Genotyping marker density and prediction models effects in long-term breeding schemes of cross-pollinated crops

Abstract

Reductions of genotyping marker density have been extensively evaluated as potential strategies to reduce the genotyping costs of genomic selection (GS). Low-density marker panels are appealing in GS because they entail lower multicollinearity and computing time and allow more individuals to be genotyped for the same cost. However, statistical models used in GS are usually evaluated with empirical data, using "static" training sets and populations. This may be adequate for making predictions during a breeding program's initial cycles but not for the long-term. Moreover, to the best of our knowledge, no GS models consider the effect of dominance, which is particularly important for breeding outcomes in cross-pollinated crops. Hence, dominance effects are important and unexplored in GS for long-term programs involving allogamous species. To address it, we employed two approaches: analysis of empirical maize datasets and simulations of long-term breeding applying phenotypic and genomic recurrent selection (intrapopulation and reciprocal schemes). In both schemes, we simulated twenty breeding cycles and assessed the effect of marker density reduction on the population mean, the best crosses, additive variance, selective accuracy, and response to selection with models (additive, additive-dominant, general (GCA), and specific combining ability (SCA)). Our results indicate that marker reduction based on linkage disequilibrium levels provides useful predictions only within a cycle, as accuracy significantly decreases over cycles. In the long-term, without training set updating, high-marker density provides the best responses to selection. The model to be used depends on the breeding scheme: additive for intrapopulation and additivedominant or SCA for reciprocal.

Comparison of genomic prediction models for general combining ability in early stages of hybrid breeding programs

“…DoVale et al. (2022) also observed the importance of high‐density markers for long‐term genetic gain in cross‐pollinated crops. On the other hand, the oracle scenario with QTL genotypes expectedly had the best performance, but only ∼16% more genetic gain and ∼20% more heterosis than using high‐density markers.…”

Elite germplasm introduction, training set composition, and genetic optimization algorithms effect in genomic selection-based breeding programs: a stochastic simulation study in self-pollinated crops

“…Initially, the optimization algorithm may effectively select individuals with favorable traits for inclusion in the TS. However, as the breeding program progresses, the algorithm's tendency to prioritize known alleles may lead to a reduction in the representation of genetic diversity, hindering its ability to predict the performance of novel genotypes (DoVale et al, 2022) or identify transgressive combinations. Furthermore, the high computational demand associated with optimization algorithms poses a practical challenge, particularly when working with large population sizes (Hidalgo and Fernandez, 2005).…”

“…On the other hand, other authors performed studies using stochastic simulations (DoVale et al, 2022), showing that the use of these algorithms may significantly reduce the accuracy over breeding cycles, where there will be changes in allele frequencies over time, and recombination, that will break the "big" haplotypes into small ones, and some of them may segregate without any maker tagging them, reducing the prediction accuracy over cycles.…”

Developing a liquid capture chip to accelerate the genetic progress of cattle

“…Among them, the marker density and the accuracy of genotypes are crucially important. Previous studies have reported that increasing the marker density can improve the prediction accuracy for traits with complex genetic architecture [9,12].…”