Assessment of the performance of different hidden Markov models for imputation in animal breeding

Abstract

In this paper we review the performance of various hidden Markov model-based imputation methods in animal breeding populations. Traditionally, heuristic-based imputation methods have been used for imputation in large animal populations due to their computational efficiency, scalability, and accuracy. However, recent advances in the area of human genetics have increased the ability of probabilistic hidden Markov model methods to perform accurate phasing and imputation in large populations. These advances may enable these methods to be useful for routine use in large animal populations. To test this, we evaluate here the accuracy and computational cost of several methods in a series of simulated populations and a real animal population. We first tested single-step (diploid) imputation, which performs both phasing and imputation. Then we tested pre-phasing followed by haploid imputation. We tested four diploid imputation methods (fastPHASE, Beagle v4.0, IMPUTE2, and MaCH), three phasing methods, (SHAPEIT2, HAPI-UR, and Eagle2), and three haploid imputation methods (IMPUTE2, Beagle v4.1, and minimac3). We found that performing pre-phasing and haploid imputation was faster and more accurate than diploid imputation. In particular, we found that pre-phasing with Eagle2 or HAPI-UR and imputing with minimac3 or IMPUTE2 gave the highest accuracies in both simulated and real data.