Multi-trait Improvement by Predicting Genetic Correlations in Breeding Crosses

Abstract

The many quantitative traits of interest to plant breeders are often genetically correlated, which can complicate progress from selection. Improving multiple traits may be enhanced by identifying parent combinations – an important breeding step – that will deliver more favorable genetic correlations (rG). Modeling the segregation of genomewide markers with estimated effects may be one method of predicting rG in a cross, but this approach remains untested. Our objectives were to: (i) use simulations to assess the accuracy of genomewide predictions of rG and the long-term response to selection when selecting crosses on the basis of such predictions; and (ii) empirically measure the ability to predict genetic correlations using data from a barley (Hordeum vulgare L.) breeding program. Using simulations, we found that the accuracy to predict rG was generally moderate and influenced by trait heritability, population size, and genetic correlation architecture (i.e., pleiotropy or linkage disequilibrium). Among 26 barley breeding populations, the empirical prediction accuracy of rG was low (-0.012) to moderate (0.42), depending on trait complexity. Within a simulated plant breeding program employing indirect selection, choosing crosses based on predicted rG increased multi-trait genetic gain by 11–27% compared to selection on the predicted cross mean. Importantly, when the starting genetic correlation was negative, such cross selection mitigated or prevented an unfavorable response in the trait under indirect selection. Prioritizing crosses based on predicted genetic correlation can be a feasible and effective method of improving unfavorably correlated traits in breeding programs.

Phenomic Characterization of Crotalaria Germplasm for Crop Improvement in Kenya

“…Correlation among quantitative variables means that it is possible to improve one of them by targeting the other. Other studies also allude that strongly correlating traits are possibly under the control of same genes and therefore when breeding, selecting for one of the traits could result in the improvement of the other trait (Neyhart et al, 2019). In the present study, other signi cant positive correlation was observed between leaf length and number of branches, pod length, pod diameter as well as pods per plant.…”

Genetic analysis of morphological traits of the spike and reproductivity elements of speltoid chemomutant Triticum aestivum

“…Analysis of morphological traits of the ear and the elements of productivity of father varieties and simple F 1 hybrids revealed that speltoid form of the ear, red colour of the glumes and non-awnedness was dominant, and such quantitative parameters as length of the main stem and the number of spikelets in the main ear were close to those of the speltoid mutant. Simultaneous improvement of all economically valuable traits is often complicated due to negative correlation between them, caused by segregation of the genes in chromosomes and presence of genes with pleiotrophic effect (Schulthess et al, 2017;Neyhart et al, 2019). One of the most important traits which breeders are willing to improve is yield.…”

Multivariate analysis unravels genetic diversity and relationship between agronomic traits, protein, and dietary fiber in yardlong bean (Vigna unguiculata subsp. sesquipedalis Verdc.)

“…Correlation analysis is a powerful statistical tool used to measure the degree of association between traits in plants (Silva et al 2016). The existence of a correlation between traits may also indicate an association between the genes that controlling those traits (Neyhart et al 2019). The positive correlation will be beneficial in selecting multiple traits based on the assertion that the genetic gain of several traits could be achieved by selecting a single trait that is strongly and positively correlated to many other traits .…”