WheatQTLdb: a QTL database for wheat

Singh, Kalpana; Batra, Renu; Sharma, Shiveta; Saripalli, Gautam; Gautam, Tinku; Singh, Ravinder; Pal, Saikat; Malik, Parveen; Kumar, Manoj; Jan, Irfat; Singh, S.R.; Kumar, Deepak; Pundir, Saksham; Chaturvedi, Deepti; Verma, Anjali; Rani, Anshu; Kumar, Anuj; Sharma, Hemant; Chaudhary, Jyoti; Kumar, Kuldeep; Kumar, Sachin; Singh, Vikas Kumar; Singh, Vivudh Pratap; Kumar, Sachin; Kumar, Rahul; Gaurav, Shailendra Singh; Sharma, Shailendra; Sharma, Pradeep; Balyan, H. S.; Gupta, P. K.

doi:10.1007/s00438-021-01796-9

Cited by 60 publications

(38 citation statements)

References 4 publications

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“…The information on QTLs for stripe rust resistance reported during the past 20 years (2000 to 2020) was obtained from WheatQTLdb 28 (Supplementary Table S1 ) and other published literature. The detailed information from each study was collected on the following aspects: (i) types of mapping populations and their parents, (ii) size of mapping population, (iii) pathotype(s) used for phenotyping, (iv) methods of QTL mapping, (v) position of QTLs and markers flanking the QTLs, (vi) logarithm of odds (LOD) value, and (vii) R 2 values of the QTLs.…”

Section: Methodsmentioning

confidence: 99%

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Meta-QTLs and candidate genes for stripe rust resistance in wheat

Jan

Saripalli

Kumar

et al. 2021

Sci Rep

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In bread wheat, meta-QTL analysis was conducted using 353 QTLs that were available from earlier studies. When projected onto a dense consensus map comprising 76,753 markers, only 184 QTLs with the required information, could be utilized leading to identification of 61 MQTLs spread over 18 of the 21 chromosomes (barring 5D, 6D and 7D). The range for mean R2 (PVE %) was 1.9% to 48.1%, and that of CI was 0.02 to 11.47 cM; these CIs also carried 37 Yr genes. Using these MQTLs, 385 candidate genes (CGs) were also identified. Out of these CGs, 241 encoded known R proteins and 120 showed differential expression due to stripe rust infection at the seedling stage; the remaining 24 CGs were common in the sense that they encoded R proteins as well as showed differential expression. The proteins encoded by CGs carried the following widely known domains: NBS-LRR domain, WRKY domains, ankyrin repeat domains, sugar transport domains, etc. Thirteen breeders’ MQTLs (PVE > 20%) including four pairs of closely linked MQTLs are recommended for use in wheat molecular breeding, for future studies to understand the molecular mechanism of stripe rust resistance and for gene cloning.

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Section: Methodsmentioning

confidence: 99%

“…Stripe rust resistance has also been treated as a quantitative trait (QT), so that interval mapping has been used to identify > 350 QTLs 27 , 28 . Several LD-based genome wide association studies (GWAS) for stripe rust resistance have also been conducted, leading to identification of a large number of marker trait associations (MTAs).…”

Section: Introductionmentioning

confidence: 99%

Meta-QTLs and candidate genes for stripe rust resistance in wheat

Jan

Saripalli

Kumar

et al. 2021

Sci Rep

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“…1d). The QTL data collected in this study has been included in the recently developed WheatQTL database (http://wheatqtldb.net/) (Singh et al 2021).…”

Section: Salient Features Of the Qtls Collected For Meta-analysismentioning

confidence: 99%

Meta-analysis reveals consensus genomic regions associated with multiple disease resistance in wheat (Triticum aestivum L.)

et al. 2022

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In wheat, meta-QTLs (MQTLs), and candidate genes (CGs) were identi ed for multiple disease resistance (MDR). For this purpose, information was collected from 58 studies for mapping QTLs for resistance to one or more of the ve diseases. As many as 493 QTLs were available from these studies, which were distributed in ve diseases as follows: septoria tritici blotch (STB) 126 QTLs; septoria nodorum blotch (SNB), 103; fusarium head blight (FHB), 184; karnal bunt (KB), 66, and loose smut (LS), 14. Of these 493 QTLs, only 291 QTLs could be projected onto a consensus genetic map, giving 63 MQTLs. The CI of the MQTLs ranged from 0.04 to 15.31 cM with an average of 3.09 cM per MQTL. This is a ~ 4.39 fold reduction from the CI of initial QTLs, which ranged from 0 to 197.6 cM, with a mean of 13.57 cM. Of 63 MQTLs, 60 were anchored to the reference physical map of wheat (the physical interval of these MQTLs ranged from 0.30 to 726.01 Mb with an average of 74.09 Mb). Thirty-eight (38) of these MQTLs were veri ed using marker-trait associations (MTAs) derived from genome-wide association studies. As many as 874 CGs were also identi ed which were further investigated for differential expression using data from ve transcriptome studies, resulting in 194 differentially expressed genes (DEGs). Among the DEGs, 85 genes had functions previously reported to be associated with disease resistance. These results should prove useful for ne mapping of MDR genes and marker-assisted breeding.

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“…In a recent study on meta QTL analysis for drought tolerance, as many as 340 QTLs identified through at least 14 interval mapping studies have been utilized (Kumar et al, 2020 ). As many as 750 MTAs, were also identified using GWAS (Gupta et al, 2017 , 2020 ; Kumar et al, 2020 ; Singh et al, 2021 ). It also seems that the QTLs and MTAs identified so far do not represent the entire genetic variation for a multitude of traits that are involved in providing drought tolerance.…”

Section: Discussionmentioning

confidence: 99%

Multi-Locus GWAS for Grain Weight-Related Traits Under Rain-Fed Conditions in Common Wheat (Triticum aestivum L.)

et al. 2021

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In wheat, a multi-locus genome-wide association study (ML-GWAS) was conducted for the four grain weight-related traits (days to anthesis, grain filling duration, grain number per ear, and grain weight per ear) using data recorded under irrigated (IR) and rain-fed (RF) conditions. Seven stress-related indices were estimated for these four traits: (i) drought resistance index (DI), (ii) geometric mean productivity (GMP), (iii) mean productivity index (MPI), (iv) relative drought index (RDI), (v) stress tolerance index (STI), (vi) yield index, and (vii) yield stability index (YSI). The association panel consisted of a core collection of 320 spring wheat accessions representing 28 countries. The panel was genotyped using 9,627 single nucleotide polymorphisms (SNPs). The genome-wide association (GWA) analysis provided 30 significant marker-trait associations (MTAs), distributed as follows: (i) IR (15 MTAs), (ii) RF (14 MTAs), and (iii) IR+RF (1 MTA). In addition, 153 MTAs were available for the seven stress-related indices. Five MTAs co-localized with previously reported QTLs/MTAs. Candidate genes (CGs) associated with different MTAs were also worked out. Gene ontology (GO) analysis and expression analysis together allowed the selection of the two CGs, which may be involved in response to drought stress. These two CGs included: TraesCS1A02G331000 encoding RNA helicase and TraesCS4B02G051200 encoding microtubule-associated protein 65. The results supplemented the current knowledge on genetics for drought tolerance in wheat. The results may also be used for future wheat breeding programs to develop drought-tolerant wheat cultivars.

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WheatQTLdb: a QTL database for wheat

Cited by 60 publications

References 4 publications

Meta-QTLs and candidate genes for stripe rust resistance in wheat

Meta-QTLs and candidate genes for stripe rust resistance in wheat

Meta-analysis reveals consensus genomic regions associated with multiple disease resistance in wheat (Triticum aestivum L.)

Multi-Locus GWAS for Grain Weight-Related Traits Under Rain-Fed Conditions in Common Wheat (Triticum aestivum L.)

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