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.