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Genotype imputation for genome-wide association studies

Key Points

  • We review the statistical methods available for carrying out genotype imputation and compare their properties and performance.

  • We also review the downstream uses of imputation, including boosting the power of genome-wide association studies, fine-mapping and allowing comparisons between studies.

  • Several factors influence imputation accuracy, such as reference panel and study sample combination, sample size, genotyping chip and allele frequency.

  • Both Bayesian and frequentist methods can be used to impute SNP genotypes to test for association.

  • We review and compare the information metrics that are commonly used when carrying out quality control of imputed genotype data.

Abstract

In the past few years genome-wide association (GWA) studies have uncovered a large number of convincingly replicated associations for many complex human diseases. Genotype imputation has been used widely in the analysis of GWA studies to boost power, fine-map associations and facilitate the combination of results across studies using meta-analysis. This Review describes the details of several different statistical methods for imputing genotypes, illustrates and discusses the factors that influence imputation performance, and reviews methods that can be used to assess imputation performance and test association at imputed SNPs.

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Figure 1: Post-imputation information measures.

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Acknowledgements

B.N.H. was funded by a National Science Foundation Graduate Research Fellowship and the Overseas Research Students Awards Scheme. J.M. acknowledges support from the Medical Research Council.

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Correspondence to Jonathan Marchini.

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Supplementary information

Supplementary information S1

Alignment of reference and study datasets (PDF 82 kb)

Supplementary information S2

HMM-based methods (XLS 36 kb)

Supplementary information S3

Imputation information measures (PDF 146 kb)

Supplementary information S4

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Supplementary information S5

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Supplementary information S6

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Supplementary information S7

Testing for association at imputed SNPs (PDF 176 kb)

Supplementary information S8

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Supplementary information S9

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Supplementary information S10

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Supplementary information S11

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Related links

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FURTHER INFORMATION

1000 Genomes Project

BEAGLE

fastPHASE and BIMBAM

GEDI

IMPUTE v1 and v2, SNPTEST and HAPGEN

MACH, MACH2DAT and MACH2QTL

PLINK

ProbABEL

SNPMSTAT

TUNA

UNPHASED

Glossary

Hidden Markov model

A class of statistical model that can be used to relate an observed process across the genome to an underlying, unobserved process of interest. Such models have been used to estimate population structure and admixture, for genotype imputation and for mutiple testing.

Linkage disequlibrium

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.

Expectation-maximization algorithm

A method for finding maximum-likelihood estimates of parameters in statistical models, in which the model depends on unobserved latent variables. It is an iterative method which alternates between performing an expectation (E) step and a maximization (M) step.

Identical by state

Two or more alleles are identical by state if they are identical. Alleles which are identical by state may or may not be identical by descent owing to the possibility of multiple mutation events.

Identical by descent

Two or more alleles are identical by descent if they are identical copies of the same ancestral allele.

Best-guess genotype

Most imputation methods provide a probabilistic prediction of the missing genotypes. The best guess genotype is that genotype which has the largest probability.

Calibration

The probabilities of events predicted by a probability model are said to be well calibrated if they accurately estimate the proportion of times the events occur. For imputation, a method is well calibrated if genotypes that are predicted with probability p are correct 100p% of the time.

Proportional hazards model

A class of survival models in statistics. Survival models relate the time that passes before some event occurs to one or more covariates that may influence that quantity. In a proportional hazards model, the unique effect of a unit increase in a covariate is multiplicative with respect to the hazard rate.

Bayesian

A statistical school of thought in which the posterior probability distribution for any unknown parameter or hypothesis given the observed data is used to carry out inference. Bayes theorem is used to construct the posterior distribution using the observed data and a prior distribution, often allowing the incorporation of useful knowledge into the analysis.

Frequentist

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 data sets) given the hypothesis or value. These theories are usually contrasted with Bayesian models.

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Marchini, J., Howie, B. Genotype imputation for genome-wide association studies. Nat Rev Genet 11, 499–511 (2010). https://doi.org/10.1038/nrg2796

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