Optimizing Plant Breeding Programs for Genomic Selection

Abstract

Plant geneticists and breeders have used marker technology since the 1980s in quantitative trait locus (QTL) identification. Marker-assisted selection is effective for large-effect QTL but has been challenging to use with quantitative traits controlled by multiple minor effect alleles. Therefore, genomic selection (GS) was proposed to estimate all markers simultaneously, thereby capturing all their effects. However, breeding programs are still struggling to identify the best strategy to implement it into their programs. Traditional breeding programs need to be optimized to implement GS effectively. This review explores the optimization of breeding programs for variety release based on aspects of the breeder’s equation. Optimizations include reorganizing field designs, training populations, increasing the number of lines evaluated, and leveraging the large amount of genomic and phenotypic data collected across different growing seasons and environments to increase heritability estimates, selection intensity, and selection accuracy. Breeding programs can leverage their phenotypic and genotypic data to maximize genetic gain and selection accuracy through GS methods utilizing multi-trait and, multi-environment models, high-throughput phenotyping, and deep learning approaches. Overall, this review describes various methods that plant breeders can utilize to increase genetic gains and effectively implement GS in breeding.

Estimation of Complex-Trait Prediction Accuracy from the Different Holo-Omics Interaction Models

“…Previously, the Hadamard product was efficiently used to study the mechanisms of multimodel learning [ 37 ]. Merrick et al [ 38 ] also studied genotype-by-environment (GE) scenarios for genomic prediction accuracy using the Hadamard product and estimated the positive correlation between environments using the variance-covariance matrix of the main effects. In contrast, Pérez-Enciso et al [ 15 ] did not find any improvements in prediction with Hadamard products while using different scenarios of host and microbiome interactions.…”

“…Moreover, we have not estimated prediction accuracy using the Kronecker product framework, which could also be another potential model for testing the prediction accuracy of complex traits. However, several studies suggest that the prediction models using the Hadamard product have continually outperformed the Kronecker product model [ 38 ] and consist of a similar covariance structure [ 39 ]. Therefore, further studies will be required to conclude the performance of the Kronecker product in the holo-omics framework.…”

GWAS and Genomic Prediction for Pre-harvest Sprouting Tolerance in Spring Wheat

“…this is partly due to different genetic architecture of each component trait (Merrick et al 2022a) and suggests that different models may need to be used, depending upon the crop, the trait and the heritability of the trait involved as done in a recent study (Kibe et al 2020).…”

“…Bayes B also uses a Markov Chain Monte Carlo to estimate the markers effect and thus are computationally expensive whereas rrBLUP uses a ridge regression coefficient to estimate the markers effect and is faster, and is mostly preferred in wheat breeding programs in addition to GBLUP (Pérez and De Los Campos 2014). More information about the working of these models is referred to Sandhu et al (2022a) and Merrick et al (2022a).…”

Variability among maize (Zea mays L.) inbred lines for agro- morphological traits

“…Heritability estimation has been used in plant breeding programs to produce relevant data (Merrick et al 2022, Egeland 2023. Therefore, when selecting genotypes for the desired traits, heritability estimates for yield component traits might be used as a starting point for additional study.…”