Journal
BIOSTATISTICS
Volume 12, Issue 1, Pages 1-17Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biostatistics/kxq039
Keywords
Genetic association; GWAS; Imputed genotypes; Statistical genetics; Uncertain genotypes
Funding
- GlaxoSmithKline
- Faculty of Biology and Medicine of the University of Lausanne, Switzerland
- Swiss National Foundation [310000-112552]
- Swiss National Science Foundation [33CSCO-122661, 3100AO-116323/1]
- Giorgi-Cavaglieri Foundation
- Swiss Institute of Bioinformatics
- European Framework Project 6 AnEuploidy
- EuroDia
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Interpretability and power of genome-wide association studies can be increased by imputing unobserved genotypes, using a reference panel of individuals genotyped at higher marker density. For many markers, genotypes cannot be imputed with complete certainty, and the uncertainty needs to be taken into account when testing for association with a given phenotype. In this paper, we compare currently available methods for testing association between uncertain genotypes and quantitative traits. We show that some previously described methods offer poor control of the false-positive rate (FPR), and that satisfactory performance of these methods is obtained only by using ad hoc filtering rules or by using a harsh transformation of the trait under study. We propose new methods that are based on exact maximum likelihood estimation and use a mixture model to accommodate nonnormal trait distributions when necessary. The new methods adequately control the FPR and also have equal or better power compared to all previously described methods. We provide a fast software implementation of all the methods studied here; our new method requires computation time of less than one computer-day for a typical genome-wide scan, with 2.5 M single nucleotide polymorphisms and 5000 individuals.
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