The Population Reference Sample, POPRES: A Resource for Population, Disease, and Pharmacological Genetics Research

Abstract

Technological and scientific advances, stemming in large part from the Human Genome and HapMap projects, have made large-scale, genome-wide investigations feasible and cost effective. These advances have the potential to dramatically impact drug discovery and development by identifying genetic factors that contribute to variation in disease risk as well as drug pharmacokinetics, treatment efficacy, and adverse drug reactions. In spite of the technological advancements, successful application in biomedical research would be limited without access to suitable sample collections. To facilitate exploratory genetics research, we have assembled a DNA resource from a large number of subjects participating in multiple studies throughout the world. This growing resource was initially genotyped with a commercially available genome-wide 500,000 single-nucleotide polymorphism panel. This project includes nearly 6,000 subjects of African-American, East Asian, South Asian, Mexican, and European origin. Seven informative axes of variation identified via principal-component analysis (PCA) of these data confirm the overall integrity of the data and highlight important features of the genetic structure of diverse populations. The potential value of such extensively genotyped collections is illustrated by selection of genetically matched population controls in a genome-wide analysis of abacavir-associated hypersensitivity reaction. We find that matching based on country of origin, identity-by-state distance, and multidimensional PCA do similarly well to control the type I error rate. The genotype and demographic data from this reference sample are freely available through the NCBI database of Genotypes and Phenotypes (dbGaP).

A female Viking warrior confirmed by genomics

“…The genetic data from the Birka warrior was merged with three different population reference data‐sets consisting of genotype SNP data from: the Human Origins dataset (Patterson et al, 2012; Lazaridis et al, 2014), the Swedish reference (Salmela et al, 2011), and the Population Reference Sample–POPRES (Nelson et al, 2008) merged with 60 Yoruban individuals from the pilot phase of the 1000 Genomes Project (The 1000 Genome Project Consortium, 2010). The analyses were restricted to nucleotide positions with minimum mapping and base quality of 30.…”

“…Maps visualizing the results of f 3 ‐statistic in which the individual from grave Bj 581 was compared to (a) Human Origins population reference panel (Lazaridis et al, 2014; Patterson et al, 2012) and (b) Population Reference Sample (POPRES) (Nelson et al, 2008). (c) The Birka warrior plotted against PC1 values for 21 Swedish subpopulations representing all counties and the total of 1525 individuals (Salmela et al, 2011).…”

Across language families: Genome diversity mirrors linguistic variation within Europe

“…Genomic data on 13 populations were found in POPRES (dbGap accession phs000145.v1.p1; Nelson et al, 2008), a public resource for genetic research including 5,886 subjects genotyped at 500,568 loci using the Affymetrix 500K SNP chip. To determine the geographic location that best represents each individual's ancestry, we used a strict criterion of sample selection excluding individuals who reported mixed grandparental ancestry.…”

Genetic ancestry inference using support vector machines, and the active emergence of a unique American population

“…The Population Reference Sample (POPRES) includes several thousand participants from around the world genotyped at 500 000 SNPs. 21 We only used individuals whose country of origin was the same as the reported ancestry of their maternal and paternal Table S1). Genome-wide genotyping of PMRP individuals was performed at the Center for Inherited Disease Research (CIDR; John Hopkins University, Baltimore, MD, USA) using the Illumina 660W-Quad Platform (Illumina, San Diego, CA, USA).…”

“…For example, the sampling locations of POPRES individuals are mostly limited to country of origin. 21 In contrast, SVM classification only requires that members of the training set belong to the relatively broad geographic classes they represent. Finally, Heath et al 38 present a method of ancestry inference in which the probability of an unknown individual belonging to a particular ancestry is weighted by his or her position along all PCs showing separation between the mean positions of input countries.…”