Accuracy of whole-genome sequence imputation using hybrid peeling in large pedigreed livestock populations
AbstractBackgroundWe demonstrate high accuracy of whole-genome sequence imputation in large livestock populations where only a small fraction of individuals (2%) had been sequenced, mostly at low coverage.MethodsWe used data from four pig populations of different sizes (18,349 to 107,815 individuals) that were broadly genotyped at densities between 15,000 and 75,000 markers genome-wide. Around 2% of the individuals in each population were sequenced (most at 1x or 2x and a small fraction at 30x; average coverage per individual: 4x). We imputed whole-genome sequence with hybrid peeling. We evaluated the imputation accuracy by removing the sequence data of a total of 284 individuals that had been sequenced at high coverage, using a leave-one-out design. We complemented these results with simulated data that mimicked the sequencing strategy used in the real populations to quantify the factors that affected the individual-wise and variant-wise imputation accuracies using regression trees.ResultsImputation accuracy was high for the majority of individuals in all four populations (median individual-wise correlation was 0.97). Individuals in the earliest generations of each population had lower accuracy than the rest, likely due to the lack of marker array data for themselves and their ancestors. The main factors that determined the individual-wise imputation accuracy were the genotyping status of the individual, the availability of marker array data for immediate ancestors, and the degree of connectedness of an individual to the rest of the population, but sequencing coverage had no effect. The main factors that determined variant-wise imputation accuracy were the minor allele frequency and the number of individuals with sequencing coverage at each variant site. These results were validated with the empirical observations.ConclusionsThe coupling of an appropriate sequencing strategy and imputation method, such as described and validated here, is a powerful strategy for generating whole-genome sequence data in large pedigreed populations with high accuracy. This is a critical step for the successful implementation of whole-genome sequence data for genomic predictions and fine-mapping of causal variants.
