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

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Relation http://oar.icrisat.org/11598/
https://doi.org/10.1007/s00122-020-03658-1
doi:10.1007/s00122-020-03658-1
 
Title Genome-based trait prediction in multi- environment breeding trials in groundnut
 
Creator 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
Varshney, R K
 
Subject Plant Breeding
Groundnut
Genetics and Genomics
 
Description 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.
 
Publisher Springer
 
Date 2020-08
 
Type Article
PeerReviewed
 
Format application/pdf
 
Language en
 
Identifier http://oar.icrisat.org/11598/1/s00122-020-03658-1.pdf
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