scholarly journals Appraising the Genetic Architecture of Kernel Traits in Hexaploid Wheat Using GWAS

2020 ◽  
Vol 21 (16) ◽  
pp. 5649
Author(s):  
Ali Muhammad ◽  
Weicheng Hu ◽  
Zhaoyang Li ◽  
Jianguo Li ◽  
Guosheng Xie ◽  
...  
Keyword(s):  
Genetic Architecture ◽  
Genome Wide Association Study ◽  
Snp Array ◽  
Significant Snps ◽  
Kernel Weight ◽  
Snp Markers ◽  
Kernel Length ◽  
A Genome ◽  
Genetic Mechanisms ◽  
Kernel Traits

Kernel morphology is one of the major yield traits of wheat, the genetic architecture of which is always important in crop breeding. In this study, we performed a genome-wide association study (GWAS) to appraise the genetic architecture of the kernel traits of 319 wheat accessions using 22,905 single nucleotide polymorphism (SNP) markers from a wheat 90K SNP array. As a result, 111 and 104 significant SNPs for Kernel traits were detected using four multi-locus GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, and pLARmEB) and three single-locus models (FarmCPU, MLM, and MLMM), respectively. Among the 111 SNPs detected by the multi-locus models, 24 SNPs were simultaneously detected across multiple models, including seven for kernel length, six for kernel width, six for kernels per spike, and five for thousand kernel weight. Interestingly, the five most stable SNPs (RAC875_29540_391, Kukri_07961_503, tplb0034e07_1581, BS00074341_51, and BobWhite_049_3064) were simultaneously detected by at least three multi-locus models. Integrating these newly developed multi-locus GWAS models to unravel the genetic architecture of kernel traits, the mrMLM approach detected the maximum number of SNPs. Furthermore, a total of 41 putative candidate genes were predicted to likely be involved in the genetic architecture underlining kernel traits. These findings can facilitate a better understanding of the complex genetic mechanisms of kernel traits and may lead to the genetic improvement of grain yield in wheat.

scholarly journals Association Mapping and Transcriptome Analysis Reveal the Genetic Architecture of Maize Kernel Size

2021 ◽  
Vol 12 ◽  
Author(s):  
Juan Ma ◽  
Lifeng Wang ◽  
Yanyong Cao ◽  
Hao Wang ◽  
Huiyong Li
Keyword(s):  
Differential Expression ◽  
Genetic Architecture ◽  
Genome Wide Association Study ◽  
Differential Expression Analysis ◽  
Inbred Lines ◽  
Kernel Size ◽  
Maize Kernel ◽  
Association Panel ◽  
Kernel Length ◽  
A Genome

Kernel length, kernel width, and kernel thickness are important traits affecting grain yield and product quality. Here, the genetic architecture of the three kernel size traits was dissected in an association panel of 309 maize inbred lines using four statistical methods. Forty-two significant single nucleotide polymorphisms (SNPs; p < 1.72E-05) and 70 genes for the three traits were identified under five environments. One and eight SNPs were co-detected in two environments and by at least two methods, respectively, and they explained 5.87–9.59% of the phenotypic variation. Comparing the transcriptomes of two inbred lines with contrasting seed size, three and eight genes identified in the association panel showed significantly differential expression between the two genotypes at 15 and 39 days after pollination, respectively. Ten and 17 genes identified by a genome-wide association study were significantly differentially expressed between the two development stages in the two genotypes. Combining environment−/method-stable SNPs and differential expression analysis, ribosomal protein L7, jasmonate-regulated gene 21, serine/threonine-protein kinase RUNKEL, AP2-EREBP-transcription factor 16, and Zm00001d035222 (cell wall protein IFF6-like) were important candidate genes for maize kernel size and development.

scholarly journals Identification of quantitative trait loci for kernel traits in a wheat cultivar Chuannong16

BMC Genetics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Jian Ma ◽  
Han Zhang ◽  
Shuiqin Li ◽  
Yaya Zou ◽  
Ting Li ◽  
...  
Keyword(s):  
Genetic Map ◽  
Quantitative Trait ◽  
Agronomic Traits ◽  
Snp Array ◽  
Kernel Weight ◽  
Phenotypic Variance ◽  
Kernel Length ◽  
Kasp Marker ◽  
Kernel Traits ◽  
Major Qtl

Abstract Background Kernel length (KL), kernel width (KW) and thousand-kernel weight (TKW) are key agronomic traits in wheat breeding. Chuannong16 (‘CN16’) is a commercial cultivar with significantly longer kernels than the line ‘20828’. To identify and characterize potential alleles from CN16 controlling KL, the previously developed recombinant inbred line (RIL) population derived from the cross ‘20828’ × ‘CN16’ and the genetic map constructed by the Wheat55K SNP array and SSR markers were used to perform quantitative trait locus/loci (QTL) analyses for kernel traits. Results A total of 11 putative QTL associated with kernel traits were identified and they were located on chromosomes 1A (2 QTL), 2B (2 QTL), 2D (3 QTL), 3D, 4A, 6A, and 7A, respectively. Among them, three major QTL, QKL.sicau-2D, QKW.sicau-2D and QTKW.sicau-2D, controlling KL, KW and TKW, respectively, were detected in three different environments. Respectively, they explained 10.88–18.85%, 17.21–21.49% and 10.01–23.20% of the phenotypic variance. Further, they were genetically mapped in the same interval on chromosome 2DS. A previously developed kompetitive allele-specific PCR (KASP) marker KASP-AX-94721936 was integrated in the genetic map and QTL re-mapping finally located the three major QTL in a 1- cM region flanked by AX-111096297 and KASP-AX-94721936. Another two co-located QTL intervals for KL and TKW were also identified. A few predicted genes involved in regulation of kernel growth and development were identified in the intervals of these identified QTL. Significant relationships between kernel traits and spikelet number per spike and anthesis date were detected and discussed. Conclusions Three major and stably expressed QTL associated with KL, KW, and TKW were identified. A KASP marker tightly linked to these three major QTL was integrated. These findings provide information for subsequent fine mapping and cloning the three co-localized major QTL for kernel traits.

scholarly journals Genetic dissection of grain architecture-related traits in a winter wheat population

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Matías Schierenbeck ◽  
Ahmad M. Alqudah ◽  
Ulrike Lohwasser ◽  
Rasha A. Tarawneh ◽  
María Rosa Simón ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Allelic Variation ◽  
Wheat Yield ◽  
Snp Array ◽  
Significant Snps ◽  
Kernel Weight ◽  
Yield Potential ◽  
Oxidoreductase Activity ◽  
Global Food Security ◽  
Kernel Length

Abstract Background The future productivity of wheat (T. aestivum L.) as the most grown crop worldwide is of utmost importance for global food security. Thousand kernel weight (TKW) in wheat is closely associated with grain architecture-related traits, e.g. kernel length (KL), kernel width (KW), kernel area (KA), kernel diameter ratio (KDR), and factor form density (FFD). Discovering the genetic architecture of natural variation in these traits, identifying QTL and candidate genes are the main aims of this study. Therefore, grain architecture-related traits in 261 worldwide winter accessions over three field-year experiments were evaluated. Results Genome-wide association analysis using 90K SNP array in FarmCPU model revealed several interesting genomic regions including 17 significant SNPs passing false discovery rate threshold and strongly associated with the studied traits. Four of associated SNPs were physically located inside candidate genes within LD interval e.g. BobWhite_c5872_589 (602,710,399 bp) found to be inside TraesCS6A01G383800 (602,699,767–602,711,726 bp). Further analysis reveals the four novel candidate genes potentially involved in more than one grain architecture-related traits with a pleiotropic effects e.g. TraesCS6A01G383800 gene on 6A encoding oxidoreductase activity was associated with TKW and KA. The allelic variation at the associated SNPs showed significant differences betweeen the accessions carying the wild and mutated alleles e.g. accessions carying C allele of BobWhite_c5872_589, TraesCS6A01G383800 had significantly higher TKW than the accessions carying T allele. Interestingly, these genes were highly expressed in the grain-tissues, demonstrating their pivotal role in controlling the grain architecture. Conclusions These results are valuable for identifying regions associated with kernel weight and dimensions and potentially help breeders in improving kernel weight and architecture-related traits in order to increase wheat yield potential and end-use quality.

scholarly journals Genome-wide association study reveals the genetic determinism of growth traits in a Gushi-Anka F2 chicken population

Heredity ◽  
2020 ◽  
Author(s):  
Yanhua Zhang ◽  
Yuzhe Wang ◽  
Yiyi Li ◽  
Junfeng Wu ◽  
Xinlei Wang ◽  
...  
Keyword(s):  
Association Study ◽  
Genome Wide Association Study ◽  
Growth Traits ◽  
Carcass Traits ◽  
Significant Snps ◽  
Genome Wide Association ◽  
Pathway Enrichment Analysis ◽  
Genome Wide ◽  
A Genome ◽  
Genetic Mechanisms

Abstract Chicken growth traits are economically important, but the relevant genetic mechanisms have not yet been elucidated. Herein, we performed a genome-wide association study to identify the variants associated with growth traits. In total, 860 chickens from a Gushi-Anka F2 resource population were phenotyped for 68 growth and carcass traits, and 768 samples were genotyped based on the genotyping-by-sequencing (GBS) method. Finally, 734 chickens and 321,314 SNPs remained after quality control and removal of the sex chromosomes, and these data were used to carry out a GWAS analysis. A total of 470 significant single-nucleotide polymorphisms (SNPs) for 43 of the 68 traits were detected and mapped on chromosomes (Chr) 1–6, -9, -10, -16, -18, -23, and -27. Of these, the significant SNPs in Chr1, -4, and -27 were found to be associated with more than 10 traits. Multiple traits shared significant SNPs, indicating that the same mutation in the region might have a large effect on multiple growth or carcass traits. Haplotype analysis revealed that SNPs within the candidate region of Chr1 presented a mosaic pattern. The significant SNPs and pathway enrichment analysis revealed that the MLNR, MED4, CAB39L, LDB2, and IGF2BP1 genes could be putative candidate genes for growth and carcass traits. The findings of this study improve our understanding of the genetic mechanisms regulating chicken growth and carcass traits and provide a theoretical basis for chicken breeding programs.

scholarly journals Genome-Wide Association Mapping of Resistance to Fusarium Head Blight Spread and Deoxynivalenol Accumulation in Chinese Elite Wheat Germplasm

2019 ◽  
Vol 109 (7) ◽  
pp. 1208-1216 ◽  
Author(s):  
Lei Wu ◽  
Yu Zhang ◽  
Yi He ◽  
Peng Jiang ◽  
Xu Zhang ◽  
...  
Keyword(s):  
Fusarium Head Blight ◽  
Genome Wide Association Study ◽  
Snp Array ◽  
Snp Markers ◽  
Genome Wide Association ◽  
Head Blight ◽  
Sources Of Resistance ◽  
Wheat Germplasm ◽  
Genome Wide ◽  
A Genome

Improving resistance to Fusarium head blight (FHB) in wheat is crucial in the integrated management of the disease and prevention of deoxynivalenol (DON) contamination in grains. To identify novel sources of resistance, a genome-wide association study (GWAS) was performed using a panel of 213 accessions of elite wheat germplasm of China. The panel was evaluated for FHB severity in four environments and DON content in grains in two environments. High correlations across environments and high heritability were observed for FHB severity and DON content in grains. The panel was also genotyped with the 90K Illumina iSelect single nucleotide polymorphism (SNP) array and 11,461 SNP markers were obtained. The GWAS revealed a total of six and three loci significantly associated with resistance to fungal spread and DON accumulation in at least two environments, respectively. QFHB-2BL.1 tagged by IWB52433 and QFHB-3A tagged by IWB50548 were responsible for resistance to both fungal spread and DON accumulation. In summary, this study provided an overview of FHB resistance resources in elite Chinese wheat germplasm and identified novel resistance loci that could be used for wheat improvement.

scholarly journals Selecting for Nut Characteristics in Macadamia Using a Genome-wide Association Study

HortScience ◽  
2019 ◽  
Vol 54 (4) ◽  
pp. 629-632 ◽  
Author(s):  
Katie O’Connor ◽  
Ben Hayes ◽  
Craig Hardner ◽  
Mobashwer Alam ◽  
Bruce Topp
Keyword(s):  
Association Study ◽  
Genome Wide Association Study ◽  
Selection Process ◽  
Kernel Weight ◽  
Snp Markers ◽  
Yield Component ◽  
Genome Wide Association ◽  
Shell Weight ◽  
Genome Wide ◽  
A Genome

Current macadamia breeding programs involve a lengthy and laborious two-stage selection process: evaluation of a large number of unreplicated seedling progeny, followed by replicated trials of clonally propagated elite seedlings. Yield component traits, such as nut-in-shell weight (NW), kernel weight (KW), and kernel recovery (KR) are commercially important, are more easily measured than yield, and have a higher heritability. A genome-wide association study (GWAS) combined with marker-assisted selection offers an opportunity to reduce the time of candidate evaluation. In this study, a total of 281 progeny from 32 families, and 18 of their 29 parents have been genotyped for 7126 single nucleotide polymorphism (SNP) markers. A GWAS was performed using ASReml with 4352 SNPs. We found five SNPs significantly associated with NW, nine with KW, and one with KR. Further, three of the top 10 markers for NW and KW were shared between the two traits. Future macadamia breeding could involve prescreening of individuals for desired traits using these significantly associated markers, with only predicted elite individuals continuing to the second stage of selection, thus potentially reducing the selection process by 7 years.

scholarly journals Identification of quantitative trait loci for kernel traits in a wheat cultivar Chuannong16

2019 ◽  
Author(s):  
Jian Ma ◽  
Han Zhang ◽  
Shuiqin Li ◽  
Yaya Zou ◽  
Ting Li ◽  
...  
Keyword(s):  
Genetic Map ◽  
Quantitative Trait ◽  
Agronomic Traits ◽  
Snp Array ◽  
Kernel Weight ◽  
Phenotypic Variance ◽  
Kernel Length ◽  
Kasp Marker ◽  
Kernel Traits ◽  
Major Qtl

Abstract Background Kernel length (KL), kernel width (KW) and thousand-kernel weight (TKW) are key agronomic traits in wheat breeding. Chuannong16 (‘CN16’) is a commercial cultivar with significantly longer kernels that the line ‘20828’. To identify and characterize potential alleles from CN16 controlling KL, the previously developed recombinant inbred line (RIL) population derived from the cross ‘20828’ × ‘CN16’ and the genetic map constructed by the Wheat55K SNP array and SSR markers were used to perform quantitative trait locus/loci (QTL) analyses for kernel traits.Results A total of 11 putative QTL associated with kernel traits were identified and they were located on chromosomes 1A (2 QTL), 2B (2 QTL), 2D (3 QTL), 3D, 4A, 6A, and 7A, respectively. Among them, three major QTL, QKL.sicau-2D , QKW.sicau-2D and QTKW.sicau-2D , controlling KL, KW and TKW, respectively, were detected in three different environments. Respectively, they explained 10.88-18.85%, 17.21-21.49% and 10.01-23.20% of the phenotypic variance. Further, they were genetically mapped in the same interval on chromosome 2DS. A previously developed kompetitive allele-specific PCR (KASP) marker KASP-AX-94721936 was integrated in the genetic map and QTL re-mapping finally located the three major QTL in a 1- cM region flanked by AX-111096297 and KASP-AX-94721936 . Another two co-located QTL intervals for KL and TKW were also identified. A few predicated genes involved in regulation of kernel growth and development were identified in the intervals of these identified QTL. Significant relationships between kernel traits and spikelet number per spike and anthesis date were detected and discussed.Conclusions Three major and stably expressed QTL associated with KL, KW, and TKW were identified. A KASP marker tightly linked to these three major QTL was integrated. These findings provide information for subsequent fine mapping and cloning the three co-localized major QTL for kernel traits.

scholarly journals Identification of quantitative trait loci for kernel traits in a wheat cultivar Chuannong16

2019 ◽  
Author(s):  
Jian Ma ◽  
Han Zhang ◽  
Shuiqin Li ◽  
Yaya Zou ◽  
Ting Li ◽  
...  
Keyword(s):  
Genetic Map ◽  
Quantitative Trait ◽  
Agronomic Traits ◽  
Snp Array ◽  
Kernel Weight ◽  
Phenotypic Variance ◽  
Kernel Length ◽  
Kasp Marker ◽  
Kernel Traits ◽  
Major Qtl

Abstract Background Kernel length (KL), kernel width (KW) and thousand-kernel weight (TKW) are key agronomic traits in wheat breeding. Chuannong16 (‘CN16’) is a commercial cultivar with significantly longer kernels that the line ‘20828’. To identify and characterize potential alleles from CN16 controlling KL, the previously developed recombinant inbred line (RIL) population derived from the cross ‘20828’ × ‘CN16’ and the genetic map constructed by the Wheat55K SNP array and SSR markers were used to perform quantitative trait locus/loci (QTL) analyses for kernel traits.Results A total of 11 putative QTL associated with kernel traits were identified and they were located on chromosomes 1A (2 QTL), 2B (2 QTL), 2D (3 QTL), 3D, 4A, 6A, and 7A, respectively. Among them, three major QTL, QKL.sicau-2D , QKW.sicau-2D and QTKW.sicau-2D , controlling KL, KW and TKW, respectively, were detected in three different environments. Respectively, they explained 10.88-18.85%, 17.21-21.49% and 10.01-23.20% of the phenotypic variance. Further, they were genetically mapped in the same interval on chromosome 2DS. A previously developed kompetitive allele-specific PCR (KASP) marker KASP-AX-94721936 was integrated in the genetic map and QTL re-mapping finally located the three major QTL in a 1- cM region flanked by AX-111096297 and KASP-AX-94721936 . Another two co-located QTL intervals for KL and TKW were also identified. A few predicated genes involved in regulation of kernel growth and development were identified in the intervals of these identified QTL. Significant relationships between kernel traits and spikelet number per spike and anthesis date were detected and discussed.Conclusions Three major and stably expressed QTL associated with KL, KW, and TKW were identified. A KASP marker tightly linked to these three major QTL was integrated. These findings provide information for subsequent fine mapping and cloning the three co-localized major QTL for kernel traits.

scholarly journals Discovery of a novel powdery mildew (Blumeria graminis) resistance locus in rye (Secale cereale L.)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
N. M. Vendelbo ◽  
K. Mahmood ◽  
P. Sarup ◽  
P. S. Kristensen ◽  
J. Orabi ◽  
...  
Keyword(s):  
Powdery Mildew ◽  
Secale Cereale ◽  
Powdery Mildew Resistance ◽  
Genome Wide Association Study ◽  
Infection Type ◽  
Snp Array ◽  
Snp Markers ◽  
Resistance Locus ◽  
Mildew Resistance ◽  
A Genome

AbstractPowdery mildew is one of the most destructive diseases in the world, causing substantial grain yield losses and quality reduction in cereal crops. At present 23 powdery mildew resistance genes have been identified in rye, of which the majority are in wheat-rye translocation lines developed for wheat improvement. Here, we investigated the genetics underlying powdery mildew resistance in the Gülzow-type elite hybrid rye (Secale cereale L.) breeding germplasm. In total, 180 inbred breeding lines were genotyped using the state-of-the-art 600 K SNP array and phenotyped for infection type against three distinct field populations of B. graminis f. sp. secalis from Northern Germany (2013 and 2018) and Denmark (2020). We observed a moderate level of powdery mildew resistance in the non-restorer germplasm population, and by performing a genome-wide association study using 261,406 informative SNP markers, we identified a powdery mildew resistance locus, provisionally denoted PmNOS1, on the distal tip of chromosome arm 7RL. Using recent advances in rye genomic resources, we investigated whether nucleotide-binding leucine-rich repeat genes residing in the identified 17 Mbp block associated with PmNOS1 on recent reference genomes resembled known Pm genes.

scholarly journals Genome-Wide Association Study of Kernel Traits in Aegilops tauschii

2021 ◽  
Vol 12 ◽  
Author(s):  
Qing Wang ◽  
Ning Yan ◽  
Hao Chen ◽  
Sirui Li ◽  
Haiyan Hu ◽  
...  
Keyword(s):  
Population Structure ◽  
Genome Wide Association Study ◽  
Aegilops Tauschii ◽  
Genome Wide Association ◽  
Kernel Width ◽  
Genome Wide ◽  
Population Structure Analysis ◽  
Kernel Length ◽  
A Genome ◽  
Kernel Traits

Aegilops tauschii is the diploid progenitor of the D subgenome of hexaploid wheat (Triticum aestivum L.). Here, the phenotypic data of kernel length (KL), kernel width (KW), kernel volume (KV), kernel surface area (KSA), kernel width to length ratio (KWL), and hundred-kernel weight (HKW) for 223 A. tauschii accessions were gathered across three continuous years. Based on population structure analysis, 223 A. tauschii were divided into two subpopulations, namely T-group (mainly included A. tauschii ssp. tauschii accessions) and S-group (mainly included A. tauschii ssp. strangulata). Classifications based on cluster analysis were highly consistent with the population structure results. Meanwhile, the extent of linkage disequilibrium decay distance (r2 = 0.5) was about 110 kb and 290 kb for T-group and S-group, respectively. Furthermore, a genome-wide association analysis was performed on these kernel traits using 6,723 single nucleotide polymorphism (SNP) markers. Sixty-six significant markers, distributed on all seven chromosomes, were identified using a mixed linear model explaining 4.82–13.36% of the phenotypic variations. Among them, 15, 28, 22, 14, 21, and 13 SNPs were identified for KL, KW, KV, KSA, KWL, and HKW, respectively. Moreover, six candidate genes that may control kernel traits were identified (AET2Gv20774800, AET4Gv20799000, AET5Gv20005900, AET5Gv20084100, AET7Gv20644900, and AET5Gv21111700). The transfer of beneficial genes from A. tauschii to wheat using marker-assisted selection will broaden the wheat D subgenome improve the efficiency of breeding.

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