DOI: 10.5061/dryad.rv15dv4f6
title: Data from: Globally-deployed sorghum aphid resistance gene RMES1 is vulnerable to biotype shifts but being bolstered by RMES2

description: Durable host plant resistance (HPR) to insect pests is critical for
 sustainable agriculture. Natural variation exists for aphid HPR in sorghum
 (Sorghum bicolor) but the genetic architecture and phenotype have not been
 clarified for most sources. To assess the threat of a sorghum aphid
 (Melanaphis sorghi) biotype shift, we characterized the phenotype of
 Resistance to Melanaphis sorghi 1 (RMES1) and contributing HPR
 architecture in globally-admixed populations selected under severe aphid
 infestation in Haiti. We developed and sequenced RMES1 near-isogenic lines
 and found RMES1 reduces sorghum aphid fecundity but not bird cherry-oat
 aphid (Rhopalosiphum padi) fecundity, suggesting a discriminant HPR
 response typical of gene-for-gene interaction. Analyzing whole-genome
 resequencing of a global diversity panel, we found resistant alleles at a
 second gene, RMES2, were more frequent than RMES1 resistant alleles in
 landraces and historic breeding lines. RMES2 contributes early and
 mid-season aphid resistance in a segregating F2 population, however, RMES1
 was only significant with mid-season fitness. In a fixed population with
 high aphid resistance, RMES1 and RMES2 were selected for demonstrating a
 lack of significant antagonistic pleiotropy. Associations with resistance
 co-located with cyanogenic glucoside biosynthesis genes support additional
 HPR sources. Globally, therefore, a vulnerable HPR source (RMES1) is
 bolstered by a second common source of resistance in breeding programs
 (RMES2) which may be staving off a biotype shift.
description: 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 - plotting allele
 information of RMES1 and RMES2 with geographic coordinates or breeding
 origin metadata
description: # Data from: Globally-deployed sorghum aphid resistance gene *RMES1* is
 vulnerable to biotype shifts but being bolstered by *RMES2*
 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
 . 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: * (BAP) * (SAP) *