Practical issues in imputation-based association mapping.

Published

Journal Article

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 information across studies, using only summary data for each SNP. Methods discussed here are implemented in the software package BIMBAM, which is available from http://stephenslab.uchicago.edu/software.html.

Full Text

Duke Authors

Cited Authors

  • Guan, Y; Stephens, M

Published Date

  • December 5, 2008

Published In

Volume / Issue

  • 4 / 12

Start / End Page

  • e1000279 -

PubMed ID

  • 19057666

Pubmed Central ID

  • 19057666

Electronic International Standard Serial Number (EISSN)

  • 1553-7404

International Standard Serial Number (ISSN)

  • 1553-7390

Digital Object Identifier (DOI)

  • 10.1371/journal.pgen.1000279

Language

  • eng