Supplemental Material for Ogawa et al., 2018
datasetposted on 04.09.2018 by Daisuke Ogawa, Yasunori Nonoue, Hiroshi Tsunematsu, Noriko Kanno, Toshio Yamamoto, Jun-ichi Yonemaru
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Figure S1 contains Principal component analysis (PCA) of the JAM lines using 16,345 SNP locations. Figure S2 contains plots showing positive relationships between grain area and grain length or width in the 372 JAM lines. Figure S3-1 to -6 contains manhattan plots of each chromosome analyzed with haplotype-based GWAS and SNP-based GWAS with naïve model. Figure S4 contains manhattan plots of GWAS on grain shape measurements. Figure S5 contains the correlations between sum of the additive effects of QTLs (SAQ) on grain length and observed values. FIgure S6 contains the correlations between sum of the additive effects of QTLs (SAQ) on grain width and observed values. Figure S7 contains haplotype effects at JAM-GL1 to -GL10 on grain length and width. Figure S8 contains haplotype effects at JAM-GW1 to -GW10 on grain length and width. Figure S9 contains GW2 alleles in the eight founders. Table S1 contains SNP positions of QTLs for grain length detected by five different methods of GWAS. Table S2 contains SNP positions of QTLs for grain width detected by five different methods of GWAS. Table S3 contains information on JAM-GL QTLs for grain length detected by haplotype-based GWAS. Table S4 contains Information on JAM-GW QTLs for grain width detected by haplotype-based GWAS. Table S5 contains putative common QTLs detected in this study and in a previous QTL study. Table S6 contains effects of previously identified genes for grain shape (from Li et al. 2018). LiteratureCited1 and LiteratureCited2 contain references for supplementary Information.