Molecular Mapping of Oil Content and Fatty Acids Using Dense Genetic Maps in Groundnut (Arachis hypogaea L.)

Enhancing seed oil content with desirable fatty acid composition is one of the most important objectives of groundnut breeding programs globally. Genomics-assisted breeding facilitates combining multiple traits faster, however, requires linked markers. In this context, we have developed two different F2 mapping populations, one for oil content (OC-population, ICGV 07368 × ICGV 06420) and another for fatty acid composition (FA-population, ICGV 06420 × SunOleic 95R). These two populations were phenotyped for respective traits and genotyped using Diversity Array Technology (DArT) and DArTseq genotyping platforms. Two genetic maps were developed with 854 (OC-population) and 1,435 (FA-population) marker loci with total map distance of 3,526 and 1,869 cM, respectively. Quantitative trait locus (QTL) analysis using genotyping and phenotyping data identified eight QTLs for oil content including two major QTLs, qOc-A10 and qOc-A02, with 22.11 and 10.37% phenotypic variance explained (PVE), respectively. For seven different fatty acids, a total of 21 QTLs with 7.6–78.6% PVE were identified and 20 of these QTLs were of major effect. Two mutant alleles, ahFAD2B and ahFAD2A, also had 18.44 and 10.78% PVE for palmitic acid, in addition to oleic (33.8 and 17.4% PVE) and linoleic (41.0 and 19.5% PVE) acids. Furthermore, four QTL clusters harboring more than three QTLs for fatty acids were identified on the three LGs. The QTLs identified in this study could be further dissected for candidate gene discovery and development of diagnostic markers for breeding improved groundnut varieties with high oil content and desirable oil quality

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

Single Nucleotide Polymorphism–based Genetic Diversity in the Reference Set of Peanut (Arachis spp.) by Developing and Applying Cost-Effective Kompetitive Allele Specific Polymerase Chain Reaction Genotyping Assays

Kompetitive allele-specific polymerase chain reaction (KASP) assays have emerged as cost-effective marker assays especially for molecular breeding applications. Therefore, a set of 96 informative single nucleotide polymorphisms (SNPs) was used to develop KASP assays in groundnut or peanut (Arachis spp.). Developed assays were designated as groundnut KASP assay markers (GKAMs) and screened on 94 genotypes (validation set) that included parental lines of 27 mapping populations, seven synthetic autotetraploid and amphidiploid lines, and 19 wild species accessions. As a result, 90 GKAMs could be validated and 73 GKAMs showed polymorphism in the validation set. Validated GKAMs were screened on 280 diverse genotypes of the reference set for estimating diversity features and elucidating genetic relationships. Cluster analysis of marker allelic data grouped accessions according to their genome type, subspecies, and botanical variety. The subspecies Arachis hypogaea L. subsp. fastigiata Waldron and A. hypogaea subsp. hypogaea formed distinct cluster; however, some overlaps were found indicating their frequent intercrossing during the course of evolution. The wild species, having diploid genomes, were grouped into a single cluster. The average polymorphism information content value for polymorphic GKAMs was 0.32 in the validation set and 0.31 in the reference set. These validated and highly informative GKAMs may be useful for genetics and breeding applications in Arachis species

Multi-dimensional crop improvement by ILRI and partners: Drivers, approaches, achievements and impact

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Editorial: Disease and pest resistance in rice

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Not AvailableThe nutritional quality and food use of groundnut (Arachis hypogaea L.) is mainly governed by oil, fatty acids, protein, and moisture content of kernels. The breeding for higher proportion of oil, protein, and oleic acid in the kernels is an important objective, which needs a non-destructive, rapid, and reliable method for routine estimation in relatively large breeding populations. The present study reports the development of calibration equations in near-infrared reflectance spectroscopy (NIRS) for rapid and non-destructive estimation of kernel quality. Mode of inheritance pattern of a high oleic trait in groundnut was also studied. The best equation for each trait was selected based on the coefficient of determination in calibration and for cross-validation. The current equation gave high fidelity with the reference to biochemical value as indicated by high values of coefficient of determination in external validation (r2 ) for oleic acid (r2 = 0.96), linoleic acid (r 2 = 0.96), moisture (r 2 = 0.96) and moderate for oil (r 2 = 0.89), protein (r 2 = 0.83) and palmitic acid (r 2 = 0.80). The study further developed an efficient NIRS equation to deploy in groundnut breeding. The high oleic trait inheritance pattern was studied in F2:3 population derived from a cross between Spanish bunch normal oleic ICGV 06420 and high oleic SunOleic 95R parents. The results showed duplicate recessive inheritance pattern with a segregation ratio of 15: 1 (normal oleic: high oleic). The outcomes from the inheritance study helps to breed groundnut cultivars for high oleic trait.Not Availabl

Variability and Trait Association Studies for Late Leaf Spot Resistance in a Groundnut MAGIC Population

Globally, late leaf spot (LLS), a foliar fungal disease is one of the most important biotic constraint in groundnut production. Multi-Parent Advanced Generation Inter Cross (MAGIC) groundnut population was developed in a convergent crossing scheme using eight founder parents to develop a mapping population for multiple traits includes LLS. The experiments conducted in light chamber using detached leaf assay, and disease field screening nurseries at two locations (ICRISAT and ARS, Kasbe Digraj) showed significant variability for LLS resistance and component of resistance traits. Total 10 MAGIC lines with longer incubation (>11.0 days) and two MAGIC lines with longer latent period (>27 days) than the resistant parent, GPBD 4 were identified. The MAGIC lines, ICGR 171413, and ICGR 171443 with a lesion diameter of <1 mm and 4.10–5.67% of leaf area damage can be valuable sources for the alleles limiting the pathogen severity. A total of 20 MAGIC lines recorded significantly superior for disease score at 105 DAP_I (5.60–6.89) compared to resistant check, GPDB 4 (6.89). Further studies to determine the type and number of genes controlling the LLS component traits in groundnut will be useful for improvement of resistance to LLS. Genomic selection approach can be valuable in groundnut breeding to harness the minor alleles contributing to the component traits of LLS resistance