Genome-wide association study and gene network analyses reveal potential candidate genes for high night temperature tolerance in rice

Bheemanahalli, Raju; Knight, Montana; Quiñones, Cherryl; Doherty, Colleen J.

doi:10.1038/s41598-021-85921-z

Cited by 15 publications

(15 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Likewise, compared with Raza et al (2020) [ 126 ], the average interval distance of heat-tolerance meta-QTLs was smaller in this study. Besides, 47 blast-resistant and 21 heat-tolerant genes published were found in meta-QTL regions ( Table 1 and Table 2 ), such as Pi27 (t) [ 65 ], Pi-Da(t) [ 66 ], Pi14 [ 67 ], Pi16 (t) [ 68 ], Pid1 (t) [ 68 ], OsHsfA7 [ 104 ], OsHsfC1a [ 104 ], OsWRKY11 [ 105 ], OsGR2 [ 106 ], OsRb1 [ 107 ] and OsHSP58.7 [ 112 ]. Therefore, this study not only narrowed the confidence interval, but also improved the credibility of meta-QTLs related to these two traits, which would lay a foundation for further mining of crucial resistant genes.…”

Section: Discussionmentioning

confidence: 99%

Selection of Candidate Genes Conferring Blast Resistance and Heat Tolerance in Rice through Integration of Meta-QTLs and RNA-Seq

Tian

Chen

et al. 2022

Genes

View full text Add to dashboard Cite

Due to global warming, high temperature is a significant environmental stress for rice production. Rice (Oryza sativa L.), one of the most crucial cereal crops, is also seriously devastated by Magnaporthe oryzae. Therefore, it is essential to breed new rice cultivars with blast and heat tolerance. Although progress had been made in QTL mapping and RNA-seq analysis in rice in response to blast and heat stresses, there are few reports on simultaneously mining blast-resistant and heat-tolerant genes. In this study, we separately conducted meta-analysis of 839 blast-resistant and 308 heat-tolerant QTLs in rice. Consequently, 7054 genes were identified in 67 blast-resistant meta-QTLs with an average interval of 1.00 Mb. Likewise, 6425 genes were obtained in 40 heat-tolerant meta-QTLs with an average interval of 1.49 Mb. Additionally, using differentially expressed genes (DEGs) in the previous research and GO enrichment analysis, 55 DEGs were co-located on the common regions of 16 blast-resistant and 14 heat-tolerant meta-QTLs. Among, OsChib3H-c, OsJAMyb, Pi-k, OsWAK1, OsMT2b, OsTPS3, OsHI-LOX, OsACLA-2 and OsGS2 were the significant candidate genes to be further investigated. These results could provide the gene resources for rice breeding with excellent resistance to these 2 stresses, and help to understand how plants response to the combination stresses of blast fungus and high temperature.

show abstract

Section: Discussionmentioning

confidence: 99%

Selection of Candidate Genes Conferring Blast Resistance and Heat Tolerance in Rice through Integration of Meta-QTLs and RNA-Seq

Tian

Chen

et al. 2022

Genes

View full text Add to dashboard Cite

show abstract

“…GWAS analysis revealed 38 genetic loci associated across treatments, 18 for control and 20 for HNT. In addition, 20 candidate marker-trait associations (MTAs) were identified for the yield and quality-related traits under HNT across experiments [150]. Crucially, several GWAS loci were in the genic region or close to previously reported genes controlling HNT tolerance, indicating that GWAS is an effective approach to identify putative genes or target regions for HNT tolerance in rice [150].…”

Section: Gwasmentioning

confidence: 92%

“…Moreover, using an indica rice diversity panel comprising 209 genotypes, the genetic architecture of high night temperature (HNT; 24 • C/29 • C, control/HNT treatments) tolerance during panicle initiation to physiological maturity periods was analysed in rice [150]. GWAS analysis revealed 38 genetic loci associated across treatments, 18 for control and 20 for HNT.…”

Section: Gwasmentioning

confidence: 99%

Reproductive-Stage Heat Stress in Cereals: Impact, Plant Responses and Strategies for Tolerance Improvement

Zenda

Wang

Dong

et al. 2022

IJMS

View full text Add to dashboard Cite

Reproductive-stage heat stress (RSHS) poses a major constraint to cereal crop production by damaging main plant reproductive structures and hampering reproductive processes, including pollen and stigma viability, pollination, fertilization, grain setting and grain filling. Despite this well-recognized fact, research on crop heat stress (HS) is relatively recent compared to other abiotic stresses, such as drought and salinity, and in particular, RSHS studies in cereals are considerably few in comparison with seedling-stage and vegetative-stage-centered studies. Meanwhile, climate change-exacerbated HS, independently or synergistically with drought, will have huge implications on crop performance and future global food security. Fortunately, due to their sedentary nature, crop plants have evolved complex and diverse transient and long-term mechanisms to perceive, transduce, respond and adapt to HS at the molecular, cell, physiological and whole plant levels. Therefore, uncovering the molecular and physiological mechanisms governing plant response and tolerance to RSHS facilitates the designing of effective strategies to improve HS tolerance in cereal crops. In this review, we update our understanding of several aspects of RSHS in cereals, particularly impacts on physiological processes and yield; HS signal perception and transduction; and transcriptional regulation by heat shock factors and heat stress-responsive genes. We also discuss the epigenetic, post-translational modification and HS memory mechanisms modulating plant HS tolerance. Moreover, we offer a critical set of strategies (encompassing genomics and plant breeding, transgenesis, omics and agronomy) that could accelerate the development of RSHS-resilient cereal crop cultivars. We underline that a judicious combination of all of these strategies offers the best foot forward in RSHS tolerance improvement in cereals. Further, we highlight critical shortcomings to RSHS tolerance investigations in cereals and propositions for their circumvention, as well as some knowledge gaps, which should guide future research priorities. Overall, our review furthers our understanding of HS tolerance in plants and supports the rational designing of RSHS-tolerant cereal crop cultivars for the warming climate.

show abstract

“…We tested the hypothesis that, under higher night temperatures, increased carbon loss due to higher respiration would lead to different levels of grain chalkiness among the tillers with the least chalkiness from primary panicles and the highest chalkiness in the later formed tillers. Regardless of the cause or differential chalkiness among tillers, the ability to quickly and accurately identify and quantify the chalkiness in rice is extremely important to help not only to understand the cause of chalkiness, but also to breed for heat tolerant nutritional rice varieties [ 19 , 26 – 28 ].…”

Section: Introductionmentioning

confidence: 99%

Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature

et al. 2022

Self Cite

View full text Add to dashboard Cite

Background Rice is a major staple food crop for more than half the world’s population. As the global population is expected to reach 9.7 billion by 2050, increasing the production of high-quality rice is needed to meet the anticipated increased demand. However, global environmental changes, especially increasing temperatures, can affect grain yield and quality. Heat stress is one of the major causes of an increased proportion of chalkiness in rice, which compromises quality and reduces the market value. Researchers have identified 140 quantitative trait loci linked to chalkiness mapped across 12 chromosomes of the rice genome. However, the available genetic information acquired by employing advances in genetics has not been adequately exploited due to a lack of a reliable, rapid and high-throughput phenotyping tool to capture chalkiness. To derive extensive benefit from the genetic progress achieved, tools that facilitate high-throughput phenotyping of rice chalkiness are needed. Results We use a fully automated approach based on convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM) to detect chalkiness in rice grain images. Specifically, we train a CNN model to distinguish between chalky and non-chalky grains and subsequently use Grad-CAM to identify the area of a grain that is indicative of the chalky class. The area identified by the Grad-CAM approach takes the form of a smooth heatmap that can be used to quantify the degree of chalkiness. Experimental results on both polished and unpolished rice grains using standard instance classification and segmentation metrics have shown that Grad-CAM can accurately identify chalky grains and detect the chalkiness area. Conclusions We have successfully demonstrated the application of a Grad-CAM based tool to accurately capture high night temperature induced chalkiness in rice. The models trained will be made publicly available. They are easy-to-use, scalable and can be readily incorporated into ongoing rice breeding programs, without rice researchers requiring computer science or machine learning expertise.

show abstract

Genome-wide association study and gene network analyses reveal potential candidate genes for high night temperature tolerance in rice

Cited by 15 publications

References 64 publications

Selection of Candidate Genes Conferring Blast Resistance and Heat Tolerance in Rice through Integration of Meta-QTLs and RNA-Seq

Selection of Candidate Genes Conferring Blast Resistance and Heat Tolerance in Rice through Integration of Meta-QTLs and RNA-Seq

Reproductive-Stage Heat Stress in Cereals: Impact, Plant Responses and Strategies for Tolerance Improvement

Deep learning based high-throughput phenotyping of chalkiness in rice exposed to high night temperature

Contact Info

Product

Resources

About