DOI: 10.5061/dryad.kd51c5bgv
title: Data for: Pangenome analysis reveals local adaptation to climate driven by introgression in oak species

description: The genetic basis of local adaptation has been extensively studied in
 natural populations. However, a comprehensive genome-wide perspective on
 the contribution of structural variants (SVs) and adaptive introgression
 to local adaptation remains limited. In this study, we performed de
 novo assembly and annotation of 22 representative accessions of
 Quercus variabilis, identifying a total of 543,372 SVs. These SVs
 play crucial roles in shaping genomic structure and influencing gene
 expression. By analysing range-wide genomic data, we identified
 both SNPs and SVs associated with local adaptation in Q.
 variabilis and Q. acutissima. Notably, SV-outliers
 exhibit selection signals that did not overlap with SNP-outliers,
 indicating that SNP-based analyses may not detect the same candidate genes
 associated with SV-outliers. Remarkably, 29−37% of candidate SNPs
 were located in a 250 kb region on chromosome 9, referred to as Chr9-ERF.
 This region contains eight duplicated ethylene-responsive
 factor (ERF) genes, which may have contributed to local
 adaptation of Q. variabilis and Q. acutissima. We also found that
 a considerable number of candidate SNPs were shared between Q.
 variabilis and Q. acutissima in the
 Chr9-ERF region, suggesting a pattern of repeated selection. We
 further demonstrated that advantageous variants in this region were
 introgressed from western populations of Q. acutissima into Q.
 variabilis, providing compelling evidence that introgression facilitates
 local adaptation. This study offers a valuable genomic resource for future
 studies on oak species and highlights the importance of pan-genome
 analysis in understanding mechanisms driving adaptation and evolution.
description: # Data for: Pangenome analysis reveals local adaptation to climate driven
 by introgression in oak species ## Description of the data and file
 structure There are two zipped files here: one contains all the codes for
 this analysis, and the other is the VCF file of SV and SNP calling used
 for this analysis. ### File: Code.zip All scripts were put in the file
 "src". #### Part 1: pan-genome analyses 1.1. assembly.sh ``` 1)
 For the reference individual 2) For the other pan-genome individual ```
 1.2. repeat elements.sh ``` 1) For the reference individual 2) For the
 other pan-genome individual ``` 1.3. annotations.sh 1.4.
 Pangenome_graph.sh ``` 1) RUN PBSV for SV calling 2) RUN SVIM for SV
 calling 3) RUN Sniffles for SV calling 4) RUN cuteSV for SV calling 5) RUN
 SVIMasm for SV calling 6) RUN Assemblytics for SV calling 7) RUN merge SVs
 for SV calling 8) RUN winnowmap for SNP calling 9) RUN deepvariant for
 indel calling 10) Construct the Pan-genome Graph ``` 1.5 Collinearity
 Analyses.sh ``` 1) Collinearity among Q. variabilis genome assemblies 2)
 Variation in synteny diversity across the genome 3) Synteny relationship
 index (SRI) between each pair of Q. variabilis genomes. ``` 1.6 SV
 calling.sh ``` 1) Genotyping SV in each sample of 257 re-seq individuals
 2) Merge vcf 3) Filtering low-quality sv ``` #### Part 2: Population
 genetic analyses 2.1 SNP_calling_filter.sh ``` 1) Build Indexes 2) Filter
 the raw data 3) Mapping 4) SNP calling using GATK 5) SNP filtering ``` 2.2
 population_structure.sh ``` 1) Input prepare 2) PCANGsd 3) NGSadmix 4) NJ
 tree ``` 2.3 summary_statistics.sh ``` 1) Calculate the number of valid
 sites 2) Calculate tP, Tajima's D 3) Calculate Fay & Wu's H
 4) Calculate ZnS 5) Calculate Fst 6) Calculate dxy 7) Calculate RND ```
 2.4 demography.sh ``` 1) Perform fsc26 ``` 2.5 local_adaptation.sh ``` 1)
 GF 2) LFMM2 3) RDA 4) Pcadapt ``` 2.6 selective_sweep.sh ``` Perform
 XP-CLR ``` 2.7 co-directional_SNPs.sh ``` Identification of co-directional
 and anti-directional SNPs of 323 shared outlier SNP ``` 2.8
 introgression.sh ``` 1) Conduct ML tree 2) Perform ELAI 3) Calculate fdm
 4) Calculate df ``` #### File: VCF\_data.tar The VCF files with SNP and SV
 calls for a total of 257 individuals across three oak species. #####
 Dataset Overview: ——————VCF_data.tar ————SNPs_data
 ——Chr*a.mac.recode.vcf.gz ————SVs_data ——Qv_sv_minQ.mis.recode.vcf.gz The
 Chr*a.mac.recode.vcf.gz files represent the SNP calls for each chromosome
 generated by GATK. It comprises 21,695,563 SNPs. The
 Qv_sv_minQ.mis.recode.vcf.gz file represents the SV calls genome-wide
 generated by vg. It comprises 20,072,814 SVs. ##### File Details: * **File
 Name**: Chr*a.mac.recode.vcf/Qv_sv_minQ.mis.recode.vcf * **File Format**:
 VCF (Variant Call Format) Version 4.2 * **Date**: October 7, 2024 *
 **Source Software**: GATK /vg ##### Data Description: The VCF file
 includes the following columns standard to the format: * `#CHROM`:
 Chromosome number * `POS`: Position of the SNP/SV on the chromosome *
 `ID`: Identifier of the SNP/SV * `REF`: Reference base * `ALT`: Alternate
 base(s) * `QUAL`: Quality score of the SNP/SV * `FILTER`: Filter status *
 `INFO`: Additional information (e.g., allele frequency, number of samples)
 * `FORMAT`: Data format * Sample columns: One per individual, containing
 genotype information ###### Specific Fields in `INFO` * `NS`: Number of
 samples with data * `AF`: Allele frequency * `DP`: Total depth of reads
 ###### Specific Fields in `FORMAT` * `GT`: Genotype * `AD`: Allele depth *
 `DP`: Read depth * `GQ`: Genotype quality * `GL`: Genotype likelihood ##
 Access information Other publicly accessible locations of the data: * All
 sequencing data from this study have been deposited at NCBI Sequence Read
 Archive under Bioproject accession numbers SAMN38056469-SAMN38056477 and
 SAMN38043932-SAMN38044038.