Enhancing Hybrid Prediction in Pearl Millet Using Genomic and/or Multi-Environment Phenotypic Information of Inbreds

Abstract

Genomic selection (GS) is an emerging methodology that helps select superior lines among experimental cultivars in plant breeding programs. It offers the opportunity to increase the productivity of cultivars by delivering increased genetic gains and reducing the breeding cycles. This methodology requires inexpensive and sufficiently dense marker information to be successful, and with whole genome sequencing, it has become an important tool in many crops. The recent assembly of the pearl millet genome has made it possible to employ GS models to improve the selection procedure in pearl millet breeding programs. Here, three GS models were implemented and compared using grain yield and dense molecular marker information of pearl millet obtained from two different genotyping platforms (C [conventional GBS RAD-seq] and T [tunable GBS tGBS]). The models were evaluated using three different cross-validation (CV) schemes mimicking real situations that breeders face in breeding programs: CV2 resembles an incomplete field trial, CV1 predicts the performance of untested hybrids, and CV0 predicts the performance of hybrids in unobserved environments. We found that (i) adding phenotypic information of parental inbreds to the calibration sets improved predictive ability, (ii) accounting for genotype-by-environment interaction also increased the performance of the models, and (iii) superior strategies should consider the use of the molecular markers derived from the T platform (tGBS).

Multi-omics-based prediction of hybrid performance in canola

“…They also represent an interesting substitute for traits that are difficult and/or expensive to score in the field. The inclusion of environmental data and their interaction with omics data can potentially improve trait predictabilities, as shown by a recent study in pearl millet (Jarquin et al 2020 ). A study on hybrid prediction in grain maize illustrated that including historic phenotypic data for training improves genomic prediction and enables optimization of hybrid variety development (Schrag et al 2019 ).…”

Genetic Gains in Pearl Millet in India: Insights Into Historic Breeding Strategies and Future Perspective

“…This work has laid a solid foundation for carrying out trait discovery, mapping, and deployment of QTLs/alleles/candidate genes linked to traits of economic interests. It also has helped toward the development and implementation of whole-genome prediction models for the pearl millet community globally (Jarquin et al, 2020 ; Srivastava et al, 2020a ). The whole-genome resequencing of Pearl Millet Inbred Germplasm Association Panel, mapping population parents, and elite hybrid parental lines have helped to develop a huge (>32 million) repository of genome-wide SNPs.…”

Response to Early Generation Genomic Selection for Yield in Wheat

“…One approach predicts the complete genetic values of individuals and focuses on both additive and non-additive effects, thereby estimating the genetic performance of candidate cultivars ( Crossa et al, 2017 ). Additive or genetic values are predicted in breeding generations using as much phenotypic information as possible obtained from different environments in a complete or incomplete (sparse) multi-environment testing scheme ( Jarquin et al, 2020 ). A second approach is predicting additive effects in early generations (bi-parental F2, or multi-parental populations) to achieve a rapid selection cycle with a short interval ( Vivek et al, 2017 ; Zhang et al, 2017 ; Beyene et al, 2021 ).…”