Dataset Information

Genome-Wide Association Study and QTL Meta-Analysis Identified Novel Genomic Loci Controlling Potassium Use Efficiency and Agronomic Traits in Bread Wheat.

ABSTRACT: Potassium use efficiency, a complex trait, directly impacts the yield potential of crop plants. Low potassium efficiency leads to a high use of fertilizers, which is not only farmer unfriendly but also deteriorates the environment. Genome-wide association studies (GWAS) are widely used to dissect complex traits. However, most studies use single-locus one-dimensional GWAS models which do not provide true information about complex traits that are controlled by multiple loci. Here, both single-locus GWAS (MLM) and multi-locus GWAS (pLARmEB, FASTmrMLM, mrMLM, FASTmrEMMA) models were used with genotyping from 90 K Infinium SNP array and phenotype derived from four normal and potassium-stress environments, which identified 534 significant marker-trait associations (MTA) for agronomic and potassium related traits: pLARmEB = 279, FASTmrMLM = 213, mrMLM = 35, MLM = 6, FASTmrEMMA = 1. Further screening of these MTA led to the detection of eleven stable loci: q1A, q1D, q2B-1, q2B-2, q2D, q4D, q5B-1, q5B-2, q5B-3, q6D, and q7A. Moreover, Meta-QTL (MQTL) analysis of four independent QTL studies for potassium deficiency in bread wheat located 16 MQTL on 13 chromosomes. One locus identified in this study (q5B-1) colocalized with an MQTL (MQTL_11 ), while the other ten loci were novel associations. Gene ontology of these loci identified 20 putative candidate genes encoding functional proteins involved in key pathways related to stress tolerance, sugar metabolism, and nutrient transport. These findings provide potential targets for breeding potassium stress resistant wheat cultivars and advocate the advantages of multi-locus GWAS models for studying complex traits.

SUBMITTER: Safdar LB

PROVIDER: S-EPMC7041172 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Genome-Wide Association Study and QTL Meta-Analysis Identified Novel Genomic Loci Controlling Potassium Use Efficiency and Agronomic Traits in Bread Wheat.

Safdar Luqman Bin LB Andleeb Tayyaba T Latif Sadia S Umer Muhammad Jawad MJ Tang Minqiang M Li Xiang X Liu Shengyi S Quraishi Umar Masood UM

Frontiers in plant science 20200218

Potassium use efficiency, a complex trait, directly impacts the yield potential of crop plants. Low potassium efficiency leads to a high use of fertilizers, which is not only farmer unfriendly but also deteriorates the environment. Genome-wide association studies (GWAS) are widely used to dissect complex traits. However, most studies use single-locus one-dimensional GWAS models which do not provide true information about complex traits that are controlled by multiple loci. Here, both single-locu ...[more]

PMID: 32133017

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Project description:BackgroundWheat is a cool seasoned crop requiring low temperature during grain filling duration and therefore increased temperature causes significant yield reduction. A set of 125 spring wheat genotypes from International Maize and Wheat Improvement Centre (CIMMYT-Mexico) was evaluated for phenological and yield related traits at three locations in Pakistan under normal sowing time and late sowing time for expose to prolonged high temperature. With the help of genome-wide association study using genotyping-by-sequencing, marker trait associations (MTAs) were observed separately for the traits under normal and late sown conditions.ResultsSignificant reduction ranging from 9 to 74% was observed in all traits under high temperature. Especially 30, 25, 41 and 66% reduction was observed for days to heading (DH), plant height (PH), spikes per plant (SPP) and yield respectively. We identified 55,954 single nucleotide polymorphisms (SNPs) using genotyping by sequencing of these 125 hexaploid spring wheat genotypes and conducted genome-wide association studies (GWAS) for days to heading (DH), grain filled duration (GFD), plant height (PH), spikes per plant (SPP), grain number per spike (GNS), thousand kernel weight (TKW) and grain yield per plot (GY). Genomic regions identified through GWAS explained up to 13% of the phenotypic variance, on average. A total of 139 marker-trait associations (MTAs) across three wheat genomes (56 on genome A, 55 on B and 28 on D) were identified for all the seven traits studied. For days to heading, 20; grain filled duration, 21; plant height, 23; spikes per plant, 13; grain numbers per spike, 8; thousand kernel weight, 21 and for grain yield, 33 MTAs were detected under normal and late sown conditions.ConclusionsThis study identifies the essential resource of genetics research and underpins the chromosomal regions of seven agronomic traits under normal and high temperature. Significant relationship was observed between the number of favored alleles and trait observations. Fourteen protein coding genes with their respective annotations have been searched with the sequence of seven MTAs which were identified in this study. These findings will be helpful in the development of a breeder friendly platform for the selection of high yielding wheat lines at high temperature areas.

Project description:ERECTA encodes a receptor-like kinase and is proposed as a candidate for determining transpiration efficiency of plants. Two genes homologous to ERECTA in Arabidopsis were identified on chromosomes 6 (TaER2) and 7 (TaER1) of bread wheat (Triticum aestivum L.), with copies of each gene on the A, B and D genomes of wheat. Similar expression patterns were observed for TaER1 and TaER2 with relatively higher expression of TaER1 in flag leaves of wheat at heading (Z55) and grain-filling (Z73) stages. Significant variations were found in the expression levels of both TaER1 and TaER2 in the flag leaves at both growth stages among 48 diverse bread wheat varieties. Based on the expression of TaER1 and TaER2, the 48 wheat varieties could be classified into three groups having high (5 varieties), medium (27 varieties) and low (16 varieties) levels of TaER expression. Significant differences were also observed between the three groups varying for TaER expression for several transpiration efficiency (TE)- related traits, including stomatal density (SD), transpiration rate, photosynthetic rate (A), instant water use efficiency (WUEi) and carbon isotope discrimination (CID), and yield traits of biomass production plant-1 (BYPP) and grain yield plant-1 (GYPP). Correlation analysis revealed that the expression of TaER1 and TaER2 at the two growth stages was significantly and negatively associated with SD (P<0.01), transpiration rate (P<0.05) and CID (P<0.01), while significantly and positively correlated with flag leaf area (FLA, P<0.01), A (P<0.05), WUEi (P<0.05), BYPP (P<0.01) and GYPP (P<0.01), with stronger correlations for TaER1 than TaER2 and at grain-filling stage than at heading stage. These combined results suggested that TaER involved in development of transpiration efficiency -related traits and yield in bread wheat, implying a function for TaER in regulating leaf development of bread wheat and contributing to expression of these traits. Moreover, the results indicate that TaER could be exploitable for manipulating important agronomical traits in wheat improvement.

Project description:BACKGROUND:Identification of loci for agronomic traits and characterization of their genetic architecture are crucial in marker-assisted selection (MAS). Genome-wide association studies (GWAS) have increasingly been used as potent tools in identifying marker-trait associations (MTAs). The introduction of new adaptive alleles in the diverse genetic backgrounds may help to improve grain yield of old or newly developed varieties of wheat to balance supply and demand throughout the world. Landraces collected from different climate zones can be an invaluable resource for such adaptive alleles. RESULTS:GWAS was performed using a collection of 298 Iranian bread wheat varieties and landraces to explore the genetic basis of agronomic traits during 2016-2018 cropping seasons under normal (well-watered) and stressed (rain-fed) conditions. A high-quality genotyping by sequencing (GBS) dataset was obtained using either all original single nucleotide polymorphism (SNP, 10938 SNPs) or with additional imputation (46,862 SNPs) based on W7984 reference genome. The results confirm that the B genome carries the highest number of significant marker pairs in both varieties (49,880, 27.37%) and landraces (55,086, 28.99%). The strongest linkage disequilibrium (LD) between pairs of markers was observed on chromosome 2D (0.296). LD decay was lower in the D genome, compared to the A and B genomes. Association mapping under two tested environments yielded a total of 313 and 394 significant (-log10 P >3) MTAs for the original and imputed SNP data sets, respectively. Gene ontology results showed that 27 and 27.5% of MTAs of SNPs in the original set were located in protein-coding regions for well-watered and rain-fed conditions, respectively. While, for the imputed data set 22.6 and 16.6% of MTAs represented in protein-coding genes for the well-watered and rain-fed conditions, respectively. CONCLUSIONS:Our finding suggests that Iranian bread wheat landraces harbor valuable alleles that are adaptive under drought stress conditions. MTAs located within coding genes can be utilized in genome-based breeding of new wheat varieties. Although imputation of missing data increased the number of MTAs, the fraction of these MTAs located in coding genes were decreased across the different sub-genomes.

Project description:The adzuki bean (Vigna angularis) is an important grain legume. Fine mapping of quantitative trait loci (QTL) and qualitative trait genes plays an important role in gene cloning, molecular-marker-assisted selection (MAS), and trait improvement. However, the genetic control of agronomic traits in the adzuki bean remains poorly understood. Single-nucleotide polymorphisms (SNPs) are invaluable in the construction of high-density genetic maps. We mapped 26 agronomic QTLs and five qualitative trait genes related to pigmentation using 1,571 polymorphic SNP markers from the adzuki bean genome via restriction-site-associated DNA sequencing of 150 members of an F2 population derived from a cross between cultivated and wild adzuki beans. We mapped 11 QTLs for flowering time and pod maturity on chromosomes 4, 7, and 10. Six 100-seed weight (SD100WT) QTLs were detected. Two major flowering time QTLs were located on chromosome 4, firstly VaFld4.1 (PEVs 71.3%), co-segregating with SNP marker s690-144110, and VaFld4.2 (PEVs 67.6%) at a 0.974 cM genetic distance from the SNP marker s165-116310. Three QTLs for seed number per pod (Snp3.1, Snp3.2, and Snp4.1) were mapped on chromosomes 3 and 4. One QTL VaSdt4.1 of seed thickness (SDT) and three QTLs for branch number on the main stem were detected on chromosome 4. QTLs for maximum leaf width (LFMW) and stem internode length were mapped to chromosomes 2 and 9, respectively. Trait genes controlling the color of the seed coat, pod, stem and flower were mapped to chromosomes 3 and 1. Three candidate genes, VaAGL, VaPhyE, and VaAP2, were identified for flowering time and pod maturity. VaAGL encodes an agamous-like MADS-box protein of 379 amino acids. VaPhyE encodes a phytochrome E protein of 1,121 amino acids. Four phytochrome genes (VaPhyA1, VaPhyA2, VaPhyB, and VaPhyE) were identified in the adzuki bean genome. We found candidate genes VaAP2/ERF.81 and VaAP2/ERF.82 of SD100WT, VaAP2-s4 of SDT, and VaAP2/ERF.86 of LFMW. A candidate gene VaUGT related to black seed coat color was identified. These mapped QTL and qualitative trait genes provide information helpful for future adzuki bean candidate gene cloning and MAS breeding to improve cultivars with desirable growth periods, yields, and seed coat color types.

Dataset Information

Genome-Wide Association Study and QTL Meta-Analysis Identified Novel Genomic Loci Controlling Potassium Use Efficiency and Agronomic Traits in Bread Wheat.

Publications

Genome-Wide Association Study and QTL Meta-Analysis Identified Novel Genomic Loci Controlling Potassium Use Efficiency and Agronomic Traits in Bread Wheat.

Similar Datasets

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