Dataset DOI: 10.5061/dryad.dr7sqvbdw

Description of the data and file structure

The dataset presented here is associated with the article "Modern pea (Lathyrus oleraceus) cultivars exhibit significantly lower genetic diversity relative to landraces, garden cultivars and genebank accessions".

File overview:

• geno_QC.bed 677.21 KB
• geno_QC.bim 207.34 KB
• geno_QC.fam 9.99 KB
• geno_QC.map 175.09 KB
• geno_QC.ped 10.78 MB
• genoDart.xlsx 49.86 MB
• genoMap2.xlsx 43.13 MB

File descriptions for:

• geno_QC.bed
• geno_QC.bim
• geno_QC.fam
• geno_QC.ped
• geno_QC.map

The .bed/.bim/.fam files are the genotype datasets in the standard binary format. They are presented after quality control (see the article for quality control metrics). Note: raw data is available in the .xlsx files. The .ped/.map file format is the classical format provided in case it is more applicable for some users, but it provides the same data as the binary format.

Data can be read and analysed using the PLINK software pipelines available at https://www.cog-genomics.org/plink/2.0/input#bed.

Format structure information is available here: https://www.youtube.com/watch?v=vZyf5aXlB-k

Note: All of these files can be opened using a standard text reader such as Notepad or TextPad.

Individual file descriptions:

Can only be read by a computer

Each row is an SNP

• col 1; chromosome nr. Set to 0 in our data, as DArT do not provide chromosome location per default for this analysis.
• col 2; SNP ID.
• col 3; position of SNP in morgans. Set to 0 as not available.
• col 4; position of SNP in basepairs. Set to 0 when not available.
• cols 5 and 6; the potential genotypes in a nucleotide coding for each SNP

Each row is an individual/accession

• col 1; Family ID. Not relevant in plant analysis; therefore, set to accession ID.
• col 2; Individual ID. Accession ID.
• cols 3 and 4; ID of parents. Set to 0 as unknown.
• col 5; sex of individual. Set to 0 as not relevant in plant analysis.
• col 6; phenotype column. Set to -9 (missing) as not relevant.

Each row is an individual/accession

• col 1; Family ID. Not relevant in plant analysis; therefore, set to accession ID.
• col 2; Individual ID. Accession ID.
• cols 3 and 4; ID of parents. Set to 0 as unknown.
• col 5; sex of individual. Set to 0 as not relevant in plant analysis.
• col 6; phenotype column. Set to -9 (missing) as not relevant.
• Remaining cols; SNP genotypes for each individual in nucleotide format.

Each row is an SNP

• col 1; chromosome nr. Set to 0 in our data, as DArT do not provide chromosome location per default for this analysis.
• col 2; SNP ID.
• col 3; position of SNP in morgans. Set to 0 as not available.
• col 4; position of SNP in basepairs. Set to 0 when not available.

File descriptions for:

• genoDart.xlsx
• genoMap2.xlsx

The raw datasets of the genotypes and the scripts used for analysis.

Note: Raw data has already been cleaned and quality control applied in the binary and classical file formats described above.

Each row is an SNP.

• col 1; Clone ID (SNP ID).
• col 2; The sequence of the Reference allele.
• col 3; Same as the full sequence, but with removed adapters in short marker tags.
• col 4; Chromosome nr according to the model reference Pisum_sativum_ZW6_1.0.
• col 5; Position(s) on contig(s) with the best alignment of marker/tag to an existing model genome (Pisum_sativum_ZW6_1.0).
• col 6; Total count of aligning markers/tags (Pisum_sativum_ZW6_1.0).
• col 7; E-value of the best alignment to an existing model genome (Pisum_sativum_ZW6_1.0).
• col 8; Strand of the marker alignment - Plus for forward and Minus for reverse (Pisum_sativum_ZW6_1.0).
• col 9; Contig(s) with the best alignment of marker/tag to an existing model genome (Pisum_sativum_v1a).
• col 10; Position(s) on contig(s) with the best alignment of marker/tag to an existing model genome (Pisum_sativum_v1a).
• col 11; Total count of aligning markers/tags (Pisum_sativum_v1a).
• col 12; E-value of the best alignment to an existing model genome (Pisum_sativum_v1a).
• Col 13; Strand of the marker alignment - Plus for forward and Minus for reverse (Pisum_sativum_v1a)
• col 14; The proportion of samples for which the genotype call is either "1" or "0", rather than "-"
• col 15; The proportion of samples for which the genotype score is "1"
• col 16; Polymorphism Information Content
• col 17; The sum of the tag read counts for all samples, divided by the number of samples with non-zero tag read counts.
• col 18: The standard deviation of the number of tag reads for all samples with non-zero tag read counts
• col 19; The average of the normalized non-zero tag read counts divided by the standard deviation of the normalized non-zero tag read counts (if the standard deviation is zero or near zero, the Qpmr value will be 100).
• col 20; The proportion of technical replicate assay pairs for which the marker score is consistent
• cols 21 and onwards: The SNP genotypes for each individual.

Each row is an SNP.

• col 1; Unique identifier for the sequence in which the SNP marker occurs
• col 2; Unique identifier for the sequence in which the SNP marker occurs
• col 3; The sequence of the Reference allele.
• col 4; The sequence of the SNP allele.
• col 5; Same as the full sequence, but with removed adapters in short marker tags.
• col 6; Same as the full sequence, but with removed adapters in short marker tags.
• col 7; Contig(s) with the best alignment of marker / tag to an existing model genome (Pisum_sativum_ZW6_1.0).
• col 8; Position(s) on contig(s) with the best alignment of marker / tag to an existing model genome (Pisum_sativum_ZW6_1.0).
• col 9; Calculated position(s) of the SNP for best aligned marker on a contig(s) to an existing model genome (Pisum_sativum_ZW6_1.0).
• col 10; Total count of aligning markers / tags with selection criteria described below (Pisum_sativum_ZW6_1.0).
• col 11; E value of the best alignment to an existing model genome (Pisum_sativum_ZW6_1.0).
• col 12; Strand of the marker alignment - Plus for forward and Minus for reverse (Pisum_sativum_ZW6_1.0).
• col 13; Contig(s) with the best alignment of marker / tag to an existing model genome (Pisum_sativum_v1a)
• col 14; Position(s) on contig(s) with the best alignment of marker / tag to an existing model genome (Pisum_sativum_v1a)
• col 15; Calculated position(s) of the SNP for best aligned marker on a contig(s) to an existing model genome (Pisum_sativum_v1a)
• col 16; Total count of aligning markers / tags with selection criteria described below (Pisum_sativum_v1a)
• col 17; E value of the best alignment to an existing model genome (Pisum_sativum_v1a)
• col 18; Strand of the marker alignment - Plus for forward and Minus for reverse (Pisum_sativum_v1a)
• col 19; The base position and base variant
• col 20; The position (zero indexed) in the sequence tag at which the defined SNP variant base occurs
• col 21; The proportion of samples for which the genotype call is either "1" or "0", rather than "-"
• col 22; The proportion of samples for which the genotype score is "1", in the Reference allele row
• col 23; The proportion of samples for which the genotype score is "1", in the SNP allele row
• col 24; The proportion of samples which score as homozygous for the Reference allele
• col 25; The proportion of samples which score as homozygous for the SNP allele
• col 26; The proportion of samples which score as heterozygous
• col 27; The polymorphism information content (PIC) for the Reference allele row
• col 28; The polymorphism information content (PIC) for the SNP allele row
• col 29; The average of the polymorphism information content (PIC) of the Reference and SNP allele rows
• col 30; The sum of the tag read counts for all samples, divided by the number of samples with non-zero tag read counts, for the Reference allele row
• col 31; The sum of the tag read counts for all samples, divided by the number of samples with non-zero tag read counts, for the SNP allele row
• col 32; The proportion of technical replicate assay pairs for which the marker score is consistent
• col 33 and onwards: The SNP genotypes for each individual

File: scripts_for_repository.zip

Description: Scripts

Code/software

Scripts are uploaded as .txt. Can be manually changed to .r for easy use in R software. 

Description of scripts in folder titled "Scripts other than PLINK related

Note - dependencies for each script is defined within the script.

• 1- update_r.txt: Simply ensures R version control
• 2- data cleanup.txt: Import raw genotype datasets, remove empty sample columns, inspect dataset structure, and create a cleaned genotype matrix for downstream genetic analyses.
• 3-convert-to-GCAT-format.txt: Convert binary SNP genotype data into allele-based nucleotide calls (A/C/G/T) using marker metadata and export the recoded genotype matrix.
• 4-generating-PED-and-MAP-binary.txt: Generate PLINK-compatible PED and MAP files from a binary genotype dataset.
• 7- data_preparation4STRUCTURE.txt: Prepare genotype data for STRUCTURE analysis by converting a PLINK dataset into STRUCTURE input format.
• 10-data-for-STRUCTURE-minus.txt: Generate a STRUCTURE-compatible genotype file from the quality-controlled PLINK dataset after excluding specified accessions.
• 14.3 - mergeAfreadyforGGtreeOgmpopdone.txt: Merge population assignment information with metadata prepared for ggtree, reorder samples according to the phylogenetic tree order, and export the final annotation table for downstream visualization.
• 15.5.2 - ggTree making the tree_Kpop2.txt: Visualize a phylogenetic tree using ggtree and annotate samples with population assignments derived from STRUCTURE analysis.
• 15.5.3-round-ggtree-making-the-tree-Kpop2.txt: Visualize a phylogenetic tree using a circular layout and annotate accessions with inferred population assignments from STRUCTURE (K = 2).
• 17 - chromosomeCoverage.txt: Prepare chromosome coverage data for visualization by importing SNP coverage information, selecting relevant variables, and exporting the processed data to Excel format.
• 18.2 - combined_Trees_multiyearTSW.txt: Create a phylogenetic tree visualization integrating population assignments, phenotypic traits, highlighted clades, and bootstrap support.
• 21 - Best K by Evanno.txt: Generate an Evanno Delta K plot from STRUCTURE results to identify the most likely number of genetic clusters.
• 22 - chisquare_test_indepence_w_assumptions.txt: Evaluate associations between categorical phenotypic traits and genetic subpopulations using chi-square tests of independence.
• 23-FST.txt: Calculate population genetic diversity statistics and genetic differentiation among populations using genotype data in PLINK format.

Description of scripts in folder titled "Scripts PLINK related"

Note - dependencies for each script is defined within the script.

• 5-QC.txt: Perform genotype quality control (QC) in PLINK, generate a genetic distance matrix, conduct Principal Coordinates Analysis (PCoA), and visualize population structure.
• 9- PCoA_K3.txt: Perform Principal Coordinates Analysis (PCoA) based on genetic distances calculated in PLINK, visualize genetic structure, and colour individuals according to STRUCTURE-derived K = 3 population assignments.
• 10-realPCA_K3.txt: Visualize genetic structure using principal component analysis (PCA) generated from PLINK and colour individuals according to STRUCTURE-derived K = 3 population assignments.
• 11 - DAPC.txt: Perform Discriminant Analysis of Principal Components (DAPC) on SNP genotype data, infer genetic clusters, optimize the number of retained principal components, and visualize genetic structure.