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  • Review Article
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Genome-wide association studies: theoretical and practical concerns

Nature Reviews Genetics volume 6, pages 109–118 (2005)Cite this article

Key Points

  • Recent improvements in genotyping technology and in our knowledge of human genetic variation have made it possible to carry out genome-wide genetic association studies to identify susceptibility genes for common disease. However, before such studies are undertaken, it is important to know what proportion of human genetic variation they are likely to survey and what the likely costs will be per true-positive result.

  • The allelic spectrum of complex diseases — that is, the range of the frequencies and effect sizes of susceptibility loci —will influence the success of genome-wide association studies. Large sample sizes will be required to search even the most accessible end of this spectrum: that is, alleles with population allele frequencies exceeding 0.1 and with odds ratios of 1.3 and above.

  • The existence of extensive regions of linkage disequilibrium in the human genome will greatly reduce the cost of genotyping in genome-wide association studies, as this allows the use of tag SNPs that provide information on a number of other SNPs that are not directly genotyped.

  • However, as much as 30% of the common variants in the genome remains unknown and the latest high-throughput technologies convert only about 50% of SNPs into robust assays. In addition, extensive resequencing and genotyping will have to be carried out to ensure that SNPs in regions of low linkage disequilibrium are surveyed comprehensively.

  • Other factors that need to be taken into account in the design and execution of initial genome-wide association studies, to avoid loss of statistical power, include possible selection bias, population substructure and misclassification errors.

Abstract

To fully understand the allelic variation that underlies common diseases, complete genome sequencing for many individuals with and without disease is required. This is still not technically feasible. However, recently it has become possible to carry out partial surveys of the genome by genotyping large numbers of common SNPs in genome-wide association studies. Here, we outline the main factors — including models of the allelic architecture of common diseases, sample size, map density and sample-collection biases — that need to be taken into account in order to optimize the cost efficiency of identifying genuine disease-susceptibility loci.

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Figure 1: Effects of allele frequency on sample-size requirements.
Figure 2: Models of the risks conferred by disease-associated variants.
Figure 3: Possible allelic spectra of human diseases.
Figure 4: Putative distribution of phenotypic-effect sizes among disease-susceptibility variants.

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Acknowledgements

W.Y.S.W. received scholarships from the University of Cambridge, the University of Sydney and Gonville and Caius College, Cambridge, UK. This work was financed by the Wellcome Trust and the Juvenile Diabetes Research Foundation International.

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Authors and Affiliations

  1. Juvenile Diabetes Research Foundation/Wellcome Trust Diabetes and Inflammation Laboratory, Cambridge Institute for Medical Research, University of Cambridge, Cambridge, CB2 2XY, UK

    William Y. S. Wang, Bryan J. Barratt, David G. Clayton & John A. Todd

  2. Basic and Clinical Genomics Laboratory, School of Medical Sciences and Institute for Biomedical Research, University of Sydney, NSW 2006, Australia

    William Y. S. Wang

  3. Research and Development Genetics, AstraZeneca, Alderley Park, Macclesfield, SK10 4TG, Cheshire, UK

    Bryan J. Barratt

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DATABASES

OMIM

Type 1 diabetes

type 2 diabetes

FURTHER INFORMATION

David Clayton's tag SNP web site

International HapMap Project

NCBI Single Nucleotide Polymorphism database web site

Perlegen Sciences, Inc.

T1DBase — a genetics and bioinformatics resource for type 1 diabetes researchers

University of Washington and Fred Hutchinson Cancer Research Center Variation Discovery Resource database

Glossary

LINKAGE ANALYSIS

Mapping genes by typing genetic markers in families to identify chromosome regions that are associated with disease or trait values within pedigrees more often than are expected by chance. Such linked regions are more likely to contain a causal genetic variant.

AFFECTED SIB-PAIR (ASP) STUDIES

Linkage studies that are based on the collection of a large number of families, consisting of affected siblings, and their parents if available. In linkage analyses, the studies rely on the principle that ASPs share half their chromosomes.

LINKAGE DISEQUILIBRIUM

The non-random association of alleles of different linked polymorphisms in a population.

MINOR ALLELE FREQUENCY

(MAF). The frequency of the less common allele of a polymorphic locus. It has a value that lies between 0 and 0.5, and can vary between populations.

ODDS RATIO

A measurement of association that is commonly used in case-control studies. It is defined as the odds of exposure to the susceptible genetic variant in cases compared with that in controls. If the odds ratio is significantly greater than one, then the genetic variant is associated with the disease.

QUANTITATIVE TRAIT LOCI

Genetic loci that contribute to variations in quantitative, that is continuous, phenotypes.

PURIFYING SELECTION

Evolutionary selective forces that reduce the frequency of specific polymorphisms that have phenotypic effects.

POSITIVE SELECTION

The effect of evolutionary selective forces that favour certain variants and tend to increase their allele frequencies.

GENETIC DRIFT

Changes in allele frequencies in a population from one generation to another as the result of chance events in mating, meiosis and number of offspring.

SIBLING RELATIVE-RECURRENCE RISK

The risk of developing disease in a sibling of an affected individual relative to that of an individual in the general population. Commonly used as an indication of the heritability of a disease.

HAPLOTYPE

A set of alleles that is present on a single chromosome.

GENE CONVERSION

A non-reciprocal recombination process that results in an alteration of the sequence of a gene to that of its homologue during meiosis.

SUBGROUP ANALYSES

In genome-wide association analyses, or any other association study, there is a very low prior probability that any given locus or region is associated with disease. If the samples or data are divided into subgroups, for example, in analysis of epistatic interactions between loci — a departure from statistical independence in the joint distributions of genotypes between the loci — then the prior probability of a true positive is even lower.

POPULATION STRATIFICATION

The presence of several population subgroups that show limited interbreeding. When such subgroups differ both in allele frequency and in disease prevalence, this can lead to erroneous results in association studies.

ADMIXTURE

The mixture of two or more genetically distinct populations.

ANCESTRY INFORMATIVE MARKERS

Genetic markers that have different frequencies between populations and can be used to readily estimate the ancestral origins of a person or population.

COHORT STUDIES

Observational studies in which defined groups of people (the cohorts) are followed over time and outcomes are compared in subsets of the cohort who were exposed to different levels of factors of interest. These studies can either be performed prospectively or retrospectively from historical records.

GENOMIC CONTROL

A statistical genetics approach that provides an adjustment of the chi-squared threshold for statistical significance in a genetic association study to help allow for population sub-structure effects.

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Wang, W., Barratt, B., Clayton, D. et al. Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet 6, 109–118 (2005). https://doi.org/10.1038/nrg1522

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