Genotype × Environment Studies on Resistance to Late Leaf Spot and Rust in Genomic Selection Training Population of Peanut (Arachis hypogaea L.)

Chaudhari, Sunil; Khare, D.; Patil, Sudam C.; Sundravadana, S.; Variath, Murali T.; Sudini, H.; Manohar, Surendra S.; Bhat, Ramesh; Janila, Pasupuleti

doi:10.3389/fpls.2019.01338

Cited by 31 publications

(24 citation statements)

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“…This training population includes 227 lines from subspecies fastigiata and 113 lines from subspecies hypogaea and has variation for key agronomical traits focussed by the Indian groundnut breeding programs. From the perspective of botanical varieties, 212 lines belong to vulgaris (Spanish bunch), 111 lines belong to botanical variety hypogaea , 10 to fastigiata (Valencia), four to peruviana and single representative line to aequatoriana , hirsuta and unknown botanical type (Chaudhari et al 2019 ). These lines were phenotyped for 11 agronomic, 7 quality and 6 foliar fungal disease resistance traits at Patancheru, Aliyarnagar and Jalgaon locations in India during two environments (Rainy 2015 and Post-Rainy 2015–2016).…”

Section: Methodsmentioning

confidence: 99%

“…The experimental trials were conducted in alpha lattice design with two replications. The detail procedure of conducting trials along with phenotyping of disease resistance at three different time intervals each for rust (rust@75 days, rust@90 days and rust@105 days) and late leaf spot (Late leaf spot@75 days, Late leaf spot@90 days and Late leaf spot@105 days) can be found in Chaudhari et al ( 2019 ). The data on agronomic traits included days to 50% flowering, days to maturity, primary branches/plant, pods/plant, plant height (cm), pod yield/plant (g), shelling %, hundred seed weight (g), seed yield/plant (g), total yield/plot (g) and pod yield/ha (kg) recorded from both the replications across environments.…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

et al. 2020

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“…Later on, ICRI-SAT worked together with national partners in India and constituted a training population with 340 elite groundnut lines compassing the trait diversity required for Indian groundnut breeding programs. This training population is being phenotyped for 11 agronomic, 7 quality and 6 foliar disease resistance traits at four locations (Patancheru, Aliyarnagar, Jalgaon and Dharwad) in India (Chaudhari et al 2019). High-density genotyping data have been generated using 58 K SNP assay, and 13,355 genome-wide polymorphic SNPs have been identified for performing GS analysis.…”

Section: Optimization Of Gs Breeding To Improve Complex Traitsmentioning

confidence: 99%

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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“…ELS and LLS are major diseases of peanuts and the range of severity is strongly influenced by G x E associated with many QTLs [21][22][23]. Only a few peanut genotypes have been identified as medium tolerant to leaf spot diseases and specific crosses were generated to develop more tolerant lines [8].…”

Section: Discussionmentioning

confidence: 99%

Association of differentially expressed R-gene candidates with leaf spot resistance in peanut (Arachis hypogaea L.)

2021

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Early leaf spot (ELS) and late leaf spot (LLS) are major fungal diseases of peanut that can severely reduce yield and quality. Development of acceptable genetic resistance has been difficult due to a strong environmental component and many major and minor QTLs. Resistance genes (R-genes) are an important component of plant immune system and have been identified in peanut. Association of specific R-genes to leaf spot resistance will provide molecular targets for marker-assisted breeding strategies. In this study, advanced breeding lines from different pedigrees were evaluated for leaf spot resistance and 76 candidate R-genes expression study was applied to susceptible and resistant lines. Thirty-six R-genes were differentially expressed and significantly correlated with resistant lines, of which a majority are receptor like kinases (RLKs) and receptor like proteins (RLPs) that sense the presence of pathogen at the cell surface and initiate protection response. The largest group was receptor-like cytoplasmic kinases (RLCKs) VII that are involved in pattern-triggered kinase signaling resulting in the production reactive oxygen species (ROS). Four R-genes were homologous to TMV resistant protein N which has shown to confer resistance against tobacco mosaic virus (TMV). When mapped to peanut genomes, 36 R-genes were represented in most chromosomes except for A09 and B09. Low levels of gene-expression in resistant lines suggest expression is tightly controlled to balance the cost of R-gene expression to plant productively. Identification and association of R-genes involved in leaf spot resistance will facilitate genetic selection of leaf spot resistant lines with good agronomic traits.

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Genotype × Environment Studies on Resistance to Late Leaf Spot and Rust in Genomic Selection Training Population of Peanut (Arachis hypogaea L.)

Cited by 31 publications

References 31 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

Association of differentially expressed R-gene candidates with leaf spot resistance in peanut (Arachis hypogaea L.)

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