@Article{Hoppmann_Schlosser_Backofen-GenTo_Use_Ortho-2016,
  author =	 {Hoppmann, Anselm S. and Schlosser, Pascal and Backofen, 
                  Rolf and Lausch, Ekkehart and Kottgen, Anna},
  title =	 {{GenToS}: {Use} of {Orthologous} {Gene} {Information} to 
                  {Prioritize} {Signals} from {Human} {GWAS}},
  journal =	 {PLoS One},
  year =	 {2016},
  volume =	 {11},
  number =	 {9},
  pages =	 {e0162466},
  user =	 {backofen},
  pmid =	 {27612175},
  doi = 	 {10.1371/journal.pone.0162466},
  issn = 	 {1932-6203},
  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.}
}