Abstract
During the last three decades, QTL analysis in wheat has been conducted for a variety of individual traits, so that thousands of QTL along with the linked markers, their genetic positions and contribution to phenotypic variation (PV) for concerned traits are now known. However, no exhaustive database for wheat QTL is currently available at a single platform. Therefore, the present database was prepared which is an exhaustive information resource for wheat QTL data from the published literature till May, 2020. QTL data from both interval mapping and genome-wide association studies (GWAS) have been included for the following classes of traits: (i) morphological traits, (ii) N and P use efficiency, (iii) traits for biofortification (Fe, K, Se, and Zn contents), (iv) tolerance to abiotic stresses including drought, water logging, heat stress, pre-harvest sprouting and salinity, (v) resistance to biotic stresses including those due to bacterial, fungal, nematode and insects, (vi) quality traits, and (vii) a variety of physiological traits, (viii) developmental traits, and (ix) yield and its related traits. For the preparation of the database, literature was searched for data on QTL/marker-trait associations (MTAs), curated and then assembled in the form of WheatQTLdb. The available information on metaQTL, epistatic QTL and candidate genes, wherever available, is also included in the database. Information on QTL in this WheatQTLdb includes QTL names, traits, associated markers, parental genotypes, crosses/mapping populations, association mapping panels and other useful information. To our knowledge, WheatQTLdb prepared by us is the largest collection of QTL (11,552), epistatic QTL (107) and metaQTL (330) data for hexaploid wheat to be used by geneticists and plant breeders for further studies involving fine mapping, cloning, and marker-assisted selection (MAS) during wheat breeding.
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Data availability
Wheat QTL, metaQTL, and epistatic QTL data provided in WheatQTLdb is a public database and is freely accessible. It can be accessed online from the following URL: www.wheatqtldb.net through any browser (Mozilla Firefox/Chrome/Safari/Internet Explorer) compatible as per the operating system (Windows/Linux/Mac OSX).
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29 September 2021
A Correction to this paper has been published: https://doi.org/10.1007/s00438-021-01812-y
Reference
Blake VC, Woodhouse MR, Lazo GR, Odell SG, Wight CP et al (2019) Grain Genes: centralized small grain resources and digital platform for geneticists and breeders. Database 2019:baz065
Dong Q, Schlueter SD, Brendel V (2014) Plant GDB, plant genome database and analysis tools. Nucleic Acids Res 32(Database issue):D354–D359
FAO (2002) FAO: World Agriculture: towards 2015/2030. Summary Report. Food and Agriculture Organization of the United Nations, Rome
FAO (2019) World food situation: FAO cereal supply and demand brief. Rome: United Nations, Food and Agriculture Organization. Retrieved 14 Dec 2016.
FAOSTAT (2014) Crops/World Total/Wheat/Area Harvested/2014 (pick list). United Nations, Food and Agriculture Organization, Statistics Division (FAOSTAT). Archived from the original on 6 Sept 2015. Retrieved 8 Dec 2016.
Gupta P, Naithani S, Tello-Ruiz MK et al (2016) Gramene database: navigating plant comparative genomics resources. Curr Plant Biol 7–8:10–15
Mauseth JD (2014) Perhaps the simplest of fruits are those of grasses (all cereals such as corn and wheat)… These fruits are caryopses. Botany. Jones & Bartlett Publishers, Burlington, p 223
Schreiber F, Colmsee C, Czauderna T et al (2012) MetaCrop 2.0: managing and exploring information about crop plant metabolism. Nucleic Acids Res 40(Database issue):D1173–D1177
Soriano JM, Alvaro F (2019) Discovering consensus genomic regions in wheat for root-related traits by QTL meta-analysis. Sci Rep 9:10537
Wilkinson PA, Winfield MO, Barker GLA et al (2016) CerealsDB 3.0: expansion of resources and data integration. BMC Bioinform 17:256
Acknowledgements
Thanks are due to Bioinformatics Infrastructure Facility, New Delhi for providing the necessary computational resources for developing the database. Thanks are also due to Indian National Science Academy (INSA), New Delhi for the award of the positions of INSA-Senior Scientist and INSA Honorary Scientist to HSB. Thanks are also due to Department of Biotechnology for providing financial support through different research projects to Shailendra Sharma.
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KS prepared the database and first draft of the manuscript. SK, RK, SSG, SS, PKS, HSB, and PKG conceived and planned the study and also finalised the manuscript. All the remaining authors collected and tabulated the data for different traits. GS, AK, and KK also jointly prepared the chromosome maps showing the markers linked to QTLs for different traits.
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Communicated by Stefan Hohmann.
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Singh, K., Batra, R., Sharma, S. et al. WheatQTLdb: a QTL database for wheat. Mol Genet Genomics 296, 1051–1056 (2021). https://doi.org/10.1007/s00438-021-01796-9
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DOI: https://doi.org/10.1007/s00438-021-01796-9