Validating, augmenting and refining genome-wide association signals

Key Points

  • Genome-wide association studies have yielded a large number of association signals with robust statistical support, but these are only markers of the true functional variants.

  • Reliable identification of the true functional variants can be notoriously difficult, but a series of methods could be helpful in this regard.

  • Large-scale exact replication to achieve robust statistical credibility of a marker should precede efforts at finding the causative variants.

  • Fine mapping and resequencing might help to identify more informative markers and multiple independent informative loci.

  • Functional information could fine tune the credibility of different variants for being the causative variant.

  • Additional insights might be obtained by more extensive phenotype mapping of proposed variants.

Abstract

Studies using genome-wide platforms have yielded an unprecedented number of promising signals of association between genomic variants and human traits. This Review addresses the steps required to validate, augment and refine such signals to identify underlying causal variants for well-defined phenotypes. These steps include: large-scale exact replication across both similar and diverse populations; fine mapping and resequencing; determination of the most informative markers and multiple independent informative loci; incorporation of functional information; and improved phenotype mapping of the implicated genetic effects. Even in cases for which replication proves that an effect exists, confident localization of the causal variant often remains elusive.

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Figure 1: Putting it in order.

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Acknowledgements

Scientific support for this project was provided through the Tufts Clinical and Translational Science Institute (Tufts CTSI) under funding from the National Institute of Health/National Center for Research Resources (UL1 RR025752 ). Points of view or opinions in this paper are those of the authors and do not necessarily represent the official position or policies of the Tufts CTSI.

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Correspondence to John P. A. Ioannidis.

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DATABASES

OMIM

Crohn's disease

SLE

type 2 diabetes

FURTHER INFORMATION

John P. A. Ioannidis' homepage

1000 Genomes Project

NHGRI GWA studies catalogue

Nature Reviews Genetics Series on Genome-wide association studies

Glossary

Pleiotropy

The effect of a gene on more than one phenotype or disease.

Meta-analysis

An analysis that combines the evidence from multiple data sets.

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 the odds of exposure in controls. If the odds ratio is significantly greater than one, then the genetic variant is associated with the disease.

Cochran–Armitage test

A genotype-based contingency table test for association that is well suited to the detection of trends across ordinal categories (in this case, genotypes).

r2

(Correlation coefficient). For linkage disequilibrium, it provides a measure of the strength and direction of a linear relationship between the genotypes of two variants expressed as a number of minor alleles.

Proxy

A highly correlated DNA variant that is an adequate substitute in an association study.

Detection probability

For a two-stage design, this is the probability that a disease-associated SNP will have a p value among the lowest ranks of p values at stage 1 and, among those SNPs selected at stage 1, that a disease-associated SNP will also have a p value among the lowest ranks of p values at stage 2.

Hardy–Weinberg equilibrium

A theoretical description of the relationship between genotype and allele frequencies that is based on an expectation in a stable population undergoing random mating in the absence of selection, new mutations and gene flow. Under these conditions, and in the absence of linkage disequilibrium, the genotype frequencies are equal to the product of the allele frequencies.

Imputation accuracy

This describes the different ways to treat missing genotypes in a data set. Imputed genotypes with less than a pre-specified accuracy can be considered missing or genotypes can be weighted in the calculations on the basis of the estimated imputation accuracy.

Population stratification

The situation that arises when a population contains several subpopulations that differ in their genetic characteristics.

Frequentist

A statistical approach for assessing whether a hypothesis is correct or an alternative should be adopted.

Markov chain Monte Carlo

An iterative computational approach for identifying the most likely model among many possible models.

Phasing

The determination of the haplotype phase (the arrangement of alleles at two loci on homologous chromosomes) from genotype data using statistical methods.

Winner's curse

The inflation of effect sizes compared with the true effect size for associations that are discovered on the basis of passing specific statistical significance or other selection thresholds.

I2

A metric of between-study heterogeneity taking values between 0 and 100%, which describes how much of the between-study heterogeneity is beyond chance.

Fixed effects model

A set of methods for combining data that assumes there is a common effect in all data sets and that observed effects only differ by chance.

Random effects model

A set of methods for combining data that assumes that genetic effects are different across different populations.

Phenotype misclassification

This describes the situation in which cases are classified as controls or controls are classified as cases for binary outcomes. The equivalent problem for continuous traits is measurement error.

Nested case–control

A design in which cases and controls are sampled from a pre-existing larger cohort.

Convenience sample

A sample of controls or of cases with a trait of interest that is available for another purpose and has not been collected for the purpose of the specific research project or with an explicit sampling scheme.

Principal components analysis

A statistical method used to simplify data sets by transforming a series of correlated variables into a smaller number of uncorrelated factors.

Copy number variant

A class of DNA sequence variants (including deletions and duplications) that lead to a departure from the expected diploid representation of DNA sequence.

Recombination hot spot

A small (usually one to a few kilobases) chromosomal region in which the frequency of meiotic recombination is much higher than average. Hot spots of recombination can be recognized by observing that all pairs of SNPs that encompass the region have a low D′ value.

Gene desert

A stretch of the genome that contains no known protein-coding gene.

Expression quantitative trait locus

A locus at which genetic allelic variation is associated with variation in gene expression.

Bayes factor

The ratio of the prior probabilities of the null hypothesis compared with the alternative hypotheses over the ratio of the posterior probabilities. This can be interpreted as the relative odds that the hypothesis is true before and after examining the data.

Regression model

A model that evaluates the association between one or multiple variables with an outcome of interest.

Overfitting

In a regression model, the tendency to obtain better fit to the available data than to other independent data.

Bayesian method

Any approach that uses a combination of prior beliefs and observed data to generate posterior beliefs.

Endophenotype

A physiological or other trait that is related to a disease trait and is measured independently of the disease.

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Ioannidis, J., Thomas, G. & Daly, M. Validating, augmenting and refining genome-wide association signals. Nat Rev Genet 10, 318–329 (2009). https://doi.org/10.1038/nrg2544

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