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1.
Front Plant Sci ; 10: 1502, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31824533

RESUMO

Genomic selection predicts the genomic estimated breeding values (GEBVs) of individuals not previously phenotyped. Several studies have investigated the accuracy of genomic predictions in maize but there is little empirical evidence on the practical performance of lines selected based on phenotype in comparison with those selected solely on GEBVs in advanced testcross yield trials. The main objectives of this study were to (1) empirically compare the performance of tropical maize hybrids selected through phenotypic selection (PS) and genomic selection (GS) under well-watered (WW) and managed drought stress (WS) conditions in Kenya, and (2) compare the cost-benefit analysis of GS and PS. For this study, we used two experimental maize data sets (stage I and stage II yield trials). The stage I data set consisted of 1492 doubled haploid (DH) lines genotyped with rAmpSeq SNPs. A subset of these lines (855) representing various DH populations within the stage I cohort was crossed with an individual single-cross tester chosen to complement each population. These testcross hybrids were evaluated in replicated trials under WW and WS conditions for grain yield and other agronomic traits, while the remaining 637 DH lines were predicted using the 855 lines as a training set. The second data set (stage II) consists of 348 DH lines from the first data set. Among these 348 best DH lines, 172 lines selected were solely based on GEBVs, and 176 lines were selected based on phenotypic performance. Each of the 348 DH lines were crossed with three common testers from complementary heterotic groups, and the resulting 1042 testcross hybrids and six commercial checks were evaluated in four to five WW locations and one WS condition in Kenya. For stage I trials, the cross-validated prediction accuracy for grain yield was 0.67 and 0.65 under WW and WS conditions, respectively. We found similar responses to selection using PS and GS for grain yield other agronomic traits under WW and WS conditions. The top 15% of hybrids advanced through GS and PS gave 21%-23% higher grain yield under WW and 51%-52% more grain yield under WS than the mean of the checks. The GS reduced the cost by 32% over the PS with similar selection gains. We concluded that the use of GS for yield under WW and WS conditions in maize can produce selection candidates with similar performance as those generated from conventional PS, but at a lower cost, and therefore, should be incorporated into maize breeding pipelines to increase breeding program efficiency.

2.
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
3.
PLoS One ; 14(9): e0222337, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31539381

RESUMO

The presence of earthworm species in crop fields is as old as agriculture itself. The earthworms Pontoscolex corethrurus (invasive) and Balanteodrilus pearsei (native) are associated with the emergence of agriculture and sedentism in the region Amazon and Maya, respectively. Both species have shifted their preference from their natural habitat to the cropland niche. They contrast in terms of intensification of agricultural land use (anthropic impact to the symbiotic soil microbiome). P. corethrurus inhabits conventional agroecosystems, while B. pearsei thrives in traditional agroecosystems, i.e., P. corethrurus has not yet been recorded in soils where B. pearsei dwells. The demographic behavior of these two earthworm species was assessed in the laboratory over 100 days, according to their origin (OE; P. corethrurus and B. pearsei) food quality (FQ; soil only, maize stubble, Mucuna pruriens), and soil moisture (SM; 25, 33, 42%). The results showed that OE, FQ, SM, and the OE x FQ interaction were highly significant for the survival, growth, and reproduction of earthworms. P. corethrurus showed a lower survival rate (> mortality). P. corethrurus survivors fed a diet of low-to-intermediate nutritional quality (soil and stubble maize, respectively) showed a greater capacity to grow and reproduce; however, it was surpassed by the native earthworm when fed a high-quality diet (M. pruriens). Besides, P. corethrurus displayed a low cocoon hatching (emergence of juveniles). These results suggest that the presence of the invasive species was associated with a negative interaction with the soil microbiota where the native species dwells, and with the absence of natural mutualistic bacteria (gut, nephridia, and cocoons). These results are consistent with the absence of P. corethrurus in milpa and pasture-type agricultural niches managed by peasants (agroecologists) to grow food regularly through biological soil management. Results reported here suggest that P. corethrurus is an invasive species that is neither wild nor domesticated, that is, its eco-evolutionary phylogeny needs to be derived based on its symbionts.

4.
Plants (Basel) ; 8(9)2019 Sep 05.
Artigo em Inglês | MEDLINE | ID: mdl-31492041

RESUMO

Eucalyptus globulus (Labill.) is one of the most important cultivated eucalypts in temperate and subtropical regions and has been successfully subjected to intensive breeding. In this study, Bayesian genomic models that include the effects of haplotype and single nucleotide polymorphisms (SNP) were assessed to predict quantitative traits related to wood quality and tree growth in a 6-year-old breeding population. To this end, the following markers were considered: (a) ~14 K SNP markers (SNP), (b) ~3 K haplotypes (HAP), and (c) haplotypes and SNPs that were not assigned to a haplotype (HAP-SNP). Predictive ability values (PA) were dependent on the genomic prediction models and markers. On average, Bayesian ridge regression (BRR) and Bayes C had the highest PA for the majority of traits. Notably, genomic models that included the haplotype effect (either HAP or HAP-SNP) significantly increased the PA of low-heritability traits. For instance, BRR based on HAP had the highest PA (0.58) for stem straightness. Consistently, the heritability estimates from genomic models were higher than the pedigree-based estimates for these traits. The results provide additional perspectives for the implementation of genomic selection in Eucalyptus breeding programs, which could be especially beneficial for improving traits with low heritability.

5.
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
6.
Environ Entomol ; 48(5): 1178-1186, 2019 09 30.
Artigo em Inglês | MEDLINE | ID: mdl-31298272

RESUMO

Monophagous insects that use discrete resources for oviposition and feeding are especially sensitive to variations in host quality and availability because their opportunities to find these resources are scarce. The monophagous tephritid fly Anastrepha spatulata Stone is a tephritid fly that uses as hosts the fruits of the non-economically important Schoepfia schreberi J. F. Gmel. Scant information of host utilization behavior in the field is available for this species. Wild individually marked flies were observed during the fruiting season. Observations of oviposition, feeding and resting on three trees were taken hourly from 0900 to 1800 hours on days with benign weather. Our results suggest that females can use fruits for oviposition or for feeding according to a temporal scale. Females were significantly more likely to feed on smaller hosts and oviposit in larger ones. Additionally, individual variation in host patch exploitation was detected. However, females that fed on a natural food source such as host fruit juice oviposited fewer eggs than females provided an artificial diet of sucrose and hydrolyzed yeast. Results indicate that females use different foraging tactics during the fruiting season and confirm that, in this case, the host plant is not the center of activity.


Assuntos
Olacaceae , Tephritidae , Animais , Feminino , Frutas , Larva , Oviposição , Óvulo
7.
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
8.
G3 (Bethesda) ; 9(8): 2425-2428, 2019 08 08.
Artigo em Inglês | MEDLINE | ID: mdl-31201204

RESUMO

The dna is the fundamental basis of genetic information, just as bits are for computers. Whenever computers are used to represent genetic data, the computational encoding must be efficient to allow the representation of processes driving the inheritance and variability. This is especially important across simulations in view of the increasing complexity and dimensions brought by genomics. This paper introduces a new binary representation of genetic information. Algorithms as bitwise operations that mimic the inheritance of a wide range of polymorphisms are also presented. Different kinds and mixtures of polymorphisms are discussed and exemplified. Proposed algorithms and data structures were implemented in C++ programming language and is available to end users in the R package "isqg" which is available at the R repository (cran). Supplementary data are available online.


Assuntos
Biologia Computacional/métodos , Genômica/métodos , Software , Algoritmos
9.
PLoS One ; 14(6): e0217571, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31173600

RESUMO

Several studies have shown differences in the abilities of maize genotypes to facilitate or impede Azospirillum brasilense colonization and to receive benefits from this association. Hence, our aim was to study the genetic control, heterosis effect and the prediction accuracy of the shoot and root traits of maize in response to A. brasilense. For that, we evaluated 118 hybrids under two contrasting scenarios: i) N stress (control) and ii) N stress plus A. brasilense inoculation. The diallel analyses were performed using mixed model equations, and the genomic prediction models accounted for the general and specific combining ability (GCA and SCA, respectively) and the presence or not of G×E effects. In addition, the genomic models were fitted considering parametric (G-BLUP) and semi-parametric (RKHS) kernels. The genotypes showed significant inoculation effect for five root traits, and the GCA and SCA were significant for both. The GCA in the inoculated treatment presented a greater magnitude than the control, whereas the opposite was observed for SCA. Heterosis was weakly influenced by the inoculation, and the heterozygosity and N status in the plant can have a role in the benefits that can be obtained from this Plant Growth-Promoting Bacteria (PGPB). Prediction accuracies for N stress plus A. brasilense ranged from 0.42 to 0.78, depending on the scenario and trait, and were higher, in most cases, than the non-inoculated treatment. Finally, our findings provide an understanding of the quantitative variation of maize responsiveness to A. brasilense and important insights to be applied in maize breeding aiming the development of superior hybrids for this association.


Assuntos
Azospirillum brasilense/fisiologia , Genômica/métodos , Vigor Híbrido/genética , Zea mays/genética , Redes Reguladoras de Genes , Genoma de Planta , Heterozigoto , Hibridização Genética , Endogamia , Fenótipo , Raízes de Plantas/genética , Raízes de Plantas/crescimento & desenvolvimento , Característica Quantitativa Herdável , Estresse Fisiológico/genética
10.
Plant Genome ; 12(1)2019 03.
Artigo em Inglês | MEDLINE | ID: mdl-30951082

RESUMO

In this study, we used genotype × environment interactions (G×E) models for hybrid prediction, where similarity between lines was assessed by pedigree and molecular markers, and similarity between environments was accounted for by environmental covariables. We use five genomic and pedigree models (M1-M5) under four cross-validation (CV) schemes: prediction of hybrids when the training set (i) includes hybrids of all males and females evaluated only in some environments (T2FM), (ii) excludes all progenies from a randomly selected male (T1M), (iii) includes all progenies from 20% randomly selected females in combination with all males (T1F), and (iv) includes one randomly selected male plus 40% randomly selected females that were crossed with it (T0FM). Models were tested on a total of 1888 wheat ( L.) hybrids including 18 males and 667 females in three consecutive years. For grain yield, the most complex model (M5) under T2FM had slightly higher prediction accuracy than the less complex model. For T1F, the prediction accuracy of hybrids for grain yield and other traits of the most complete model was 0.50 to 0.55. For T1M, Model M3 exhibited high prediction accuracies for flowering traits (0.71), whereas the more complex model (M5) demonstrated high accuracy for grain yield (0.5). For T0FM, the prediction accuracy for grain yield of Model M5 was 0.61. Including genomic and pedigree gave relatively high prediction accuracy even when both parents were untested. Results show that it is possible to predict unobserved hybrids when modeling genomic general combining ability (GCA) and specific combining ability (SCA) and their interactions with environments.


Assuntos
Hibridização Genética , Modelos Genéticos , Triticum/genética , Interação Gene-Ambiente , Linhagem , Melhoramento Vegetal
11.
G3 (Bethesda) ; 9(5): 1355-1369, 2019 05 07.
Artigo em Inglês | MEDLINE | ID: mdl-30819822

RESUMO

Evidence that genomic selection (GS) is a technology that is revolutionizing plant breeding continues to grow. However, it is very well documented that its success strongly depends on statistical models, which are used by GS to perform predictions of candidate genotypes that were not phenotyped. Because there is no universally better model for prediction and models for each type of response variable are needed (continuous, binary, ordinal, count, etc.), an active area of research aims to develop statistical models for the prediction of univariate and multivariate traits in GS. However, most of the models developed so far are for univariate and continuous (Gaussian) traits. Therefore, to overcome the lack of multivariate statistical models for genome-based prediction by improving the original version of the BMTME, we propose an improved Bayesian multi-trait and multi-environment (BMTME) R package for analyzing breeding data with multiple traits and multiple environments. We also introduce Bayesian multi-output regressor stacking (BMORS) functions that are considerably efficient in terms of computational resources. The package allows parameter estimation and evaluates the prediction performance of multi-trait and multi-environment data in a reliable, efficient and user-friendly way. We illustrate the use of the BMTME with real toy datasets to show all the facilities that the software offers the user. However, for large datasets, the BME() and BMTME() functions of the BMTME R package are very intense in terms of computing time; on the other hand, less intensive computing is required with BMORS functions BMORS() and BMORS_Env() that are also included in the BMTME package.


Assuntos
Teorema de Bayes , Biologia Computacional/métodos , Interação Gene-Ambiente , Genômica/métodos , Característica Quantitativa Herdável , Software , Algoritmos , Modelos Estatísticos , Zea mays/genética
12.
G3 (Bethesda) ; 9(4): 1231-1247, 2019 04 09.
Artigo em Inglês | MEDLINE | ID: mdl-30796086

RESUMO

Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype × environment (G × E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program of the International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregón, México over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker- and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker- and pedigree-based models were marginal when correcting for days to heading. Our results demonstrate the potential of using hyperspectral imaging to predict grain yield within a multi-environment context and also support further studies on the integration of hyperspectral reflectance phenotyping into breeding programs.


Assuntos
Melhoramento Vegetal/métodos , Triticum/genética , Interação Gene-Ambiente , Marcadores Genéticos , Genoma de Planta , Genótipo , México , Fenótipo , Seleção Genética , Triticum/crescimento & desenvolvimento
13.
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
14.
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
15.
G3 (Bethesda) ; 8(9): 3019-3037, 2018 08 30.
Artigo em Inglês | MEDLINE | ID: mdl-30021830

RESUMO

Plant and animal breeders are interested in selecting the best individuals from a candidate set for the next breeding cycle. In this paper, we propose a formal method under the Bayesian decision theory framework to tackle the selection problem based on genomic selection (GS) in single- and multi-trait settings. We proposed and tested three univariate loss functions (Kullback-Leibler, KL; Continuous Ranked Probability Score, CRPS; Linear-Linear loss, LinLin) and their corresponding multivariate generalizations (Kullback-Leibler, KL; Energy Score, EnergyS; and the Multivariate Asymmetric Loss Function, MALF). We derived and expressed all the loss functions in terms of heritability and tested them on a real wheat dataset for one cycle of selection and in a simulated selection program. The performance of each univariate loss function was compared with the standard method of selection (Std) that does not use loss functions. We compared the performance in terms of the selection response and the decrease in the population's genetic variance during recurrent breeding cycles. Results suggest that it is possible to obtain better performance in a long-term breeding program using the single-trait scheme by selecting 30% of the best individuals in each cycle but not by selecting 10% of the best individuals. For the multi-trait approach, results show that the population mean for all traits under consideration had positive gains, even though two of the traits were negatively correlated. The corresponding population variances were not statistically different from the different loss function during the 10th selection cycle. Using the loss function should be a useful criterion when selecting the candidates for selection for the next breeding cycle.


Assuntos
Genoma , Modelos Genéticos , Característica Quantitativa Herdável , Seleção Genética , Teorema de Bayes
16.
Plant Genome ; 11(2)2018 07.
Artigo em Inglês | MEDLINE | ID: mdl-30025028

RESUMO

New methods and algorithms are being developed for predicting untested phenotypes in schemes commonly used in genomic selection (GS). The prediction of disease resistance in GS has its own peculiarities: a) there is consensus about the additive nature of quantitative adult plant resistance (APR) genes, although epistasis has been found in some populations; b) rust resistance requires effective combinations of major and minor genes; and c) disease resistance is commonly measured based on ordinal scales (e.g., scales from 1-5, 1-9, etc.). Machine learning (ML) is a field of computer science that uses algorithms and existing samples to capture characteristics of target patterns. In this paper we discuss several state-of-the-art ML methods that could be applied in GS. Many of them have already been used to predict rust resistance in wheat. Others are very appealing, given their performance for predicting other wheat traits with similar characteristics. We briefly describe the proposed methods in the Appendix.


Assuntos
Aprendizado de Máquina , Melhoramento Vegetal/métodos , Triticum/genética , Triticum/microbiologia , Basidiomycota/patogenicidade , Resistência à Doença/genética , Genoma de Planta , Genômica/métodos , Modelos Lineares , Modelos Genéticos , Doenças das Plantas/genética , Doenças das Plantas/microbiologia , Máquina de Vetores de Suporte
17.
Theor Appl Genet ; 131(9): 1873-1890, 2018 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-29858950

RESUMO

KEY MESSAGE: We were able to obtain good prediction accuracy in genomic selection with ~ 2000 GBS-derived SNPs. SNPs in genic regions did not improve prediction accuracy compared to SNPs in intergenic regions. Since genotyping can represent an important cost in genomic selection, it is important to minimize it without compromising the accuracy of predictions. The objectives of the present study were to explore how a decrease in the unit cost of genotyping impacted: (1) the number of single nucleotide polymorphism (SNP) markers; (2) the accuracy of the resulting genotypic data; (3) the extent of coverage on both physical and genetic maps; and (4) the prediction accuracy (PA) for six important traits in barley. Variations on the genotyping by sequencing protocol were used to generate 16 SNP sets ranging from ~ 500 to ~ 35,000 SNPs. The accuracy of SNP genotypes fluctuated between 95 and 99%. Marker distribution on the physical map was highly skewed toward the terminal regions, whereas a fairly uniform coverage of the genetic map was achieved with all but the smallest set of SNPs. We estimated the PA using three statistical models capturing (or not) the epistatic effect; the one modeling both additivity and epistasis was selected as the best model. The PA obtained with the different SNP sets was measured and found to remain stable, except with the smallest set, where a significant decrease was observed. Finally, we examined if the localization of SNP loci (genic vs. intergenic) affected the PA. No gain in PA was observed using SNPs located in genic regions. In summary, we found that there is considerable scope for decreasing the cost of genotyping in barley (to capture ~ 2000 SNPs) without loss of PA.


Assuntos
Hordeum/genética , Melhoramento Vegetal , Polimorfismo de Nucleotídeo Único , Mapeamento Cromossômico , Epistasia Genética , Marcadores Genéticos , Técnicas de Genotipagem , Modelos Genéticos , Fenótipo
18.
G3 (Bethesda) ; 8(5): 1771-1785, 2018 05 04.
Artigo em Inglês | MEDLINE | ID: mdl-29588381

RESUMO

Genomic selection (GS) has become a tool for selecting candidates in plant and animal breeding programs. In the case of quantitative traits, it is common to assume that the distribution of the response variable can be approximated by a normal distribution. However, it is known that the selection process leads to skewed distributions. There is vast statistical literature on skewed distributions, but the skew normal distribution is of particular interest in this research. This distribution includes a third parameter that drives the skewness, so that it generalizes the normal distribution. We propose an extension of the Bayesian whole-genome regression to skew normal distribution data in the context of GS applications, where usually the number of predictors vastly exceeds the sample size. However, it can also be applied when the number of predictors is smaller than the sample size. We used a stochastic representation of a skew normal random variable, which allows the implementation of standard Markov Chain Monte Carlo (MCMC) techniques to efficiently fit the proposed model. The predictive ability and goodness of fit of the proposed model were evaluated using simulated and real data, and the results were compared to those obtained by the Bayesian Ridge Regression model. Results indicate that the proposed model has a better fit and is as good as the conventional Bayesian Ridge Regression model for prediction, based on the DIC criterion and cross-validation, respectively. A computing program coded in the R statistical package and C programming language to fit the proposed model is available as supplementary material.


Assuntos
Genômica , Modelos Genéticos , Teorema de Bayes , Simulação por Computador , Resistência à Doença/genética , Método de Monte Carlo , Doenças das Plantas/genética , Análise de Regressão , Zea mays/genética
19.
Trends Plant Sci ; 22(11): 961-975, 2017 11.
Artigo em Inglês | MEDLINE | ID: mdl-28965742

RESUMO

Genomic selection (GS) facilitates the rapid selection of superior genotypes and accelerates the breeding cycle. In this review, we discuss the history, principles, and basis of GS and genomic-enabled prediction (GP) as well as the genetics and statistical complexities of GP models, including genomic genotype×environment (G×E) interactions. We also examine the accuracy of GP models and methods for two cereal crops and two legume crops based on random cross-validation. GS applied to maize breeding has shown tangible genetic gains. Based on GP results, we speculate how GS in germplasm enhancement (i.e., prebreeding) programs could accelerate the flow of genes from gene bank accessions to elite lines. Recent advances in hyperspectral image technology could be combined with GS and pedigree-assisted breeding.


Assuntos
Genoma de Planta , Modelos Genéticos , Melhoramento Vegetal/métodos , Seleção Genética , Produtos Agrícolas/genética , Interação Gene-Ambiente , Sequenciamento de Nucleotídeos em Larga Escala , Aprendizado de Máquina , Polimorfismo de Nucleotídeo Único , Locos de Características Quantitativas , Zea mays/genética
20.
Plant Genome ; 10(2)2017 07.
Artigo em Inglês | MEDLINE | ID: mdl-28724079

RESUMO

Genomic prediction models have been commonly used in plant breeding but only in reduced datasets comprising a few hundred genotyped individuals. However, pedigree information for an entire breeding population is frequently available, as are historical data on the performance of a large number of selection candidates. The single-step method extends the genomic relationship information from genotyped individuals to pedigree information from a larger number of phenotyped individuals in order to combine relationship information on all members of the breeding population. Furthermore, genomic prediction models that incorporate genotype × environment interactions (G × E) have produced substantial increases in prediction accuracy compared with single-environment genomic prediction models. Our main objective was to show how to use single-step genomic and pedigree models to assess the prediction accuracy of 58,798 CIMMYT wheat ( L.) lines evaluated in several simulated environments in Ciudad Obregon, Mexico, and to predict the grain yield performance of some of them in several sites in South Asia (India, Pakistan, and Bangladesh) using a reaction norm model that incorporated G × E. Another objective was to describe the statistical and computational challenges encountered when developing the pedigree and single-step models in such large datasets. Results indicate that the genomic prediction accuracy achieved by models using pedigree only, markers only, or both pedigree and markers to predict various environments in India, Pakistan, and Bangladesh is higher (0.25-0.38) than prediction accuracy of models that use only phenotypic prediction (0.20) or do not include the G × E term.


Assuntos
Interação Gene-Ambiente , Genótipo , Linhagem , Triticum/genética , Ásia , Genes de Plantas , Software
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