Single-trait, multi-locus and multi-trait GWAS using four different models for yield traits in bread wheat

Malik, Parveen; Kumar, Jitendra; Singh, S.R.; Sharma, Shiveta; Meher, Prabina Kumar; Sharma, Mukesh Kumar; Roy, Joy; Sharma, Pradeep; Balyan, H. S.; Gupta, P. K.

doi:10.1007/s11032-021-01240-1

Cited by 14 publications

(10 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The genotyping data for 210 SNP markers (distributed on 21 chromosomes) suggested a low level of population structure in the association panel ( Figure 2 ), which is a desirable feature for GWAS, as also shown in our earlier studies involving the same association panel with minor differences ( Kumar et al., 2018 ; Gahlaut et al., 2019 ; Malik et al., 2021a ; Malik et al., 2021b ; Malik et al., 2022 ). In earlier studies involving different association panels also, the number of sub-populations ranged from three (for example, Wang et al., 2017 ; Rahimi et al., 2019 ) to six (for example, Li et al., 2016 ; Qaseem et al., 2018 ; Jamil et al., 2019 ), suggesting that in majority of studies in wheat, the level of population structure is low.…”

Section: Discussionsupporting

confidence: 73%

“…High level of variability (as revealed by descriptive statistics) for each of the three traits suggested that the panel was suitable for a study of the genetics of quantitative traits. The same panel was earlier utilized by us in GWAS for several other traits including the following: (i) yield related traits ( Sehgal et al., 2017 ; Malik et al., 2021a ; Malik et al., 2021b ; Malik et al., 2022 ); (ii) Fe, Zn, β-carotene, GPC content ( Kumar et al., 2018 ); and, (iii) drought tolerance ( Gahlaut et al., 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…At least a dozen models are now available showing significant improvement in GWAS. In the recent past several genome wide association studies have been conducted, where several models have been used and the results compared ( Ayana et al., 2018 ; Ward et al., 2019 ; Chaurasia et al., 2020 ; Alemu et al., 2021 ; Malik et al., 2021a ; Sandhu et al., 2021 ; Soumya et al., 2021 ). In this study, we selected five models for identification of main effect MTAs and PLINK for epistatic interactions.…”

Section: Discussionmentioning

confidence: 99%

“…Epistatic interactions are often ignored in GWAS, although recently studied for the following traits: (1) flowering time ( Reif et al., 2011 ; Langer et al., 2014 ), (2) stem rust resistance ( Yu et al., 2011 ), (3) agronomic traits ( Sehgal et al., 2017 ), (iv) yield related traits, micronutrients, and grain morphology ( Jaiswal et al., 2016 ; Kumar et al., 2018 ; Malik et al., 2021a , Malik et al., 2022 ). Epistatic interactions were also detected through interval mapping ( Li et al., 2011 ; Xu et al., 2012 ; Rouse et al., 2014 ; Boeven et al., 2020 ).…”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Genetics of spot blotch resistance in bread wheat (Triticum aestivum L.) using five models for GWAS

Singh

Gaurav²,

Vasistha³

et al. 2023

Front. Plant Sci.

Self Cite

View full text Add to dashboard Cite

Genetic architecture of resistance to spot blotch in wheat was examined using a Genome-Wide Association Study (GWAS) involving an association panel comprising 303 diverse genotypes. The association panel was evaluated at two different locations in India including Banaras Hindu University (BHU), Varanasi (Uttar Pradesh), and Borlaug Institute for South Asia (BISA), Pusa, Samastipur (Bihar) for two consecutive years (2017-2018 and 2018-2019), thus making four environments (E1, BHU 2017-18; E2, BHU 2018-19; E3, PUSA, 2017-18; E4, PUSA, 2018-19). The panel was genotyped for 12,196 SNPs based on DArT-seq (outsourced to DArT Ltd by CIMMYT); these SNPs included 5,400 SNPs, which could not be assigned to individual chromosomes and were therefore, described as unassigned by the vendor. Phenotypic data was recorded on the following three disease-related traits: (i) Area Under Disease Progress Curve (AUDPC), (ii) Incubation Period (IP), and (iii) Lesion Number (LN). GWAS was conducted using each of five different models, which included two single-locus models (CMLM and SUPER) and three multi-locus models (MLMM, FarmCPU, and BLINK). This exercise gave 306 MTAs, but only 89 MTAs (33 for AUDPC, 30 for IP and 26 for LN) including a solitary MTA detected using all the five models and 88 identified using four of the five models (barring SUPER) were considered to be important. These were used for further analysis, which included identification of candidate genes (CGs) and their annotation. A majority of these MTAs were novel. Only 70 of the 89 MTAs were assigned to individual chromosomes; the remaining 19 MTAs belonged to unassigned SNPs, for which chromosomes were not known. Seven MTAs were selected on the basis of minimum P value, number of models, number of environments and location on chromosomes with respect to QTLs reported earlier. These 7 MTAs, which included five main effect MTAs and two for epistatic interactions, were considered to be important for marker-assisted selection (MAS). The present study thus improved our understanding of the genetics of resistance against spot blotch in wheat and provided seven MTAs, which may be used for MAS after due validation.

show abstract

Section: Discussionsupporting

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Genetics of spot blotch resistance in bread wheat (Triticum aestivum L.) using five models for GWAS

Singh

Gaurav²,

Vasistha³

et al. 2023

Front. Plant Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The approach has already been successfully utilized to dissect the genetic architecture associated with important agronomic and quality traits in several crops, such as maize (Zhang et al, 2018 ; Zhu et al, 2018 ; An et al, 2020 ), rice (Cui et al, 2018 ; Liu et al, 2020 ), barley (Hu et al, 2018 ), cotton (Li et al, 2018 ; Su et al, 2018 ), soybean (Ziegler et al, 2018 ), and foxtail millet (Jaiswal et al, 2019 ). In wheat also, ML-GWAS has been used to identify genomic regions associated with different agronomic and yield associated traits (Jaiswal et al, 2016 ; Ward et al, 2019 ; Hanif et al, 2021 ; Malik et al, 2021a ; Muhammad et al, 2021 ), grain architecture-related traits (Schierenbeck et al, 2021 ), spike-layer uniformity-related traits (Malik et al, 2021b ), potassium use efficiency (Safdar et al, 2020 ), nutrient accumulation (Bhatta et al, 2018 ; Kumar et al, 2018 ; Alomari et al, 2021 ), disease resistance (Cheng et al, 2020 ; Habib et al, 2020 ; Tomar et al, 2021 ), and salinity tolerance (Chaurasia et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Genome wide association in Spanish bread wheat landraces identifies six key genomic regions that constitute potential targets for improving grain yield related traits

López-Fernández,

García-Abadillo,

Uauy

et al. 2023

Theor Appl Genet

View full text Add to dashboard Cite

Key message Association mapping conducted in 189 Spanish bread wheat landraces revealed six key genomic regions that constitute stable QTLs for yield and include 15 candidate genes. Abstract Genetically diverse landraces provide an ideal population to conduct association analysis. In this study, association mapping was conducted in a collection of 189 Spanish bread wheat landraces whose genomic diversity had been previously assessed. These genomic data were combined with characterization for yield-related traits, including grain size and shape, and phenological traits screened across five seasons. The association analysis revealed a total of 881 significant marker trait associations, involving 434 markers across the genome, that could be grouped in 366 QTLs based on linkage disequilibrium. After accounting for days to heading, we defined 33 high density QTL genomic regions associated to at least four traits. Considering the importance of detecting stable QTLs, 6 regions associated to several grain traits and thousand kernel weight in at least three environments were selected as the most promising ones to harbour targets for breeding. To dissect the genetic cause of the observed associations, we studied the function and in silico expression of the 413 genes located inside these six regions. This identified 15 candidate genes that provide a starting point for future analysis aimed at the identification and validation of wheat yield related genes.

show abstract

Single-trait, multi-locus and multi-trait GWAS using four different models for yield traits in bread wheat

Cited by 14 publications

References 75 publications

Genetics of spot blotch resistance in bread wheat (Triticum aestivum L.) using five models for GWAS

Genetics of spot blotch resistance in bread wheat (Triticum aestivum L.) using five models for GWAS

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

Genome wide association in Spanish bread wheat landraces identifies six key genomic regions that constitute potential targets for improving grain yield related traits

Contact Info

Product

Resources

About