5Gs for crop genetic improvement

Varshney, Rajeev K.; Sinha, Pallavi; Singh, Vikas K.; Kumar, Arvind; Zhang, Qifa; Bennetzen, Jeffrey L.

doi:10.1016/j.pbi.2019.12.004

Cited by 163 publications

(117 citation statements)

References 67 publications

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“…It is noteworthy that among the selected set of genotypes for haplotype analysis, the two leading varieties, ICPL 8863 and ICPL 151, do not possess any superior haplotype for drought responsiveness. Hence, these varieties can be further improved or better drought‐tolerant varieties could be developed using haplotype‐based breeding strategy (Varshney et al ., 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Several GAB approaches such as marker‐assisted selection (MAS), marker‐assisted backcrossing (MABC) and marker‐assisted recurrent selection (MARS) have been suggested to transfer/assemble superior alleles into elite genetic background(s). Recently, a 5G (genome, germplasm, gene function, genomic breeding and genome editing) breeding approach has been proposed to bring precision and enhancing breeding efficiency for crop genetic improvement (Varshney et al ., 2020). Genomic selection (GS) through genomic‐estimated breeding values (GEBVs)‐based prediction breeding approaches have also become popular for crop improvement (Crossa et al ., 2017; Varshney et al ., 2012).…”

Section: Introductionmentioning

confidence: 99%

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Superior haplotypes for haplotype‐based breeding for drought tolerance in pigeonpea (Cajanus cajan L.)

Sinha

Singh

Saxena

et al. 2020

Plant Biotechnology Journal

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Haplotype-based breeding, a recent promising breeding approach to develop tailor-made crop varieties, deals with identification of superior haplotypes and their deployment in breeding programmes. In this context, whole genome re-sequencing data of 292 genotypes from pigeonpea reference set were mined to identify the superior haplotypes for 10 droughtresponsive candidate genes. A total of 83, 132 and 60 haplotypes were identified in breeding lines, landraces and wild species, respectively. Candidate gene-based association analysis of these 10 genes on a subset of 137 accessions of the pigeonpea reference set revealed 23 strong marker-trait associations (MTAs) in five genes influencing seven drought-responsive component traits. Haplo-pheno analysis for the strongly associated genes resulted in the identification of most promising haplotypes for three genes regulating five component drought traits. The haplotype C. cajan_23080-H2 for plant weight (PW), fresh weight (FW) and turgid weight (TW), the haplotype C. cajan_30211-H6 for PW, FW, TW and dry weight (DW), the haplotype C. cajan_26230-H11 for FW and DW and the haplotype C. cajan_26230-H5 for relative water content (RWC) were identified as superior haplotypes under drought stress condition. Furthermore, 17 accessions containing superior haplotypes for three drought-responsive genes were identified. The identified superior haplotypes and the accessions carrying these superior haplotypes will be very useful for deploying haplotype-based breeding to develop nextgeneration tailor-made better drought-responsive pigeonpea cultivars.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Superior haplotypes for haplotype‐based breeding for drought tolerance in pigeonpea (Cajanus cajan L.)

Sinha

Singh

Saxena

et al. 2020

Plant Biotechnology Journal

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“…Sustainable improvement in crop production is crucial for supporting the demand from an increasing global population, particularly considering that there are 821 M people who lack su cient food to support their daily lives [1]. Recent technological advances in genome biology like next-generation sequencing, genome editing and genomic selection have paved the way for crop breeders to identify, characterize, transfer, or modify the genes responsible for grain yield or quality traits in a rapid and precise way [2].…”

Section: Introductionmentioning

confidence: 99%

Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning

Yang

Gao

Xiao

et al. 2020

Preprint

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Background : Identification and characterization of new traits with a sound physiological foundation is essential for crop breeding and management. Deep learning has been widely used in image data analysis to explore spatial and temporal information on crop growth and development, thus strengthening the power of the identification of physiological traits. This study aims to develop a novel trait that indicates source and sink relation in japonica rice based on deep learning. Results : We applied a deep learning approach to accurately segment leaf and panicle and subsequently developed the procedure of GvCrop to calculate the leaf to panicle ratio (LPR) of rice populations during grain filling. Images of the training dataset were captured in the field experiments, with large variations in camera shooting angle, the elevation angle and the azimuth angle of the sun, rice genotype, and plant phenological stages. Accurately labeled by manually annotating all the panicle and leaf regions, the resulting dataset were used to train FPN-Mask (Feature Pyramid Network Mask) models, consisting of a backbone network and a task-specific sub-network. The model with the highest accuracy is then selected to study the variations in LPR among 192 rice germplasms and among agronomical practices. Despite the challenging field conditions, FPN-Mask models achieved a high detection accuracy, with Pixel Accuracy being 0.99 for panicles and 0.98 for leaves. The calculated LPRs showed large spatial and temporal variations as well as genotypic differences. Conclusion : Deep learning techniques can achieve high accuracy in simultaneously detecting panicle and leaf data from complex rice field images. The proposed FPN-Mask model is applicable for detecting and quantifying crop performance under field conditions. The newly identified trait of LPR should provide a high throughput protocol for breeders to select superior rice cultivars as well as for agronomists to precisely manage field crops that have a good balance of source and sink.

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“…Another promising strategy is the establishment of trait-specific donor panels harboring adapted genomic signatures (Figure 1). A first-generation ideal plant type (iGen-IPT) could then be developed by combining superior trait-specific near isogenic lines (NILs) through genomic-based breeding [14]. Multiomic analyses of these iGen-IPTs could redefine molecular networks and regulators, gene-gene interactions, genetic background effects, a highresolution computational genome, and thus an improved iGen-IPT.…”

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confidence: 99%