Genetic Dissection of Grain Yield of Maize and Yield-Related Traits Through Association Mapping and Genomic Prediction.
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ABSTRACT:
SUBMITTER: Ma J
PROVIDER: S-EPMC8319912 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Understanding the genetic basis of maize grain yield and other traits under low-nitrogen (N) stressed environments could improve selection efficiency. In this study, five doubled haploid (DH) populations were evaluated under optimum and N-stressed conditions, during the main rainy season and off-season in Kenya and Rwanda, from 2014 to 2015. Identifying the genomic regions associated with grain yield (GY), anthesis date (AD), anthesis-silking interval (ASI), plant height (PH), ear height (EH), ear position (EPO), and leaf senescence (SEN) under optimum and N-stressed environments could facilitate the use of marker-assisted selection to develop N-use-efficient maize varieties. DH lines were genotyped with genotyping by sequencing. A total of 13, 43, 13, 25, 30, 21, and 10 QTL were identified for GY, AD ASI, PH, EH, EPO, and SEN, respectively. For GY, PH, EH, and SEN, the highest number of QTL was found under low-N environments. No common QTL between optimum and low-N stressed conditions were identified for GY and ASI. For secondary traits, there were some common QTL for optimum and low-N conditions. Most QTL conferring tolerance to N stress was on a different chromosome position under optimum conditions.
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Project description: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.
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Project description:Genomic selection (GS) is a strategy to predict the genetic merits of individuals using genome-wide markers. However, GS prediction accuracy is affected by many factors, including missing rate and minor allele frequency (MAF) of genotypic data, GS models, trait features, etc. In this study, we used one wheat population to investigate prediction accuracies of various GS models on yield and yield-related traits from various quality control (QC) scenarios, missing genotype imputation, and genome-wide association studies (GWAS)-derived markers. Missing rate and MAF of single nucleotide polymorphism (SNP) markers were two major factors in QC. Five missing rate levels (0%, 20%, 40%, 60%, and 80%) and three MAF levels (0%, 5%, and 10%) were considered and the five-fold cross validation was used to estimate the prediction accuracy. The results indicated that a moderate missing rate level (20% to 40%) and MAF (5%) threshold provided better prediction accuracy. Under this QC scenario, prediction accuracies were further calculated for imputed and GWAS-derived markers. It was observed that the accuracies of the six traits were related to their heritability and genetic architecture, as well as the GS prediction model. Moore-Penrose generalized inverse (GenInv), ridge regression (RidgeReg), and random forest (RForest) resulted in higher prediction accuracies than other GS models across traits. Imputation of missing genotypic data had marginal effect on prediction accuracy, while GWAS-derived markers improved the prediction accuracy in most cases. These results demonstrate that QC on missing rate and MAF had positive impact on the predictability of GS models. We failed to identify one single combination of QC scenarios that could outperform the others for all traits and GS models. However, the balance between marker number and marker quality is important for the deployment of GS in wheat breeding. GWAS is able to select markers which are mostly related to traits, and therefore can be used to improve the prediction accuracy of GS.
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Project description:The suboptimal productivity of maize systems in sub-Saharan Africa (SSA) is a pressing issue, with far-reaching implications for food security, nutrition, and livelihood sustainability within the affected smallholder farming communities. Dissecting the genetic basis of grain protein, starch and oil content can increase our understanding of the governing genetic systems, improve the efficacy of future breeding schemes and optimize the end-use quality of tropical maize. Here, four bi-parental maize populations were evaluated in field trials in Kenya and genotyped with mid-density single nucleotide polymorphism (SNP) markers. Genotypic (G), environmental (E) and G×E variations were found to be significant for all grain quality traits. Broad sense heritabilities exhibited substantial variation (0.18-0.68). Linkage mapping identified multiple quantitative trait loci (QTLs) for the studied grain quality traits: 13, 7, 33, 8 and 2 QTLs for oil content, protein content, starch content, grain texture and kernel weight, respectively. The co-localization of QTLs identified in our research suggests the presence of shared genetic factors or pleiotropic effects, implying that specific genomic regions influence the expression of multiple grain quality traits simultaneously. Genomic prediction accuracies were moderate to high for the studied traits. Our findings highlight the polygenic nature of grain quality traits and demonstrate the potential of genomic selection to enhance genetic gains in maize breeding. Furthermore, the identified genomic regions and single nucleotide polymorphism markers can serve as the groundwork for investigating candidate genes that regulate grain quality traits in tropical maize. This, in turn, can facilitate the implementation of marker-assisted selection (MAS) in breeding programs focused on improving grain nutrient levels.
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Project description:Genotype-by-environment interaction (GEI) is among the greatest challenges for maize breeding programs. Strong GEI limits both the prediction of genotype performance across variable environmental conditions and the identification of genomic regions associated with grain yield. Incorporating GEI into yield prediction models has been shown to improve prediction accuracy of yield; nevertheless, more work is needed to further understand this complex interaction across populations and environments. The main objectives of this study were to: 1) assess GEI in maize grain yield based on reaction norm models and predict hybrid performance across a gradient of environmental (EG) conditions and 2) perform a genome-wide association study (GWAS) and post-GWAS analyses for maize grain yield using data from 2014 to 2017 of the Genomes to Fields initiative hybrid trial. After quality control, 2,126 hybrids with genotypic and phenotypic data were assessed across 86 environments representing combinations of locations and years, although not all hybrids were evaluated in all environments. Heritability was greater in higher-yielding environments due to an increase in genetic variability in these environments in comparison to the low-yielding environments. GWAS was carried out for yield and five single nucleotide polymorphisms (SNPs) with the highest magnitude of effect were selected in each environment for follow-up analyses. Many candidate genes in proximity of selected SNPs have been previously reported with roles in stress response. Genomic prediction was performed to assess prediction accuracy of previously tested or untested hybrids in environments from a new growing season. Prediction accuracy was 0.34 for cross validation across years (CV0-Predicted EG) and 0.21 for cross validation across years with only untested hybrids (CV00-Predicted EG) when compared to Best Linear Unbiased Prediction (BLUPs) that did not utilize genotypic or environmental relationships. Prediction accuracy improved to 0.80 (CV0-Predicted EG) and 0.60 (CV00-Predicted EG) when compared to the whole-dataset model that used the genomic relationships and the environmental gradient of all environments in the study. These results identify regions of the genome for future selection to improve yield and a methodology to increase the number of hybrids evaluated across locations of a multi-environment trial through genomic prediction.
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Project description:Heterosis contributes a big proportion to hybrid performance in maize, especially for grain yield. It is attractive to explore the underlying genetic architecture of hybrid performance and heterosis. Considering its complexity, different from former mapping method, we developed a series of linear mixed models incorporating multiple polygenic covariance structures to quantify the contribution of each genetic component (additive, dominance, additive-by-additive, additive-by-dominance, and dominance-by-dominance) to hybrid performance and midparent heterosis variation and to identify significant additive and non-additive (dominance and epistatic) quantitative trait loci (QTL). Here, we developed a North Carolina II population by crossing 339 recombinant inbred lines with two elite lines (Chang7-2 and Mo17), resulting in two populations of hybrids signed as Chang7-2 × recombinant inbred lines and Mo17 × recombinant inbred lines, respectively. The results of a path analysis showed that kernel number per row and hundred grain weight contributed the most to the variation of grain yield. The heritability of midparent heterosis for 10 investigated traits ranged from 0.27 to 0.81. For the 10 traits, 21 main (additive and dominance) QTL for hybrid performance and 17 dominance QTL for midparent heterosis were identified in the pooled hybrid populations with two overlapping QTL. Several of the identified QTL showed pleiotropic effects. Significant epistatic QTL were also identified and were shown to play an important role in ear height variation. Genomic selection was used to assess the influence of QTL on prediction accuracy and to explore the strategy of heterosis utilization in maize breeding. Results showed that treating significant single nucleotide polymorphisms as fixed effects in the linear mixed model could improve the prediction accuracy under prediction schemes 2 and 3. In conclusion, the different analyses all substantiated the different genetic architecture of hybrid performance and midparent heterosis in maize. Dominance contributes the highest proportion to heterosis, especially for grain yield, however, epistasis contributes the highest proportion to hybrid performance of grain yield.
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Project description:Maize yield components including row number, kernel number per row, kernel thickness, kernel width, kernel length, 100-kernel weight, and volume weight affect grain yield directly. Previous studies mainly focused on dissecting the genetic basis of per se performances for yield-related traits, but the genetic basis of general combining ability (GCA) for these traits is still unclear. In the present study, 328 RILs were crossed as males to two testers according to the NCII mating design, resulting in a hybrid panel composed of 656 hybrids. Both the hybrids and parental lines were evaluated in four environments in 2015 and 2016. Correlation analysis showed the performances of GCA effects were significantly correlated to the per se performances of RILs for all yield-related traits (0.17 ≤ r ≤ 0.64, P > 0.01). Only 17 of 95 QTL could be detected for both per se performances of RILs and GCA effects for eight yield-related traits. The QTL qKN7-1 and qHKW1-3, which could explain more than 10% of the variation in the GCA effects of KN and HKW, were also detected for per se performances for the traits. The pleiotropic loci qRN3-1 and qRN6, which together explained 14.92% of the observed variation in GCA effects for RN, were associated with the GCA effects of KW and HKW, but not with per se performances for these traits. In contrast, Incw1, which was related to seed weight in maize, was mapped to the region surrounding MK2567 at the qHKW5-2 locus, but no GCA effect was detected. The QTL identified in present study for per se performances and corresponding GCA effects for yield-related traits might be useful for maize hybrid breeding.
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Project description:Drought stress (DS) is a major constraint to maize yield production. Heat stress (HS) alone and in combination with DS are likely to become the increasing constraints. Association mapping and genomic prediction (GP) analyses were conducted in a collection of 300 tropical and subtropical maize inbred lines to reveal the genetic architecture of grain yield and flowering time under well-watered (WW), DS, HS, and combined DS and HS conditions. Out of the 381,165 genotyping-by-sequencing SNPs, 1549 SNPs were significantly associated with all the 12 trait-environment combinations, the average PVE (phenotypic variation explained) by these SNPs was 4.33%, and 541 of them had a PVE value greater than 5%. These significant associations were clustered into 446 genomic regions with a window size of 20 Mb per region, and 673 candidate genes containing the significantly associated SNPs were identified. In addition, 33 hotspots were identified for 12 trait-environment combinations and most were located on chromosomes 1 and 8. Compared with single SNP-based association mapping, the haplotype-based associated mapping detected fewer number of significant associations and candidate genes with higher PVE values. All the 688 candidate genes were enriched into 15 gene ontology terms, and 46 candidate genes showed significant differential expression under the WW and DS conditions. Association mapping results identified few overlapped significant markers and candidate genes for the same traits evaluated under different managements, indicating the genetic divergence between the individual stress tolerance and the combined drought and HS tolerance. The GP accuracies obtained from the marker-trait associated SNPs were relatively higher than those obtained from the genome-wide SNPs for most of the target traits. The genetic architecture information of the grain yield and flowering time revealed in this study, and the genomic regions identified for the different trait-environment combinations are useful in accelerating the efforts on rapid development of the stress-tolerant maize germplasm through marker-assisted selection and/or genomic selection.
| S-EPMC6363715 | biostudies-literature
Project description:In sub-Saharan Africa, one of the major challenges to smallholder farmers is soil with low fertility and inability to apply nitrogen fertilizer externally due to the cost. Development of maize hybrids, which perform better in nitrogen depleted soils, is one of the promising solutions. However, breeding maize for nitrogen use efficiency (NUE) is hindered by expensive phenotypic evaluations and trait complexity under low N stress. Genome-wide association study (GWAS) and genomic prediction (GP) are promising tools to circumvent this interference. Here, we evaluated a mapping panel in diverse environments both under optimum and low N management. The objective of this study was to identify SNPs significantly associated with grain yield (GY) and other traits through GWAS and assess the potential of GP under low N and optimum conditions. Testcross progenies of 411 inbred lines were planted under optimum and low N conditions in several locations in Africa and Latin America. In all locations, low N fields were previously depleted over several seasons, and no N fertilizer was applied throughout the growing season. All inbred lines were genotyped with genotyping by sequencing. Genotypic and GxE interaction variances were significant, and heritability estimates were moderate to high for all traits under both optimum and low N conditions. Genome-wide LD decay at r 2 = 0.2 and r 2 = 0.34 were 0.24 and 0.19 Mbp, respectively. Chromosome-specific LD decays ranged from 0.13 to 0.34 Mbps with an average of 0.22 Mbp at r 2 = 0.2. GWAS analyses revealed 38 and 45 significant SNPs under optimum and low N conditions, respectively. Out of these 83 significant SNPs, 3 SNPs on chromosomes 1, 2, and 6 were associated either with different traits or the same trait under different management conditions, suggesting pleiotropic effects of genes. A total of 136 putative candidate genes were associated with the significant SNPs, of which seven SNPs were linked with four known genes. Prediction accuracies were moderate to high for all traits under both optimum and low N conditions. These results can be used as useful resources for further applications to develop hybrids or lines with better performance under low N conditions.
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Project description:Quantitative trait loci (QTLs) mapped in different genetic populations are of great significance for marker-assisted breeding. In this study, an F2:3 population were developed from the crossing of two maize inbred lines SG-5 and SG-7 and applied to QTL mapping for seven yield-related traits. The seven traits included 100-kernel weight, ear length, ear diameter, cob diameter, kernel row number, ear weight, and grain weight per plant. Based on an ultra-high density linkage map, a total of thirty-three QTLs were detected for the seven studied traits with composite interval mapping (CIM) method, and fifty-four QTLs were indentified with genome-wide composite interval mapping (GCIM) methods. For these QTLs, Fourteen were both detected by CIM and GCIM methods. Besides, eight of the thirty QTLs detected by CIM were identical to those previously mapped using a F2 population (generating from the same cross as the mapping population in this study), and fifteen were identical to the reported QTLs in other recent studies. For the fifty-four QTLs detected by GCIM, five of them were consistent with the QTLs mapped in the F2 population of SG-5 × SG-7, and twenty one had been reported in other recent studies. The stable QTLs associated with grain weight were located on maize chromosomes 2, 5, 7, and 9. In addition, differentially expressed genes (DEGs) between SG-5 and SG-7 were obtained from the transcriptomic profiling of grain at different developmental stages and overlaid onto the stable QTLs intervals to predict candidate genes for grain weight in maize. In the physical intervals of confirmed QTLs qKW-7, qEW-9, qEW-10, qGWP-6, qGWP-8, qGWP-10, qGWP-11 and qGWP-12, there were 213 DEGs in total. Finally, eight genes were predicted as candidate genes for grain size/weight. In summary, the stable QTLs would be reliable and the candidate genes predicted would be benefit for maker assisted breeding or cloning.
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