Genome‐wide association mapping for phenotypic plasticity in rice

Kikuchi, Shinichi; Bheemanahalli, Raju; Jagadish, Krishna; Kumagai, Etsushi; Masuya, Yusuke; Kuroda, Eiki; Raghavan, Chitra; Dingkuhn, Michaël; Abe, Akira; Shimono, Hiroyuki

doi:10.1111/pce.12955

Cited by 52 publications

(79 citation statements)

References 44 publications

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“…Two different data sets with different missing SNP imputation from genotype-by-sequencing data were recently used in GWAS analysis for this panel (i.e. the 90K SNPs data set with 22.8% missing imputation by Rebolledo et al [2016] and the 45K SNPs data set with 8.75% missing imputation by Kikuchi et al [2017]). In addition, this panel also was genotyped with a 700K SNPs data set and recently used in a GWAS (Al-Tamimi et al, 2016).…”

Section: Snp Genotyping Datamentioning

confidence: 99%

“…S1; Supplemental Data Set S1). This panel was carefully assembled at the International Rice Research Institute for the Phenomics of Rice Adaptation and Yield Potential project for use in GWAS (Al-Tamimi et al, 2016;Rebolledo et al, 2016;Kikuchi et al, 2017) in the context of the GRiSP Global Rice Phenotyping Network (http://ricephenonetwork.irri.org/).…”

Section: Plant Materialsmentioning

confidence: 99%

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Genetic Control of Plasticity in Root Morphology and Anatomy of Rice in Response to Water Deficit

et al. 2017

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Elucidating the genetic control of rooting behavior under water-deficit stress is essential to breed climate-robust rice (Oryza sativa) cultivars. Using a diverse panel of 274 indica genotypes grown under control and water-deficit conditions during vegetative growth, we phenotyped 35 traits, mostly related to root morphology and anatomy, involving 45,000 root-scanning images and nearly 25,000 cross sections from the root-shoot junction. The phenotypic plasticity of these traits was quantified as the relative change in trait value under water-deficit compared with control conditions. We then carried out a genome-wide association analysis on these traits and their plasticity, using 45,608 high-quality single-nucleotide polymorphisms. One hundred four significant loci were detected for these traits under control conditions, 106 were detected under water-deficit stress, and 76 were detected for trait plasticity. We predicted 296 (control), 284 (water-deficit stress), and 233 (plasticity) a priori candidate genes within linkage disequilibrium blocks for these loci. We identified key a priori candidate genes regulating root growth and development and relevant alleles that, upon validation, can help improve rice adaptation to water-deficit stress.

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Section: Snp Genotyping Datamentioning

confidence: 99%

Section: Plant Materialsmentioning

confidence: 99%

Genetic Control of Plasticity in Root Morphology and Anatomy of Rice in Response to Water Deficit

et al. 2017

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“…As plants grown at low density face reduced competition for solar radiation, water and soil nutrients, and as a consequence, plants with greater plasticity are able to adapt and utilize this resourceful environmental condition more efficiently (Kumagai et al 2015, MolinaMontenegro et al 2016. In terms of resource enrichment, the response of the phenotypic plasticity of plants in low density planting should mimic the response towards e[CO2] (Kumagai et al 2015, Kikuchi et al 2017. The proposed similar behavior of phenotypic plasticity and e[CO2] responsiveness has been applied and confirmed for rice in terms of panicle number (Shimono et al 2014) and for soybean in terms of seed yield (Kumagai et al 2015).…”

Section: Introductionmentioning

confidence: 72%

“…Positive gains in yield components and biomass in low density planting in comparison with high density planting is used as a measure of phenotypic plasticy. Therefore the ratio of a specific phenotypic parameter of the low density planting and normal density planting (LD : ND) is considered as the 'relative response' of a particular genotype (Kikuchi et al 2017). The performed analysis was conducted to evaluate the range of the relative responses of the traits as well as to compare the two different maturity group.…”

Section: Methodsmentioning

confidence: 99%

“…(Shimono 2011) introduced a simple and novel idea to use planting geometry as an alternative pre-screening technique by using phenotypic plasticity as a proxy of [CO 2 ] responsiveness. Phenotypic plasticity is defined as the ability of changing physiological, molecular or metabolic performance of a genotype to cope up with the heterogeneous environmental variation (Price et al 2003, Kikuchi et al 2017. As plants grown at low density face reduced competition for solar radiation, water and soil nutrients, and as a consequence, plants with greater plasticity are able to adapt and utilize this resourceful environmental condition more efficiently (Kumagai et al 2015, MolinaMontenegro et al 2016.…”

Section: Introductionmentioning

confidence: 99%

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Relative Response of Indigenous Rice Genotypes to Low Versus Normal Planting Density for Determination of Differential Phenotypic Plasticity in Traits Related to Grain Yield

Tareq

Rahman

Jewel

et al. 2018

Plant Tissue Cult. & Biotech.

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Increase in atmospheric CO 2 ([CO 2 ]) improves the efficiency of the unsaturated photosynthetic system of C 3 plants, like rice, which leads to high crop productivity and increased biomass production. Planting geometry using lower than standard planting density has been shown to be an alternative pre-screening technique for phenotypic plasticity as a proxy of [CO 2 ] responsiveness. More than 200 indigenous rice genotypes were tested for several traits, such as plant height, tiller number, panicle number, panicle dry weight, straw dry weight, total dry weight and harvest index. Their relative response for these phenotypic traits at low density planting versus normal density was determined to assess the plasticity of the genotypes. Ten genotypes were identified as significantly [CO2] responsive based on their higher panicle dry weight and panicle number. Even though it was observed that genotypes with higher days to maturity (DM) were more plastic, rice genotypes with low DM had a significantly higher relative response to harvest index, implying that rice with low DM may also be considered to be responsive to higher [CO2]. In order to associate phenotypic plasticity with the genotype, 23 SSR markers physically close to genes involved in CO2 metabolism were used to amplify selected responsive and non-responsive rice. Out of 3 alleles amplified using RM17, allele A was found to be significantly associated with genotypes with higher phenotypic plasticity whereas allele C was associated with genotypes with negligible response. The identified rice genotypes and the associated marker RM17 can therefore be tested further for breeding of superior rice responsive to higher [CO2] envisaged in a changing climatic scenario of the future.

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Identification of genomic regions associated with the plasticity of carbon 13 ratio in soybean

et al. 2022

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Improving water use efficiency (WUE) for soybean [Glycine max (L.) Merr.] through selection for high carbon isotope (C13) ratio may increase drought tolerance, but increased WUE may limit growth in productive environments. An ideal genotype would be plastic for C13 ratio; that is, be able to alter C13 ratio in response to the environment. Our objective was to identify genomic regions associated with C13 ratio plasticity, C13 ratio stability, and overall C13 ratio in two panels of diverse Maturity Group IV soybean accessions. A second objective was to identify accessions that differed in their C13 ratio plasticity. Panel 1 (205 accessions) was evaluated in seven irrigated and four drought environments, and Panel 2 (373 accessions) was evaluated in four environments. Plasticity was quantified as the slope from regressing C13 ratio of individual genotypes against an environmental index calculated based on the mean within and across environments. The regression intercept was considered a measure of C13 ratio over all environments, and the root mean square error was considered a measure of stability. Combined over both panels, genome‐wide association mapping (GWAM) identified 19 single nucleotide polymorphisms (SNPs) for plasticity, 39 SNPs for C13 ratio, and 16 SNPs for stability. Among these SNPs, 71 candidate genes had annotations associated with transpiration or water conservation and transport, root development, root hair elongation, and stomatal complex morphogenesis. The genomic regions associated with plasticity and stability identified in the current study will be a useful resource for implementing genomic selection for improving drought tolerance in soybean.

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Genome‐wide association mapping for phenotypic plasticity in rice

Cited by 52 publications

References 44 publications

Genetic Control of Plasticity in Root Morphology and Anatomy of Rice in Response to Water Deficit

Genetic Control of Plasticity in Root Morphology and Anatomy of Rice in Response to Water Deficit

Relative Response of Indigenous Rice Genotypes to Low Versus Normal Planting Density for Determination of Differential Phenotypic Plasticity in Traits Related to Grain Yield

Identification of genomic regions associated with the plasticity of carbon 13 ratio in soybean

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