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      Integrating genomics for chickpea improvement: achievements and opportunities

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          Abstract

          Key message

          Integration of genomic technologies with breeding efforts have been used in recent years for chickpea improvement. Modern breeding along with low cost genotyping platforms have potential to further accelerate chickpea improvement efforts.

          Abstract

          The implementation of novel breeding technologies is expected to contribute substantial improvements in crop productivity. While conventional breeding methods have led to development of more than 200 improved chickpea varieties in the past, still there is ample scope to increase productivity. It is predicted that integration of modern genomic resources with conventional breeding efforts will help in the delivery of climate-resilient chickpea varieties in comparatively less time. Recent advances in genomics tools and technologies have facilitated the generation of large-scale sequencing and genotyping data sets in chickpea. Combined analysis of high-resolution phenotypic and genetic data is paving the way for identifying genes and biological pathways associated with breeding-related traits. Genomics technologies have been used to develop diagnostic markers for use in marker-assisted backcrossing programmes, which have yielded several molecular breeding products in chickpea. We anticipate that a sequence-based holistic breeding approach, including the integration of functional omics, parental selection, forward breeding and genome-wide selection, will bring a paradigm shift in development of superior chickpea varieties. There is a need to integrate the knowledge generated by modern genomics technologies with molecular breeding efforts to bridge the genome-to-phenome gap. Here, we review recent advances that have led to new possibilities for developing and screening breeding populations, and provide strategies for enhancing the selection efficiency and accelerating the rate of genetic gain in chickpea.

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          Most cited references72

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          GS3, a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein.

          The GS3 locus located in the pericentromeric region of rice chromosome 3 has been frequently identified as a major QTL for both grain weight (a yield trait) and grain length (a quality trait) in the literature. Near isogenic lines of GS3 were developed by successive crossing and backcrossing Minghui 63 (large grain) with Chuan 7 (small grain), using Minghui 63 as the recurrent parent. Analysis of a random subpopulation of 201 individuals from the BC3F2 progeny confirmed that the GS3 locus explained 80-90% of the variation for grain weight and length in this population. In addition, this locus was resolved as a minor QTL for grain width and thickness. Using 1,384 individuals with recessive phenotype (large grain) from a total of 5,740 BC3F2 plants and 11 molecular markers based on sequence information, GS3 was mapped to a DNA fragment approximately 7.9 kb in length. A full-length cDNA corresponding to the target region was identified, which provided complete sequence information for the GS3 candidate. This gene consists of five exons and encodes 232 amino acids with a putative PEBP-like domain, a transmembrane region, a putative TNFR/NGFR family cysteine-rich domain and a VWFC module. Comparative sequencing analysis identified a nonsense mutation, shared among all the large-grain varieties tested in comparison with the small grain varieties, in the second exon of the putative GS3 gene. This mutation causes a 178-aa truncation in the C-terminus of the predicted protein, suggesting that GS3 may function as a negative regulator for grain size. Cloning of such a gene provided the opportunity for fully characterizing the regulatory mechanism and related processes during grain development.
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            The Arabidopsis cytochrome P450 CYP707A encodes ABA 8'-hydroxylases: key enzymes in ABA catabolism.

            The hormonal action of abscisic acid (ABA) in plants is controlled by the precise balance between its biosynthesis and catabolism. In plants, ABA 8'-hydroxylation is thought to play a predominant role in ABA catabolism. ABA 8'-hydroxylase was shown to be a cytochrome P450 (P450); however, its corresponding gene had not been identified. Through phylogenetic and DNA microarray analyses during seed imbibition, the candidate genes for this enzyme were narrowed down from 272 Arabidopsis P450 genes. These candidate genes were functionally expressed in yeast to reveal that members of the CYP707A family, CYP707A1-CYP707A4, encode ABA 8'-hydroxylases. Expression analyses revealed that CYP707A2 is responsible for the rapid decrease in ABA level during seed imbibition. During drought stress conditions, all CYP707A genes were upregulated, and upon rehydration a significant increase in mRNA level was observed. Consistent with the expression analyses, cyp707a2 mutants exhibited hyperdormancy in seeds and accumulated six-fold greater ABA content than wild type. These results demonstrate that CYP707A family genes play a major regulatory role in controlling the level of ABA in plants.
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              Genetic dissection of drought tolerance in chickpea (Cicer arietinum L.)

              Key message Analysis of phenotypic data for 20 drought tolerance traits in 1–7 seasons at 1–5 locations together with genetic mapping data for two mapping populations provided 9 QTL clusters of which one present on CaLG04 has a high potential to enhance drought tolerance in chickpea improvement. Abstract Chickpea (Cicer arietinum L.) is the second most important grain legume cultivated by resource poor farmers in the arid and semi-arid regions of the world. Drought is one of the major constraints leading up to 50 % production losses in chickpea. In order to dissect the complex nature of drought tolerance and to use genomics tools for enhancing yield of chickpea under drought conditions, two mapping populations—ICCRIL03 (ICC 4958 × ICC 1882) and ICCRIL04 (ICC 283 × ICC 8261) segregating for drought tolerance-related root traits were phenotyped for a total of 20 drought component traits in 1–7 seasons at 1–5 locations in India. Individual genetic maps comprising 241 loci and 168 loci for ICCRIL03 and ICCRIL04, respectively, and a consensus genetic map comprising 352 loci were constructed (http://cmap.icrisat.ac.in/cmap/sm/cp/varshney/). Analysis of extensive genotypic and precise phenotypic data revealed 45 robust main-effect QTLs (M-QTLs) explaining up to 58.20 % phenotypic variation and 973 epistatic QTLs (E-QTLs) explaining up to 92.19 % phenotypic variation for several target traits. Nine QTL clusters containing QTLs for several drought tolerance traits have been identified that can be targeted for molecular breeding. Among these clusters, one cluster harboring 48 % robust M-QTLs for 12 traits and explaining about 58.20 % phenotypic variation present on CaLG04 has been referred as “QTL-hotspot”. This genomic region contains seven SSR markers (ICCM0249, NCPGR127, TAA170, NCPGR21, TR11, GA24 and STMS11). Introgression of this region into elite cultivars is expected to enhance drought tolerance in chickpea. Electronic supplementary material The online version of this article (doi:10.1007/s00122-013-2230-6) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                m.roorkiwal@cgiar.org
                r.k.varshney@cgiar.org
                Journal
                Theor Appl Genet
                Theor. Appl. Genet
                TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0040-5752
                1432-2242
                6 April 2020
                6 April 2020
                2020
                : 133
                : 5
                : 1703-1720
                Affiliations
                [1 ]GRID grid.419337.b, ISNI 0000 0000 9323 1772, Center of Excellence in Genomics and Systems Biology, , International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), ; Hyderabad, India
                [2 ]GRID grid.1012.2, ISNI 0000 0004 1936 7910, The UWA Institute of Agriculture, The University of Western Australia, ; Perth, Australia
                [3 ]GRID grid.418105.9, ISNI 0000 0001 0643 7375, ICAR—Indian Agricultural Research Institute (IARI), ; Delhi, India
                [4 ]GRID grid.412419.b, ISNI 0000 0001 1456 3750, Department of Genetics, , Osmania University, ; Hyderabad, India
                [5 ]ICAR—Indian Institute of Pulses Research (IIPR), Kanpur, India
                [6 ]Rani Lakshmi Bai Central Agricultural University, Jhansi, India
                [7 ]International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Addis Ababa, Ethiopia
                [8 ]International Center for Agriculture Research in the Dry Areas (ICARDA), Cairo, Egypt
                [9 ]International Center for Agriculture Research in the Dry Areas (ICARDA), Rabat, Morocco
                [10 ]International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Nairobi, Kenya
                [11 ]GRID grid.25152.31, ISNI 0000 0001 2154 235X, Department of Plant Sciences, , University of Saskatchewan, ; Saskatoon, Canada
                [12 ]GRID grid.463251.7, ISNI 0000 0001 2195 6683, Ethiopian Institute of Agricultural Research (EIAR), ; Debre Zeit, Ethiopia
                Author notes

                Communicated by Henry T. Nguyen.

                Author information
                https://orcid.org/0000-0001-6595-281X
                https://orcid.org/0000-0002-4562-9131
                Article
                3584
                10.1007/s00122-020-03584-2
                7214385
                32253478
                098a2baf-66e4-4851-8f81-5b95129e1e01
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 27 December 2019
                : 18 March 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000865, Bill and Melinda Gates Foundation;
                Award ID: Tropical Legumes
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001843, Science and Engineering Research Board;
                Award ID: ECR/2018/000323
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001409, Department of Science and Technology, Ministry of Science and Technology;
                Award ID: IFA14-LSPA23
                Award Recipient :
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                © Springer-Verlag GmbH Germany, part of Springer Nature 2020

                Genetics
                Genetics

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