README: Data from: Globally-deployed sorghum aphid resistance gene RMES1 is vulnerable to biotype shifts but being bolstered by RMES2

https://doi.org/10.5061/dryad.rv15dv4f6

Data and scripts relevant for four analyses are included here:
-NIL genome characterization - newly generated resequencing data of near-isogenic lines and their parents was used to determine the genomic contribution of NILs
-Resequencing SAP,BAP association - previously generated phenotype data (Poosapati et al., 2022) and resequencing data (Boatwright et al., 2022, LeBauer et al., 2020) was combined to newly generate marker-trait association data
-Haitian breeding population association - newly generated DART sequencing and phenotype data was used to generate marker-trait association data
-Geographic mapping of RMES1 and RMES2

Description of the data and file structure

NIL genome characterization

• Raw .fastq files are included in NIL_genome_characterization/data. SCAR_1, SCAR_2, and SCAR_5 are RMES1+, SCAR_2 and SCAR_3 are Rmes1-. SCAR_1 and SCAR_3 are used as NIL+ and NIL-, respectively.

• Scripts for processing fastq files through VCF generation are in NIL_genome_characterization/scripts.
• Remaining files in NIL_genome_characterization/scripts are used for parallelization of pipeline. Final filtered output is in NIL_genome_characterization/results.
• The RTx430v2.1 genome required for mapping can be downloaded from https://phytozome-next.jgi.doe.gov/info/SbicolorRTx430_v2_1.

NIL genome characterization

• data
  • 1_S1_L003_R1_001.fastq.gz #raw fastq data for sample 1 read 1
  • 1_S1_L003_R2_001.fastq.gz #raw fastq data for sample 1 read 2
  • (remaining 27 samples continue)
• scripts and resources
  • chromosomes.tsv #list of chromosome groups (ID, chrom listing chromosomes 1-10)
  • gdb2vcf-chromo-array.sh #generate vcf from gvcf database using gatk, genotypegvcfs
  • gdb2vcf-sg-array.sh #generate vcf from gvcf database, scaffold groups using gatk, genotypegvcfs
  • genomics-db-array.sh #generate gvcf database with gatk, gdb import
  • genomics-db-scaffold-array.sh #generate gvcf database from scaffolds using gatk, gdb import
  • gvcf-array-job.sh #generate gvcf file using gatk, haplotype caller
  • map-array-job.sh #map reads to reference using BWA
  • mark-dups.sh #mark duplicate reads using picard
  • numbered-chromosomes_Chr.tsv #list of chromosomes used by scripts (index and chr = ChrXX)
  • numbered-chromosomes.tsv #list of chromosomes used by scripts (index and chr = chromosome_XX)
  • numbered-sample-bams.tsv #list of sample bams used by scripts (index, sample IDs, bamopts = input field for bam files)
  • numbered-samples.tsv #list of samples used by scripts (index 1-28, sample S1-S28)
  • numbered-scaff-groups.tsv #list of scaffolds used by scripts (index, sg = scaffold group)
  • numbered-units.tsv #metadata relevant for bioinformatic analysis and interpretation (index, sample = SXX in running order, library metadata [library, platform, flowcell, lane], sample ID = genotype name and replicate, adapter barcode, path, kilobyte size of samples)
  • scaffold_groups.tsv #list of scaffold groups (ID, chrom = scaffold name, length, and cumulative length)
  • trim-samples.sh #trim samples using trimmomatic

Resequencing SAP,BAP association - beagle_PI_Sorghum_d8.665samples.vcf.gz contains genotype data for available SAP and BAP lines in VCF format.

Poosapati_Traits_norm.txt contains phenotype data, previously normalized and published (Poosapati et al., 2022).

The tassel_pca.sh script was used to generate popstr_PC.txt and popstr_PC_rmes2fixed.txt which contain population structure (PC1,PC2,PC3) and fixed covariate for RMES2.

The tassel_glm.sh and tassel_glm_rmes2fixed.sh script was used to generate marker-trait associations.

Top association outputs can be found in the supplementary data of the accompanying article.

Resequencing SAP,BAP association

• tassel_pca.sh #generates PCA data (popstr_PC) used for GWAS using TASSEL
• beagle_PI_Sorghum_d8.665samples.vcf.gz #imputed vcf data for 665 genotypes in the SAP and BAP
• Poosapati_Traits_norm.txt #resistance traits normalized and reported previously in Poosapati et al. 2022 (first column = PI genotype, second column = normalized phenotypes)
• popstr_PC_rmes2fixed.txt #population structure (PC 1,2,3) (same as popstr_PC.txt) (first column = PI genotype, column 2 - 4 = PC1 - PC3)
• popstr_PC.txt #population structure (PC 1,2,3) (first column = PI genotype, column 2 - 4 = PC1 - PC3)
• tassel_glm_rmes2fixed.sh #generates associations for resistance with RMES2 as fixed covariate using TASSEL
• tassel_glm.sh #generates associations for resistance using TASSEL

Haitian breeding population association - Data generated by DArT sequencing of Haitian breeding lines are in Report_SilicoDArT_1.csv, Report_SNP_2.csv, and Report_SNP_mapping_2.csv.

• DArT_data_processing.R was used to process raw data.
• HBP_2021_Dart_Indeldata_filtered_IndCall0.5_SNPcall0.5_Rep0.9_numeric_BeagleImputed.Rdata and
• HBP_2021_Dart_SNPdata_filtered_IndCall0.5_SNPcall0.5_Rep0.9_numeric_BeagleImputed.Rdata contains imputed genotype data.
• HBP_2021_PopulationStructure_PCs_and_Hclust_and_PhenoPC.csv and Kinship_rTassel_2021HBP_SNPs.Rdata contains population structure, phenotype, and kinship matrix for Haitian lines.
• The script ASreml_MTA.R was used to generate marker-trait associations.
• Top association outputs can be found in the supplementary data of the accompanying article.

Haitian breeding population association

• ASreml_MTA.R #generates associations with survival using ASreml
• DArT_data_processing.R #R script for processing raw DART sequencing data
• HBP_2021_Dart_Indeldata_filtered_IndCall0.5_SNPcall0.5_Rep0.9_numeric_BeagleImputed.Rdata #imputed genotype data
• HBP_2021_Dart_SNPdata_filtered_IndCall0.5_SNPcall0.5_Rep0.9_numeric_BeagleImputed.Rdata #imputed genotype data
• HBP_2021_PopulationStructure_PCs_and_Hclust_and_PhenoPC.csv #population structure, phenotype, and kinship data for genotypes (Taxa = genotype ID, PC1 - PC5, ID = running number, plate ID = dart genotyping plate, phenotype data = stand count, rows, number of leaves without colonies, yellowing binary score, survival binary score)
• Kinship_rTassel_2021HBP_SNPs.Rdata #kisnhip data determined tassel
• Report_SilicoDArT_1.csv #raw DART sequencing data recieved
• Report_SNP_2.csv #raw DART sequencing data recieved
• Report_SNP_mapping_2.csv #raw DART sequencing data recieved

Geographic mapping - Chr06_09_SNPS.recode.vcf and Chr09_INDEL_62669680.recode.vcf contain RMES1 and RMES2 allelic data (resequencing, unimputed) for lines with known landrace or breeding origin. The script geographic_mapping.R was used to plot allele information. Metadata columns with NA and sample ID was unused in analysis.

Geographic mapping

• Chr06_09_SNPs.recode.vcf #unimputed marker data for RMES1 and RMES2 resistance associated SNPs
• Chr09_INDEL_62669680.recode.vcf #unimputed marker data for RMES2 resistance associated INDEL
• geographic_mapping.R #R script for generating geographic maps and pie charts
• Sorghum_Metadata.csv #metadata and germplasm origin for genotypes (ISnum = NA, PInumber = NA, Duplicate remove = libraries kept, LIB = sample ID matching vcf genotypes, Sample = alternative sample ID, PlantName = informative name, Germplasm = NA/unused, BotanicalType = NA/unused, CountryOfOrigin = NA/unused, Region = NA/unused, lat = latitude, lon = longitude, category_cv = germplasm origin designation)

Sharing/Access information

Data was derived from the following sources:

• https://doi.org/10.5061/dryad.4b8gtht99 (BAP)
• https://www.ebi.ac.uk/ena/browser/view/PRJEB50066 (SAP)
• https://pubmed.ncbi.nlm.nih.gov/35020066/