DOI: 10.5061/dryad.gm18v5f
title: Data from: Combining high-throughput micro-CT-RGB phenotyping and genome-wide association study to dissect the genetic architecture of tiller growth in rice

description: Manual phenotyping of rice tillers is time consuming and labor intensive
 and lags behind the rapid development of rice functional genomics. Thus,
 automated, non-destructive phenotyping of rice tiller traits at a high
 spatial resolution and high-throughput for large-scale assessment of 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 could
 explain 30% of the grain yield variance from 2 tiller traits assessed in
 the early growth stages. 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 rice
 accessions containing haplotype H3 displayed much smaller tiller angles.
 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.
description: Supplementary Figures
 S1-S10 Notes S1-S9 Tables S1-S7Fig. S1. Plastic round
 pipes screened in HCR system. Fig. S2. Experimental design. Fig. S3.
 Experimental conditions. Fig. S4. Control flow of image acquisition. Fig.
 S5. Workflow chart of operation HCR. Fig. S6. Sequence diagram of
 micro-CT-RGB phenotyping system. Fig. S7. Diagram of image processing and
 feature extraction. Fig. S8. Dynamic growth curve of rice phenotypic
 traits. Fig. S9. Modeling results of grain yield using different number of
 traits. Fig. S10. Workflow chart of database. Note S1. The source code of
 sonogram extraction. Note S2. The source code of computed tomography
 reconstruction. Note S3. The source code of tiller extraction. Note S4.
 The source code of tiller rotation. Note S5. The source code of tiller
 diameter extraction. Note S6. The source code of tiller angle extraction.
 Note S7. The source code of fill holes. Note S8. The source code of tiller
 area traits extraction. Note S9. Definition of the traits. Table S1. Main
 specifications of micro-CT-RGB inspection unit. Table S2. Manual
 measurements of 8 plastic pipes with 2 workers. Table S3. Automatic
 measurements of 8 plastic pipes with 10 replications. Table S4. Comparison
 of rice with automated measurements and manual measurements. Table S5. The
 Comparison of actual TTA/TPA and predicated TTA/TPA with 6 models. Table
 S6. The statistical analysis results of one-way ANOVA using SPSS software.
 Table S7. The statistical analysis results of t-test using SPSS
 spftware.Supplementary Figures Notes and
 Tables.docPhenotypic
 traitsRice accession
 information and phenotypic traits (RGB, CT, and manual traits) used in
 this work.Supplemental Data
 S1.xlsxGWAS
 resultsGWAS of 732 traits
 (including all 730 traits measured by HCR as well as final grain yield and
 biomass) identified 402 significantly associated loci.Supplemental Data
 S2.xlsx