DOI: 10.5061/dryad.bzkh189pc
title: A genotype-by-sequencing dataset and identity-by-state matrix of genetic variation in Pinus radiata from 16 counties

description: Pinus radiata is a commercially important softwood species planted
 extensively worldwide, while its native populations remain endangered and
 of high conservation concern. This dataset provides genome-wide SNP
 genotyping data generated via genotype-by-sequencing (GBS) using
 double-digest RAD sequencing (ddRADseq) on needle-derived DNA. Samples
 were collected from 16 countries and include both domesticated breeding
 material and wild-origin individuals from native populations. The dataset
 includes a SNP-by-sample matrix and an identity-by-state (IBS) matrix
 describing pairwise genetic similarity among individuals. These data
 support applications in population structure analysis, breeding program
 design, genetic resource management, and conservation planning for P.
 radiata.
description: # Dryad dataset Dataset DOI:
 [10.5061/dryad.bzkh189pc](10.5061/dryad.bzkh189pc) ## Description of the
 data and file structure Needles were collected from four positions around
 each tree canopy and pooled per tree. DNA was extracted from homogenized
 needle tissue using the Qiagen DNeasy Plant Pro Kit, with replicate
 extractions pooled to maximize yield and representation. Genotyping was
 performed using genotype-by-sequencing (GBS/ddRADseq) with EcoRI and MseI
 digestion, ligation of barcoded adapters, size selection (280–375 bp), and
 sequencing on an Illumina NovaSeq 6000. Reads were demultiplexed,
 clustered de novo, and SNPs called using a Bayesian genotyping model.
 Variant data were filtered and combined with sample metadata to produce
 the final SNP dataset. Identity-by-state (IBS) genetic similarity matrices
 were also generated using PLINK. ### Files and variables #### File:
 Similarity_reduced_GBS_GLOBAL_June25_updated.csv
 **Description:** Similarity matrix of all samples compared to one another
 across the entire dataset. Sample names link to the metadata where GPS
 locations and country source. ##### Variables * Sample columns: One per
 individual, containing similarity #### File: GBS_metadata.csv
 **Description:** Metadata file for each of the samples. Contains sample
 names, country of collected sample, whether the sample was from an endemic
 or introduced source and which endemic populations the endemic samples
 belonged to. This is accompanied by GPS locations for each sample. #####
 Variables * Sample_Name * Country * Endemic_Introduced * Endemic_Source *
 Latitude * Longitude #### File: NXGSQCAGRF24050329-3_variants_cleaned.tsv
 **Description:** This dataset is derived from a Variant Call Format (VCF)
 file that has been exported into tab-separated values (TSV) format and
 contains Single Nucleotide Polymorphism (SNP) data from 821 individuals.
 ##### Variables * `#CHROM`: Chromosome number * `POS`: Position of the SNP
 on the chromosome * `REF`: Reference base * `ALT`: Alternate base(s) *
 Sample columns: One per individual, containing genotype information ##
 Code/software The .tsv file (tab-separated values) can be opened and
 analyzed using a wide range of software and tools, depending on what you
 want to do: * **R / Python (pandas, tidyverse, etc.):** Ideal for more
 complex statistical analysis, filtering variants, or creating custom
 visualizations. * **Database systems (SQLite, PostgreSQL, etc.):** If the
 file is large, importing into a database makes it easier to query specific
 variants or samples. * **Spreadsheet software (Excel, Google Sheets,
 LibreOffice Calc):** Easily open and explore the table, filter columns,
 and perform simple analyses. The size of the file makes this option
 difficult. * **Custom scripts / pipelines:** Since the file is plain-text
 tabular, you can parse it with bash, awk, or other scripting tools. The
 CSV similarity matrix can be opened with various tools and software.
 PRIMER and R based programs are common tools for analysis of this type of
 file.