Genetic Dissection of Grain Yield Component Traits Under High Nighttime Temperature Stress in a Rice Diversity Panel.

Anuj Kumar,Andy Pereira,Julie Thomas,Chirag Gupta

doi:10.3389/fpls.2021.712167

Anuj Kumar, Andy Pereira + Show 2 more

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https://doi.org/10.3389/fpls.2021.712167

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Abstract

To dissect the genetic complexity of rice grain yield (GY) and quality in response to heat stress at the reproductive stage, a diverse panel of 190 rice accessions in the United States Department of Agriculture (USDA) rice mini-core collection (URMC) diversity panel were treated with high nighttime temperature (HNT) stress at the reproductive stage of panicle initiation. The quantifiable yield component response traits were then measured. The traits, panicle length (PL), and number of spikelets per panicle (NSP) were evaluated in subsets of the panel comprising the rice subspecies Oryza sativa ssp. Indica and ssp. Japonica. Under HNT stress, the Japonica ssp. exhibited lower reductions in PL and NSP and a higher level of genetic variation compared with the other subpopulations. Whole genome sequencing identified 6.5 million single nucleotide polymorphisms (SNPs) that were used for the genome-wide association studies (GWASs) of the PL and NSP traits. The GWAS analysis in the Combined, Indica, and Japonica populations under HNT stress identified 83, 60, and 803 highly significant SNPs associated with PL, compared to the 30, 30, and 11 highly significant SNPs associated with NSP. Among these trait-associated SNPs, 140 were coincident with genomic regions previously reported for major GY component quantitative trait loci (QTLs) under heat stress. Using extents of linkage disequilibrium in the rice populations, Venn diagram analysis showed that the highest number of putative candidate genes were identified in the Japonica population, with 20 putative candidate genes being common in the Combined, Indica and Japonica populations. Network analysis of the genes linked to significant SNPs associated with PL and NSP identified modules that were involved in primary and secondary metabolisms. The findings in this study could be useful to understand the pathways/mechanisms involved in rice GY and its components under HNT stress for the acceleration of rice-breeding programs and further functional analysis by molecular geneticists.

Highlights

Rice (Oryza sativa L.) is the main food source for more than half the world population and one of the most important cereal crops after wheat, supplying 35–60% of the dietary calorie intake for an estimated three billion people worldwide (Fageria, 2007; FAO, 2009; GRISP, 2013)
The Venn diagram analysis revealed remarkable findings, in which 12 putative potential candidate genes identified through highly significant single nucleotide polymorphisms (SNPs) associated with panicle length (PL) were found to be common in all the three rice populations (Combined, Indica, and Japonica), 173 putative candidate genes were common in the Indica and Japonica populations, and 83 putative candidate genes identified by genome-wide association studies (GWASs) SNPs associated with number of spikelets per panicle (NSP) were common in the Indica and Japonica populations
Trait correlation analysis determined that PL was positively correlated with NSP in the Combined, Indica, and Japonica populations under high nighttime temperature (HNT) stress, and, like other important grain yield (GY) components, panicle size and NSP were crucial components contributing to GY enhancement in rice

Summary

Introduction

Rice (Oryza sativa L.) is the main food source for more than half the world population and one of the most important cereal crops after wheat, supplying 35–60% of the dietary calorie intake for an estimated three billion people worldwide (Fageria, 2007; FAO, 2009; GRISP, 2013). It is considered as the most diverse and versatile crop in the world, grown between 53◦N in northeastern China to 35◦S in New South Wales, Australia (Mae, 1997; Santos et al, 2003), and distributed across tropical, subtropical, and temperate regions (Vaughan, 1989) worldwide. By 2030, meeting future demand could be hindered by changing climate conditions, as water scarcity and the increased frequency of extreme weather events have shown negative impacts on rice yield (Satake and Yoshida, 1978; Jagadish et al, 2007, 2010; Mohammed and Tarpley, 2009a,b; Foley et al, 2011; Coast et al, 2015; Röth et al, 2016; Lesjak and Calderini, 2017)

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