Crop-model assisted phenomics and genome-wide association study for climate adaptation of indica rice. 2. Thermal stress and spikelet sterility

Abstract

Low night and high day temperatures during sensitive reproductive stages cause spikelet sterility in rice. Phenotyping of tolerance traits in the field is difficult because of temporal interactions with phenology and organ temperature differing from ambient. Physiological models can be used to separate these effects. A 203-accession indica rice diversity panel was phenotyped for sterility in ten environments in Senegal and Madagascar and climate data were recorded. Here we report on sterility responses while a companion study reported on phenology. The objectives were to improve the RIDEV model of rice thermal sterility, to estimate response traits by fitting model parameters, and to link the response traits to genomic regions through genome-wide association studies (GWAS). RIDEV captured 64% of variation of sterility when cold acclimation during vegetative stage was simulated, but only 38% when it was not. The RIDEV parameters gave more and stronger quantitative trait loci (QTLs) than index variables derived more directly from observation. The 15 QTLs identified at P<1 × 10-5 (33 at P<1 × 10-4) were related to sterility effects of heat, cold, cold acclimation, or unexplained causes (baseline sterility). Nine annotated genes were found on average within the 50% linkage disequilibrium (LD) region. Among them, one to five plausible candidate genes per QTL were identified based on known expression profiles (organ, stage, stress factors) and function. Meiosis-, development- and flowering-related genes were frequent, as well a stress signaling kinases and transcription factors. Putative epigenetic factors such as DNA methylases or histone-related genes were frequent in cold-acclimation QTLs, and positive-effect alleles were frequent in cold-tolerant highland rice from Madagascar. The results indicate that epigenetic control of acclimation may be important in indica rice genotypes adapted to cool environments.

MCRiceRepGP: a framework for the identification of genes associated with sexual reproduction in rice

“…Such studies have been successfully used to uncover genomic regions containing loci associated with agronomic traits (Huang et al ., ; Yano et al ., ). Although high‐density SNP maps give good resolution to GWAS studies, several candidate genes within the region of interest are usually identified (Dingkuhn et al ., ). Use of additional lines of evidence such as those used to identify SexRep genes can help point to higher‐confidence candidates within the sections of the genome identified by GWAS.…”

“…Use of additional lines of evidence such as those used to identify SexRep genes can help point to higher‐confidence candidates within the sections of the genome identified by GWAS. We compared the genomic locations of recently identified SNPs linked to heat stress‐associated sterility in rice (Dingkuhn et al ., ) with coordinates of SexRep genes and identified six SexRep genes potentially related to sterility (Table S14). The number of SexRep genes found in the vicinity of sterility‐associated SNPs (closer to the SNP than any other gene) was higher than would be expected by chance (chi square, P < 0.01).…”

Role of Modelling in International Crop Research: Overview and Some Case Studies

“…Potato disease modelling, foresight, further model development Kroschel et al, 2013 [167]; Sporleder et al, 2013 [168]; Condori et al, 2014 [169]; Carli et al, 2014 [170]; Kleinwechter et al, 2016 [171]; Kroschel et al, 2017 [172]; Fleisher et al, 2017 [173]; Raymundo et al, 2017 [174]; Raymundo et al, 2017 [175]; Quiroz et al, 2017 [176]; Ramirez et al, 2017 [177]; Mujica et al, 2017 [178]; Scott and Kleinwechter, 2017 [179]; Petsakos et al, 2018 [180] AfricaRice: Model improvement, yield gap analysis, genotype × environment interactions, impact of climate change van Oort et al, 2014 [181]; van Oort et al, 2015 [182]; van Oort et al, 2015 [183]; Dingkuhn et al, 2015 [184]; van Oort et al, 2016 [185]; El-Namaky and van Oort, 2017 [186]; van Oort et al, 2017 [187]; Dingkuhn et al, 2017 [104,105]; van Oort and Zwart, 2018 [188]; van Oort, 2018 [189], Duku et al, 2018 [190] ICRAF: Agroforestry and intercropping modelling Africa Luedeling et al, 2014 [191]; Araya et al, 2015 [192]; Luedeling et al, 2016 [193]; Smethurst et al, 2017 [194], Masikati et al, 2017 [195] ILRI: crop-livestock-farm interactions Van Wijk et al, 2014 [196]; Herrero et al, 2014 [197] IITA: Modelling on Yams in West Africa Marcos et al, 2011 [198]; Cornet et al, 2015 [199]; Cornet et al, 2016 [200] ICARDA: Climate variability and change impact studies, foresight, conservation agriculture impact, genotype × environment interactions Sommer et al, 2013 [201]; Bobojonov and Aw-Hassan, 2014…”

Physiological and genetic dissection of rice tolerance to water-deficit stress

“…Mangin et al (2017) showed that crop models can be used to develop "stress indicators" that explain yield variation across multiple environments, facilitating GWAS application to identify relevant QTLs for yield in response to environmental stresses. Similarly, Dingkuhn et al (2017a) and Dingkuhn et al (2017b) have shown that the crop model RIDEV can dissect phenology and spikelet sterility, respectively, into their components, thereby heuristically strengthening the phenotyping and GWAS analysis of these two traits. These studies demonstrated benefit of crop modelling in GWAS analysis.…”

“…To the best of our knowledge only few (and very recent) studies were conducted on linking GWAS with crop growth modelling (Mangin et al, 2017;Dingkuhn et al, 2017a;Dingkuhn et al, 2017b). Mangin et al (2017) showed that crop models can be used to develop "stress indicators" that explain yield variation across multiple environments, facilitating GWAS application to identify relevant QTLs for yield in response to environmental stresses.…”