Breeding Jatropha curcas by genomic selection: A pilot assessment of the accuracy of predictive models

Genomic wide selection is a promising approach for improving the selection accuracy in plant breeding, particularly in species with long life cycles, such as Jatropha. Therefore, the objectives of this study were to estimate the genetic parameters for grain yield (GY) and the weight of 100 seeds (W100S) using restricted maximum likelihood (REML); to compare the performance of GWS methods to predict GY and W100S; and to estimate how many markers are needed to train the GWS model to obtain the maximum accuracy. Eight GWS models were compared in terms of predictive ability. The impact that the marker density had on the predictive ability was investigated using a varying number of markers, from 2 to 1,248. Because the genetic variance between evaluated genotypes was significant, it was possible to obtain selection gain. All of the GWS methods tested in this study can be used to predict GY and W100S in Jatropha. A training model fitted using 1,000 and 800 markers is sufficient to capture the maximum genetic variance and, consequently, maximum prediction ability of GY and W100S, respectively. This study demonstrated the applicability of genome-wide prediction to identify useful genetic sources of GY and W100S for Jatropha breeding. Further research is needed to confirm the applicability of the proposed approach to other complex traits

Potential of maize (Zea mays L.) populations derived from commercial single-cross hybrids for extraction of partially inbred lines under different nitrogen availability

Extraction of inbred lines is a very important step in maize breeding since these lines will be used to obtain hybrids intended for the market. However, this expensive process, hinders genotype evaluation in contrasting environments, especially regarding nitrogen (N) content. This study aimed to evaluate the potential of maize populations for line extraction and select partially inbred lines under different soil nitrogen (N) availability. Five populations were evaluated regarding their potential for line extraction. Fifty-five S1 partially inbred lines were extracted from these populations. The top-cross method was used to evaluate lines by crossing them with two testers. Hybrids evaluation used 110 top-cross hybrids, five base populations, and six checks. Two trials were carried out in Dourados and Caarapó. One trial had adequate fertilization (high N) while the other was under stress condition (low N). Hybrid DKB 789 showed potential for extraction of partially inbred lines, aiming at selecting N use efficiency. Base population BP (07) was the most suitable for the extraction of partially inbred lines aiming at N use efficiency. Partially inbred lines BP (07) 13, BP (07) 14, and BP (07) 17 are the most suitable for the extraction of top-cross hybrids with high grain yield, efficiency, and responsiveness to N. Highlights Extraction of inbred lines is a very important step in maize breeding. Hybrid DKB 789 showed potential for extraction of partially inbred lines. BP (07) 13, BP (07) 14, and BP (07) 17 are the most suitable for the extraction of top-cross hybrids.Extraction of inbred lines is a very important step in maize breeding since these lines will be used to obtain hybrids intended for the market. However, this expensive process, hinders genotype evaluation in contrasting environments, especially regarding nitrogen (N) content. This study aimed to evaluate the potential of maize populations for line extraction and select partially inbred lines under different soil nitrogen (N) availability. Five populations were evaluated regarding their potential for line extraction. Fifty-five S1 partially inbred lines were extracted from these populations. The top-cross method was used to evaluate lines by crossing them with two testers. Hybrids evaluation used 110 top-cross hybrids, five base populations, and six checks. Two trials were carried out in Dourados and Caarapó. One trial had adequate fertilization (high N) while the other was under stress condition (low N). Hybrid DKB 789 showed potential for extraction of partially inbred lines, aiming at selecting N use efficiency. Base population BP (07) was the most suitable for the extraction of partially inbred lines aiming at N use efficiency. Partially inbred lines BP (07) 13, BP (07) 14, and BP (07) 17 are the most suitable for the extraction of top-cross hybrids with high grain yield, efficiency, and responsiveness to N. Highlights Extraction of inbred lines is a very important step in maize breeding. Hybrid DKB 789 showed potential for extraction of partially inbred lines. BP (07) 13, BP (07) 14, and BP (07) 17 are the most suitable for the extraction of top-cross hybrids

Leveraging genomic prediction to scan germplasm collection for crop improvement

The objective of this study was to explore the potential of genomic prediction (GP) for soybean resistance against Sclerotinia sclerotiorum (Lib.) de Bary, the causal agent of white mold (WM). A diverse panel of 465 soybean plant introduction accessions was phenotyped for WM resistance in replicated field and greenhouse tests. All plant accessions were previously genotyped using the SoySNP50K BeadChip. The predictive ability of six GP models were compared, and the impact of marker density and training population size on the predictive ability was investigated. Cross-prediction among environments was tested to determine the effectiveness of the prediction models. GP models had similar prediction accuracies for all experiments. Predictive ability did not improve significantly by using more than 5k SNPs, or by increasing the training population size (from 50% to 90% of the total of individuals). The GP model effectively predicted WM resistance across field and greenhouse experiments when each was used as either the training or validation population. The GP model was able to identify WM-resistant accessions in the USDA soybean germplasm collection that had previously been reported and were not included in the study panel. This study demonstrated the applicability of GP to identify useful genetic sources of WM resistance for soybean breeding. Further research will confirm the applicability of the proposed approach to other complex disease resistance traits and in other crops

MUDANÇAS CLIMÁTICAS E SEUS POTENCIAIS IMPACTOS SOBRE OS MÉTODOS DE MANEJO DE DOENÇAS DE PLANTA

As mudanças climáticas poderão alterar o desenvolvimento de doenças de plantas e afetar a eficácia dos métodos de manejo. Resultados indicam que as mudanças climáticas provavelmente alterarão a distribuição geográfica de doenças de plantas e modificação a resistência genética do hospedeiro. Além disso, caso ocorra o deslocamento das áreas de cultivo, novas doenças poderão surgir em determinadas regiões e outras poderão perde ou aumentar sua importância econômica. Estima-se também que as possíveis alterações na fisiologia da interação patógeno-hospedeiro, influenciarão a eficácia do manejo biológico das doenças de planta. Isto possivelmente ocorrerá devido ao efeito das mudanças climáticas tanto sobre a interação patógeno-hospedeiro, bem como sobre a população de organismos presentes no sítio de infecção. Outro importante fator é a redução da eficácia dos produtos químico empregados no manejo de doenças de plantas. Portanto novas tecnologias e táticas de aplicação de agroquímicos deverão ser empregadas, visando minimizar as possíveis perdas e otimizar o uso destes produtos no manejo de doenças de plantas. Desta forma, a intensificação de pesquisas sobre a relação das mudanças climáticas com manejo de doenças de plantas, poderá elucidar os reais impactos destas complexas alterações que possivelmente ocorrerão no futuro

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART): Study protocol for a randomized controlled trial

Background: Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality. Alveolar recruitment followed by ventilation at optimal titrated PEEP may reduce ventilator-induced lung injury and improve oxygenation in patients with ARDS, but the effects on mortality and other clinical outcomes remain unknown. This article reports the rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART). Methods/Design: ART is a pragmatic, multicenter, randomized (concealed), controlled trial, which aims to determine if maximum stepwise alveolar recruitment associated with PEEP titration is able to increase 28-day survival in patients with ARDS compared to conventional treatment (ARDSNet strategy). We will enroll adult patients with ARDS of less than 72 h duration. The intervention group will receive an alveolar recruitment maneuver, with stepwise increases of PEEP achieving 45 cmH(2)O and peak pressure of 60 cmH2O, followed by ventilation with optimal PEEP titrated according to the static compliance of the respiratory system. In the control group, mechanical ventilation will follow a conventional protocol (ARDSNet). In both groups, we will use controlled volume mode with low tidal volumes (4 to 6 mL/kg of predicted body weight) and targeting plateau pressure <= 30 cmH2O. The primary outcome is 28-day survival, and the secondary outcomes are: length of ICU stay; length of hospital stay; pneumothorax requiring chest tube during first 7 days; barotrauma during first 7 days; mechanical ventilation-free days from days 1 to 28; ICU, in-hospital, and 6-month survival. ART is an event-guided trial planned to last until 520 events (deaths within 28 days) are observed. These events allow detection of a hazard ratio of 0.75, with 90% power and two-tailed type I error of 5%. All analysis will follow the intention-to-treat principle. Discussion: If the ART strategy with maximum recruitment and PEEP titration improves 28-day survival, this will represent a notable advance to the care of ARDS patients. Conversely, if the ART strategy is similar or inferior to the current evidence-based strategy (ARDSNet), this should also change current practice as many institutions routinely employ recruitment maneuvers and set PEEP levels according to some titration method.Hospital do Coracao (HCor) as part of the Program 'Hospitais de Excelencia a Servico do SUS (PROADI-SUS)'Brazilian Ministry of Healt

Approach on models, covariables and accuracy in the genomic selection

A seleção genômica (SG) tem se tormado uma ferramenta de grande potencial no melhoramento de plantas. Além dela, o estudo de associação genômica (EAGA) e a seleção assistida por marcadores moleculares (SAM) também são metodologias com aplicabilidade no melhoramento. A diferença básica entre essas metodologias é que enquanto a SAM utiliza mapas de ligação e o EAGA utiliza mapas de associação para identificar marcadores significativos, a SG utiliza todos os marcadores disponíveis sem a necessidade de nenhum tipo de mapa. Portanto os objetivos desta pesquisa foram: 1) avaliar modelos utilizando os SNPs significativos encontrados pelos SAM e EAGA como efeito fixo nos modelos comumente utilizados na SG, em que no modelo tradicional, todos os SNPs são estabelecidos como de efeito aleatório. Estes modelos foram comparados com o modelo padrão utilizado na SG (RRBLUP bayesiano); 2) comparar os métodos tradicionais de seleção genômica (todos os SNPs como efeito aleatório); 3) verificar como a herdabilidade e o número de QTLs que controlam a característica podem influenciar na predição do valor genético; 4) estabelecer uma equação de predição da correlação genética em função da correlação fenotípica; 5) estabelecer o número ideal de indivíduos para compor a população de treinamento e; 6) estabelecer a quantidade necessária de marcadores para obter máxima acurácia pelos métodos de seleção genômica. Foram simuladas populações F 2 com 1.000 indivíduos em diferentes cenários. As populações foram simuladas com 4.500 (objetivo 1) e 3.000 marcadores (demais objetivos). Foram simuladas características com diferentes herdabilidades (5, 20, 40, 60, 80 e 99%) e o número de QTLs (60, 120, 180 e 240) (objetivos 2, 3 e 4). Foram estimados para todos os cenários a capacidade preditiva fenotípica e genotípica, a acurácia fenotipica e genotípica, a herdabilidade genômica, a variância genética, o ganho com a seleção, o índice de coincidência e o tempo de processamento. Foi utilizado a cross validação 5-fold com 50 repetições. As principais conclusões desta pesquisa foram: 1) A utilização de um modelo de SG com as marcas significativas encontradas pelo EAGA como efeito fixo e as demais marcas como efeito aleatório é uma boa estratégia para selecionar indivíduos superiores com alta acurácia; 2) A introdução no modelo de SG de QTLs que já foram descritos previamente para a característica em estudo, como efeito fixo, permite a seleção de indivíduos superiores de forma mais acurada; 3) os modelos de seleção genômica para predição em populações F 2 devem ser compostos por 200 a 900 marcadores de maior efeito sobre a característica e mais de 600 indivíduos na população de treinamento.Genomic selection (GS) has become a high potential tool in plant breeding. Moreover, genomic wide association study (GWAS) and marker-assisted selection (MAS) are also methodologies with great potential in plant breeding. The basic difference among them is while MAS requires linkage mapping and GWAS requires association mapping to identify significant markers, GWS performs all available markers without any mapping. Therefore, the objectives in this research were: 1) to evaluate models using significant SNPs found by GWAS and MAS as fixed effect in the widely GS models, which, in the traditional model all SNPs are treated as random effect. These models were compared with the standart GS model (Bayesian RRBLUP); 2) To compare the most GS traditional models (all SNPs as random effect); 3) to verify how the heritability and number of QTLs which control a specific trait can influence for predicting genetic value; 4) to establish a prediction equation to estimate the genetic correlation based on phenotypic correlation; 5) to establish the optimal number of individuals to compose the training population and; 6) to establish the number of markers needed to obtain the maximum accuracy by the genomic selection methods. F 2 population was simulated with 1,000 individuals in several scenarios. Populations were simulated with 4,500 (objective 1) and 3,000 markers (other objectives). Traits with different heritability (5%, 20%, 40%, 60%, 80% and 99%) and numbers of QTLs (60, 120, 180 and 240 – objectives 2, 3, and 4) were simulated. Phenotypic and genotypic predictive ability, phenotypic and genotypic accuracy, genomic heritability, genetic variance, selection gain, conincidence index, and processing time were estimated for all scenarios. 5-fold cross validation was repeated 50 times. The mainly conclusion in this research were: 1) SG model performed with the significant markers found by GWAS as fixed effect and the remaining SNPs as random effects is a useful strategy to select superior individuals with high accuracy; 2) GS model performed with the QTLs, previously reported for the traits in study, as fixed effect allows the selection of superior individuals more accurate; 3) Genomic selection models should be composed with number of markers ranged from 200 to 900 and number of individuals in the training population beyond 600.Conselho Nacional de Desenvolvimento Científico e Tecnológic

Artificial neural networks in prediction of genetics value

Os objetivos do presente trabalho foram a partir das populações simuladas, fazer a replicação ou a ampliação de conjuntos populacionais, com as mesmas características pontuais de média, herdabilidade e coeficiente de variação e de estruturação (matriz de covariância ou de correlações), por meio da utilização da t écnica de decomposição espectral, e avaliar a eficiência da utilização das redes neurais artificiais na predição do valor genético em experimentos em blocos casualizados. O delineamento utilizado para simulação dos experimentos foi blocos casualizados contendo seis repetições. Foram simuladas 80 cenários, que possuíam valores estabelecidos para a média, a herdabilidade e coeficiente de variação experimental. Os experimentos simulados foram utilizadas para treinamento (experimento com 5000 genótipos) e validação (100 experimentos com 150 ou 200 genótipos para cada cenário). Para medir a eficiência da RNA na predição do valor genético comparou-se a correlação do valor de rede com o valor genético e a correlação do valor fenotípico com a valor genético. A média, herdabilidade, CV e a matriz de variância e covariância foram mantidos constantes em todos os experimentos simulados. Isso foi possível através da utilização da técnica de decomposição espectral. As redes neurais foram eficientes para predição do valor genético com ganho de 0,64 a 10,3% em relação ao valor fenotípico independente do tamanho de população utilizada, da herdabilidade ou do coeficiente de variação simulado. Assim concluiu-se que a preservação da matriz de variâncias e covariâncias de dados experimentais foi eficientemente realizada por meio do uso da decomposição espectral da matriz observada. Portanto os conjuntos de dados simulados podem ser utilizados para treinamento das RNAs mantendo as características da população original. Assim verificou-se que as RNAs é uma técnica mais eficiente na predição do valor genético em experimentos balanceados, quando comparado ao valor fenotípico (média).The aim this present work were from the simulated population, make replication or enlargement of populations sets, with the same characteristics off from average, herdability and variation coefficient and organization (covariance or correlation matrix), by means of spectral decomposition, and evaluable efficiency of use artificial neural network in the prediction genetics value in randomized blocks experiments. The design used to simulation of the experiments was randomized blocks with six repetitions. They were simulated 80 conformation, when get established values of average, herdability and experimental variation coefficient. Experiments simulated were used to training (experiment with 5000 genotypes) and validation (100 experiment with 150n and 200 genotypes by each conformation). Thus to measurement the efficiency of the ANN in prediction of genetic value compared its correlation between network value with genetic value and correlation between phenotypic value with genetic value. Average, herdability, variation coefficient and variance and covariance matrix were maintained constant in all experiments simulated. It was possible through spectral decomposition techniques. Neural network were efficient for prediction of genetic value with gain 0,64 until 10,3% in relation phenotypic value regardless of size from populations, herdability or variation coefficient simulated. Thus concluded that preservation variance and covariance matrix of experimental data was efficiently performed by using spectral decomposition of the matrix observed. Therefore datasets simulated can be used to training of the ANNs maintaining original populations characteristics. Thus it was found that ANNs is a technique more efficient by prediction of genetic value in balanced experiments, when compared with phenotype value (average).Coordenação de Aperfeiçoamento de Pessoal de Nível Superio