From pixel to knowledge: how high-throughput phenotyping helps to dissect the genetic architecture and improves predictive ability in maize under inoculation with plant growth-promoting bacteria

Detalhes bibliográficos
Ano de defesa: 2022
Autor(a) principal: Yassue, Rafael Massahiro
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Biblioteca Digitais de Teses e Dissertações da USP
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Aprendizado de máquina
Fenômica
Genomic prediction
GWAS
Hiperspectral
Hyperspectral
Machine learning
Multispectral
Multispectral images
Phenomics
Predição genômica
Shovelomics
Link de acesso: https://www.teses.usp.br/teses/disponiveis/11/11137/tde-07122022-122617/
Resumo: Plant growth-promoting bacteria (PGPB) may play an important role in the agriculture in the future due to the ability of these bacteria in promote growth without causing any type of environmental damage. Besides, they can increase the plant resilience against biotic and abiotic stress and improve nutrient uptake. Nevertheless, only a few works have studied the genetic architecture of the response to PGPB. Another emerging field is the high-throughput phenotyping (HTP) which can be used to improve the assessment of the new phenotypes and be integrated in genetics studies. Based on this, we study the genetic architect of the response to PGPB using a public tropical association panel containing 360 inbreeds lines genotyped using genotype-by-sequence methodology with 13,826 single-nucleotide polymorphisms using RGB, multi, and hyperspectral cameras, besides the traditional phenotypes. Also, we develop a low-cost HTP platform for greenhouses experiments. In addition, several single-trait, multi-trait, machine learning models and its application in the context of genetics studies is discussed. Collectively, our results reveal the usefulness of PGPB in increase plant resilience and the applications of HTP phenotypes in genetics studies to dissect the genetic architecture and improve the accuracy in predictive models.
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spelling From pixel to knowledge: how high-throughput phenotyping helps to dissect the genetic architecture and improves predictive ability in maize under inoculation with plant growth-promoting bacteriaDo pixel a informação: como fenotipagem de alto rendimento pode avaliar a arquitetura genética e melhor a capacidade preditiva em milho sob inoculação de bactérias promotoras de crescimento de plantas?Aprendizado de máquinaFenômicaGenomic predictionGWASGWASHiperspectralHyperspectralMachine learningMultispectralMultispectral imagesPhenomicsPredição genômicaShovelomicsShovelomicsPlant growth-promoting bacteria (PGPB) may play an important role in the agriculture in the future due to the ability of these bacteria in promote growth without causing any type of environmental damage. Besides, they can increase the plant resilience against biotic and abiotic stress and improve nutrient uptake. Nevertheless, only a few works have studied the genetic architecture of the response to PGPB. Another emerging field is the high-throughput phenotyping (HTP) which can be used to improve the assessment of the new phenotypes and be integrated in genetics studies. Based on this, we study the genetic architect of the response to PGPB using a public tropical association panel containing 360 inbreeds lines genotyped using genotype-by-sequence methodology with 13,826 single-nucleotide polymorphisms using RGB, multi, and hyperspectral cameras, besides the traditional phenotypes. Also, we develop a low-cost HTP platform for greenhouses experiments. In addition, several single-trait, multi-trait, machine learning models and its application in the context of genetics studies is discussed. Collectively, our results reveal the usefulness of PGPB in increase plant resilience and the applications of HTP phenotypes in genetics studies to dissect the genetic architecture and improve the accuracy in predictive models.Bactérias promotoras de crescimento de plantas (BPCP) podem ter um papel crucial no futuro da agricultura devido a sua capacidade de promover o crescimento de plantas, sem causar nenhum tipo de dano ambiental. Além disso, BPCP possuem a capacidade de aumentar a resiliência do seu hospedeiro contra estresses bióticos e abióticos, além de promover o aumento da absorção de nutrientes. No entanto, poucos trabalhos estudaram a arquitetura genética da resposta ao BPCP. Outro campo emergente é a fenotipagem de alto rendimento (FAR) que pode ser usada para melhorar a avaliação dos novos fenótipos e ser integrada em estudos de arquitetura genética. Com base nisso, estudamos a arquitetura genética da resposta as BPCP usando um painel público de associação de milho tropical contendo 360 linhagens genotipadas usando a metodologia genotype-by-sequence com um total de 13.826 polimorfismos de nucleotídeo único (SNPs). Para as avaliações foram utilizadas câmeras RGB, multi e hiperespectral, além dos fenótipos tradicionais. Além disso, desenvolvemos uma plataforma de FAR de baixo custo para experimentos em casa de vegetações. No trabalho são discutidos vários modelos single, multi-trait e de aprendizado de máquina, e suas aplicações no contexto de estudos genéticos. Coletivamente, nossos resultados revelam a utilidade do BPCP no aumento da resiliência das plantas e as aplicações dos fenótipos FAR em estudos genéticos para dissecar a arquitetura genética e melhorar a acurácia em modelos preditivos.Biblioteca Digitais de Teses e Dissertações da USPFritsche Neto, RobertoYassue, Rafael Massahiro2022-09-09info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfhttps://www.teses.usp.br/teses/disponiveis/11/11137/tde-07122022-122617/reponame:Biblioteca Digital de Teses e Dissertações da USPinstname:Universidade de São Paulo (USP)instacron:USPLiberar o conteúdo para acesso público.info:eu-repo/semantics/openAccesseng2022-12-12T17:49:52Zoai:teses.usp.br:tde-07122022-122617Biblioteca Digital de Teses e Dissertaçõeshttp://www.teses.usp.br/PUBhttp://www.teses.usp.br/cgi-bin/mtd2br.plvirginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.bropendoar:27212022-12-12T17:49:52Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv From pixel to knowledge: how high-throughput phenotyping helps to dissect the genetic architecture and improves predictive ability in maize under inoculation with plant growth-promoting bacteria
Do pixel a informação: como fenotipagem de alto rendimento pode avaliar a arquitetura genética e melhor a capacidade preditiva em milho sob inoculação de bactérias promotoras de crescimento de plantas?
title From pixel to knowledge: how high-throughput phenotyping helps to dissect the genetic architecture and improves predictive ability in maize under inoculation with plant growth-promoting bacteria
spellingShingle From pixel to knowledge: how high-throughput phenotyping helps to dissect the genetic architecture and improves predictive ability in maize under inoculation with plant growth-promoting bacteria
Yassue, Rafael Massahiro
Aprendizado de máquina
Fenômica
Genomic prediction
GWAS
GWAS
Hiperspectral
Hyperspectral
Machine learning
Multispectral
Multispectral images
Phenomics
Predição genômica
Shovelomics
Shovelomics
title_short From pixel to knowledge: how high-throughput phenotyping helps to dissect the genetic architecture and improves predictive ability in maize under inoculation with plant growth-promoting bacteria
title_full From pixel to knowledge: how high-throughput phenotyping helps to dissect the genetic architecture and improves predictive ability in maize under inoculation with plant growth-promoting bacteria
title_fullStr From pixel to knowledge: how high-throughput phenotyping helps to dissect the genetic architecture and improves predictive ability in maize under inoculation with plant growth-promoting bacteria
title_full_unstemmed From pixel to knowledge: how high-throughput phenotyping helps to dissect the genetic architecture and improves predictive ability in maize under inoculation with plant growth-promoting bacteria
title_sort From pixel to knowledge: how high-throughput phenotyping helps to dissect the genetic architecture and improves predictive ability in maize under inoculation with plant growth-promoting bacteria
author Yassue, Rafael Massahiro
author_facet Yassue, Rafael Massahiro
author_role author
dc.contributor.none.fl_str_mv Fritsche Neto, Roberto
dc.contributor.author.fl_str_mv Yassue, Rafael Massahiro
dc.subject.por.fl_str_mv Aprendizado de máquina
Fenômica
Genomic prediction
GWAS
GWAS
Hiperspectral
Hyperspectral
Machine learning
Multispectral
Multispectral images
Phenomics
Predição genômica
Shovelomics
Shovelomics
topic Aprendizado de máquina
Fenômica
Genomic prediction
GWAS
GWAS
Hiperspectral
Hyperspectral
Machine learning
Multispectral
Multispectral images
Phenomics
Predição genômica
Shovelomics
Shovelomics
description Plant growth-promoting bacteria (PGPB) may play an important role in the agriculture in the future due to the ability of these bacteria in promote growth without causing any type of environmental damage. Besides, they can increase the plant resilience against biotic and abiotic stress and improve nutrient uptake. Nevertheless, only a few works have studied the genetic architecture of the response to PGPB. Another emerging field is the high-throughput phenotyping (HTP) which can be used to improve the assessment of the new phenotypes and be integrated in genetics studies. Based on this, we study the genetic architect of the response to PGPB using a public tropical association panel containing 360 inbreeds lines genotyped using genotype-by-sequence methodology with 13,826 single-nucleotide polymorphisms using RGB, multi, and hyperspectral cameras, besides the traditional phenotypes. Also, we develop a low-cost HTP platform for greenhouses experiments. In addition, several single-trait, multi-trait, machine learning models and its application in the context of genetics studies is discussed. Collectively, our results reveal the usefulness of PGPB in increase plant resilience and the applications of HTP phenotypes in genetics studies to dissect the genetic architecture and improve the accuracy in predictive models.
publishDate 2022
dc.date.none.fl_str_mv 2022-09-09
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/doctoralThesis
format doctoralThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://www.teses.usp.br/teses/disponiveis/11/11137/tde-07122022-122617/
url https://www.teses.usp.br/teses/disponiveis/11/11137/tde-07122022-122617/
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv
dc.rights.driver.fl_str_mv Liberar o conteúdo para acesso público.
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Liberar o conteúdo para acesso público.
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.coverage.none.fl_str_mv
dc.publisher.none.fl_str_mv Biblioteca Digitais de Teses e Dissertações da USP
publisher.none.fl_str_mv Biblioteca Digitais de Teses e Dissertações da USP
dc.source.none.fl_str_mv
reponame:Biblioteca Digital de Teses e Dissertações da USP
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Biblioteca Digital de Teses e Dissertações da USP
collection Biblioteca Digital de Teses e Dissertações da USP
repository.name.fl_str_mv Biblioteca Digital de Teses e Dissertações da USP - Universidade de São Paulo (USP)
repository.mail.fl_str_mv virginia@if.usp.br|| atendimento@aguia.usp.br||virginia@if.usp.br
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