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. 2010;13(2):72-9.
doi: 10.1159/000218711. Epub 2009 May 13.

Genetic variants associated with complex human diseases show wide variation across multiple populations

Affiliations

Genetic variants associated with complex human diseases show wide variation across multiple populations

A Adeyemo et al. Public Health Genomics. 2010.

Abstract

Background: The wide use of genome wide association studies (GWAS) has led to the successful identification of multiple genetic susceptibility variants to several complex human diseases. Given the limited amount of data on genetic variation at these loci in populations of non-European origin, we investigated population variation among 11 population groups for loci showing strong and consistent association from GWAS with several complex human diseases.

Methods: Data from the International HapMap Project Phase 3, comprising 11 population groups, were used to estimate allele frequencies at loci showing strong and consistent association from GWAS with any of 26 complex human diseases and traits. Allele frequency summary statistics and F(ST) at each locus were used to estimate population differentiation.

Results: There is wide variation in allele frequencies and F(ST) across the 11 population groups for susceptibility loci to these complex human diseases and traits. Allele frequencies varied widely across populations, often by as much as 20- to 40-fold. F(ST), as a measure of population differentiation, also varied widely across the loci studied (for example, 0.019 to 0.201 for type 2 diabetes, 0.022 to 0.520 for prostate cancer loci, and 0.006 to 0.520 for serum lipid levels).

Conclusions: The public health risk posed by any of these risk alleles is likely to show wide variation across populations simply as a function of its frequency, and this risk difference may be amplified by gene-gene and gene-environment interactions. These analyses offer compelling reasons for including multiple human populations from different parts of the world in the international effort to use genomic tools to understand disease etiology and differential distribution of diseases across ethnic groups.

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Figures

Fig. 1
Fig. 1
Allele frequency for each susceptibility locus across all 11 HapMap populations grouped by disease/trait. Each line represents a SNP and the allele frequencies for each population are plotted as colored dots along the line. The legend shows the color code for the populations. ASW (African ancestry in Southwest USA), CEU (Utah residents with Northern and Western European ancestry from the CEPH collection), CHB (Han Chinese in Beijing, China), CHD (Chinese in Metropolitan Denver, Colorado), GIH (Gujarati Indians in Houston, Texas), JPT (Japanese in Tokyo, Japan), LWK (Luhya in Webuye, Kenya), MEX (Mexican ancestry in Los Angeles, California), MKK (Maasai in Kinyawa, Kenya), TSI (Tuscans in Italy), and YRI (Yoruba in Ibadan, Nigeria).
Fig. 2
Fig. 2
Boxplots showing distribution of FST values by disease/trait. The dots represent outliers or extreme values.
Fig. 3
Fig. 3
Pair-wise population scatter diagram showing correlation between allele frequencies across all 621 loci. Abbreviations of the different groups are the same as in figure 1.
Fig. 4
Fig. 4
Distribution of FST values by type of SNP. NS_coding: non-synonymous coding, SYN_coding: synonymous coding.

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References

    1. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–575. - PMC - PubMed
    1. McCarthy MI. Casting a wider net for diabetes susceptibility genes. Nat Genet. 2008;40:1039–1040. - PubMed
    1. Alamanos Y, Drosos AA. Epidemiology of adult rheumatoid arthritis. Autoimmun Rev. 2005;4:130–136. - PubMed
    1. Jablensky A. Epidemiology of schizophrenia: the global burden of disease and disability. Eur Arch Psychiatry Clin Neurosci. 2000;250:274–285. - PubMed
    1. Karvonen M, Viik-Kajander M, Moltchanova E, Libman I, LaPorte R, Tuomilehto J. Incidence of childhood type 1 diabetes worldwide. Diabetes Mondiale (DiaMond) Project Group. Diabetes Care. 2000;23:1516–1526. - PubMed

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