Supplemental Material for Bhatta et al., 2019
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 Manhattan and quantile-quantile (Q-Q) plots for 35 traits in 143 diverse wheat accessions obtained from a genome-wide association study.
Figure S2 contains principal component biplot analysis among agronomic, diseases resistance, and grain quality traits in 143 diverse wheat accessions.
Figure S3. Physical distribution of 46,268 genotyping-by-sequencing derived SNPs within a 1-Mb window size on 21 chromosomes (Chr) of 143 diverse accession of wheat.
Table S1 contain details of the 143 diverse wheat accessions used in this study. Table S2. Genotype-by-sequencing derived SNPs identified in 143 diverse wheat lines.
Table S3 contain details of significant markers associated with 35 traits in 143 diverse wheat accessions grown in two seasons (2017 and 2018) in Siberia.
Table S4 contains details of potential candidate gene functions harboring SNPs affecting agronomic, disease resistance, and grain quality traits from two years (2017 and 2018) of experiments conducted in Siberia using 143 diverse wheat accessions.
Table S5. Genomic fingerprinting of 143 diverse accessions of wheat showing distribution of favorable (1), unfavorable (0) and heterozygote (0.5) alleles of significant marker-trait associations identified in this study.