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:
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.