GWAS with principal component analysis identifies a gene comprehensively controlling rice architecture

Yano, Kenji; Morinaka, Yoichi; Wang, Fanmiao; Huang, Peng; Takehara, Shoichiro; Hirai, Takanori; Ito, Aya; Koketsu, Eriko; Kawamura, Morio; Kotake, Kunihiko; Yoshida, Shigeo; Endo, Masaki; Tamiya, Gen; Kitano, Hidemi; Ueguchi-Tanaka, Miyako; Hirano, Ken; Matsuoka, Makoto

doi:10.1073/pnas.1904964116

Cited by 157 publications

(105 citation statements)

References 38 publications

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“…Only two genes recently characterized, namely NAL8 and OsFMDL1, which are associated to spikelet number and the development of leaf and flower, respectively, (Chen et al 2019;Tao et al 2018) were associated to panicle morphological trait diversity in O. glaberrima using our cutoff. Several association studies of panicle morphological trait diversity have been recently conducted for O. sativa (Bai et al 2016;Crowell et al 2016;Rebolledo et al 2016;Ta et al 2018;Yano et al 2019). Only a few overlaps of GWAS regions were observed between the two rice crop species, including a cluster of GWAS sites related to panicle and yield traits reported on chromosome 4 in O. sativa (Crowell et al 2016).…”

Section: Limited Overlap Of Flowering Time and Panicle Architecture Gmentioning

confidence: 99%

Genome Wide Association Study Pinpoints Key Agronomic QTLs in African Rice Oryza glaberrima

Cubry

Pidon

et al. 2020

Rice

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Background African rice, Oryza glaberrima, is an invaluable resource for rice cultivation and for the improvement of biotic and abiotic resistance properties. Since its domestication in the inner Niger delta ca. 2500 years BP, African rice has colonized a variety of ecologically and climatically diverse regions. However, little is known about the genetic basis of quantitative traits and adaptive variation of agricultural interest for this species. Results Using a reference set of 163 fully re-sequenced accessions, we report the results of a Genome Wide Association Study carried out for African rice. We investigated a diverse panel of traits, including flowering date, panicle architecture and resistance to Rice yellow mottle virus. For this, we devised a pipeline using complementary statistical association methods. First, using flowering time as a target trait, we found several association peaks, one of which co-localised with a well described gene in the Asian rice flowering pathway, OsGi, and identified new genomic regions that would deserve more study. Then we applied our pipeline to panicle- and resistance-related traits, highlighting some interesting genomic regions and candidate genes. Lastly, using a high-resolution climate database, we performed an association analysis based on climatic variables, searching for genomic regions that might be involved in adaptation to climatic variations. Conclusion Our results collectively provide insights into the extent to which adaptive variation is governed by sequence diversity within the O. glaberrima genome, paving the way for in-depth studies of the genetic basis of traits of interest that might be useful to the rice breeding community.

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Section: Limited Overlap Of Flowering Time and Panicle Architecture Gmentioning

confidence: 99%

Genome Wide Association Study Pinpoints Key Agronomic QTLs in African Rice Oryza glaberrima

Cubry

Pidon

et al. 2020

Rice

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“…DELLA proteins are central regulators of plant productivity and stress responses, and the hormonal crosstalk mediated by these proteins is essential for plant plasticity to endure stress. As other hormone modulators, DELLA activity is regulated by post-translational modifications, such as ubiquitination, phosphorylation, O -fucosylation, O -GlcNAcylation, and SUMOylation (Yano et al, 2019; Zentella et al, 2016; Zentella et al, 2017; Conti et al, 2014; Dai and Xue, 2010; Itoh et al, 2003). Recently, SUMO proteases were linked to salt stress response in rice (Srivastava et al, 2016b; 2017).…”

Section: Discussionmentioning

confidence: 99%

“…The SUMOylation of SLR1 could also be affecting the interaction with upstream regulatory proteins. For instance, SUMO attachment could affect SLR1 interaction with SPY, which enhances SLR1 activity through O -fucosylation and is a major determinant of rice plant architecture (Yano et al, 2019). Crosstalk between SUMOylation and O -fucosylation could play a role in GA-promoted growth regulation by decreasing TF-interactions that result in growth repression (Camut et al, 2017).…”

Section: Discussionmentioning

confidence: 99%

SUMOylation of rice DELLA SLR1 modulates transcriptional responses and improves yield under salt stress

Gonçalves¹,

Fernandes²,

Nunes

et al. 2020

Preprint

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Author Contributions: 15 NMG participated in the conception of research, executed most experiments, and 16 wrote the article with contributions from all authors. TF did protein interaction 17 experiments. MTGR characterized SUMO-protease rice lines. CCM analysed data 18 and completed the writing. MAAR purified recombinant proteins. PMB analysed RNA 19 seq. MMO supervised experiments and analysed results. IAA conceived original 20 project, supervised experiments, and finalised the article. 31 32 Short title (50 characters): SUMOylation of rice SLR1 increases salt tolerance 33 34 ABSTRACT 35 DELLA proteins modulate GA signalling and are major regulators of plant plasticity to 36 endure stress. DELLAs are mostly regulated at the post-translational level, and their 37 activity relies on the interaction with upstream regulators and transcription factors 38 (TFs). SUMOylation is a post-translational modification (PTM) capable of changing 39protein interaction and found to influence DELLA activity in Arabidopsis. We 40 determined that SUMOylation of the single rice DELLA SLENDER RICE1 (SLR1) 41 occurs in a lysine residue different from the one identified in Arabidopsis 42 REPRESSOR OF GA (RGA), which suggests differential DELLA regulation by 43SUMOylation in monocots and dicots. Remarkably, artificially increasing 44 SUMOylated SLR1 (SUMO1SLR1) levels attenuated the penalty of salt stress on 45 plant yield. Gene expression analysis revealed that the overexpression of 46 SUMOylated SLR1 regulates key dioxygenases that modulate active GA levels, 47namely GA20ox2 and GA2ox3, which could partially explain the sustained 48 productivity upon salt stress imposition. Besides, SLR1 SUMOylation blocked the 49 interaction with the growth regulator YAB4, which may fine-tune GA20ox2 50 expression. Mechanistically, we propose that SLR1 SUMOylation disrupts the 51 interaction with members of several transcription factor families to modulate gene 52 expression. We found that SLR1 SUMOylation represents a novel mechanism 53 modulating DELLA activity, which attenuates the impact of stress on plant 54 performance. 55 56 gibberellin 58 59 One sentence summary: Rice plants show increased yield under salt stress when 60 its gibberellin transcriptional regulator DELLA protein is artificially SUMOylated.

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“…The second question is how to utilize extensive lists of phenotypic traits in genetic mapping. One good example is performing principal component (PC) analysis to extract PCs of a specific phenotypic trait category and combining the PC scores and GWAS to identify a gene controlling rice architecture (Yano et al, 2019). The third question is how to rapidly clone candidate genes from the large number of QTLs.…”

Section: High-throughput Phenotyping and Genetic Mappingmentioning

confidence: 99%

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

et al. 2020

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Since whole-genome sequencing of many crops has been achieved, crop functional genomics studies have stepped into the big-data and high-throughput era. However, acquisition of large-scale phenotypic data has become one of the major bottlenecks hindering crop breeding and functional genomics studies. Nevertheless, recent technological advances provide us potential solutions to relieve this bottleneck and to explore advanced methods for large-scale phenotyping data acquisition and processing in the coming years. In this article, we review the major progress on high-throughput phenotyping in controlled environments and field conditions as well as its use for post-harvest yield and quality assessment in the past decades. We then discuss the latest multi-omics research combining high-throughput phenotyping with genetic studies. Finally, we propose some conceptual challenges and provide our perspectives on how to bridge the phenotype-genotype gap. It is no doubt that accurate high-throughput phenotyping will accelerate plant genetic improvements and promote the next green revolution in crop breeding.

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GWAS with principal component analysis identifies a gene comprehensively controlling rice architecture

Cited by 157 publications

References 38 publications

Genome Wide Association Study Pinpoints Key Agronomic QTLs in African Rice Oryza glaberrima

Genome Wide Association Study Pinpoints Key Agronomic QTLs in African Rice Oryza glaberrima

SUMOylation of rice DELLA SLR1 modulates transcriptional responses and improves yield under salt stress

Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives

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