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  • Review Article
  • Published:

Genome-wide association studies in diverse populations

Nature Reviews Genetics volume 11, pages 356–366 (2010)Cite this article

Subjects

Key Points

  • Genome-wide association (GWA) studies have identified large numbers of genetic variants that contribute to disease risk.

  • Most GWA studies have been performed primarily in populations of European descent.

  • Phenotypes differ in prevalence across populations, and risk variants differ in frequency, linkage-disequilibrium patterns and effect-size across populations. Diverse populations are therefore required for fully characterizing risk variants.

  • For a given population, both intrinsic population-genetic properties and the properties of genomic resources affect the utility of tag SNPs and the performance of genotype-imputation methods.

  • Population-genetic modelling provides a basis for examining GWA phenomena in diverse populations and for testing the potential of new statistical methods for improving GWA in diverse populations.

  • A combination of population-genetic modelling, statistical methods targeted to diverse populations and new genomic resources will help to address challenges involved in extending GWA to diverse populations.

Abstract

Genome-wide association (GWA) studies have identified a large number of SNPs associated with disease phenotypes. As most GWA studies have been performed in populations of European descent, this Review examines the issues involved in extending the consideration of GWA studies to diverse worldwide populations. Although challenges exist with issues such as imputation, admixture and replication, investigation of a greater diversity of populations could make substantial contributions to the goal of mapping the genetic determinants of complex diseases for the human population as a whole.

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Figure 1: Differences in 'mappability' of a risk variant between two populations with different linkage disequilibrium patterns.
Figure 2: Effect of frequency in Europe on the occurrence of an allele in other regions.
Figure 3: Excess SNP variability in Europeans resulting from ascertainment bias.
Figure 4: Genotype imputation accuracy in 29 populations, with and without external reference panels.
Figure 5: Imputation in admixed populations.

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Acknowledgements

We thank L. Hindorff for detailed information on the National Human Genome Research Institute (NHGRI) catalogue of GWA studies, the DIAGRAM Consortium for use of prepublication data, J. Li and S. Zöllner for helpful discussions, and N. Patterson and an anonymous reviewer for comments on a draft of the manuscript. We are grateful to M. DeGiorgio, M. Jakobsson, S. Reddy and P. Scheet for assistance with Box 1 and with figure preparation. Support was provided by US National Institutes of Health grants DK062370, GM081441, HG000376 and HL090564, and by grants from the Burroughs Wellcome Fund and the Alfred P. Sloan Foundation.

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Author notes

  1. Lucy Huang, Ethan M. Jewett, Zachary A. Szpiech, Ivana Jankovic and Michael Boehnke: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Human Genetics, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Noah A. Rosenberg

  2. Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Noah A. Rosenberg, Lucy Huang, Ethan M. Jewett, Zachary A. Szpiech & Ivana Jankovic

  3. Life Sciences Institute, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Noah A. Rosenberg

  4. Department of Biostatistics, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Noah A. Rosenberg & Michael Boehnke

  5. Center for Statistical Genetics, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Michael Boehnke

Authors
  1. Noah A. Rosenberg
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  2. Lucy Huang
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Correspondence to Noah A. Rosenberg.

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FURTHER INFORMATION

1000 Genomes Project

Human Genome Diversity Cell Line Panel

International HapMap Project

Nature Reviews Genetics article series on Genome-wide association studies

NHGRI list of GWA studies

Glossary

Genome-wide association studies

Study designs in which many markers spread across a genome are genotyped, and tests of statistical association with a phenotype are performed locally along the genome.

Genotype imputation

Probabilistic prediction of genotypes that have not been measured experimentally.

Principal component

A composite variable that summarizes the variation across a larger number of variables, each represented by a column of a matrix.

Loading

In a principal components analysis, a quantity that represents the contribution of one of the original variables (columns of the data matrix) to one of the principal components.

SNP

A nucleotide site at which two or more variants exist in a population. Most SNPs in genome-wide association studies are biallelic.

Tag SNP

A SNP chosen from a larger set of available SNPs for use in an association study. Tag SNPs are generally selected on the basis of favourable linkage disequilibrium properties.

Linkage disequilibrium

A statistical association in the occurrence of alleles at separate loci.

Tag-SNP portability

The utility of SNPs chosen as tags in one population for use as tags in another population.

Minor allele frequency

The frequency of the less frequent allele at a biallelic genetic locus.

Expected heterozygosity

The probability for a locus that two alleles drawn from its allele-frequency distribution are distinct.

Ascertainment bias

A distortion in results due to the use of a subsample that, in a systematic manner, fails to properly represent a larger sample.

Admixed population

A population formed recently from the mixing of two or more groups whose ancestors had long been separated.

Microsatellite

A type of genetic marker in which individuals vary in their number of tandemly repeated copies of a short DNA unit.

Coalescent

A specific stochastic process that describes the relationship among genetic lineages sampled in a population.

Recombination hot spot

A region of the genome in which the per-generation recombination rate is substantially elevated above the genome-wide average.

Contingency table

A table of observations of two or more variables that might have a statistical relationship of interest. For each variable, a contingency table places each observation into one of a series of categories.

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Rosenberg, N., Huang, L., Jewett, E. et al. Genome-wide association studies in diverse populations. Nat Rev Genet 11, 356–366 (2010). https://doi.org/10.1038/nrg2760

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