title: Evolutionary triangulation: informing genetic association studies with evolutionary evidence.
abstract: Genetic studies of human diseases have identified many variants associated with pathogenesis and severity. However, most studies have used only statistical association to assess putative relationships to disease, and ignored other factors for evaluation. For example, evolution is a factor that has shaped disease risk, changing allele frequencies as human populations migrated into and inhabited new environments. Since many common variants differ among populations in frequency, as does disease prevalence, we hypothesized that patterns of disease and population structure, taken together, will inform association studies. Thus, the population distributions of allelic risk variants should reflect the distributions of their associated diseases. Evolutionary Triangulation (ET) exploits this evolutionary differentiation by comparing population structure among three populations with variable patterns of disease prevalence. By selecting populations based on patterns where two have similar rates of disease that differ substantially from a third, we performed a proof of principle analysis for this method. We examined three disease phenotypes, lactase persistence, melanoma, and Type 2 diabetes mellitus. We show that for lactase persistence, a phenotype with a simple genetic architecture, ET identifies the key gene, lactase. For melanoma, ET identifies several genes associated with this disease and/or phenotypes related to it, such as skin color genes. ET was less obviously successful for Type 2 diabetes mellitus, perhaps because of the small effect sizes in known risk loci and recent environmental changes that have altered disease risk. Alternatively, ET may have revealed new genes involved in conferring disease risk for diabetes that did not meet nominal GWAS significance thresholds. We also compared ET to another method used to filter for phenotype associated genes, population branch statistic (PBS), and show that ET performs better in identifying genes known to associate with diseases appropriately distributed among populations. Our results indicate that ET can filter association results to improve our ability to discover disease loci.
authors.affiliation: Department of Genetics, Dartmouth College, Geisel School of Medicine, Hanover, NH USA ; Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH USA.
authors.forename: Minjun
authors.initials: M
authors.lastname: Huang
authors.affiliation: Department of Genetics, Dartmouth College, Geisel School of Medicine, Hanover, NH USA ; Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH USA.
authors.forename: Britney E
authors.initials: BE
authors.lastname: Graham
authors.affiliation: Human Genetics Division, Cincinnati Children's Hospital Medical Center, Cincinnati, OH USA.
authors.forename: Ge
authors.initials: G
authors.lastname: Zhang
authors.affiliation: Department of Genetics, Dartmouth College, Geisel School of Medicine, Hanover, NH USA ; Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH USA.
authors.forename: Reed
authors.initials: R
authors.lastname: Harder
authors.affiliation: Department of Genetics, Dartmouth College, Geisel School of Medicine, Hanover, NH USA ; Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH USA.
authors.forename: Nuri
authors.initials: N
authors.lastname: Kodaman
authors.affiliation: Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH USA ; Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA.
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
authors.affiliation: Center for Prevention of Preterm Birth, Perinatal Institute, Cincinnati Children's Hospital Medical Center, and March of Dimes Prematurity Research Center Ohio Collaborative, Cincinnati, OH USA.
authors.forename: Louis
authors.initials: L
authors.lastname: Muglia
authors.affiliation: Department of Genetics, Dartmouth College, Geisel School of Medicine, Hanover, NH USA ; Institute for Quantitative Biomedical Sciences, Dartmouth College, Hanover, NH USA ; Present Address: Department of Epidemiology and Biostatistics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106 USA.
authors.forename: Scott M
authors.initials: SM
authors.lastname: Williams
keywords: Genetic association; Health disparity; Population differentiation; Selection