Although population association studies are not new, there remain many areas of disagreement over appropriate statistical analyses. This article provides an overview of statistical methods, including areas of controversy and ongoing developments. It does not consider family-based association studies, nor linkage or admixture studies.
I first cover analyses that are preliminary to association testing: testing for Hardy–Weinberg equilibrium; imputing missing genotype data; inferring haplotype from genotype data; measures of linkage disequilibrium and estimates of recombination rates; and choosing tag SNPs.
Among tests of association, I cover case–control, quantitative and ordered phenotypes, and analyses that are based on single SNPs, multiple SNPs and haplotypes. There is a discussion of issues that are relevant to genome-wide association studies.
I discuss Genomic Control and other approaches to the problem of population stratification.
I give particular attention to the problem of multiple testing, and discuss both frequentist and Bayesian approaches to addressing the problem.
Although genetic association studies have been with us for many years, even for the simplest analyses there is little consensus on the most appropriate statistical procedures. Here I give an overview of statistical approaches to population association studies, including preliminary analyses (Hardy–Weinberg equilibrium testing, inference of phase and missing data, and SNP tagging), and single-SNP and multipoint tests for association. My goal is to outline the key methods with a brief discussion of problems (population structure and multiple testing), avenues for solutions and some ongoing developments.
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I thank W. Astle and E. Waldron for help with drawing figures, and W. Astle, L. Cardon, A. Lewin, D. Lunn, A. Morris, D. Schaid, J. Whittaker and D. Zabaneh for discussions and comments on drafts of the manuscript. The author is supported in part by the UK Medical Research Council.
The author declares no competing financial interests.
A combination of alleles at different loci on the same chromosome.
- Population stratification
Refers to a situation in which the population of interest includes subgroups of individuals that are on average more related to each other than to other members of the wider population.
- Multiple-testing problem
Refers to the problem that arises when many null hypotheses are tested; some significant results are likely even if all the hypotheses are false.
- Hardy–Weinberg equilibrium
Holds at a locus in a population when the two alleles within an individual are not statistically associated.
- Significance level
Usually denoted, and chosen by the researcher to be the greatest probability of type-1 error that is tolerated for a statistical test. It is conventional to choose α = 5% for the overall analysis, which might consist of many tests each with a much lower significance level.
- Test statistic
A numerical summary of the data that is used to measure support for the null hypothesis. Either the test statistic has a known probability distribution (such as χ2) under the null hypothesis, or its null distribution is approximated computationally.
- Common-disease common-variant hypothesis
The hypothesis that many genetic variants that underlie complex diseases are common, and therefore susceptible to detection using current population association study designs. An alternative possibility is that genetic contributions to complex diseases arise from many variants, all of which are rare.
- Effective population size
The size of a theoretical population that best approximates a given natural population under an assumed model. Human effective population size is often taken to mean the size of a constant-size, panmictic population of breeding adults that generates the same level of polymorphism under neutrality as observed in an actual human population.
- Maximum-likelihood estimate
The value of an unknown parameter that maximizes the probability of the observed data under the assumed statistical model.
The information that is needed to determine the two haplotypes that underlie a multi-locus genotype within a chromosomal segment.
- Regression models
A class of statistical models that relate an outcome variable to one or more explanatory variables. The goal might be to predict further values of the outcome variable given the explanatory variables, or to identify a minimal set of explanatory variables with good predictive power.
- Prospective study design
Studies in which individuals are followed forward in time and disease events are recorded as they arise. DNA and biomarker samples, and data on environmental exposures and lifestyle factors, are usually obtained at the start of the study.
- Retrospective study design
Studies in which individuals are identified for inclusion in the study on the basis of their disease state. Data on previous environmental exposures and lifestyle factors are then recorded, and samples for DNA and biomarker studies might be obtained.
- Time to event
Refers to data in which the time to an event of interest is recorded, such as the time from the start of the study to disease onset, if any. This is potentially more informative than simply recording case or control status at the end of the study.
- Linkage disequilibrium
The statistical association, within gametes in a population, of the alleles at two loci. Although linkage disequilibrium can be due to linkage, it can also arise at unlinked loci; for example, because of selection or non-random mating.
- Type-1 error
The rejection of a true null hypothesis; for example, concluding that HWE does not hold when in fact it does. By contrast, the power of a test is the probability of correctly rejecting a false null hypothesis.
- Degrees of freedom
This term is used in different senses both within statistics and in other fields. It can often be interpreted as the number of values that can be defined arbitrarily in the specification of a system; for example, the number of coefficients in a regression model. It is often sufficient to regard degrees of freedom as a parameter that is used to define particular probability distributions.
A statistical school of thought that, in contrast to the frequentist school, holds that inferences about any unknown parameter or hypothesis should be encapsulated in a probability distribution, given the observed data. Bayes theorem is a celebrated result in probability theory that allows one to compute the posterior distribution for an unknown from the observed data and its assumed prior distribution.
- Likelihood-ratio test
A statistical test that is based on the ratio of likelihoods under alternative and null hypotheses. If the null hypothesis is a special case of the alternative hypothesis, then the likelihood-ratio statistic typically has a χ2 distribution with degrees of freedom equal to the number of additional parameters under the alternative hypothesis.
Describes a variable with a finite number, say k, of possible outcomes; in the cases k = 2 and k = 3, the terms binomial and trinomial are also used.
- Principal-components analysis
A statistical technique for summarizing many variables with minimal loss of information: the first principal component is the linear combination of the observed variables with the greatest variance; subsequent components maximize the variance subject to being uncorrelated with the preceding components.
- Stepwise selection procedure
Describes a class of statistical procedures that identify from a large set of variables (such as SNPs) a subset that provides a good fit to a chosen statistical model (for example, a regression model that predicts case–control status) by successively including or discarding terms from the model.
- Shrinkage methods
In this approach a prior distribution for regression coefficients is concentrated at zero, so that in the absence of a strong signal of association, the corresponding regression coefficient is 'shrunk' to zero. This mitigates the effects of too many variables (degrees of freedom) in the statistical model.
A name for the school of statistical thought in which support for a hypothesis or parameter value is assessed using the probability of the observed data (or more 'extreme' datasets) given the hypothesis or value. Usually contrasted with Bayesian.
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Balding, D. A tutorial on statistical methods for population association studies. Nat Rev Genet 7, 781–791 (2006). https://doi.org/10.1038/nrg1916
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