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

Abstract

Key message Comparative assessment identified naïve interaction model, and naïve and informed interaction GS models suitable for achieving higher prediction accuracy in groundnut keeping in mind the high genotype × environment interaction for complex traits. 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.

The Genetic Basis of Tomato Aroma

“…Genomic prediction is particularly useful for traits for which phenotyping is expensive, difficult or time consuming, since no phenotyping is needed for selection, once a good prediction model based on data of a representative training population is available. This technique has been widely applied in animal selection [83][84][85], while its practical application in plant breeding is still limited to major crops, such as maize and wheat [82,86,87], in which QTLs for important traits, such as yield, have already been fixed in the elite germplasm [88], or to tree crops, where early selection is very useful and cost-effective [89].…”

Construction of ddRADseq-Based High-Density Genetic Map and Identification of Quantitative Trait Loci for Trans-resveratrol Content in Peanut Seeds

“…In addition, more studies could be conducted to evaluate the resveratrol contents, variations as well as their relationships in different peanut tissues, which might help developing diverse high resveratrol peanut products. Along with the reducing cost of high-throughput sequencing or genotyping, genome-based trait prediction models (Pandey et al, 2020 ) for the resveratrol content could be build and might be used in genomic selection (Crossa et al, 2017 ) to breed high resveratrol varieties.…”

Improved Genetic Map Identified Major QTLs for Drought Tolerance- and Iron Deficiency Tolerance-Related Traits in Groundnut

“…In fact, SNP arrays also have some demerits as they do not allow for the discovery of new SNPs; however, they have uniform genome coverage and recombination rates, whereas GBS may have low and non-uniform genome coverage [ 49 ]. In addition, the recent study using SNP array also demonstrated its suitability for deployment in genomic selection for improving complex traits in groundnut [ 12 ].…”

“…Modern technologies and breeding strategies have great potential to improve such complex traits and achieve sustainable yield under water-limited and iron-deficit conditions. Additionally, to enhance varietal replacement rate in farmers’ fields [ 11 ], new methods and technologies such as genomics tools, rapid generation advancements, and genomic selection can help in developing better groundnut varieties at a faster pace [ 12 , 13 , 14 ]. Recently, the 5G breeding approach has been suggested by Varshney et al [ 15 ] for integrating modern genomics and genetic technologies with crop improvement programs.…”

“…However, the majority of these studies failed to provide conclusive results due to a lack of sufficiently dense genetic map (≈56–347 loci) and reference genome required for gene discovery underlying QTL regions. During the last decade, modern genotyping platforms such as GBS (genotyping by sequencing), WGRS (whole-genome re-sequencing), ddRAD-seq (double digest restriction site-associated DNA), and single nucleotide polymorphism (SNP) array were successfully deployed for trait mapping and high-resolution mapping in groundnut including the most recent deployment of SNP array in genomic selection [ 12 ]. High-density SNP array is a promising approach that generates high-quality genotyping data with minimum missing call rates and uniform genome coverage for high-resolution trait mapping.…”