Genomic mating in outbred species: predicting cross usefulness with additive and total genetic covariance matrices

Abstract

Diverse crops are both outbred and clonally propagated. Breeders typically use truncation selection of parents and invest significant time, land and money evaluating the progeny of crosses to find exceptional genotypes. We developed and tested genomic mate selection criteria suitable for organisms of arbitrary homozygosity level where the full-sibling progeny are of direct interest as future parents and/or cultivars. We extended cross variance and covariance variance prediction to include dominance effects and predicted the multivariate selection index genetic variance of crosses based on haplotypes of proposed parents, marker effects and recombination frequencies. We combined the predicted mean and variance into usefulness criteria for parent and variety development. We present an empirical study of cassava (Manihot esculenta), a staple tropical root crop. We assessed the potential to predict the multivariate genetic distribution (means, variances and trait covariances) of 462 cassava families in terms of additive and total value using cross-validation. We were able to predict all genetic variances and most covariances with non-zero accuracy. We also tested a directional dominance model and found significant inbreeding depression for most traits and a boost in total merit accuracy for root yield. We predicted 47,083 possible crosses of 306 parents and contrasted them to those previously tested to show how mate selection can reveal new potential within the germplasm. We enable breeders to consider the potential of crosses to produce future parents (progeny with excellent breeding values) and varieties (progeny with top performance).Author SummaryBreeders typically use truncation selection and invest significant resources evaluating progeny to find exceptional genotypes. We extended genetic variance and trait covariance prediction to include dominance and predicting the multivariate selection index variance. We enable mate selection based on potential to produce future parents (progeny with excellent breeding values) and/or varieties (progeny with top performance). Using cross-validation, we demonstrate that genetic variances and covariances can be predicted with non-zero accuracy in cassava, a staple tropical root crop.

Genomic selection of parents and crosses beyond the native gene pool of a breeding program

“…Furthermore, pedigree information for plant material from other programs is in most cases scarce and not systematically available, rendering the genomic selection of potential crossing parents and combinations an interesting strategy. A plethora of genomic cross prediction methods have been suggested for this purpose (De Beukelaer et al, 2017;Moeinizade et al, 2019;Woolliams et al, 2015), amongst which the usage of mate selection indices like the usefulness criterion (Schnell & Utz, 1975) or superior progeny value (Zhong & Jannink, 2007) and its extensions (Lehermeier et al, 2017;Osthushenrich et al, 2017;Wolfe et al, 2021) have gained a large popularity in plant breeding programs. The goals of this study were thus to compare the influence of the training population composition and statistical modelling on genomic predictions as well as assessing the potential of genomic predictions for preselecting adapted elite parents and choosing particular crossing combinations within and beyond a given breeding program.…”

Increasing cassava root yield: Additive-dominant genetic models for selection of parents and clones

“…Other important genomic selection goals are breeding population size reduction, time required to develop a new variety, and the ability to grow breeding populations outside the variety's recommended location, allowing selection for biotic and abiotic disturbances outside the endemic region (Fergunson et al, 2012). New prediction methodologies are consistently being published (Meuwissen et al, 2001;Park and Casella, 2008;Habier et al, 2011;Legarra et al, 2011;Azevedo et al, 2015;Wolfe et al, 2021). Application of the appropriate methodology to a trait of interest may increase selection gains and simultaneously reduce the work required in phenotypic evaluations, which are mostly high in cost and low in yield (Fergunson et al, 2012).…”