Genetic parameters via predictivity in large populations under strong genomic selection

Estimation of accurate genetic parameters require using all data that were available upon selection. Existing methods for parameter estimation become impractical with large genomic data, and estimation becomes complex if parameters vary over time. This study aimed to test a new method called ‘GPP’ (Genetic parameters via predictivity), which combines within and across traits predictivity formulas with a deterministic formula to predict the accuracy of genomic breeding values. The GPP method can handle any data size and can estimate parameters across time slices.

We tested GPP using simulated datasets with negatively and positively correlated traits: primary production (h² = 0.40), secondary production (h² = 0.10) and a fitness (h² = 0.10) trait. Genomic selection was based on the primary production trait Two scenarios included either 5000 (A) or 100,000 (B) genotyped animals per generation for 10 generations. Across the two scenarios, GPP estimates closely matched the realized values, with a slight, variable bias. Using incorrect variances to compute GEBV prior to GPP had little impact on estimates. With the smaller data sets, estimates by REML using only one generation were highly variable, and estimates by GREML had lower standard errors. For large data, REML estimates were slightly biased. Genetic correlations obtained with GPP were non-symmetric, meaning they varied depending on which trait adjusted phenotype was used as a benchmark in the formula. We observed lower bias when predictivity involved adjusted phenotypes for the secondary trait. The GPP method performed equally well across both positively and negatively correlated traits, as well as for weak and strong genetic correlation scenarios, in both small and large datasets. Computations took 55 min and 11 s for a one-time slice run of GPP in the large dataset (scenario B). GPP had an approximately linear cost with the number of genotyped animals.

Genetic Parameters via predictivity is a fast, flexible, and accurate approach for estimating dynamic genetic parameters for positively or negatively correlated traits and also for weak and strong genetic correlation scenarios across time slices in large datasets, subject to genomic evaluation, where predictivity computation is feasible.