Journal of Experimental BotanyGeneticsPeer-reviewed journal article
Combining high-throughput micro-CT-RGB phenotyping and genome-wide association study to dissect the genetic architecture of tiller growth in rice
Di WuiD, Zilong GuoiD, Junli YeiD, Hui FengiD, Jianxiao LiuiD, Guoxing CheniD, Jingshan Zheng, Dongmei Yan, Xiaoquan YangiD, Xiong XiongiD, Qian LiuiD, Zhiyou NiuiD, Alan P. GayiD, John H. DoonaniD, Lizhong XiongiD, Wanneng YangiD
Abstract
Traditional phenotyping of rice tillers is time consuming and labor intensive and lags behind the rapid development of rice functional genomics. Thus, dynamic phenotyping of rice tiller traits at a high spatial resolution and high-throughput for large-scale rice accessions is urgently needed. In this study, we developed a high-throughput micro-CT-RGB (HCR) imaging system to non-destructively extract 730 traits from 234 rice accessions at 9 time points. We used these traits to predict the grain yield in the early growth stage, and 30% of the grain yield variance was explained by 2 tiller traits in the early growth stage. A total of 402 significantly associated loci were identified by GWAS, and dynamic and static genetic components were found across the nine time points. A major locus associated with tiller angle was detected at nine time points, which contained a major gene TAC1. Significant variants associated with tiller angle were enriched in the 3'-UTR of TAC1. Three haplotypes for the gene were found and tiller angles of rice accessions containing haplotype H3 were much smaller. Further, we found two loci contained associations with both vigor-related HCR traits and yield. The superior alleles would be beneficial for breeding of high yield and dense planting.\n\nHighlightCombining high-throughput micro-CT-RGB phenotyping facility and genome-wide association study to dissect the genetic architecture of rice tiller development by using the indica subpopulation.
- organism: plant (other)
- assay: imaging
- research focus: GWAS / association mapping
- approach: statistical model
- Citations
- 94
- FWCI
- 6.90
- Field percentile
- top 2%
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