370 research outputs found
Biotechnology and Plant Breeding : Applications and Approaches for Developing Improved Cultivars /
No full textThis book discusses applications of biotechnology in plant breeding. It covers key topics such as biometry applied to molecular analysis of genetic diversity and genetically modified plants, and goes beyond recombinant DNA technology to bring together key information and references on new biotech tools for cultivar development.Online resource; title from PDF title page (ScienceDirect, viewed Feb. 12, 2014).Includes bibliographical references and index.1. Plant breeding and biotechnological advances / Aluizio Borem, Valdir Diola, and Roberto Fritsche-Neto -- 2. Molecular markers / Eveline Teixeira Caixeta [and three others] -- 3. Biometrics applied to molecular analysis in genetic diversity / Cosme Damiao Cruz, Caio Cesio Salgado, and Leonardo Lopes Bhering -- 4. Genome-wide association studies (GWAS) / Marcos Deon Vilela de Resende [and three others] -- 5. Genome-wide selection (GWS) / Marcos Deon Vilela de Resende [and three others] -- 6. Genes prospection / Valdir Diola and Roberto Fritsche-Neto -- 7. Tissue culture applications for the genetic improvement of plants / Moacir Pasqual, Joyce Doria Rodrigues Soares, and Filipe Almendagna Rodrigues -- 8. Transgenic plants / Francisco Murilo Zerbini [and three others] -- 9. Double haploids / Roberto Fritsche-Neto, Deoclecio Domingos Garbuglio, and Aluizio Borem -- 10. Tools for the future breeder / Valdir Diola, Aluizio Borem, and Natalia Arruda Sanglard.This book discusses applications of biotechnology in plant breeding. It covers key topics such as biometry applied to molecular analysis of genetic diversity and genetically modified plants, and goes beyond recombinant DNA technology to bring together key information and references on new biotech tools for cultivar development.Elsevie
TCGA: A TROPICAL CORN GERMPLASM ASSEMBLY FOR GENOMIC PREDICTION AND HIGH-THROUGHPUT PHENOTYPING STUDIES
No full textGenomic prediction (GS) studies using diversity panels are essential to identify genetic variations associated with traits of interest in maize. Unfortunately, most of these studies have been conducted on temperate germplasm and on a global germplasm collection in which tropical genotypes are under-represented. Nonetheless, a continuous effort has been directed to improving the accuracy of GS. While genotyping is currently a precise and efficient mechanized process, phenotyping is still laborious, low-throughput, and highly sensitive to environmental variations. Also, extreme shifts in the weather pattern due to climate change complicates the selection of superior genotypes with broad adaptability. In this context, adopting GS models that account for genotypes x environments reaction norms, crop growth models, and environmental covariates should increase the accuracy of genomic predictions. Thus, the objective of this project is to develop a diversity panel of tropical maize for genomic prediction studies that incorporate high-throughput phenotyping, plant growth models, and environmental covariables. For that, 360 tropical maize lines from ESALQ-USP, IAPAR, IAC, and CIMMYT will be genotyped and phenotyped using traditional methods and multispectral imaging in eight environments (two locations, two years, and two seasons). With this data, several GS models will be tested and compared for prediction accuracy and selection coincidence. Besides the development of novel GS models and high-throughput phenotyping protocols in tropical maize, we will also organize, characterize, and publicize a panel of tropical maize lines (data and genetic material) to the scientific community that will serve as the benchmark for new studies of this nature in tropical maize.
FAPESP (2017/24327-0
Datasets of "Association mapping for image-based root traits in tropical maize under water stress in semi-arid regions"
No full textWater stress is the factor that most negatively impacts agricultural production. In this context, root system traits, such as length, surface area, volume, and mass, are paramount in water deficit studies, as they play a central role in plant growth, allocation, and acquisition of soil resources. However, the plant evaluation for them and under water stress is very difficult. Therefore, an alternative has been to obtain surrogate variables from image processing. Moreover, identifying genomic regions or genes associated with the expression of the root system under water deficit may allow breeding programs to outline more effective strategies for obtaining efficient genotypes. Hence, a public diversity panel composed of 360 inbred maize lines was evaluated via image-based root traits at phenological stage V6 (six expanded leaves) under well-water (WW) and water-stress (WS) conditions. Then, genetic association analyses (GWAS) were conducted for each image-based trait in WW and WS using the Fixed and Random Model Circulating Probability Unification (FarmCPU) method. A total of 23 markers were identified in association with all the traits in the two water supply conditions, 12 only in WW, four associated with traits in WW and WS, and seven exclusives to WS. All those genomic regions are associated with physiological mechanisms and molecular responses related to water deficit tolerance that can be explored in subsequent studies and by breeding programs to obtain more resilient genotypes for this condition. Furthermore, image-based features are a valuable tool to dissect root traits in WS conditions.'Here you can find all the data and scripts used to perform this study
Tropical interspecific raspberry panel data
No full textMake available all datasets related to the "Tropical interspecific raspberry panel"https://bv.fapesp.br/pt/auxilios/106448/componentes-epidemiologicos-caracterizacao-de-danos-e-controle-de-ferrugens-tropicais-e-temperadas-e
Genomic Dataset of the Commercial Germplasm in Brazil
No full textA few breeding companies dominate the maize (Zea mays L.) hybrid market in Brazil: Mon- santo® (35%), DuPont Pioneer® (30%), Dow Agrosciences® (15%), Syngenta® (10%) and Helix Sementes (4%). Therefore, it is important to monitor the genetic diversity in commer- cial germplasms as breeding practices, registration and marketing of new cultivars can lead to a significant reduction of the genetic diversity. Reduced genetic variation may lead to crop vulnerabilities, food insecurity and limited genetic gains following selection. The aim of this study was to evaluate the genetic vulnerability risk by examining the relationship between the commercial Brazilian maize germplasms and the Nested Association Mapping (NAM) Parents. For this purpose, we used the commercial hybrids with the largest market share in Brazil and the NAM parents. The hybrids were genotyped for 768 single nucleotide polymorphisms (SNPs), using the Illumina Goldengate® platform. The NAM parent geno- mic data, comprising 1,536 SNPs for each line, were obtained from the Panzea data bank. The population structure, genetic diversity and the correlation between allele frequencies were analyzed. Based on the estimated effective population size and genetic variability, it was found that there is a low risk of genetic vulnerability in the commercial Brazilian maize germplasms. However, the genetic diversity is lower than those found in the NAM parents. Furthermore, the Brazilian germplasms presented no close relations with most NAM parents, except B73. This indicates that B73, or its heterotic group (Iowa Stiff Stalk Syn- thetic), contributed to the development of the commercial Brazilian germplasms.
The hybrids (20) are in the rows and the markers (768) are in the columns. Quality control over data (MAF and Call Rate) was not performed. Genotypes are referred to by nitrogenous bases (eg G / G) and information lost by - / -
USP tropical maize hybrid panel
No full text906 maize single-crosses obtained from a full dial- lel, according to Griffing’s method 4, divided into two heterotic groups, flint and dent, with 34 and 15 lines, respec- tively. Moreover, each heterotic group has a representative line, frequently used as the tester in our breeding program.
The experimental scheme used to evaluate the hybrids was an augmented block design (unreplicated trial) consisted of small blocks, each with 16 unique hybrids and two checks. Trials were carried out in Anhembi (22°50′51′′S, 48°01′06′′W, 466 m) and Piracicaba, at São Paulo State, Brazil (22°42′23′′S, 47°38′14′′W, 535 m), during the second growing season of 2016 and 2017, cultivated between January to June. In both sites and years, the hybrids were evaluated under two nitrogen (N) levels, low (LN) with 30 kg N ha−1, and normal (NN) with 100 kg N ha−1.
The genotyping of the 49 tropical inbred lines was per- formed by Affymetrix® platform, containing about 614,000 SNPs (Unterseer et al. 2014). Then, markers with low call rate (< 95%), minor allele frequency (MAF < 0.05) and heterozygous loci on at least one individual were removed. The missing markers were imputed using the snpReady R package. Finally, the resulting 146,365 SNPs high-quality polymorphic SNPs were used to build the artificial hybrids genomic matrix, deduced by combining the genotypes from its two parents
USP tropical maize hybrid panel
No full text906 maize single-crosses obtained from a full dial- lel, according to Griffing’s method 4, divided into two heterotic groups, flint and dent, with 34 and 15 lines, respec- tively. Moreover, each heterotic group has a representative line, frequently used as the tester in our breeding program.
The experimental scheme used to evaluate the hybrids was an augmented block design (unreplicated trial) consisted of small blocks, each with 16 unique hybrids and two checks. Trials were carried out in Anhembi (22°50′51′′S, 48°01′06′′W, 466 m) and Piracicaba, at São Paulo State, Brazil (22°42′23′′S, 47°38′14′′W, 535 m), during the second growing season of 2016 and 2017, cultivated between January to June. In both sites and years, the hybrids were evaluated under two nitrogen (N) levels, low (LN) with 30 kg N ha−1, and normal (NN) with 100 kg N ha−1.
The genotyping of the 49 tropical inbred lines was per- formed by Affymetrix® platform, containing about 614,000 SNPs (Unterseer et al. 2014). Then, markers with low call rate (< 95%), minor allele frequency (MAF < 0.05) and heterozygous loci on at least one individual were removed. The missing markers were imputed using the snpReady R package. Finally, the resulting 146,365 SNPs high-quality polymorphic SNPs were used to build the artificial hybrids genomic matrix, deduced by combining the genotypes from its two parents
Correction to: female reproductive organs of Brassica napus are more sensitive than male to transient heat stress
No full textThe article Female reproductive organs of Brassica napus are more sensitive than male to transient heat stress, written by Sheng Chen, Renu Saradadevi, Miriam S. Vidotti, Roberto Fritsche-Neto, Jose Crossa, Kadambot H. M. Siddique, Wallace A. Cowling, was originally published Online First without Open Access. After publication in volume 217: 117 the author decided to opt for Open Choice and to make the article an Open Access publication. Therefore, the copyright of the article has been changed t
Correction to: Female reproductive organs of Brassica napus are more sensitive than male to transient heat stress (Euphytica, (2021), 217, 6, (117), 10.1007/s10681-021-02859-z)
No full textThe article Female reproductive organs of Brassica napus are more sensitive than male to transient heat stress, written by Sheng Chen, Renu Saradadevi, Miriam S. Vidotti, Roberto Fritsche-Neto, Jose Crossa, Kadambot H. M. Siddique, Wallace A. Cowling, was originally published Online First without Open Access. After publication in volume 217: 117 the author decided to opt for Open Choice and to make the article an Open Access publication. Therefore, the copyright of the article has been changed to © The Author(s) 2021 and the article is forthwith distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
Envirômica, kernels não-lineares e otimização de populações de treinamento na predição genômica inteligente para o clima com foco na plasticidade fenotípica em milho
No full textLarge-scale envirotyping (environmental + typing) or simply enviromics, is an emerging field of data science, applied both in agronomic research and plant breeding. This \"omics\" consists of gathering and processing reliable environmental information, respecting the crop-specific ecophysiology aspects, then for further integration of this data into quantitative genetics and prediction-based breeding. However, most of the current prediction-based platforms are based on genotype-phenotype relationships (i.e., the phenotype-genotype association enabled by whole-genome markers), in which the state-of-art of this approach in the context of predictive breeding is so-called genomic selection or prediction (GP). Despite the success of its use in preliminary breeding stages, mostly conducted under restricted environmental variations (e.g., few number of environments or a single environment), the occurrence of low accuracy values are still a reality under multiple environmental conditions, in which is detected the presence of the so-called \"genotype by environment interaction\" (G×E). On the other hand, knowledge of crop ecophysiology can be the alternative to boost the accuracy of GP under G×E. This environmental variation shapes genotype-specific phenotypic responses to a given gradient of soil, climate and management factors i.e., the reaction norm. In this thesis, we conducted three studies aimed to investigate the use of GP enviromics under G×E scenarios, using for this the grain yield of two datasets of tropical maize hybrids. The first study of this thesis involves the development of the first open-source software dedicated to envirotyping in genomic prediction. In this study, we elucidate the use of remote sensing to popularize the use of envirotyping, as well as aspects of ecophysiology useful to understand and define the concepts of \'environment\', \'enviromics\' and \'envirotyping\'. In the second chapter, we verify the accuracy gains acquired by the adoption of non-linear kernels (Gaussian Kernel, GK; Deep Kernel, DK) for modeling non-additive effects (e.g., dominance and envirotyping-enabled reaction-norms) using the traditional GBLUP (genomic best linear unbiased predictor) as a reference method. Our results suggest that non-linear kernels (GK and DK) are the best alternative to model non-additive and reaction norm effects. The adoption of GK or DK reduced the computational time in running the models, as well as increased the accuracy to predict complex G×E interactions (variations in the rank of genotypes across environments). Finally, we observe that the use of GK or DK for modeling non-additive effects is critical to expand GP\'s resolve to predict the interaction of a particular maize hybrid across multiple environments. Finally, in the third chapter we propose the concept of \'envirotype marker\', developed by reconciling classical concepts of ecophysiology (Shelford\'s Law) and characterization of the environmental typology (i.e., frequency of occurrence of qualitative classes of environmental factors over time and over time. space). The approach was exemplified with two case studies covering the hypothetical use of GP under evaluation trials in maize hybrids in different environments. The combined use of enviromics and genomics made it possible to design a prediction platform (called E-GP) that reconciles selective phenotyping (reduction of training populations for GP) and prediction of future scenarios (i.e., unknown G×E). We observed that the increase in phenotypic information in various environments does not always correspond to the increase in the accuracy of GP. Therefore, the representativeness of each hybrid under evaluation at the experimental network (most representative genotypes, evaluated in \"key\" environments) is more important than the number of genotypes and environments considered for training GP. Through E-GP together with genetic algorithms, we were able to select the most representative G×E combinations, which directly reflected in a drastic reduction in the size of the experimental network, reconciling increased accuracy. Finally, we found that GBLUP without any envirotyping information is inefficient in predicting the phenotypic plasticity of maize hybrids under multiple environments and unknown G×E. With E-GP it was possible to screen the best hybrids, in terms of phenotypic plasticity, using reduced phenotypic information and supplemented by the wide use of genomics and enviromics. Such results allow us to envision smart approaches to climate, involving the drastic reduction of field-testing efforts as the conscious use of enviromics (and envirotyping) combined with genomics increases.A tipagem de ambientes em larga escala, ou simplesmente a envirômica, é um campo emergente de ciência de dados, tanto na pesquisa agrícola como nas rotinas de programas de melhoramento. Esta \"omica\" consiste em reunir e processar informações ambientais, respeitando a ecofisiologia do cultivo para, por fim, integrá-las na genômica quantitativa e na seleção baseada em modelos preditivos. No entanto, a maioria das atuais plataformas baseadas em predição aplicáveis ao melhoramento de plantas são baseadas nas relações genótipo-fenótipo, isto é; na modelagem a variação fenotípica em função da variação genômica caracterizada por marcadores moleculares, na qual o estado da arte é denominado por seleção ou predição genômica (GP). Apesar do sucesso de seu uso em estágios preliminares de melhoramento, sob condições restritas variações ambientais (p.ex: poucos ambientes ou um único ambiente), baixas acurácias ainda são observadas sob múltiplas condições ambientais, na presença de \"interação genótipo por ambiente\" (G×E). Por outro lado, o conhecimento da ecofisiologia dos cultivos pode ser a alternativa para impulsionar aumentar a acurácia da GP sob G×E. Esta variação ambiental molda respostas fenotípicas específicas de cada genótipo a um dado gradiente de fatores de solo, clima e manejo isto é, a norma de reação. Nesta tese, buscamos estudar esses aspectos, através da realização de três estudos voltados para o uso de envirômica com GP sob cenários de G×E, usando para isso o rendimento de grãos de dois conjuntos de dados de híbridos de milho tropical. O primeiro estudo desta tese envolve o desenvolvimento do primeiro software de código aberto dedicado a ambitipagem (tradução proposta para o termo envirotyping) em predição genômica. Neste estudo, elucidamos o uso de sensoriamento remoto para popularizar o uso da ambitipagem, assim como aspectos de ecofisiologia úteis para compreender e definir os conceitos de \'ambiente\', \'envirômica\' e \'ambitipagem\'. No segundo capítulo, verificamos os ganhos de acurácia adquiridos pela adoção de kernels não lineares (Gaussian Kernel, GK; Deep Kernel, DK) para modelagem de efeitos não-aditivos (p.ex: dominância e ambitipagem), usando o tradicional GBLUP (genomic best linear unbiased predictor) como método de referência. Nossos resultados sugerem que os kernels não lineares (GK e DK) são a melhor alternativa para modelar efeitos não-aditivos e de norma de reação. A adoção de GK ou DK reduziu o tempo computacional na execução dos modelos, como também aumentou a precisão para prever interações G×E complexas/cruzadas (variações no rank dos genótipos através dos ambientes). Por fim, observamos que o uso de GK ou DK para modelagem de efeitos não-aditivos é fundamental para expandir a resolução da GP em predizer a interação de um hibrido de milho particular através de múltiplos ambientes. Finalmente, no terceiro capítulo propomos o conceito de \"marcador qualitativo de ambiente\", desenvolvido conciliando conceitos clássicos de ecofisiologia (Lei de Shelford) e caracterização da tipologia ambiental (isto é, frequência de ocorrência de classes qualitativas de fatores ambientais através do tempo e do espaço). A abordagem foi exemplificada com dois estudos de caso abrangendo o uso hipotético de GP sob ensaios de avaliação de em híbridos de milho em diversos ambientes. O uso combinado de envirômica e genômica possibilitou conceber uma plataforma de predição (denominada E-GP) que concilia fenotipagem seletiva (redução das populações de treinamento para GP) e predição de cenários futuros (isto é, G×E desconhecidas). Observamos que o aumento de informações fenotípicas em vários ambientes nem sempre corresponde ao aumento de acurácia da GP. Portanto, a representatividade da rede de avaliação de híbridos (genótipos mais representativos, avaliados nos ambientes \"chave\") é mais importante que o número de genótipos e ambientes considerados. Através de E-GP juntamente a algoritmos genéticos, fomos capazes de selecionar as combinações G×E mais representativas, o que refletiu diretamente em uma redução drástica do tamanho da rede experimental, conciliando aumento de acurácia. Por fim, constatamos que o GBLUP sem nenhuma informação de ambitipagem é ineficiente em predizer a plasticidade fenotípica dos híbridos de milho sob múltiplos ambientes e G×E desconhecida. Com E-GP foi possível realizar uma triagem dos melhores híbridos, em termos de plasticidade fenotípica, usando reduzidas informações fenotípicas e suplementadas pelo amplo uso de genômica e envirômica. Tais resultados permitem vislumbrar abordagens inteligentes para o clima, envolvendo a redução drástica dos esforços de testes de campo à medida que aumenta o uso consciente de envirômica (e ambitipagem) combinada com genômica
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