Combining Image Analysis, Genome Wide Association Studies and Different Field Trials to Reveal Stable Genetic Regions Related to Panicle Architecture and the Number of Spikelets per Panicle in Rice

Abstract

Number of spikelets per panicle (NSP) is a key trait to increase yield potential in rice (O. sativa). The architecture of the rice inflorescence which is mainly determined by the length and number of primary (PBL and PBN) and secondary (SBL and SBN) branches can influence NSP. Although several genes controlling panicle architecture and NSP in rice have been identified, there is little evidence of (i) the genetic control of panicle architecture and NSP in different environments and (ii) the presence of stable genetic associations with panicle architecture across environments. This study combines image phenotyping of 225 accessions belonging to a genetic diversity array of indica rice grown under irrigated field condition in two different environments and Genome Wide Association Studies (GWAS) based on the genotyping of the diversity panel, providing 83,374 SNPs. Accessions sown under direct seeding in one environement had reduced Panicle Length (PL), NSP, PBN, PBL, SBN, and SBL compared to those established under transplanting in the second environment. Across environments, NSP was significantly and positively correlated with PBN, SBN and PBL. However, the length of branches (PBL and SBL) was not significantly correlated with variables related to number of branches (PBN and SBN), suggesting independent genetic control. Twenty- three GWAS sites were detected with P ≤ 1.0E-04 and 27 GWAS sites with p ≤ 5.9E−04. We found 17 GWAS sites related to NSP, 10 for PBN and 11 for SBN, 7 for PBL and 11 for SBL. This study revealed new regions related to NSP, but only three associations were related to both branching number (PBN and SBN) and NSP. Two GWAS sites associated with SBL and SBN were stable across contrasting environments and were not related to genes previously reported. The new regions reported in this study can help improving NSP in rice for both direct seeded and transplanted conditions. The integrated approach of high-throughput phenotyping, multi-environment field trials and GWAS has the potential to dissect complex traits, such as NSP, into less complex traits and to match single nucleotide polymorphisms with relevant function under different environments, offering a potential use for molecular breeding.

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

“…Most of the root traits are complex polygenic in nature, and we expected that the effect of the individual underlying loci would be small. Therefore, we chose a suggestive threshold of P # 1.00E-04 to detect significant associations, as followed recently for the same population (Rebolledo et al, 2016) and in many other rice GWAS (Zhao et al, 2011;Norton et al, 2014;Dimkpa et al, 2016). The similar threshold also was used in another GWAS for rice root traits (Courtois et al, 2013 …”

“…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).…”

“…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/).…”

Identification of new sources of resistance to RHBV- rice hoja blanca virus

“…The analysis was carried out with the package Structure V2.3 using 83,374 SNPs (Rebolledo, Peña, Duitama, Cruz, Dingkuhn, Grenier & Tohme, 2016). Parameters were K=1 to 10 subpopulations, tested with 16 replications, a burning period of 20,000 iterations, and sampling period of 40,000 iterations.…”

Genotypic variation in source and sink traits affects the response of photosynthesis and growth to elevated atmospheric CO2

“… Table S1 Description of the 5 cultivars selected within the PRAY diversity panel for the present study: Values are mean of the two years (2013, 2014) in field experiments at CIAT, Colombia (Rebolledo et al, ). Local C source‐sink ratio computed as flag leaf area divided by fertile spike number on the main stem.…”