eprintid: 11365
rev_number: 9
eprint_status: archive
userid: 3170
dir: disk0/00/01/13/65
datestamp: 2020-02-12 05:44:20
lastmod: 2020-02-12 05:44:20
status_changed: 2020-02-12 05:44:20
type: article
metadata_visibility: show
creators_name: Jarquin, D
creators_name: Howard, R
creators_name: Liang, Z
creators_name: Gupta, S K
creators_name: Schnable, J C
creators_name: Crossa, J
icrisatcreators_name: Gupta, S K
affiliation: Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln,(NE)
affiliation: Department of Statistics, University of Nebraska-Lincoln, Lincoln, (NE)
affiliation: ICRISAT (Patancheru)
affiliation: International Maize and Wheat Improvement Center (CIMMYT), Ciudad de Mexico, (Mexico)
country: USA
country: India
country: Mexico
title: Enhancing Hybrid Prediction in Pearl Millet Using Genomic and/or Multi-Environment Phenotypic Information of Inbreds
ispublished: pub
subjects: PLB1
subjects: S1.5.1
subjects: s2.13
divisions: CRPS2
full_text_status: public
keywords: Genomic selection, Hybrid prediction, Genotype-by-environment interaction G×E, General combining ability, Specific combining ability, Conventional, Tunable GBS
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).
date: 2020-01
date_type: published
publication: Frontiers in Genetics (TSI)
volume: 10
number: 1294
publisher: Frontiers Media
pagerange: 1-11
id_number: doi:10.3389/fgene.2019.01294
refereed: TRUE
issn: 1664-8021
official_url: https://doi.org/10.3389/fgene.2019.01294
related_url_url: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Enhancing+Hybrid+Prediction+in+Pearl+Millet+Using+Genomic+and%2For+Multi-Environment+Phenotypic+Information+of+Inbreds&btnG=
related_url_type: pub
funders: USDA National Institute of Food and Agriculture to JS
citation:   Jarquin, D and Howard, R and Liang, Z and Gupta, S K and Schnable, J C and Crossa, J  (2020) Enhancing Hybrid Prediction in Pearl Millet Using Genomic and/or Multi-Environment Phenotypic Information of Inbreds.  Frontiers in Genetics (TSI), 10 (1294).  pp. 1-11.  ISSN 1664-8021     
document_url: http://oar.icrisat.org/11365/1/Enhancing%20Hybrid%20Prediction.pdf