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Practical issues in imputation-based association mapping (2008)
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| Venue: | PLoS Genet |
| Citations: | 42 - 7 self |
BibTeX
@ARTICLE{Guan08practicalissues,
author = {Yongtao Guan and Matthew Stephens},
title = {Practical issues in imputation-based association mapping},
journal = {PLoS Genet},
year = {2008},
pages = {1000279}
}
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Abstract
Imputation-based association methods provide a powerful framework for testing untyped variants for association with phenotypes and for combining results from multiple studies that use different genotyping platforms. Here, we consider several issues that arise when applying these methods in practice, including: (i) factors affecting imputation accuracy, including choice of reference panel; (ii) the effects of imputation accuracy on power to detect associations; (iii) the relative merits of Bayesian and frequentist approaches to testing imputed genotypes for association with phenotype; and (iv) how to quickly and accurately compute Bayes factors for testing imputed SNPs. We find that imputation-based methods can be robust to imputation accuracy and can improve power to detect associations, even when average imputation accuracy is poor. We explain how ranking SNPs for association by a standard likelihood ratio test gives the same results as a Bayesian procedure that uses an unnatural prior assumption—specifically, that difficult-to-impute SNPs tend to have larger effects— and assess the power gained from using a Bayesian approach that does not make this assumption. Within the Bayesian framework, we find that good approximations to a full analysis can be achieved by simply replacing unknown genotypes with a point estimate—their posterior mean. This approximation considerably reduces computational expense compared with published sampling-based approaches, and the methods we present are practical on a genome-wide scale with very modest computational resources (e.g., a single desktop computer). The approximation also facilitates combining
Keyphrases
imputation-based association mapping practical issue imputation accuracy frequentist approach full analysis average imputation accuracy reference panel untyped variant single desktop computer posterior mean imputed genotype genome-wide scale ranking snp powerful framework unnatural prior assumption imputed snp computational expense bayesian procedure different genotyping platform several issue difficult-to-impute snp tend bayesian approach good approximation imputation-based method standard likelihood ratio test sampling-based approach modest computational resource relative merit multiple study compute bayes factor imputation-based association method bayesian framework unknown genotype
