Improvement of three popular Indian groundnut varieties for foliar disease resistance and high oleic acid using SSR markers and SNP array in marker-assisted backcrossing

Shasidhar, Yaduru; Variath, Murali T.; Vishwakarma, Manish K.; Manohar, Surendra S.; Gangurde, Sunil S.; Sriswathi, Manda; Sudini, H.; Dobariya, K. L.; Bera, SK; Radhakrishnan, T.; Pandey, Manish K.; Janila, Pasupuleti; Varshney, Rajeev K.

doi:10.1016/j.cj.2019.07.001

Cited by 56 publications

(54 citation statements)

References 32 publications

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“…Majority of the breeding programs have been relying on phenotype-based selection approaches with some efforts dedicated toward using marker-assisted selection (MAS) or marker-assisted backcrossing (MABC) including groundnut (Pandey et al 2016;Varshney 2016;Varshney et al 2019). The MAS and MABC efforts are now routine in few groundnut breeding programs; however, these breeding methods are mostly successful for simple traits for which diagnostic markers are being developed through trait mapping approaches (Pandey et al 2020). The major problem lies with complex traits for which generating precise and repeatable phenotyping data for complex traits is challenging as a consequence of high G × E interaction.…”

Section: Discussionmentioning

confidence: 99%

“…Among the agronomic traits, days to maturity, pods/ plant, shelling %, hundred seed weight and yield/ha along with nutritional quality traits such as oil content and protein content are the key priority traits in groundnut governed by polygenes and are complex in nature. However, the resistance to LLS and rust in groundnut are governed by major quantitative trait loci (Sujay et al 2012;Kolekar et al 2016;Shirasawa et al 2018) and used for introgression of LLS and rust resistance into elite varieties (Varshney et al 2014;Janila et al 2016;Shasidhar et al 2020). The quantitative inheritance with additive effect of minor genes has been reported for LLS and rust resistance in groundnut (Janila et al 2013).…”

Section: Discussionmentioning

confidence: 99%

“…Last decade witnessed rapid development of genomic resources such as large scale molecular markers (Wang et al 2012), genetic maps (Gautami et al 2012) and genome sequences (Bertioli et al 2019;Chen et al 2019;Zhuang et al 2019) and deployment in genomics-assisted breeding (GAB) in groundnut (see Pandey et al 2016Pandey et al , 2020Varshney 2016;Varshney et al 2019). There are three GAB approaches, namely marker-assisted backcrossing (MABC), marker-assisted recurrent selection (MARS) and genomic selection (GS).…”

Section: Introductionmentioning

confidence: 99%

“…This approach has been utilized extensively in livestock breeding (Hays and Goddard 2010;van der Werf 2013;Hays et al 2013;Meuwissen et al 2016) and is still evolving in plant breeding. If integrated with rapid generation advancement technology such as speed breeding, the GS can make remarkable achievement and positive impact on breeding programs (Watson et al 2019) including groundnut (Pandey et al 2020).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…The tenth successful MABC effort was made to improve three popular Indian varieties, namely GJG 9, GG 20 and GJGHPS 1, for foliar disease resistance (FDR) and high oleic acid. As a result, > 50 FDR MABC lines and > 80 high oleic lines in BC 3 F 7 generation were developed which are now being now subjected to seed multiplication (Shasidhar et al 2020). More importantly, this study also deployed 58 K Axiom_Arachis array and identified molecular breeding lines with up to 94% recurrent parent genome recovery among second and third backcross progenies.…”

Section: Marker-assisted Pyramiding Of Resistance To Rust and Lls Andmentioning

confidence: 95%

Translational genomics for achieving higher genetic gains in groundnut

Pandey

Kumar

et al. 2020

Theor Appl Genet

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Key message Groundnut has entered now in post-genome era enriched with optimum genomic and genetic resources to facilitate faster trait dissection, gene discovery and accelerated genetic improvement for developing climate-smart varieties. Abstract Cultivated groundnut or peanut (Arachis hypogaea), an allopolyploid oilseed crop with a large and complex genome, is one of the most nutritious food. This crop is grown in more than 100 countries, and the low productivity has remained the biggest challenge in the semiarid tropics. Recently, the groundnut research community has witnessed fast progress and achieved several key milestones in genomics research including genome sequence assemblies of wild diploid progenitors, wild tetraploid and both the subspecies of cultivated tetraploids, resequencing of diverse germplasm lines, genome-wide transcriptome atlas and cost-effective high and low-density genotyping assays. These genomic resources have enabled high-resolution trait mapping by using germplasm diversity panels and multi-parent genetic populations leading to precise gene discovery and diagnostic marker development. Furthermore, development and deployment of diagnostic markers have facilitated screening early generation populations as well as marker-assisted backcrossing breeding leading to development and commercialization of some molecular breeding products in groundnut. Several new genomics applications/technologies such as genomic selection, speed breeding, mid-density genotyping assay and genome editing are in pipeline. The integration of these new technologies hold great promise for developing climate-smart, high yielding and more nutritious groundnut varieties in the post-genome era.

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Genetic diversity and distinctness based on morphological and SSR markers in peanut

Hong

Pandey

et al. 2021

Agronomy Journal

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The morphological and molecular diversity of 101 peanut varieties from South China were analyzed to identify distinctness among these varieties. No significant difference was observed for six morphological characteristics whereas a range of 0.25-0.51 of diversity index was observed for 11 morphological characteristics, with an average value of 0.39. Molecular characterization with 40 highly polymorphic simple sequence repeats (SSRs) generated a total of 167 alleles ranging from two to six alleles per marker with average 4.18 alleles per marker. The polymorphism information content (PIC) of these markers varied from 0.79 to 0.26 with an average value of 0.55 per marker. The diversity analysis using morphological and genotyping data grouped all the varieties into seven and six clusters, respectively, and varieties released by the same province tended to be grouped in the same cluster. Mantel testing revealed that the correlations between the similarity coefficient matrixes of the morphological characteristics and SSR markers of different varieties were weak (r = .347), implying that deployment of more SSR markers is needed for achieving distinctness among these peanut varieties. Nevertheless, the combination of morphological characteristics and SSR markers will effectively increase the accuracy of distinctiveness identification.

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Improvement of three popular Indian groundnut varieties for foliar disease resistance and high oleic acid using SSR markers and SNP array in marker-assisted backcrossing

Cited by 56 publications

References 32 publications

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

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

Translational genomics for achieving higher genetic gains in groundnut

Genetic diversity and distinctness based on morphological and SSR markers in peanut

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