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

Authors

Diego Jarquin, University of Nebraska-LincolnFollow
Reka Howard, University of Nebraska-LincolnFollow
Zhikai Liang, University of Nebraska-Lincoln
Shashi K. Gupta, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT)
James C. Schnable, University of Nebraska-LincolnFollow
Jose Crossa, International Maize and Wheat Improvement Center (CIMMYT)

Document Type

Article

Date of this Version

1-24-2020

Citation

2020 Jarquin, Howard, Liang, Gupta, Schnable and Crossa

Comments

Jarquin D, Howard R, Liang Z, Gupta SK, Schnable JC and Crossa J (2020) Enhancing Hybrid Prediction in Pearl Millet Using Genomic and/or Multi-Environment Phenotypic Information of Inbreds. Front. Genet. 10:1294. doi: 10.3389/fgene.2019.01294

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).