eprintid: 11598
rev_number: 11
eprint_status: archive
userid: 3170
dir: disk0/00/01/15/98
datestamp: 2020-09-06 07:03:45
lastmod: 2020-09-06 07:03:45
status_changed: 2020-09-06 07:03:45
type: article
metadata_visibility: show
creators_name: Pandey, M K
creators_name: Chaudhari, S
creators_name: Jarquin, D
creators_name: Janila, P
creators_name: Crossa, J
creators_name: Patil, S C
creators_name: Sundravadana, S
creators_name: Khare, D
creators_name: Bhat, R S
creators_name: Radhakrishnan, T
creators_name: Hickey, J M
creators_name: Varshney, R K
creators_gender: Female
icrisatcreators_name: Pandey, M K
icrisatcreators_name: Chaudhari, S
icrisatcreators_name: Janila, P
icrisatcreators_name: Varshney, R K
affiliation: ICRISAT (Patancheru)
affiliation: CIMMYT, Mexico
affiliation: Mahatma Phule Krishi Vidyapeeth (MPKV), Jalgaon
affiliation: Tamil Nadu Agricultural University (TNAU), Coimbatore
affiliation: Jawaharlal Nehru Krishi Vishwa Vidyalaya (JNKVV), Jabalpur
affiliation: University of Agricultural Sciences (UAS)-Dharwad, Dharwad
affiliation: ICAR-Directorate of Groundnut Research (DGR), Junagadh
affiliation: The Roslin Institute, The University of Edinburgh, Edinburgh, Scotland
country: India
country: Mexico
country: UK
title: Genome-based trait prediction in multi- environment breeding trials in groundnut
ispublished: pub
subjects: PLB1
subjects: s1.3
subjects: s2.13
divisions: CRPS2
divisions: CRPS3
crps: CG1
full_text_status: public
keywords: Groundnut, Genomics, Breeding
note: The authors are thankful for financial support
from Department of Biotechnology (DBT) of Ministry of Science &
Technology, Government of India, India and Bill & Melinda Gates
Foundation, USA. The work reported in this article was undertaken
as a part of the DBT-India and CGIAR Research Program on Grain
Legumes and Dryland Cereals (GLDC). ICRISAT is a member of the
CGIAR.
abstract: Genomic selection (GS) can be an efficient and cost-effective breeding approach which captures both small- and
large-effect genetic factors and therefore promises to achieve higher genetic gains for complex traits such as yield and oil content in groundnut. A training population was constituted with 340 elite lines followed by genotyping with 58 K ‘Axiom_Arachis’ SNP array and phenotyping for key agronomic traits at three locations in India. Four GS models were tested using three different random cross-validation schemes (CV0, CV1 and CV2). These models are: (1) model 1 (M1 = E + L) which includes the main effects of environment (E) and line (L); (2) model 2 (M2 = E + L + G) which includes the main effects of markers (G) in addition to E and L; (3) model 3 (M3 = E + L + G + GE), a naïve interaction model; and (4) model 4 (E + L + G + LE + GE), a naïve and informed interaction model. Prediction accuracy estimated for four models indicated clear advantage of the inclusion of marker information which was reflected in better prediction accuracy achieved with models M2, M3 and M4 as compared to M1 model. High prediction accuracies (> 0.600) were observed for days to 50% flowering, days to maturity, hundred seed weight, oleic acid, rust@90 days, rust@105 days and late leaf spot@90 days, while
medium prediction accuracies (0.400–0.600) were obtained for pods/plant, shelling %, and total yield/plant. Assessment of comparative prediction accuracy for different GS models to perform selection for untested genotypes, and unobserved and unevaluated environments provided greater insights on potential application of GS breeding in groundnut.
date: 2020-08
date_type: published
publication: Theoretical and Applied Genetics (TSI)
publisher: Springer
id_number: doi:10.1007/s00122-020-03658-1
refereed: TRUE
issn: 0040-5752
official_url: https://doi.org/10.1007/s00122-020-03658-1
related_url_url: https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=10.1007%2Fs00122-020-03658-1&btnG=
related_url_type: pub
citation:   Pandey, M K and Chaudhari, S and Jarquin, D and Janila, P and Crossa, J and Patil, S C and Sundravadana, S and Khare, D and Bhat, R S and Radhakrishnan, T and Hickey, J M and Varshney, R K  (2020) Genome-based trait prediction in multi- environment breeding trials in groundnut.  Theoretical and Applied Genetics (TSI).   ISSN 0040-5752     
document_url: http://oar.icrisat.org/11598/1/s00122-020-03658-1.pdf