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

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

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Not AvailableMulti-environment testing at five locations for rust and late leaf spot (LLS) resistance with 41 introgressed lines (ILs) bred using marker-assisted backcross breeding in the genetic background Spanish-type groundnut varieties identified significant genotype, and genotype 9 environment interactions (GEI) for LLS disease resistance and yield parameters. Significant GEI effects suggest the need to identify location specific breeding lines to achieve gains in pod yield and LLS resistance. The observed variable LLS disease reaction among the ILs in part suggests influence of background genotype on the level of resistance. A breeding scheme with early generation selection using molecular markers followed by phenotyping for LLS, and multi-location testing of fixed breeding lines was optimized to enhance selection intensity and accuracy in groundnut breeding. The ILs, ICGVs 14431, 14436 and 14438 with pooled LLS score at 90 DAS of 3.5–3.7 were superior to respective recurrent parent for pod yield, with early maturing similar to recurrent parents. The pod yield advantage in ILs is attributed by more number of pods, besides resistance to LLS that contributes to better filling.Springe