Murdoch University Research Repository

Welcome to the Murdoch University Research Repository

The Murdoch University Research Repository is an open access digital collection of research
created by Murdoch University staff, researchers and postgraduate students.

Learn more

Genome-based trait prediction in multi- environment breeding trials in groundnut

Pandey, M.K., Chaudhari, S., Jarquin, D., Janila, P., Crossa, J., Patil, S.C., Sundravadana, S., Khare, D., Bhat, R.S., Radhakrishnan, T., Hickey, J.M. and Varshney, R.K.ORCID: 0000-0002-4562-9131 (2020) Genome-based trait prediction in multi- environment breeding trials in groundnut. Theoretical and Applied Genetics, 133 (11). pp. 3101-3117.

[img]
Preview
PDF - Published Version
Download (3MB) | Preview
Free to read: https://doi.org/10.1007/s00122-020-03658-1
*No subscription required

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.

Item Type: Journal Article
Publisher: Springer Verlag
Copyright: © 2020 The Authors.
URI: http://researchrepository.murdoch.edu.au/id/eprint/60098
Item Control Page Item Control Page

Downloads

Downloads per month over past year