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1.
Nat Genet ; 51(10): 1530-1539, 2019 10.
Artigo em Inglês | MEDLINE | ID: mdl-31548720

RESUMO

Bread wheat improvement using genomic tools is essential for accelerating trait genetic gains. Here we report the genomic predictabilities of 35 key traits and demonstrate the potential of genomic selection for wheat end-use quality. We also performed a large genome-wide association study that identified several significant marker-trait associations for 50 traits evaluated in South Asia, Africa and the Americas. Furthermore, we built a reference wheat genotype-phenotype map, explored allele frequency dynamics over time and fingerprinted 44,624 wheat lines for trait-associated markers, generating over 7.6 million data points, which together will provide a valuable resource to the wheat community for enhancing productivity and stress resilience.


Assuntos
Resistência à Doença/genética , Genômica/métodos , Locos de Características Quantitativas , Estresse Fisiológico/imunologia , Triticum/crescimento & desenvolvimento , Triticum/imunologia , Ascomicetos/fisiologia , Mapeamento Cromossômico , Grão Comestível/genética , Grão Comestível/crescimento & desenvolvimento , Estudos de Associação Genética , Marcadores Genéticos , Genoma de Planta , Estudo de Associação Genômica Ampla , Melhoramento Vegetal , Doenças das Plantas/genética , Doenças das Plantas/microbiologia , Seleção Genética , Estresse Fisiológico/genética , Triticum/genética
2.
G3 (Bethesda) ; 9(9): 2913-2924, 2019 09 04.
Artigo em Inglês | MEDLINE | ID: mdl-31289023

RESUMO

Kernel methods are flexible and easy to interpret and have been successfully used in genomic-enabled prediction of various plant species. Kernel methods used in genomic prediction comprise the linear genomic best linear unbiased predictor (GBLUP or GB) kernel, and the Gaussian kernel (GK). In general, these kernels have been used with two statistical models: single-environment and genomic × environment (GE) models. Recently near infrared spectroscopy (NIR) has been used as an inexpensive and non-destructive high-throughput phenotyping method for predicting unobserved line performance in plant breeding trials. In this study, we used a non-linear arc-cosine kernel (AK) that emulates deep learning artificial neural networks. We compared AK prediction accuracy with the prediction accuracy of GB and GK kernel methods in four genomic data sets, one of which also includes pedigree and NIR information. Results show that for all four data sets, AK and GK kernels achieved higher prediction accuracy than the linear GB kernel for the single-environment and GE multi-environment models. In addition, AK achieved similar or slightly higher prediction accuracy than the GK kernel. For all data sets, the GE model achieved higher prediction accuracy than the single-environment model. For the data set that includes pedigree, markers and NIR, results show that the NIR wavelength alone achieved lower prediction accuracy than the genomic information alone; however, the pedigree plus NIR information achieved only slightly lower prediction accuracy than the marker plus the NIR high-throughput data.


Assuntos
Genômica/métodos , Modelos Genéticos , Melhoramento Vegetal/métodos , Espectrofotometria/métodos , Bases de Dados Genéticas , Aprendizado Profundo , Genômica/estatística & dados numéricos , Fenótipo , Espectrofotometria/estatística & dados numéricos , Triticum/genética , Zea mays/genética
3.
G3 (Bethesda) ; 9(9): 2925-2934, 2019 09 04.
Artigo em Inglês | MEDLINE | ID: mdl-31300481

RESUMO

Genome-enabled prediction plays an essential role in wheat breeding because it has the potential to increase the rate of genetic gain relative to traditional phenotypic and pedigree-based selection. Since the performance of wheat lines is highly influenced by environmental stimuli, it is important to accurately model the environment and its interaction with genetic factors in prediction models. Arguably, multi-environmental best linear unbiased prediction (BLUP) may deliver better prediction performance than single-environment genomic BLUP. We evaluated pedigree and genome-based prediction using 35,403 wheat lines from the Global Wheat Breeding Program of the International Maize and Wheat Improvement Center (CIMMYT). We implemented eight statistical models that included genome-wide molecular marker and pedigree information as prediction inputs in two different validation schemes. All models included main effects, but some considered interactions between the different types of pedigree and genomic covariates via Hadamard products of similarity kernels. Pedigree models always gave better prediction of new lines in observed environments than genome-based models when only main effects were fitted. However, for all traits, the highest predictive abilities were obtained when interactions between pedigree, genomes, and environments were included. When new lines were predicted in unobserved environments, in almost all trait/year combinations, the marker main-effects model was the best. These results provide strong evidence that the different sources of genetic information (molecular markers and pedigree) are not equally useful at different stages of the breeding pipelines, and can be employed differentially to improve the design and prediction of the outcome of future breeding programs.


Assuntos
Genoma de Planta , Modelos Genéticos , Triticum/fisiologia , Interação Gene-Ambiente , Marcadores Genéticos , Fenótipo , Melhoramento Vegetal , Distribuição Aleatória , Reprodutibilidade dos Testes , Triticum/genética
4.
Int J Mol Sci ; 20(13)2019 Jun 26.
Artigo em Inglês | MEDLINE | ID: mdl-31247965

RESUMO

Karnal bunt disease of wheat, caused by the fungus Neovossia indica, is one of the most important challenges to the grain industry as it affects the grain quality and also restricts the international movement of infected grain. It is a seed-, soil- and airborne disease with limited effect of chemical control. Currently, this disease is contained through the deployment of host resistance but further improvement is limited as only a few genotypes have been found to carry partial resistance. To identify genomic regions responsible for resistance in a set of 339 wheat accessions, genome-wide association study (GWAS) was undertaken using the DArTSeq® technology, in which 18 genomic regions for Karnal bunt resistance were identified, explaining 5-20% of the phenotypic variation. The identified quantitative trait loci (QTL) on chromosome 2BL showed consistently significant effects across all four experiments, whereas another QTL on 5BL was significant in three experiments. Additional QTLs were mapped on chromosomes 1DL, 2DL, 4AL, 5AS, 6BL, 6BS, 7BS and 7DL that have not been mapped previously, and on chromosomes 4B, 5AL, 5BL and 6BS, which have been reported in previous studies. Germplasm with less than 1% Karnal bunt infection have been identified and can be used for resistance breeding. The SNP markers linked to the genomic regions conferring resistance to Karnal bunt could be used to improve Karnal bunt resistance through marker-assisted selection.


Assuntos
Basidiomycota , Resistência à Doença/genética , Estudo de Associação Genômica Ampla , Interações Hospedeiro-Patógeno/genética , Doenças das Plantas/genética , Doenças das Plantas/microbiologia , Triticum/genética , Marcadores Genéticos , Variação Genética , Genoma Fúngico , Melhoramento Vegetal , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas , Sementes
5.
G3 (Bethesda) ; 9(5): 1545-1556, 2019 05 07.
Artigo em Inglês | MEDLINE | ID: mdl-30858235

RESUMO

Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between traits with mixed phenotypes in order to improve prediction accuracy in the context of genomic selection (GS). For this reason, when breeders have mixed phenotypes, they usually analyze them using univariate models, and thus are not able to exploit the correlation between traits, which many times helps improve prediction accuracy. In this paper we propose applying deep learning for analyzing multiple traits with mixed phenotype data in terms of prediction accuracy. The prediction performance of multiple-trait deep learning with mixed phenotypes (MTDLMP) models was compared to the performance of univariate deep learning (UDL) models. Both models were evaluated using predictors with and without the genotype × environment (G×E) interaction term (I and WI, respectively). The metric used for evaluating prediction accuracy was Pearson's correlation for continuous traits and the percentage of cases correctly classified (PCCC) for binary and ordinal traits. We found that a modest gain in prediction accuracy was obtained only in the continuous trait under the MTDLMP model compared to the UDL model, whereas for the other traits (1 binary and 2 ordinal) we did not find any difference between the two models. In both models we observed that the prediction performance was better for WI than for I. The MTDLMP model is a good alternative for performing simultaneous predictions of mixed phenotypes (binary, ordinal and continuous) in the context of GS.


Assuntos
Aprendizado Profundo , Estudos de Associação Genética , Genoma , Genômica , Modelos Genéticos , Fenótipo , Característica Quantitativa Herdável , Algoritmos , Genoma de Planta , Genômica/métodos , Genótipo , Melhoramento Vegetal , Reprodutibilidade dos Testes , Seleção Genética
6.
Theor Appl Genet ; 132(6): 1705-1720, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-30778634

RESUMO

Genomic selection (GS) models have been validated for many quantitative traits in wheat (Triticum aestivum L.) breeding. However, those models are mostly constrained within the same growing cycle and the extension of GS to the case of across cycles has been a challenge, mainly due to the low predictive accuracy resulting from two factors: reduced genetic relationships between different families and augmented environmental variances between cycles. Using the data collected from diverse field conditions at the International Wheat and Maize Improvement Center, we evaluated GS for grain yield in three elite yield trials across three wheat growing cycles. The objective of this project was to employ the secondary traits, canopy temperature, and green normalized difference vegetation index, which are closely associated with grain yield from high-throughput phenotyping platforms, to improve prediction accuracy for grain yield. The ability to predict grain yield was evaluated reciprocally across three cycles with or without secondary traits. Our results indicate that prediction accuracy increased by an average of 146% for grain yield across cycles with secondary traits. In addition, our results suggest that secondary traits phenotyped during wheat heading and early grain filling stages were optimal for enhancing the prediction accuracy for grain yield.


Assuntos
Genética Populacional , Genoma de Planta , Genômica/métodos , Melhoramento Vegetal/métodos , Seleção Genética , Triticum/genética , Marcadores Genéticos , Fenótipo , Triticum/crescimento & desenvolvimento
7.
Theor Appl Genet ; 132(1): 177-194, 2019 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-30341493

RESUMO

Genomic selection and high-throughput phenotyping (HTP) are promising tools to accelerate breeding gains for high-yielding and climate-resilient wheat varieties. Hence, our objective was to evaluate them for predicting grain yield (GY) in drought-stressed (DS) and late-sown heat-stressed (HS) environments of the International maize and wheat improvement center's elite yield trial nurseries. We observed that the average genomic prediction accuracies using fivefold cross-validations were 0.50 and 0.51 in the DS and HS environments, respectively. However, when a different nursery/year was used to predict another nursery/year, the average genomic prediction accuracies in the DS and HS environments decreased to 0.18 and 0.23, respectively. While genomic predictions clearly outperformed pedigree-based predictions across nurseries, they were similar to pedigree-based predictions within nurseries due to small family sizes. In populations with some full-sibs in the training population, the genomic and pedigree-based prediction accuracies were on average 0.27 and 0.35 higher than the accuracies in populations with only one progeny per cross, indicating the importance of genetic relatedness between the training and validation populations for good predictions. We also evaluated the item-based collaborative filtering approach for multivariate prediction of GY using the green normalized difference vegetation index from HTP. This approach proved to be the best strategy for across-nursery predictions, with average accuracies of 0.56 and 0.62 in the DS and HS environments, respectively. We conclude that GY is a challenging trait for across-year predictions, but GS and HTP can be integrated in increasing the size of populations screened and evaluating unphenotyped large nurseries for stress-resilience within years.


Assuntos
Clima , Modelos Genéticos , Melhoramento Vegetal/métodos , Triticum/genética , Grão Comestível/genética , Genoma de Planta , Genômica , Genótipo , Ensaios de Triagem em Larga Escala , Modelos Lineares , Linhagem , Fenótipo , Característica Quantitativa Herdável
8.
G3 (Bethesda) ; 9(2): 601-618, 2019 02 07.
Artigo em Inglês | MEDLINE | ID: mdl-30593512

RESUMO

Genomic selection is revolutionizing plant breeding. However, still lacking are better statistical models for ordinal phenotypes to improve the accuracy of the selection of candidate genotypes. For this reason, in this paper we explore the genomic based prediction performance of two popular machine learning methods: the Multi Layer Perceptron (MLP) and support vector machine (SVM) methods vs. the Bayesian threshold genomic best linear unbiased prediction (TGBLUP) model. We used the percentage of cases correctly classified (PCCC) as a metric to measure the prediction performance, and seven real data sets to evaluate the prediction accuracy, and found that the best predictions (in four out of the seven data sets) in terms of PCCC occurred under the TGLBUP model, while the worst occurred under the SVM method. Also, in general we found no statistical differences between using 1, 2 and 3 layers under the MLP models, which means that many times the conventional neuronal network model with only one layer is enough. However, although even that the TGBLUP model was better, we found that the predictions of MLP and SVM were very competitive with the advantage that the SVM was the most efficient in terms of the computational time required.


Assuntos
Melhoramento Vegetal/métodos , Máquina de Vetores de Suporte , Teorema de Bayes , Característica Quantitativa Herdável , Seleção Artificial
9.
Plant Genome ; 11(3)2018 11.
Artigo em Inglês | MEDLINE | ID: mdl-30512047

RESUMO

The Item-Based Collaborative Filtering for Multitrait and Multienvironment Data (IBCF.MTME) package was developed to implement the item-based collaborative filtering (IBCF) algorithm for continuous phenotypic data in the context of plant breeding where data are collected for various traits and environments. The main difference between this package and the other available packages that can implement IBCF is that this one was developed for continuous phenotypic data, which cannot be implemented in the current packages because they can implement IBCF only for binary and ordinary phenotypes. In the following article, we will show how to both install the package and use it for studying the prediction accuracy of multitrait and multienvironment data under phenotypic and genomic selection. We illustrate its use with seven examples (with information from two datasets, Wheat_IBCF and Year_IBCF, which are included in the package) comprising multienvironment data, multitrait data, and both multitrait and multienvironment data that cover scenarios in which breeding scientists are interested. The package offers many advantages for studying the genomic-enabled prediction accuracy of multitrait and multienvironment data, ultimately helping plant breeders make better decisions.


Assuntos
Algoritmos , Interação Gene-Ambiente , Conjuntos de Dados como Assunto , Genótipo , Fenótipo
10.
Plant Genome ; 11(3)2018 11.
Artigo em Inglês | MEDLINE | ID: mdl-30512048

RESUMO

Genomic selection (GS) has been promising for increasing genetic gains in several species. Therefore, we evaluated the potential integration of GS for grain yield (GY) in bread wheat ( L.) in CIMMYT's elite yield trial nurseries. We observed that the genomic prediction accuracies within nurseries (0.44 and 0.35) were substantially higher than across-nursery accuracies (0.15 and 0.05) for GY evaluated in the bed and flat planting systems, respectively. The accuracies from using only a subset of 251 genotyping-by-sequencing markers were comparable to the accuracies using all 2038 markers. We also used the item-based collaborative filtering approach for incorporating other related traits in predicting GY and observed that it outperformed genomic predictions across nurseries, but was less predictive when trait correlations with GY were low. Furthermore, we compared GS and phenotypic selections (PS) and observed that at a selection intensity of 0.5, GS could select a maximum of 70.9 and 61.5% of the top lines and discard 71.5 and 60.5% of the poor lines selected or discarded by PS within and across nurseries, respectively. Comparisons of GS and pedigree-based predictions revealed that the advantage of GS over the pedigree was moderate in populations without full-sibs. However, GS was less advantageous for within-family selections in elite families with few full-sibs and minimal Mendelian sampling variance. Overall, our results demonstrate the importance of applying GS for GY at the appropriate stage of the breeding cycle, and we speculate that gains can be maximized if it is implemented in early-generation within-family selections.


Assuntos
Melhoramento Vegetal , Seleção Genética , Triticum/genética , Agricultura , Grão Comestível , Marcadores Genéticos , Genoma de Planta , Linhagem , Fenótipo
11.
Int J Mol Sci ; 19(12)2018 Dec 14.
Artigo em Inglês | MEDLINE | ID: mdl-30558200

RESUMO

Spot blotch (SB) is an important fungal disease of wheat in South Asia and South America. Host resistance is regarded as an economical and environmentally friendly approach of controlling SB, and the inheritance of resistance is mostly quantitative. In order to gain a better understanding on the SB resistance mechanism in CIMMYT germplasm, two bi-parental mapping populations were generated, both comprising 232 F2:7 progenies. Elite CIMMYT breeding lines, BARTAI and WUYA, were used as resistant parents, whereas CIANO T79 was used as susceptible parent in both populations. The two populations were evaluated for field SB resistance at CIMMYT's Agua Fria station for three consecutive years, from the 2012⁻2013 to 2014⁻2015 cropping seasons. Phenological traits like plant height (PH) and days to heading (DH) were also determined. Genotyping was performed using the DArTSeq genotyping-by-sequencing (GBS) platform, and a few D-genome specific SNPs and those for phenological traits were integrated for analysis. The most prominent quantitative trait locus (QTL) in both populations was found on chromosome 5AL at the Vrn-A1 locus, explaining phenotypic variations of 7⁻27%. Minor QTL were found on chromosomes 1B, 3A, 3B, 4B, 4D, 5B and 6D in BARTAI and on chromosomes 1B, 2A, 2D and 4B in WUYA, whereas minor QTL contributed by CIANO T79 were identified on chromosome 1B, 1D, 3A, 4B and 7A. In summary, resistance to SB in the two mapping populations was controlled by multiple minor QTL, with strong influence from Vrn-A1.


Assuntos
Mapeamento Cromossômico/métodos , Resistência à Doença , Locos de Características Quantitativas , Triticum/genética , Cromossomos de Plantas/genética , Genótipo , Fenótipo , Melhoramento Vegetal , Doenças das Plantas/microbiologia , Sementes/genética , Triticum/anatomia & histologia , Triticum/crescimento & desenvolvimento
12.
Sci Rep ; 8(1): 13526, 2018 09 10.
Artigo em Inglês | MEDLINE | ID: mdl-30201978

RESUMO

Wheat is an important staple that acts as a primary source of dietary energy, protein, and essential micronutrients such as iron (Fe) and zinc (Zn) for the world's population. Approximately two billion people suffer from micronutrient deficiency, thus breeders have crossed high Zn progenitors such as synthetic hexaploid wheat, T. dicoccum, T. spelta, and landraces to generate wheat varieties with competitive yield and enhanced grain Zn that are being adopted by farmers in South Asia. Here we report a genome-wide association study (GWAS) using the wheat Illumina iSelect 90 K Infinitum SNP array to characterize grain Zn concentrations in 330 bread wheat lines. Grain Zn phenotype of this HarvestPlus Association Mapping (HPAM) panel was evaluated across a range of environments in India and Mexico. GWAS analysis revealed 39 marker-trait associations for grain Zn. Two larger effect QTL regions were found on chromosomes 2 and 7. Candidate genes (among them zinc finger motif of transcription-factors and metal-ion binding genes) were associated with the QTL. The linked markers and associated candidate genes identified in this study are being validated in new biparental mapping populations for marker-assisted breeding.


Assuntos
Biofortificação , Grão Comestível/genética , Locos de Características Quantitativas , Triticum/genética , Zinco/análise , Mapeamento Cromossômico , Cromossomos de Plantas/genética , Grão Comestível/química , Genoma de Planta/genética , Estudo de Associação Genômica Ampla , Índia , México , Melhoramento Vegetal/métodos , Proteínas de Plantas/genética , Plantas Geneticamente Modificadas/química , Plantas Geneticamente Modificadas/genética , Polimorfismo de Nucleotídeo Único , Sementes/química , Sementes/genética , Triticum/química , Dedos de Zinco/genética
13.
Theor Appl Genet ; 131(7): 1405-1422, 2018 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-29589041

RESUMO

KEY MESSAGE: Genome-wide association mapping in conjunction with population sequencing map and Ensembl plants was used to identify markers/candidate genes linked to leaf rust, stripe rust and tan spot resistance in wheat. Leaf rust (LR), stripe rust (YR) and tan spot (TS) are some of the important foliar diseases in wheat (Triticum aestivum L.). To identify candidate resistance genes for these diseases in CIMMYT's (International Maize and Wheat Improvement Center) International bread wheat screening nurseries, we used genome-wide association studies (GWAS) in conjunction with information from the population sequencing map and Ensembl plants. Wheat entries were genotyped using genotyping-by-sequencing and phenotyped in replicated trials. Using a mixed linear model, we observed that seedling resistance to LR was associated with 12 markers on chromosomes 1DS, 2AS, 2BL, 3B, 4AL, 6AS and 6AL, and seedling resistance to TS was associated with 14 markers on chromosomes 1AS, 2AL, 2BL, 3AS, 3AL, 3B, 6AS and 6AL. Seedling and adult plant resistance (APR) to YR were associated with several markers at the distal end of chromosome 2AS. In addition, YR APR was also associated with markers on chromosomes 2DL, 3B and 7DS. The potential candidate genes for these diseases included several resistance genes, receptor-like serine/threonine-protein kinases and defense-related enzymes. However, extensive LD in wheat that decays at about 5 × 107 bps, poses a huge challenge for delineating candidate gene intervals and candidates should be further mapped, functionally characterized and validated. We also explored a segment on chromosome 2AS associated with multiple disease resistance and identified seventeen disease resistance linked genes. We conclude that identifying candidate genes linked to significant markers in GWAS is feasible in wheat, thus creating opportunities for accelerating molecular breeding.


Assuntos
Mapeamento Cromossômico , Resistência à Doença/genética , Genes de Plantas , Doenças das Plantas/genética , Triticum/genética , Basidiomycota , Estudos de Associação Genética , Marcadores Genéticos , Genótipo , Modelos Lineares , Desequilíbrio de Ligação , Fenótipo , Doenças das Plantas/microbiologia , Triticum/microbiologia
14.
G3 (Bethesda) ; 8(1): 131-147, 2018 01 04.
Artigo em Inglês | MEDLINE | ID: mdl-29097376

RESUMO

In genomic-enabled prediction, the task of improving the accuracy of the prediction of lines in environments is difficult because the available information is generally sparse and usually has low correlations between traits. In current genomic selection, although researchers have a large amount of information and appropriate statistical models to process it, there is still limited computing efficiency to do so. Although some statistical models are usually mathematically elegant, many of them are also computationally inefficient, and they are impractical for many traits, lines, environments, and years because they need to sample from huge normal multivariate distributions. For these reasons, this study explores two recommender systems: item-based collaborative filtering (IBCF) and the matrix factorization algorithm (MF) in the context of multiple traits and multiple environments. The IBCF and MF methods were compared with two conventional methods on simulated and real data. Results of the simulated and real data sets show that the IBCF technique was slightly better in terms of prediction accuracy than the two conventional methods and the MF method when the correlation was moderately high. The IBCF technique is very attractive because it produces good predictions when there is high correlation between items (environment-trait combinations) and its implementation is computationally feasible, which can be useful for plant breeders who deal with very large data sets.


Assuntos
Interação Gene-Ambiente , Genoma de Planta , Modelos Estatísticos , Melhoramento Vegetal/métodos , Característica Quantitativa Herdável , Triticum/genética , Zea mays/genética , Algoritmos , Produtos Agrícolas , Genótipo , Modelos Genéticos , Fenótipo , Ploidias , Polimorfismo de Nucleotídeo Único , Seleção Genética
15.
Plant Genome ; 10(2)2017 07.
Artigo em Inglês | MEDLINE | ID: mdl-28724084

RESUMO

The leaf spotting diseases in wheat that include Septoria tritici blotch (STB) caused by , Stagonospora nodorum blotch (SNB) caused by , and tan spot (TS) caused by pose challenges to breeding programs in selecting for resistance. A promising approach that could enable selection prior to phenotyping is genomic selection that uses genome-wide markers to estimate breeding values (BVs) for quantitative traits. To evaluate this approach for seedling and/or adult plant resistance (APR) to STB, SNB, and TS, we compared the predictive ability of least-squares (LS) approach with genomic-enabled prediction models including genomic best linear unbiased predictor (GBLUP), Bayesian ridge regression (BRR), Bayes A (BA), Bayes B (BB), Bayes Cπ (BC), Bayesian least absolute shrinkage and selection operator (BL), and reproducing kernel Hilbert spaces markers (RKHS-M), a pedigree-based model (RKHS-P) and RKHS markers and pedigree (RKHS-MP). We observed that LS gave the lowest prediction accuracies and RKHS-MP, the highest. The genomic-enabled prediction models and RKHS-P gave similar accuracies. The increase in accuracy using genomic prediction models over LS was 48%. The mean genomic prediction accuracies were 0.45 for STB (APR), 0.55 for SNB (seedling), 0.66 for TS (seedling) and 0.48 for TS (APR). We also compared markers from two whole-genome profiling approaches: genotyping by sequencing (GBS) and diversity arrays technology sequencing (DArTseq) for prediction. While, GBS markers performed slightly better than DArTseq, combining markers from the two approaches did not improve accuracies. We conclude that implementing GS in breeding for these diseases would help to achieve higher accuracies and rapid gains from selection.


Assuntos
Ascomicetos/isolamento & purificação , Perfilação da Expressão Gênica , Modelos Genéticos , Doenças das Plantas/microbiologia , Triticum/microbiologia , Teorema de Bayes , Genes de Plantas , Marcadores Genéticos , Desequilíbrio de Ligação , Fenótipo , Locos de Características Quantitativas , Triticum/genética
16.
Theor Appl Genet ; 130(7): 1415-1430, 2017 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-28393303

RESUMO

KEY MESSAGE: Genomic prediction for seedling and adult plant resistance to wheat rusts was compared to prediction using few markers as fixed effects in a least-squares approach and pedigree-based prediction. The unceasing plant-pathogen arms race and ephemeral nature of some rust resistance genes have been challenging for wheat (Triticum aestivum L.) breeding programs and farmers. Hence, it is important to devise strategies for effective evaluation and exploitation of quantitative rust resistance. One promising approach that could accelerate gain from selection for rust resistance is 'genomic selection' which utilizes dense genome-wide markers to estimate the breeding values (BVs) for quantitative traits. Our objective was to compare three genomic prediction models including genomic best linear unbiased prediction (GBLUP), GBLUP A that was GBLUP with selected loci as fixed effects and reproducing kernel Hilbert spaces-markers (RKHS-M) with least-squares (LS) approach, RKHS-pedigree (RKHS-P), and RKHS markers and pedigree (RKHS-MP) to determine the BVs for seedling and/or adult plant resistance (APR) to leaf rust (LR), stem rust (SR), and stripe rust (YR). The 333 lines in the 45th IBWSN and the 313 lines in the 46th IBWSN were genotyped using genotyping-by-sequencing and phenotyped in replicated trials. The mean prediction accuracies ranged from 0.31-0.74 for LR seedling, 0.12-0.56 for LR APR, 0.31-0.65 for SR APR, 0.70-0.78 for YR seedling, and 0.34-0.71 for YR APR. For most datasets, the RKHS-MP model gave the highest accuracies, while LS gave the lowest. GBLUP, GBLUP A, RKHS-M, and RKHS-P models gave similar accuracies. Using genome-wide marker-based models resulted in an average of 42% increase in accuracy over LS. We conclude that GS is a promising approach for improvement of quantitative rust resistance and can be implemented in the breeding pipeline.


Assuntos
Resistência à Doença/genética , Doenças das Plantas/genética , Triticum/genética , Basidiomycota , Marcadores Genéticos , Genômica/métodos , Genótipo , Modelos Lineares , Modelos Genéticos , Fenótipo , Doenças das Plantas/microbiologia , Locos de Características Quantitativas , Triticum/microbiologia
17.
G3 (Bethesda) ; 7(5): 1595-1606, 2017 05 05.
Artigo em Inglês | MEDLINE | ID: mdl-28364037

RESUMO

When a plant scientist wishes to make genomic-enabled predictions of multiple traits measured in multiple individuals in multiple environments, the most common strategy for performing the analysis is to use a single trait at a time taking into account genotype × environment interaction (G × E), because there is a lack of comprehensive models that simultaneously take into account the correlated counting traits and G × E. For this reason, in this study we propose a multiple-trait and multiple-environment model for count data. The proposed model was developed under the Bayesian paradigm for which we developed a Markov Chain Monte Carlo (MCMC) with noninformative priors. This allows obtaining all required full conditional distributions of the parameters leading to an exact Gibbs sampler for the posterior distribution. Our model was tested with simulated data and a real data set. Results show that the proposed multi-trait, multi-environment model is an attractive alternative for modeling multiple count traits measured in multiple environments.


Assuntos
Interação Gene-Ambiente , Modelos Genéticos , Melhoramento Vegetal/métodos , Característica Quantitativa Herdável , Teorema de Bayes , Distribuição de Poisson , Triticum/genética
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