In silico simulation of future hybrid performance to evaluate heterotic pool formation in a self-pollinating crop.

Hybrid vigour has the potential to substantially increase the yield of self-pollinating crops such as wheat and rice, but future hybrid performance may depend on the initial strategy to form heterotic pools. We used in silico stochastic simulation of future hybrid performance in a self-pollinating crop to evaluate three strategies of forming heterotic pools in the founder population. The model included either 500, 2000 or 8000 quantitative trait nucleotides (QTN) across 10 chromosomes that contributed to a quantitative trait with population mean 100 and variance 10. The average degree of dominance at each QTN was either 0.2, 0.4 or 0.8 with variance 0.2. Three strategies for splitting the founder population into two heterotic pools were compared: (i) random split; (ii) split based on genetic distance according to principal component analysis of SNP genotypes; and (iii) optimized split based on F1 hybrid performance in a diallel cross among the founders. Future hybrid performance was stochastically simulated over 30 cycles of reciprocal recurrent selection based on true genetic values for additive and dominance effects. The three strategies of forming heterotic pools produced similar future hybrid performance, and superior future hybrids to a control population selected on inbred line performance when the number of quantitative trait nucleotides was ≥2000 and/or the average degree of dominance was ≥0.4.

A hybrid method for the imputation of genomic data in livestock populations.

BACKGROUND: This paper describes a combined heuristic and hidden Markov model (HMM) method to accurately impute missing genotypes in livestock datasets. Genomic selection in breeding programs requires high-density genotyping of many individuals, making algorithms that economically generate this information crucial. There are two common classes of imputation methods, heuristic methods and probabilistic methods, the latter being largely based on hidden Markov models. Heuristic methods are robust, but fail to impute markers in regions where the thresholds of heuristic rules are not met, or the pedigree is inconsistent. Hidden Markov models are probabilistic methods which typically do not require specific family structures or pedigree information, making them very flexible, but they are computationally expensive and, in some cases, less accurate. RESULTS: We implemented a new hybrid imputation method that combined heuristic and HMM methods, AlphaImpute and MaCH, and compared the computation time and imputation accuracy of the three methods. AlphaImpute was the fastest, followed by the hybrid method and then the HMM. The computation time of the hybrid method and the HMM increased linearly with the number of iterations used in the hidden Markov model, however, the computation time of the hybrid method increased almost linearly and that of the HMM quadratically with the number of template haplotypes. The hybrid method was the most accurate imputation method for low-density panels when pedigree information was missing, especially if minor allele frequency was also low. The accuracy of the hybrid method and the HMM increased with the number of template haplotypes. The imputation accuracy of all three methods increased with the marker density of the low-density panels. Excluding the pedigree information reduced imputation accuracy for the hybrid method and AlphaImpute. Finally, the imputation accuracy of the three methods decreased with decreasing minor allele frequency. CONCLUSIONS: The hybrid heuristic and probabilistic imputation method is able to impute all markers for all individuals in a population, as the HMM. The hybrid method is usually more accurate and never significantly less accurate than a purely heuristic method or a purely probabilistic method and is faster than a standard probabilistic method.