&lt;b&gt;QTLs identification for characteristics of the root system in upland rice through DNA microarray

Terra, Thiago Gledson Rios; Rodrigues, Haroldo Silva; Rangel, P. H. N.; Tomaz, Rafael Simões; Cruz, Cosme Damião; Borém, Aluízio

doi:10.4025/actasciagron.v38i4.30534

Cited by 5 publications

(4 citation statements)

References 18 publications

(21 reference statements)

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“…The SITIS phenotyping platform provided quality data for assessing the root system throughout plant development up to heading, from the top layer and down to 60 cm, in 20 cm layers. Despite high CVs, as commonly found in root studies (Courtois et al 2013 ; Terra et al 2016 ; Li et al 2017 ), the SITIS platform brought out systematic highly significant effects of accessions and water treatments, as well as frequent interaction effects (Table 2 ), confirming its good performance for root screening.…”

Section: Discussionmentioning

confidence: 57%

“…The SITIS phenotyping platform provided quality data for assessing the root system throughout plant development up to heading, from the top layer and down to 60 cm, in 20 cm layers. Despite high CVs, as commonly found in root studies (Courtois et al 2013;Terra et al 2016;Li Fig. 4 Shoot dry weight (SDW) of 17 accessions under two water conditions.…”

Section: Discussionmentioning

confidence: 89%

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Phenotyping Root Systems in a Set of Japonica Rice Accessions: Can Structural Traits Predict the Response to Drought?

Guimarães

Lima

Castro

et al. 2020

Rice

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Background The root system plays a major role in plant growth and development and root system architecture is reported to be the main trait related to plant adaptation to drought. However, phenotyping root systems in situ is not suited to high-throughput methods, leading to the development of non-destructive methods for evaluations in more or less controlled root environments. This study used a root phenotyping platform with a panel of 20 japonica rice accessions in order to: (i) assess their genetic diversity for a set of structural and morphological root traits and classify the different types; (ii) analyze the plastic response of their root system to a water deficit at reproductive phase and (iii) explore the ability of the platform for high-throughput phenotyping of root structure and morphology. Results High variability for the studied root traits was found in the reduced set of accessions. Using eight selected traits under irrigated conditions, five root clusters were found that differed in root thickness, branching index and the pattern of fine and thick root distribution along the profile. When water deficit occurred at reproductive phase, some accessions significantly reduced root growth compared to the irrigated treatment, while others stimulated it. It was found that root cluster, as defined under irrigated conditions, could not predict the plastic response of roots under drought. Conclusions This study revealed the possibility of reconstructing the structure of root systems from scanned images. It was thus possible to significantly class root systems according to simple structural traits, opening up the way for using such a platform for medium to high-throughput phenotyping. The study also highlighted the uncoupling between root structures under non-limiting water conditions and their response to drought.

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Section: Discussionmentioning

confidence: 57%

“…The SITIS phenotyping platform provided quality data for assessing the root system throughout plant development up to heading, from the top layer and down to 60 cm, in 20 cm layers. Despite high CVs, as commonly found in root studies (Courtois et al 2013;Terra et al 2016;Li Fig. 4 Shoot dry weight (SDW) of 17 accessions under two water conditions.…”

Section: Discussionmentioning

confidence: 89%

Phenotyping Root Systems in a Set of Japonica Rice Accessions: Can Structural Traits Predict the Response to Drought?

Guimarães

Lima

Castro

et al. 2020

Rice

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“…Li et al, 2015T. Li et al, , 2017Liu et al, 2005;Qu et al, 2008;Solis et al, 2018;Terra et al, 2016). In the Basmati rice background, a 'Super Basmati' and IR55419-04 RIL population was exploited for QTL identification associated with drought tolerance traits through SSR markers under greenhouse controlled conditions (Sabar et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Drought response QTLs in a Super Basmati × Azucena population by high‐density GBS‐based SNP linkage mapping

et al. 2021

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Basmati rice is favoured for its grain type and aroma, but its production is vulnerable due to climate change and freshwater accessibility. Discovery and refinement of genomic regions conferring drought tolerance are needed to develop new drought tolerant Basmati varieties. Using Recombinant Inbred Lines (RILs) derived from a cross between drought-sensitive elite Basmati variety, 'Super Basmati', and droughttolerant japonica cultivar, 'Azucena', a linkage map was constructed with 862 highquality GBS-based SNP markers. Thirty-three QTLs were detected for yield-related and physiological traits. Of five QTL clusters, two were identified as novel on chromosome 7 and 11 for shoot dry weight and water uptake that could be designated as promising QTLs, and their candidate gene analysis suggested contributions to yield in the 'Super Basmati' background. Comparing the greenhouse and field results, this study suggests that selection of drought tolerance donor varieties must be matched with the target growth stage of drought tolerance needed. Along with fine mapping and functional marker selection, the QTL clusters identified can be further tested to contribute to Basmati rice improvement.

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“…QTL analysis has been used to identify molecular markers responsible for the variation in an observed trait (DHARIWAL et al, 2018;LI et al, 2016). In our study on genomic analysis, the detection of QTLs associated with a characteristic of interest is represented in Figure 4, and was established using the simple interval method (CRUZ, 2016;TERRA et al, 2016).…”

Section: Recognition Of Groups Of Factorial Linkage and Linkage Group...mentioning

confidence: 99%

Análise de fatores para redução de dimensionalidade em estudos de predição genômica

Oliveira¹

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O conceito de seleção genômica tem como base o desequilíbrio de ligação (LD) entre locos de características quantitativas (QTLs) e marcadores. Uma variação genética que se relaciona com a forma que o fenótipo é expresso conduz a múltiplas associações estatísticas em marcadores próximos em termos de ligação fatorial ou de desequilíbrio, podendo estas associações ser ou não de causa e efeito. Assim ao construir modelos preditivos, em geral não é conhecido quais SNPs possuem de fato associação de causa e efeito com o fenótipo de interesse, consequentemente o modelo é construído utilizando todas as informações genotípicas. Com o intuito de aumentar a acurácia dos modelos de predição, diferentes abordagens de seleção de marcadores foram propostas. São estratégias utilizadas para isto selecionar SNPs relatados anteriormente em estudos de associação para a característica de interesse, estimar a significância dos SNPs no conjunto de dados para cada característica utilizando um modelo preditivo e o efeito dos marcadores estimados pelo modelo, ou a seleção subconjuntos dos marcadores uniformemente espaçados ao longo do genoma. Dentre as abordagens citadas anteriormente, a seleção uniformemente espaçada ao longo do genoma é a mais versátil, uma vez que um painel de baixa densidade formado por meio dela pode ser utilizado em estudos de predição de valores genéticos de qualquer característica, diferentemente das outras abordagens citadas. Porém esta seleção está sujeita a possibilidade de excluir por completo blocos de haplótipos em LD relacionados com o fenótipo de interesse. Este trabalho foi desenvolvido com o objetivo de propor uma abordagem de seleção de marcadores espaçados dentro de blocos de haplótipos construídos utilizando Análise de Fatores (AF). Mostramos, utilizando dados simulados que a Análise de Fatores pode ser utilizada para construir os blocos de haplótipos, sendo ela capaz de sintetizar a relação linear entre marcadores e criar fatores comuns que podem ser interpretados como blocos de LD. Em seguida utilizamos em um conjunto de dados de soja, contendo 41985 marcadores do tipo SNPs com informação de 20087 acessos de soja, esta abordagem para construir os blocos e então foi feito a seleção espaçada dentro dos blocos formados a partir da AF. Três painéis de SNPs foram considerados, contendo 1%, 5% e 100% dos marcadores. Para avaliar o êxito desta abordagem, foi considerado a acurácia em uma tarefa de predição do valor fenotípico dos indivíduos utilizando os painéis reduzidos e o painel completo. Os resultados mostram que ao utilizar os painéis reduzidos não há diferença significativa de acurácia seletiva comparado a acurácia obtida utilizando o painel completo e para uma das características avaliadas também não foi encontrada diferença significativa para acurácia preditiva. Palavras-chave: SNP. GWS. Seleção de Marcadores. Análise Fatorial. Soja. Aprendizado de Máquina. Blocos de Haplótipos.

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<b>QTLs identification for characteristics of the root system in upland rice through DNA microarray

Cited by 5 publications

References 18 publications

Phenotyping Root Systems in a Set of Japonica Rice Accessions: Can Structural Traits Predict the Response to Drought?

Phenotyping Root Systems in a Set of Japonica Rice Accessions: Can Structural Traits Predict the Response to Drought?

Drought response QTLs in a Super Basmati × Azucena population by high‐density GBS‐based SNP linkage mapping

Análise de fatores para redução de dimensionalidade em estudos de predição genômica

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