63 A comparison of clustering methods for cross-validation of genomic predictors when training on phenotypes or deregressed Estimated Breeding Values

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Abstract The objective of the study was to evaluate the impact of clustering methods for cross-validation on the accuracy of prediction of molecular breeding values (MBV) in Red Angus cattle (n = 9,763) and in simulation. Individuals were clustered using seven methods [k-means, k-medoids, principal component analysis on the numerator relationship matrix (A) and identical-by-state genomic matrix (G) as data and covariance matrices, and random] and two response variables [deregressed Estimated Breeding Values (DEBV) and adjusted phenotypes]. Genotypes were imputed to a 50K reference panel. Using cross-validation and a Bayes C model, MBV were estimated for traits including birth weight (BWT), marbling (MARB), rib-eye area (REA), and yearling weight (YWT) for DEBV and BWT, YWT, and ultrasonically measured intramuscular fat percentage and rib eye area for adjusted phenotypes. A bivariate animal model was used to estimate prediction accuracies calculated using the genetic correlation between estimated MBV and the associated response variable. To quantify the difference between true and estimated accuracies, a simulation mimicking a cattle population was replicated five times. The same clustering methods were used as with the Red Angus data with the addition of forward validation and two genotyping methods (random selection and selection of the top 25% of animals). Predicted accuracies were estimated similarly and true accuracies were estimated using the residual correlation of a bivariate model using MBV and true breeding values (TBV). The Rand index was used to quantify the similarity between clustering methods, showing relationship-based clusters were clearly different from random clusters. In simulation, random genotyping led to higher estimated accuracies than selection of top individuals; however, estimated accuracies over predicted true accuracies with random genotyping but under predicted true accuracies with the selection of top individuals. When forward validation was evaluated within simulation, results suggested DEBV led to less biased estimates of MBV accuracy.