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Worldwide population differentiation at disease-associated SNPs.

ABSTRACT: BACKGROUND:Recent genome-wide association (GWA) studies have provided compelling evidence of association between genetic variants and common complex diseases. These studies have made use of cases and controls almost exclusively from populations of European ancestry and little is known about the frequency of risk alleles in other populations. The present study addresses the transferability of disease associations across human populations by examining levels of population differentiation at disease-associated single nucleotide polymorphisms (SNPs). METHODS:We genotyped ~1000 individuals from 53 populations worldwide at 25 SNPs which show robust association with 6 complex human diseases (Crohn's disease, type 1 diabetes, type 2 diabetes, rheumatoid arthritis, coronary artery disease and obesity). Allele frequency differences between populations for these SNPs were measured using Fst. The Fst values for the disease-associated SNPs were compared to Fst values from 2750 random SNPs typed in the same set of individuals. RESULTS:On average, disease SNPs are not significantly more differentiated between populations than random SNPs in the genome. Risk allele frequencies, however, do show substantial variation across human populations and may contribute to differences in disease prevalence between populations. We demonstrate that, in some cases, risk allele frequency differences are unusually high compared to random SNPs and may be due to the action of local (i.e. geographically-restricted) positive natural selection. Moreover, some risk alleles were absent or fixed in a population, which implies that risk alleles identified in one population do not necessarily account for disease prevalence in all human populations. CONCLUSION:Although differences in risk allele frequencies between human populations are not unusually large and are thus likely not due to positive local selection, there is substantial variation in risk allele frequencies between populations which may account for differences in disease prevalence between human populations.

SUBMITTER: Myles S

PROVIDER: S-EPMC2440747 | biostudies-literature | 2008 Jun

REPOSITORIES: biostudies-literature

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Worldwide population differentiation at disease-associated SNPs.

Myles Sean S Davison Dan D Barrett Jeffrey J Stoneking Mark M Timpson Nic N

BMC medical genomics 20080604

<h4>Background</h4>Recent genome-wide association (GWA) studies have provided compelling evidence of association between genetic variants and common complex diseases. These studies have made use of cases and controls almost exclusively from populations of European ancestry and little is known about the frequency of risk alleles in other populations. The present study addresses the transferability of disease associations across human populations by examining levels of population differentiation a ...[more]

PMID: 18533027

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Project description:BACKGROUND:Obesity is emerging as a global health problem, with more than one-third of the world's adult population being overweight or obese. In this study, we investigated worldwide population differentiation in allele frequencies of obesity-associated SNPs (single nucleotide polymorphisms). RESULTS:We collected a total of 225 obesity-associated SNPs from a public database. Their population-level allele frequencies were derived based on the genotype data from 1000 Genomes Project (phase 3). We used hypergeometric model to assess whether the effect allele at a given SNP is significantly enriched or depleted in each of the 26 populations surveyed in the 1000 Genomes Project with respect to the overall pooled population. Our results indicate that 195 out of 225 SNPs (86.7%) possess effect alleles significantly enriched or depleted in at least one of the 26 populations. Populations within the same continental group exhibit similar allele enrichment/depletion patterns whereas inter-continental populations show distinct patterns. Among the 225 SNPs, 15 SNPs cluster in the first intron region of the FTO gene, which is a major gene associated with body-mass index (BMI) and fat mass. African populations exhibit much smaller blocks of LD (linkage disequilibrium) among these15 SNPs while European and Asian populations have larger blocks. To estimate the cumulative effect of all variants associated with obesity, we developed the personal composite genetic risk score for obesity. Our results indicate that the East Asian populations have the lowest averages of the composite risk scores, whereas three European populations have the highest averages. In addition, the population-level average of composite genetic risk scores is significantly correlated (R2 = 0.35, P = 0.0060) with obesity prevalence. CONCLUSIONS:We have detected substantial population differentiation in allele frequencies of obesity-associated SNPs. The results will help elucidate the genetic basis which may contribute to population disparities in obesity prevalence.

Project description:Existing methods to ascertain small sets of markers for the identification of human population structure require prior knowledge of individual ancestry. Based on Principal Components Analysis (PCA), and recent results in theoretical computer science, we present a novel algorithm that, applied on genomewide data, selects small subsets of SNPs (PCA-correlated SNPs) to reproduce the structure found by PCA on the complete dataset, without use of ancestry information. Evaluating our method on a previously described dataset (10,805 SNPs, 11 populations), we demonstrate that a very small set of PCA-correlated SNPs can be effectively employed to assign individuals to particular continents or populations, using a simple clustering algorithm. We validate our methods on the HapMap populations and achieve perfect intercontinental differentiation with 14 PCA-correlated SNPs. The Chinese and Japanese populations can be easily differentiated using less than 100 PCA-correlated SNPs ascertained after evaluating 1.7 million SNPs from HapMap. We show that, in general, structure informative SNPs are not portable across geographic regions. However, we manage to identify a general set of 50 PCA-correlated SNPs that effectively assigns individuals to one of nine different populations. Compared to analysis with the measure of informativeness, our methods, although unsupervised, achieved similar results. We proceed to demonstrate that our algorithm can be effectively used for the analysis of admixed populations without having to trace the origin of individuals. Analyzing a Puerto Rican dataset (192 individuals, 7,257 SNPs), we show that PCA-correlated SNPs can be used to successfully predict structure and ancestry proportions. We subsequently validate these SNPs for structure identification in an independent Puerto Rican dataset. The algorithm that we introduce runs in seconds and can be easily applied on large genome-wide datasets, facilitating the identification of population substructure, stratification assessment in multi-stage whole-genome association studies, and the study of demographic history in human populations.

Dataset Information

Worldwide population differentiation at disease-associated SNPs.

Publications

Worldwide population differentiation at disease-associated SNPs.

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