DOI: 10.5061/dryad.jdfn2z3dp
title: Genetic architecture of disease resistance and tolerance in Douglas-fir trees

description: Understanding the genetic architecture of tolerance and resistance to
 pathogens is important to monitor and maintain resilient tree populations.
 Here we investigate the genetic basis of tolerance and resistance to
 needle cast disease in Douglas-fir (Pseudotsuga menziesii) caused by two
 fungal pathogens: Swiss needle cast (SNC) caused by Nothophaeocryptopus
 gaeumannii, and Rhabdocline needle cast (RNC) caused by Rhabdocline
 pseudotsugae). We performed a case-control genome-wide association
 analysis (GWA) and found these traits to be polygenic and under selection.
 We showed that stomatal regulation as well as ethylene and jasmonic acid
 pathways are important for resisting SNC infection and secondary
 metabolite pathways play a role in tolerating SNC once the plant is
 infected. We identified a key upstream transcription factor of plant
 defence, ERF1, as the main candidate for RNC resistance. Our findings
 contribute to the understanding of the highly polygenic architectures
 underlying disease resistance and tolerance in Douglas-fir and have
 important implications for forestry and conservation as the climate
 changes.
description: We used a pool-sequencing approach that targeted exon regions.
 For probe design details, see Lind et al. (2022). Briefly, the sequence
 capture probes were designed using genes identified in Douglas-fir RNA-seq
 data from (1) daily and cyclic-induced experiments (Cronn et al., 2017)
 and (2) needle samples infected by the fungal pathogen causing
 SNC <em>Nothophaeocryptopus gaeumannii </em>and wasp
 (unpublished). Exon sequences with a length of at least 100bp were
 submitted to Roche NimbleGen for Custom SeqCap EZ probe design. 
 DNA was extracted from Douglas-fir diploid needle tissue. DNA
 samples were normalised at 10ng/μl and 10 to 20 individuals were pooled
 per site (See File S2) by combining equimolar amounts of individual DNA
 samples prior to library preparation. Barcoded (Kapa, Dual-Indexed Adapter
 Kit) libraries were made using 100-150ng of DNA from each pooled DNA
 sample with an approximately 450-bp mean insert size. SeqCap library
 preparation was performed using custom Nimblegen SeqCap probes according
 to the NimbleGen SeqCap EZ HyperCap Workflow User’s Guide Ver 2 (Roche
 Sequencing Solutions, Inc., CA USA). Following capture, each library was
 sequenced in a 150bp paired-end format on an Illumina NovaSeq 6000
 instrument at the Genome Quebec Innovation Centre (McGill University,
 Montreal, Canada).
description: # Genetic architecture of disease resistance and tolerance in Douglas-fir
 trees
 [https://doi.org/10.5061/dryad.jdfn2z3dp](https://doi.org/10.5061/dryad.jdfn2z3dp) ·       Understanding the genetic basis of how plants defend against pathogens is important to monitor and maintain resilient tree populations. Swiss needle cast (SNC) and Rhabdocline needle cast (RNC) epidemics are responsible for major damage of forest ecosystems in North America. ·       Here we investigate the genetic architecture of tolerance and resistance to needle cast diseases in Douglas-fir (*Pseudotsuga menziesii*) caused by two fungal pathogens: SNC caused by *Nothophaeocryptopus gaeumannii*, and RNC caused by *Rhabdocline pseudotsugae*. ·       We performed case-control genome-wide association analyses and found disease resistance and tolerance in Douglas-fir to be polygenic and under strong selection. We show that stomatal regulation as well as ethylene and jasmonic acid pathways are important for resisting SNC infection, and secondary metabolite pathways play a role in tolerating SNC once the plant is infected. We identify a major transcriptional regulator of plant defence, ERF1, as the top candidate for RNC resistance. ·       Our findings shed light on the highly polygenic architectures underlying fungal disease resistance and tolerance and have important implications for forestry and conservation as the climate changes. ## Description of the data and file structure RNC_IR_candidate_genes.fasta This file contains fasta sequences for the candidate genes identified in this study. SNC_TR_candidate_genes.fasta This file contains fasta sequences for the candidate genes identified in this study. SNC_IT_candidate_genes.fasta This file contains fasta sequences for the candidate genes identified in this study. SNC_IR_candidate_genes.fasta This file contains fasta sequences for the candidate genes identified in this study. Rh_data-varscan_all_bedfiles_SNP_maf_RD-recalculated_CMH-test-results.txt CMH test output results from this study. SNC-varscan_all_bedfiles_SNP_maf_RD-recalculated_R-column-pairs_I-T-test_CMH-test-results.txt CMH test output results from this study. SNC-varscan_all_bedfiles_SNP_maf_RD-recalculated_R-column-pairs_T-R-test_CMH-test-results.txt CMH test output results from this study. SNC-varscan_all_bedfiles_SNP_maf_RD-recalculated_R-column-pairs_I-R-test_CMH-test-results.txt CMH test output results from this study. SNC-varscan_all_bedfiles_SNP_maf_RD-recalculated.FREQ.txt SNP allele frequency table from this study. Rh_data-varscan_all_bedfiles_SNP_maf_RD-recalculated.FREQ.txt SNP allele frequency table from this study. tpr1_jcf7190000014959.vcf.anno Annotated vcf file for the TPR1 gene. DF.merged.final_annotation.gff Extended annotation of Douglas fir generated in this study/ README File describing content of this repository. DF_trans_v2020_2_transcriptome.fa Douglas fir transcriptome assembly that was used to design the pool-seq probes and the extended annotation in this study. ## Sharing/Access information Other related code for this project can be found here: [https://github.com/CoAdapTree/cmh_test](https://github.com/CoAdapTree/cmh_test) [https://github.com/poojasingh09/2022_Singh_et_al_GWAS_SNC](https://github.com/poojasingh09/2022_Singh_et_al_GWAS_SNC) [https://github.com/CoAdapTree/varscan_pipeline](https://github.com/CoAdapTree/varscan_pipeline) ## Code/Software categorising_infection_severity.R This Rscript was used to categorise the disease infection severity of pools. Input data for this script can be found here: https://github.com/poojasingh09/2022_Singh_et_al_GWAS_SNC/tree/main/methods_sampling