Dataset Information

Genetic Architecture of Grain Yield-Related Traits in Sorghum and Maize.

ABSTRACT: Grain size, grain number per panicle, and grain weight are crucial determinants of yield-related traits in cereals. Understanding the genetic basis of grain yield-related traits has been the main research object and nodal in crop science. Sorghum and maize, as very close C4 crops with high photosynthetic rates, stress tolerance and large biomass characteristics, are extensively used to produce food, feed, and biofuels worldwide. In this review, we comprehensively summarize a large number of quantitative trait loci (QTLs) associated with grain yield in sorghum and maize. We placed great emphasis on discussing 22 fine-mapped QTLs and 30 functionally characterized genes, which greatly hinders our deep understanding at the molecular mechanism level. This review provides a general overview of the comprehensive findings on grain yield QTLs and discusses the emerging trend in molecular marker-assisted breeding with these QTLs.

SUBMITTER: Baye W

PROVIDER: S-EPMC8909957 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

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Publications

Genetic Architecture of Grain Yield-Related Traits in Sorghum and Maize.

Baye Wodajo W Xie Qi Q Xie Peng P

International journal of molecular sciences 20220222 5

Grain size, grain number per panicle, and grain weight are crucial determinants of yield-related traits in cereals. Understanding the genetic basis of grain yield-related traits has been the main research object and nodal in crop science. Sorghum and maize, as very close C4 crops with high photosynthetic rates, stress tolerance and large biomass characteristics, are extensively used to produce food, feed, and biofuels worldwide. In this review, we comprehensively summarize a large number of quan ...[more]

PMID: 35269548

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Project description:BackgroundThe 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.ResultsGenome-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.ConclusionsThese 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.

Project description:BackgroundDrought is a major abiotic stress in sub-Saharan Africa, impacting maize growth and development leading to severe yield loss. Drought tolerance is a complex trait regulated by multiple genes, making direct grain yield selection ineffective. To dissect the genetic architecture of grain yield and flowering traits under drought stress, a genome-wide association study (GWAS) was conducted on a panel of 236 maize lines testcrossed and evaluated under managed drought and optimal growing conditions in multiple environments using seven multi-locus GWAS models (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, ISIS EM-BLASSO, and FARMCPU) from mrMLM and GAPIT R packages. Genomic prediction with RR-BLUP model was applied on BLUEs across locations under optimum and drought conditions.ResultsA total of 172 stable and reliable quantitative trait nucleotides (QTNs) were identified, of which 77 are associated with GY, AD, SD, ASI, PH, EH, EPO and EPP under drought and 95 are linked to GY, AD, SD, ASI, PH, EH, EPO and EPP under optimal conditions. Among these QTNs, 17 QTNs explained over 10% of the phenotypic variation (R2 ≥ 10%). Furthermore, 43 candidate genes were discovered and annotated. Two major candidate genes, Zm00001eb041070 closely associated with grain yield near peak QTN, qGY_DS1.1 (S1_216149215) and Zm00001eb364110 closely related to anthesis-silking interval near peak QTN, qASI_DS8.2 (S8_167256316) were identified, encoding AP2-EREBP transcription factor 60 and TCP-transcription factor 20, respectively under drought stress. Haplo-pheno analysis identified superior haplotypes for qGY_DS1.1 (S1_216149215) associated with the higher grain yield under drought stress. Genomic prediction revealed moderate to high prediction accuracies under optimum and drought conditions.ConclusionThe lines carrying superior haplotypes can be used as potential donors in improving grain yield under drought stress. Integration of genomic selection with GWAS results leads not only to an increase in the prediction accuracy but also to validate the function of the identified candidate genes as well increase in the accumulation of favorable alleles with minor and major effects in elite breeding lines. This study provides valuable insight into the genetic architecture of grain yield and secondary traits under drought stress.

Dataset Information

Genetic Architecture of Grain Yield-Related Traits in Sorghum and Maize.

Publications

Genetic Architecture of Grain Yield-Related Traits in Sorghum and Maize.

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