Crop-model assisted phenomics and genome-wide association study for climate adaptation of indica rice. 1. Phenology

Abstract

Phenology and time of flowering are crucial determinants of rice adaptation to climate variation. A previous study characterized flowering responses of 203 diverse indica rices (the ORYTAGE panel) to ten environments in Senegal (six sowing dates) and Madagascar (two years and two altitudes) under irrigation in the field. This study used the physiological phenology model RIDEV V2 to heuristically estimate component traits of flowering such as cardinal temperatures (base temperature (Tbase) and optimum temperature), basic vegetative phase, photoperiod sensitivity and cold acclimation, and to conduct a genome-wide association study for these traits using 16 232 anonymous single-nucleotide polymorphism (SNP) markers. The RIDEV model after genotypic parameter optimization explained 96% of variation in time to flowering for Senegal alone and 91% for Senegal and Madagascar combined. The latter was improved to 94% by including an acclimation parameter reducing Tbase when the crop experienced low temperatures during early vegetative development. Eighteen significant (P<1.0 × 10-5) quantitative trait loci (QTLs) were identified, namely ten for RIDEV parameters and eight for climatic index variables (difference in time to flowering between key environments). Co-localization of QTLs for different traits were rare. RIDEV parameters gave QTLs that were mostly more significant and distinct from QTLs for index variables. Candidate genes were investigated within the estimated 50% linkage disequilibrium regions of 39 kB. In addition to several known flowering network genes, they included genes related to thermal stress adaptation and epigenetic control mechanisms. The peak SNP for a QTL for the crop parameter Tbase (P=2.0 × 10-7) was located within HD3a, a florigen that was recently identified as implicated in flowering under cool conditions.

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

“…Similar results have been recently reported for flowering time as a complex trait (Dingkuhn et al 2017a). Despite this advantage of model-based dissection analysis over complex traits like grain yield per se, the latter approach cannot be replaced completely.…”

“…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.…”

Analysis and Modeling of Processes Involved with Salt Tolerance and Rice

“…The explicit representation of key ecophysiological processes is thus crucial to allow the model to properly interpret the complex genotype ´ environment ´ management interactions characterizing the underlying system. Moreover, the direct relationships between model parameters and plant tolerance traits make this model suitable for decomposing complex traits ("salt tolerance") into simple ones, in turn allowing the exploration of associations between traits and molecular markers (Dingkuhn et al, 2017). As an example, the parameter "threshold leaf sodium concentration" could relate to candidate genes NHX and AVP involved with the sequestration of Na + into leaf vacuoles (Munns and Tester, 2008).…”

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…”