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Doctoral Thesis
DOI
https://doi.org/10.11606/T.11.2022.tde-07122022-122617
Document
Author
Full name
Rafael Massahiro Yassue
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
Piracicaba, 2022
Supervisor
Committee
Fritsche Neto, Roberto (President)
Consoli, Fernando Luis
Pereira, Guilherme da Silva
Title in English
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
Keywords in English
Genomic prediction
GWAS
Hyperspectral
Machine learning
Multispectral images
Phenomics
Shovelomics
Abstract in English
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.
Title in Portuguese
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?
Keywords in Portuguese
Aprendizado de máquina
Fenômica
GWAS
Hiperspectral
Multispectral
Predição genômica
Shovelomics
Abstract in Portuguese
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.
 
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Publishing Date
2022-12-12
 
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