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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-016-0091-7) contains supplementary material, which is available to authorized users.
Highlights
IntroductionAs humans moved out of Africa, population patterns of genetic variation changed dramatically, due to both random (e.g., genetic drift that can cause serial founder events) and non-random (environment-specific selection) processes
As humans moved out of Africa, population patterns of genetic variation changed dramatically, due to both random and non-random processes
Evolutionary Triangulation (ET) comparisons among populations were chosen to reflect the distribution of several phenotypes, ranging from genetically simple to increasingly complex, that differ in prevalence among HapMap populations
Summary
As humans moved out of Africa, population patterns of genetic variation changed dramatically, due to both random (e.g., genetic drift that can cause serial founder events) and non-random (environment-specific selection) processes. We hypothesize that adding a third population with similar disease prevalence to one of the other two populations being compared increases our ability to define genomic regions of particular interest with respect to disease or phenotypic variation by removing the vast majority of loci or SNPs that, highly differentiated, are unlikely to associate with phenotypes of interest (Fig. 1b). The intersection of variants that have similar allele frequencies in populations with similar disease prevalences, and different allele frequencies between populations with different prevalences, should yield an enrichment of genes that associate with a given disease. We define such variants as “appropriately distributed” with respect to a given phenotype. Diseases that we will call “appropriately distributed” are those that have prevalences distributed consistently with the allele frequency patterns of variation
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