scholarly journals Genetic Dissection of Grain Yield of Maize and Yield-Related Traits Through Association Mapping and Genomic Prediction

2021 ◽  
Vol 12 ◽  
Author(s):  
Juan Ma ◽  
Yanyong Cao
Keyword(s):  
Grain Yield ◽  
Genomic Prediction ◽  
Fixed Effects ◽  
Prediction Accuracy ◽  
Reproducing Kernel ◽  
Genome Wide Association Study ◽  
Kernel Weight ◽  
Primary Objective ◽  
High Yield ◽  
Nucleotide Polymorphisms

High yield is the primary objective of maize breeding. Genomic dissection of grain yield and yield-related traits contribute to understanding the yield formation and improving the yield of maize. In this study, two genome-wide association study (GWAS) methods and genomic prediction were made on an association panel of 309 inbred lines. GWAS analyses revealed 22 significant trait–marker associations for grain yield per plant (GYP) and yield-related traits. Genomic prediction analyses showed that reproducing kernel Hilbert space (RKHS) outperformed the other four models based on GWAS-derived markers for GYP, ear weight, kernel number per ear and row, ear length, and ear diameter, whereas genomic best linear unbiased prediction (GBLUP) showed a slight superiority over other modes in most subsets of the trait-associated marker (TAM) for thousand kernel weight and kernel row number. The prediction accuracy could be improved when significant single-nucleotide polymorphisms were fitted as the fixed effects. Integrating information on population structure into the fixed model did not improve the prediction performance. For GYP, the prediction accuracy of TAMs derived from fixed and random model Circulating Probability Unification (FarmCPU) was comparable to that of the compressed mixed linear model (CMLM). For yield-related traits, CMLM-derived markers provided better accuracies than FarmCPU-derived markers in most scenarios. Compared with all markers, TAMs could effectively improve the prediction accuracies for GYP and yield-related traits. For eight traits, moderate- and high-prediction accuracies were achieved using TAMs. Taken together, genomic prediction incorporating prior information detected by GWAS could be a promising strategy to improve the grain yield of maize.

scholarly journals Assessing causality in associations between cannabis use and schizophrenia risk: a two-sample Mendelian randomization study

2016 ◽  
Vol 47 (5) ◽  
pp. 971-980 ◽  
Author(s):  
S. H. Gage ◽  
H. J. Jones ◽  
S. Burgess ◽  
J. Bowden ◽  
G. Davey Smith ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Genome Wide Association Study ◽  
Mendelian Randomization ◽  
Causal Effect ◽  
Positive Control ◽  
Negative Control ◽  
Study Data ◽  
Nucleotide Polymorphisms ◽  
Genome Wide ◽  
Genome Wide Data

BackgroundObservational associations between cannabis and schizophrenia are well documented, but ascertaining causation is more challenging. We used Mendelian randomization (MR), utilizing publicly available data as a method for ascertaining causation from observational data.MethodWe performed bi-directional two-sample MR using summary-level genome-wide data from the International Cannabis Consortium (ICC) and the Psychiatric Genomics Consortium (PGC2). Single nucleotide polymorphisms (SNPs) associated with cannabis initiation (p < 10−5) and schizophrenia (p < 5 × 10−8) were combined using an inverse-variance-weighted fixed-effects approach. We also used height and education genome-wide association study data, representing negative and positive control analyses.ResultsThere was some evidence consistent with a causal effect of cannabis initiation on risk of schizophrenia [odds ratio (OR) 1.04 per doubling odds of cannabis initiation, 95% confidence interval (CI) 1.01–1.07, p = 0.019]. There was strong evidence consistent with a causal effect of schizophrenia risk on likelihood of cannabis initiation (OR 1.10 per doubling of the odds of schizophrenia, 95% CI 1.05–1.14, p = 2.64 × 10−5). Findings were as predicted for the negative control (height: OR 1.00, 95% CI 0.99–1.01, p = 0.90) but weaker than predicted for the positive control (years in education: OR 0.99, 95% CI 0.97–1.00, p = 0.066) analyses.ConclusionsOur results provide some that cannabis initiation increases the risk of schizophrenia, although the size of the causal estimate is small. We find stronger evidence that schizophrenia risk predicts cannabis initiation, possibly as genetic instruments for schizophrenia are stronger than for cannabis initiation.

scholarly journals Unlocking the relationships among population structure, plant architecture, growing season, and environmental adaptation in Henan wheat cultivars

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Jian Yang ◽  
Yanjie Zhou ◽  
Weiguo Hu ◽  
Yu’e Zhang ◽  
Yong Zhou ◽  
...  
Keyword(s):  
Grain Yield ◽  
Plant Architecture ◽  
Selective Sweep ◽  
Association Studies ◽  
Growing Season ◽  
Kernel Weight ◽  
High Yield ◽  
Population Divergence ◽  
Genome Wide Association Studies ◽  
Wheat Cultivars

Abstract Background Ecological environments shape plant architecture and alter the growing season, which provides the basis for wheat genetic improvement. Therefore, understanding the genetic basis of grain yield and yield-related traits in specific ecological environments is important. Results A structured panel of 96 elite wheat cultivars grown in the High-yield zone of Henan province in China was genotyped using an Illumina iSelect 90 K SNP assay. Selection pressure derived from ecological environments of mountain front and plain region provided the initial impetus for population divergence. This determined the dominant traits in two subpopulations (spike number and spike percentage were dominance in subpopulation 2:1; thousand-kernel weight, grain filling rate (GFR), maturity date (MD), and fertility period (FP) were dominance in subpopulation 2:2), which was also consistent with their inheritance from the donor parents. Genome wide association studies identified 107 significant SNPs for 12 yield-related traits and 10 regions were pleiotropic to multiple traits. Especially, GY was co-located with MD/FP, GFR and HD at QTL-ple5A, QTL-ple7A.1 and QTL-ple7B.1 region. Further selective sweep analysis revealled that regions under selection were around QTLs for these traits. Especially, grain yield (GY) is positively correlated with MD/FP and they were co-located at the VRN-1A locus. Besides, a selective sweep signal was detected at VRN-1B locus which was only significance to MD/FP. Conclusions The results indicated that extensive differential in allele frequency driven by ecological selection has shaped plant architecture and growing season during yield improvement. The QTLs for yield and yield components detected in this study probably be selectively applied in molecular breeding.

Responsiveness of barley cultivars to nitrogen fertiliser

1997 ◽  
Vol 37 (2) ◽  
pp. 199 ◽  
Author(s):  
G. Fathi ◽  
G. K. McDonald ◽  
R. C. M. Lance
Keyword(s):  
Grain Size ◽  
Grain Yield ◽  
Initial Response ◽  
Kernel Weight ◽  
High Yield ◽  
Malting Quality ◽  
Yield Response ◽  
Genotypic Differences ◽  
Barley Cultivars ◽  
N Rates

Summary. Genotypic differences in responses to nitrogen (N) fertiliser of 6 cultivars of barley (Clipper, Stirling, Weeah, Schooner, Chebec, Skiff) grown at 8 different rates of N were examined in 2 seasons. Measurements of vegetative growth, N content, grain yield, grain protein concentration (GPC) and yield components were taken to identify traits that may contribute to high yield responsiveness. The optimum rates of N for dry matter production at ear emergence (DMee) were greater than 80 kg N/ha for all cultivars and often growth increased up to 105 kg N/ha. Optimum rates of N for grain yield (Nopt) were lower and ranged, on average, from 50 kg N/ha for Clipper to 96 kg N/ha for Chebec. The initial response to N varied from 13–14 kg/kg N in Chebec, Weeah and Schooner, to 36 kg/kg N in Skiff. The Nopt for the semi-dwarf cultivar Skiff was 71 kg N/ha and it tended to show the greatest yield response to N. It produced 19 kernels/g DMee, compared with 15–17 kernels/g DMee in the other cultivars. Unlike most other cultivars, Skiff’s yield was consistently and positively correlated with ears/m2; Stirling was the only other cultivar to show a similar relationship. However, the average kernel weight of Skiff was up to 5 mg lower than that of Clipper, Weeah and Schooner, and varied more than these cultivars between sites, suggesting that consistent grain size may be a problem in this cultivar. By comparison, Clipper and Schooner had lower Nopt (51 kg/ha) and a less variable kernel weight. There were no signs of differences in GPC of the 6 cultivars used here at 3 N-responsive sites. Adding N increased GPC up to the highest rate of N and the responses were generally linear, but GPC at Nopt exceeded the upper limit for malting quality of 11.8% in all cultivars. Average N rates of between 38 kg N/ha (Schooner) and 58 kg N/ha (Skiff) were sufficient to raise GPC above 11.8%. The experiments showed that the N rates for optimum yields varied considerably among cultivars, but applyi1ng rates to achieve maximum yields may cause GPC to exceed the maximum value for malting barley. The use of semi-dwarf cultivars, such as Skiff, which are very responsive to N, can provide some leeway in the choice of N, but there may be a trade-off in quality associated with reduced grain size.

scholarly journals Single-Nucleotide Polymorphism Variations Associated With Specific Genes Putatively Identified Enhanced Genetic Predisposition for 305-Day Milk Yield in the Girolando Crossbreed

2021 ◽  
Vol 11 ◽  
Author(s):  
Alex Silva da Cruz ◽  
Danilo Conrado Silva ◽  
Lysa Bernardes Minasi ◽  
Larissa Kamídia de Farias Teixeira ◽  
Flávia Melo Rodrigues ◽  
...  
Keyword(s):  
Milk Production ◽  
Milk Yield ◽  
Genetic Predisposition ◽  
Fixed Effects ◽  
Genome Wide Association Study ◽  
Significant Snps ◽  
Nucleotide Polymorphisms ◽  
Production Traits ◽  
Single Nucleotide ◽  
A Genome

Milk production phenotypes are the main focus of genetic selection in dairy herds, and although there are many genes identified as related to the biology of these traits in pure breeds, little is known about crossbreed animals. This study aimed to identify potential genes associated with the 305-day milk yield in 337 crossbreed Gir × Holstein (Girolando) animals. Milk production records were genotyped for 45,613 single-nucleotide polymorphisms (SNPs). This dataset was used for a genome-wide association study (GWAS) using the 305-day milk yield adjusted for the fixed effects of herd and year and linear and quadratic effects of age at calving (in days) and calving factor averaged per animal. Genes within the significant SNPs were retrieved from the Bos taurus ARS-UCD1.2 assembly (bosTau9) for gene ontology analysis. In summary, the GWAS identified 52 SNPs associated [p ≤ 10–4, false discovery rate (FDR) = 8.77%] with milk production, including NUB1 and SLC24A2, which were previously described as related to milk production traits in cattle. The results suggest that SNPs associated mainly with NUB1 and SLC24A2 could be useful to understand milk production in Girolando and used as predictive markers for selecting genetic predisposition for milk yield in Girolando.

scholarly journals Variability of maize inbred lines in nitrogen use effciency

Genetika ◽  
2020 ◽  
Vol 52 (2) ◽  
pp. 585-596
Author(s):  
Vesna Dragicevic ◽  
Snezana Mladenovic-Drinic ◽  
Milena Simic ◽  
Milan Brankov ◽  
Zoran Dumanovic ◽  
...  
Keyword(s):  
Grain Yield ◽  
Nitrogen Loss ◽  
Resource Scarcity ◽  
Kernel Weight ◽  
Point Of View ◽  
Utilization Efficiency ◽  
High Yield ◽  
Nitrogen Use ◽  
N Utilization ◽  
Primary Focus

Nitrogen (N) is an important element for many physiological processes in crops, and grain yield realisation. Nitrogen loss could be significant through leaching and evaporation, and from this reason lower quantities for fertilization are required. A genotype could be an important source for improved N management in crops. Breeding for high yield and nutrient-efficient genotypes is the most important strategy to enable food security, resolve resource scarcity and environmental pollution. Variability of 36 maize lines grown in optimal and low-N (without fertilization) conditions was assessed through grain yield, 1000 kernel weight, N utilization efficiency (NUtE) and N apparent recovery fraction (nitrogen use efficiency - NUE), during seasons 2017 and 2018. The genotype and year are important sources for variation of grain yield, 1000 kernel weight and NUtE, as a factor which defines N utilization efficiency. The lines, such as L1, L6, L13, L16, L26, L27, L32 and L34 are able to achieve higher grain yield when grown on low-N. Furthermore, L16, L22, L24 and L26 have high NUtE values in both experimental years (even in 2017, season with low and unequal precipitation level), especially in low-N treatment. From that point of view, they could be characterized as efficient N users, even in low-N conditions, as well as tolerant to stressful conditions. Nevertheless, L1, L6 and L27 are the lines with negative NUE, what gives them attribute as the best N users in low-N conditions. Based on the similarity of NUtE values, the genotypes such as L2, L3, L4, L8, L11, L12, L14, L15, L16, L18, L19, L24, L26, L32, L33, L34could be considered as the primary focus for further breeding programs, due to the fact that they don?t have only improved NUE, but also high grain yield (even in unfavourable years), which indicates improved tolerance to various abiotic stressful factors.

scholarly journals Complex Genetic System Involved in Fusarium Ear Rot Resistance in Maize as Revealed by GWAS, Bulked Sample Analysis, and Genomic Prediction

Plant Disease ◽  
2020 ◽  
Vol 104 (6) ◽  
pp. 1725-1735 ◽  
Author(s):  
Zifeng Guo ◽  
Cheng Zou ◽  
Xiaogang Liu ◽  
Shanhong Wang ◽  
Wen-Xue Li ◽  
...  
Keyword(s):  
Confidence Level ◽  
Genomic Prediction ◽  
Complex Traits ◽  
Genome Wide Association Study ◽  
Fusarium Verticillioides ◽  
Chromosome 1 ◽  
Nucleotide Polymorphisms ◽  
Fusarium Ear Rot ◽  
Ear Rot ◽  
Sample Analysis

Fusarium ear rot (FER) caused by Fusarium verticillioides is one of the most prevalent maize diseases in China and worldwide. Resistance to FER is a complex trait controlled by multiple genes highly affected by environment. In this paper, genome-wide association study (GWAS), bulked sample analysis (BSA), and genomic prediction were performed for understanding FER resistance using 509 diverse inbred lines, which were genotyped by 37,801 high-quality single-nucleotide polymorphisms (SNPs). Ear rot evaluation was performed using artificial inoculation in four environments in China: Xinxiang, Henan, and Shunyi, Beijing, during 2017 and 2018. Significant phenotypic and genetic variation for FER severity was observed, and FER resistance was significantly correlated among the four environments with a generalized heritability of 0.78. GWAS identified 23 SNPs that were associated with FER resistance, 2 of which (1_226233417 on chromosome 1 and 10_14501044 on chromosome 10) were associated at threshold of 2.65 × 10−7 [−log(0.01/37,801)]. Using BSA, resistance quantitative trait loci were identified on chromosomes 3, 4, 7, 9, and 10 at the 90% confidence level and on chromosomes 3 and 10 at the 95% confidence level. A key region, bin 10.03, was detected by both GWAS and BSA. Genomic prediction for FER resistance showed that the prediction accuracy by trait-related markers was higher than that by randomly selected markers under different levels of marker density. Marker-assisted selection using genomic prediction could be an efficient strategy for genetic improvement for complex traits like FER resistance.

scholarly journals Genetic Dissection of Hybrid Performance and Heterosis for Yield-Related Traits in Maize

2021 ◽  
Vol 12 ◽  
Author(s):  
Dongdong Li ◽  
Zhiqiang Zhou ◽  
Xiaohuan Lu ◽  
Yong Jiang ◽  
Guoliang Li ◽  
...  
Keyword(s):  
Grain Yield ◽  
Recombinant Inbred ◽  
Fixed Effects ◽  
Prediction Accuracy ◽  
Genetic Architecture ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Recombinant Inbred Lines ◽  
Inbred Lines ◽  
Hybrid Performance

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.

scholarly journals Genetic Dissection of Resistance to Common Rust (Puccinia Sorghi) in Tropical Maize (Zea mays L.) by Combined Genetic Mapping and Genomic Prediction

2020 ◽  
Author(s):  
Xuecai Zhang ◽  
Jiaojiao Ren ◽  
Zhimin Li ◽  
Penghao Wu ◽  
Alexander Loladze ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Linkage Mapping ◽  
Rust Resistance ◽  
Genome Wide Association Study ◽  
Genetic Dissection ◽  
Nucleotide Polymorphisms ◽  
Maize Breeding ◽  
Dh Population ◽  
Association Panel ◽  
Maize Varieties

Abstract Background: Common rust is one of the major foliar diseases of maize, leading to significant grain yield losses and poor grain quality. The most sustainable strategy for controlling common rust is to develop resistant maize varieties, which requires a further understanding of genetic dissection of common rust resistance. Results: In this study, an association panel and two bi-parental doubled haploid (DH) populations were used to perform genome-wide association study (GWAS), linkage mapping, and genomic prediction analyses. All the populations were phenotyped in multi-environment trials for common rust resistance and genotyped with genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). GWAS revealed six SNPs significantly associated with common rust resistance at bins 1.05, 1.10, 3.04, 3.05, 4.08, and 10.04, respectively. The SNP effect of each SNP ranged from 0.13 to 0.17. Linkage mapping identified six quantitative trait loci (QTL) in the first DH population (DH1) and two QTL in the second DH population (DH2), distributed on chromosomes 1, 2, 3, 4, 6, 7, and 9, respectively. The phenotypic variation explained (PVE) of each QTL ranged from 3.55% to 12.45%. A new major QTL was detected in DH1 on chromosome 7 in the region between 144,585,945 and 149,528,489 bp, it had the highest LOD score of 7.82 and the largest PVE value of 12.45%. The genomic regions located at bins 1.05, 1.10, and 4.08 were detected by both GWAS and linkage mapping. GRMZM2G114893 (bin 1.05) and GRMZM2G138949 (bin 4.08) were identified as the putative candidate genes conferring common rust resistance. The genomic prediction accuracies observed in the association panel and two bi-parental DH populations were 0.61, 0.51, and 0.10, respectively. Conclusions: These results provided new insight into the genetic architecture of common rust resistance in maize and a better understanding of the application of genomic prediction for common rust resistance in maize breeding.

scholarly journals Genome‑wide association study and genomic prediction for growth traits in yellow-plumage chicken using genotyping-by-sequencing

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Ruifei Yang ◽  
Zhenqiang Xu ◽  
Qi Wang ◽  
Di Zhu ◽  
Cheng Bian ◽  
...  
Keyword(s):  
Association Study ◽  
Genomic Prediction ◽  
Genetic Background ◽  
Prediction Accuracy ◽  
Genome Wide Association Study ◽  
Growth Traits ◽  
Genome Wide Association ◽  
Genomic Feature ◽  
Genomic Information ◽  
Genome Wide

Abstract Background Growth traits are of great importance for poultry breeding and production and have been the topic of extensive investigation, with many quantitative trait loci (QTL) detected. However, due to their complex genetic background, few causative genes have been confirmed and the underlying molecular mechanisms remain unclear, thus limiting our understanding of QTL and their potential use for the genetic improvement of poultry. Therefore, deciphering the genetic architecture is a promising avenue for optimising genomic prediction strategies and exploiting genomic information for commercial breeding. The objectives of this study were to: (1) conduct a genome-wide association study to identify key genetic factors and explore the polygenicity of chicken growth traits; (2) investigate the efficiency of genomic prediction in broilers; and (3) evaluate genomic predictions that harness genomic features. Results We identified five significant QTL, including one on chromosome 4 with major effects and four on chromosomes 1, 2, 17, and 27 with minor effects, accounting for 14.5 to 34.1% and 0.2 to 2.6% of the genomic additive genetic variance, respectively, and 23.3 to 46.7% and 0.6 to 4.5% of the observed predictive accuracy of breeding values, respectively. Further analysis showed that the QTL with minor effects collectively had a considerable influence, reflecting the polygenicity of the genetic background. The accuracy of genomic best linear unbiased predictions (BLUP) was improved by 22.0 to 70.3% compared to that of the conventional pedigree-based BLUP model. The genomic feature BLUP model further improved the observed prediction accuracy by 13.8 to 15.2% compared to the genomic BLUP model. Conclusions A major QTL and four minor QTL were identified for growth traits; the remaining variance was due to QTL effects that were too small to be detected. The genomic BLUP and genomic feature BLUP models yielded considerably higher prediction accuracy compared to the pedigree-based BLUP model. This study revealed the polygenicity of growth traits in yellow-plumage chickens and demonstrated that the predictive ability can be greatly improved by using genomic information and related features.

scholarly journals Characterization of Gamma-Rays-Induced Spring Wheat Mutants for Morphological and Quality Traits through Multivariate and GT Bi-Plot Analysis

Agronomy ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2288
Author(s):  
Sana Zulfiqar ◽  
Shumila Ishfaq ◽  
Muhammad Ikram ◽  
Muhammad Amjad Nawaz ◽  
Mehboob-ur- Rahman
Keyword(s):  
Grain Yield ◽  
Plant Height ◽  
Gamma Rays ◽  
Complex Traits ◽  
Grain Quality ◽  
Principal Component ◽  
Kernel Weight ◽  
High Yield ◽  
Quality Traits ◽  
Mutant Lines

Exploiting new genetic resources is an effective way to achieve sustainable wheat production. To this end, we exposed wheat seeds of the “Punjab-11” cultivar to gamma rays. A total of 32 stable mutants (M7) were developed, followed by characterization by conducting multilocation trials over two seasons. Principal component analysis (PCA) showed that the first six components accounted for 90.28% of the total variation among the plant height, tillers per plant, 1000-kernel weight, grain yield, and quality traits. All mutants were grouped into three clusters based on high yield index values. The genotype by trait (GT) bi-plot revealed significant associations between yield and its components among the mutants. Positive correlations were estimated for tillers per plant, plant height, 1000-kernel weight, and grain yield; however, yield components showed negative associations with protein, moisture, and gluten contents. The mutant lines Pb-M-59 waxy, Pb-M-1272 waxy, Pb-M-2260, Pb-M-1027 waxy, Pb-M-1323 waxy, and Pb-M-1854 exhibited maximum grain yield, 1000-grain weight, and tillers per plant values. Likewise, Pb-M-2725, Pb-M-2550, and Pb-M-2728 were found to be the best mutant lines in terms of grain quality; thus, the use of gamma radiation is effective in improving the desirable traits, including yield and grain quality. It is suggested that these traits can be improved beyond the performance of corresponding traits in their parent genotypes. The newly produced mutants can also be used to explore the genetic mechanisms of complex traits in the future.

Sign in / Sign up

Export Citation Format

Share Document