cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15454/pnqquq/b0ko5y
title: asso-279_DLVitis_mbw_lme4_geno-values.tsv

description: BLUPs of genotypic value for diversity panel population and mean berry weight trait.
DOI: 10.15454/pnqquq/f4obvb
title: asso-279_DLVitis_shiktar_lme4_geno-values.tsv

description: BLUPs of genotypic value for diversity panel population and shikimic / tartaric acid at ripe stage trait.
DOI: 10.15454/pnqquq/jtfjzq
title: asso-279_DLVitis_samplday_lme4_geno-values.tsv

description: BLUPs of genotypic value for diversity panel population and maturity date trait.
DOI: 10.15454/pnqquq/roe4sm
title: asso-279_DLVitis_nbclu_lme4_geno-values.tsv

description: BLUPs of genotypic value for diversity panel population and number of clusters trait.
DOI: 10.15454/pnqquq/gk8zgk
title: asso-279_DLVitis_vermatu_lme4_geno-values.tsv

description: BLUPs of genotypic value for diversity panel population and interval between veraison and maturity trait.
DOI: 10.15454/pnqquq/gml0zr
title: asso-279_DLVitis_clucomp_lme4_geno-values.tsv

description: BLUPs of genotypic value for diversity panel population and cluster compactness trait.
DOI: 10.15454/pnqquq/lfr4ne
title: asso-279_DLVitis_mcl_lme4_geno-values.tsv

description: BLUPs of genotypic value for diversity panel population and mean cluster length trait.
DOI: 10.15454/pnqquq/sdvhgk
title: asso-279_DLVitis_mcw_lme4_geno-values.tsv

description: BLUPs of genotypic value for diversity panel population and mean cluster weight trait.
DOI: 10.15454/pnqquq/vvknav
title: asso-279_DLVitis_malnorm_lme4_geno-values.tsv

description: BLUPs of genotypic value for diversity panel population and malic acid at ripe stage trait.
DOI: 10.15454/pnqquq/yruvwu
title: asso-279_DLVitis_shinorm_lme4_geno-values.tsv

description: BLUPs of genotypic value for diversity panel population and shikimic acid at ripe stage trait.
DOI: 10.15454/pnqquq/dszaqz
title: asso-279_DLVitis_maltar_lme4_geno-values.tsv

description: BLUPs of genotypic value for diversity panel population and malic / tartaric acid at ripe stage trait.
DOI: 10.15454/pnqquq/fdnmxd
title: asso-279_DLVitis_vigour_lme4_geno-values.tsv

description: BLUPs of genotypic value for diversity panel population and vigour trait.
DOI: 10.15454/pnqquq/rpwbvl
title: asso-279_DLVitis_verday_lme4_geno-values.tsv

description: BLUPs of genotypic value for diversity panel population and veraison date.
DOI: 10.15454/pnqquq/wo5hu6
title: asso-279_DLVitis_mcwi_lme4_geno-values.tsv

description: BLUPs of genotypic value for diversity panel population and mean cluster width trait.
DOI: 10.15454/pnqquq/ywchvm
title: asso-279_DLVitis_tarnorm_lme4_geno-values.tsv

description: BLUPs of genotypic value for diversity panel population and tartaric acid at ripe stage trait.
DOI: 10.15454/pnqquq/b6ewq2
title: blups_p279_chapitre_all-traits_Flutre2020.tsv

description: This file is a table with BLUPs of genotypic values for diversity panel population and the 15 studied traits.
DOI: 10.15454/pnqquq/lx3gju
title: Variant_callingPOP39.filtered.gq40.env.noreplicate3.chr0.recode.vcf

description: This is the file with genotypic data in vcf format for Terret Noir x Cabernet-Sauvignon cross, with 63 genotypes and 180,423 markers (after filtering). The pipeline is available in an associated dataverse: https://doi.org/10.15454/99SLGP.
DOI: 10.15454/pnqquq/qb49qh
title: Variant_callingPOP34.filtered.gq40.env.noreplicate3.chr0.recode.vcf

description: This is the file with genotypic data in vcf format for Terret Noir x Pinot Noir cross, with 62 genotypes and 207,203 markers (after filtering). The pipeline is available in an associated dataverse: https://doi.org/10.15454/99SLGP.
DOI: 10.15454/pnqquq/qicbk3
title: Variant_callingPOP36.filtered.gq40.env.noreplicate3.chr0.recode.vcf

description: This is the file with genotypic data in vcf format for Grenache x Syrah cross, with 61 genotypes and 174,765 markers (after filtering). The pipeline is available in an associated dataverse: https://doi.org/10.15454/99SLGP.
DOI: 10.15454/pnqquq/sfqc2q
title: compile_explo-BLUPs_p279.Rmd

description: compiles the BLUPs of genotypic values derived by Flutre et al. 2020 for the 15 traits studied (listed in « Phenotypic data »). It generates `blups_p279_chapitre_all-traits_Flutre2020.tsv`.
DOI: 10.15454/pnqquq/0apcqq
title: Variant_callingPOP33.filtered.gq40.env.noreplicate2.chr0.vcf

description: This is the file with genotypic data in vcf format for Grenache x Pinot Noir cross, with 65 genotypes and 157,875 markers (after filtering). The pipeline is available in an associated dataverse: https://doi.org/10.15454/99SLGP.
DOI: 10.15454/pnqquq/ebggcp
title: Variant_callingPOP37.filtered.gq40.env.noreplicate3.chr0.recode.vcf

description: This is the file with genotypic data in vcf format for Terret Noir x Syrah cross, with 65 genotypes and 184,129 markers (after filtering). The pipeline is available in an associated dataverse: https://doi.org/10.15454/99SLGP.
DOI: 10.15454/pnqquq/g287qv
title: Variant_callingPOP38.filtered.gq40.env.noreplicate3.chr0.recode.vcf

description: This is the file with genotypic data in vcf format for Grenache x Cabernet-Sauvignon cross, with 65 genotypes and 112,751 markers (after filtering). The pipeline is available in an associated dataverse: https://doi.org/10.15454/99SLGP.
DOI: 10.15454/pnqquq/h56on0
title: Variant_callingPOP31.filtered.gq40.env.sansdoublon3.chr0.recode.vcf

description: This is the file with genotypic data in vcf format for Syrah x Pinot Noir cross, with 60 genotypes and 173,609 markers (after filtering). The pipeline is available in an associated dataverse: https://doi.org/10.15454/99SLGP.
DOI: 10.15454/pnqquq/hrlkrs
title: Variant_callingPOP32.filtered.gq40.env.noreplicate2.chr0.vcf

description: This is the file with genotypic data in vcf format for Cabernet-Sauvignon x Pinot Noir cross, with 68 genotypes and 157,131 markers (after filtering). The pipeline is available in an associated dataverse: https://doi.org/10.15454/99SLGP.
DOI: 10.15454/pnqquq/jsmvya
title: Variant_callingPOP35.filtered.gq40.env.noreplicate3.chr0.recode.vcf

description: This is the file with genotypic data in vcf format for Cabernet-Sauvignon x Syrah cross, with 66 genotypes and 250,749 markers (after filtering). The pipeline is available in an associated dataverse: https://doi.org/10.15454/99SLGP.
DOI: 10.15454/pnqquq/asjmei
title: study_p279_diallel_relationship.Rmd

description: This script analyzes genomic data from half-diallel and diversity panel populations. It takes as inputs `geno-diallel_formated.tsv`, `geno-p279_formated.tsv` and `origin_CC279_UTF8.tsv`. It calculates additive relationships between the two populations and genomic PCA of the diversity panel. It generates tables with additive relationship between half-diallel parents, between diversity panel and half-diallel crosses; and distances on the PCA along the first or first two axes. It plots Figures 1a and S1.
DOI: 10.15454/pnqquq/cbsbx6
title: Variant_callingPOP40.filtered.gq40.env.noreplicate2.chr0.vcf

description: This is the file with genotypic data in vcf format for Terret Noir x Grenache cross, with 69 genotypes and 153,931 markers (after filtering). The pipeline is available in an associated dataverse: https://doi.org/10.15454/99SLGP.
DOI: 10.15454/pnqquq/miui9m
title: diallel_markers_fimpute.Rmd

description: This script takes as inputs `pedigree_diallel_parents_FImpute.tsv` and `genomic_data_gene-dose_diallel-all-pops_for_runFimpute_missdatmax-marker-0.8.Rdata` and impute missing marker data with FImpute3 software (version 2.2, Sargolzaei et al. 2014). It generates the file `genotypes_diallel_ped_86k_fimpute_missdatmax-marker-0.8.tsv`.
DOI: 10.15454/pnqquq/tqj7bh
title: format_diallel_markers.Rmd

description: This script takes as input genotypic data in vcf format (see above) and generates `genomic_data_gene-dose_diallel-all-pops_for_runFimpute_missdatmax-marker-0.8.Rdata`, the genomic data for the 10 crosses with all available markers. Markers were checked for segregation distortion and markers with more than 80% missing data were removed.
DOI: 10.15454/pnqquq/zsxhqd
title: compare_BLUPs_diallel-p279_pops.Rmd

description: This script aims at study and highlight differences between both populations, by comparing broad-sense heritability (Figure 1b), distribution of genotypic values (Figure 1c-e, Figure S3).It also plots the mean offspring genotypic value vs parental average predicted genotypic value (Figure S12) and a PCA with half-diallel genotypic vallues for all traits for both half-diallel and diversity panel populations (Figure S4).
DOI: 10.15454/pnqquq/boms0y
title: pheno_lme4_diallel_verday.Rmd

description: This script takes as input "pheno-diallel_with-outlier-columns.tsv". It performs mixed model selection (after raw data transformation when necessary), checks underluying assumptions, and estimates variance components, heritability values and BLUPs of genotypic values. This script generates two tables: one with mixed model fitting information and one with the BLUPs of genotypic values.
DOI: 10.15454/pnqquq/i8wfve
title: get_colours_diallel.R

description: This script is a small R-script with a function defining a specific color for each half-diallel cross, panel subpopulation and half-diallel parent.
DOI: 10.15454/pnqquq/iy1zsy
title: pheno_lme4_diallel_tar.ripe.Rmd

description: This script takes as input "pheno-diallel_with-outlier-columns.tsv". It performs mixed model selection (after raw data transformation when necessary), checks underluying assumptions, and estimates variance components, heritability values and BLUPs of genotypic values. This script generates two tables: one with mixed model fitting information and one with the BLUPs of genotypic values.
DOI: 10.15454/pnqquq/tlwhzt
title: pheno_lme4_diallel_mbw.Rmd

description: This script takes as input "pheno-diallel_with-outlier-columns.tsv". It performs mixed model selection (after raw data transformation when necessary), checks underluying assumptions, and estimates variance components, heritability values and BLUPs of genotypic values. This script generates two tables: one with mixed model fitting information and one with the BLUPs of genotypic values.
DOI: 10.15454/pnqquq/yy1i3f
title: analyze_results_genom-pred_diallel.Rmd

description: vThis script loads predictive abilities and predicted individual genotypic values from all scenarios and methods (e.g. 'GP_S1a/1b/2_RR/LASSO_per-cross-summary' and 'GP_S1a/1b/2_RR/LASSO_pred-obs.Rdata'). It combines results for all scenarios, traits and crosses for the best methods are in 'table_results_best-pred-ability_all-scenarios_per-cross.tsv'. It generates Figure 4, S6, S7, S8 and S9.
DOI: 10.15454/pnqquq/awdee9
title: pheno_lme4_diallel_mcl.Rmd

description: This script takes as input "pheno-diallel_with-outlier-columns.tsv". It performs mixed model selection (after raw data transformation when necessary), checks underluying assumptions, and estimates variance components, heritability values and BLUPs of genotypic values. This script generates two tables: one with mixed model fitting information and one with the BLUPs of genotypic values.
DOI: 10.15454/pnqquq/er9dip
title: pheno_lme4_diallel_vigour.Rmd

description: This script takes as input "pheno-diallel_with-outlier-columns.tsv". It performs mixed model selection (after raw data transformation when necessary), checks underluying assumptions, and estimates variance components, heritability values and BLUPs of genotypic values. This script generates two tables: one with mixed model fitting information and one with the BLUPs of genotypic values.
DOI: 10.15454/pnqquq/iudqli
title: pheno_lme4_diallel_vermatu.Rmd

description: This script takes as input "pheno-diallel_with-outlier-columns.tsv". It performs mixed model selection (after raw data transformation when necessary), checks underluying assumptions, and estimates variance components, heritability values and BLUPs of genotypic values. This script generates two tables: one with mixed model fitting information and one with the BLUPs of genotypic values.
DOI: 10.15454/pnqquq/lyazh9
title: pheno_lme4_diallel_mcw.Rmd

description: This script takes as input "pheno-diallel_with-outlier-columns.tsv". It performs mixed model selection (after raw data transformation when necessary), checks underluying assumptions, and estimates variance components, heritability values and BLUPs of genotypic values. This script generates two tables: one with mixed model fitting information and one with the BLUPs of genotypic values.
DOI: 10.15454/pnqquq/yspnmu
title: pheno_lme4_diallel_nbclu.Rmd

description: This script takes as input "pheno-diallel_with-outlier-columns.tsv". It performs mixed model selection (after raw data transformation when necessary), checks underluying assumptions, and estimates variance components, heritability values and BLUPs of genotypic values. This script generates two tables: one with mixed model fitting information and one with the BLUPs of genotypic values.
DOI: 10.15454/pnqquq/8kovsr
title: pheno_lme4_diallel_shiktar.ripe.Rmd

description: This script takes as input "pheno-diallel_with-outlier-columns.tsv". It performs mixed model selection (after raw data transformation when necessary), checks underluying assumptions, and estimates variance components, heritability values and BLUPs of genotypic values. This script generates two tables: one with mixed model fitting information and one with the BLUPs of genotypic values.
DOI: 10.15454/pnqquq/gonwrl
title: pheno_lme4_diallel_mcwi.Rmd

description: This script takes as input "pheno-diallel_with-outlier-columns.tsv". It performs mixed model selection (after raw data transformation when necessary), checks underluying assumptions, and estimates variance components, heritability values and BLUPs of genotypic values. This script generates two tables: one with mixed model fitting information and one with the BLUPs of genotypic values.
DOI: 10.15454/pnqquq/jr8wxe
title: pheno_lme4_diallel_shik.ripe.Rmd

description: This script takes as input "pheno-diallel_with-outlier-columns.tsv". It performs mixed model selection (after raw data transformation when necessary), checks underluying assumptions, and estimates variance components, heritability values and BLUPs of genotypic values. This script generates two tables: one with mixed model fitting information and one with the BLUPs of genotypic values.
DOI: 10.15454/pnqquq/pj84nv
title: pheno_lme4_diallel_maltar.ripe.Rmd

description: This script takes as input "pheno-diallel_with-outlier-columns.tsv". It performs mixed model selection (after raw data transformation when necessary), checks underluying assumptions, and estimates variance components, heritability values and BLUPs of genotypic values. This script generates two tables: one with mixed model fitting information and one with the BLUPs of genotypic values.
DOI: 10.15454/pnqquq/zlig9b
title: pheno_lme4_diallel_clucomp.Rmd

description: This script takes as input "pheno-diallel_with-outlier-columns.tsv". It performs mixed model selection (after raw data transformation when necessary), checks underluying assumptions, and estimates variance components, heritability values and BLUPs of genotypic values. This script generates two tables: one with mixed model fitting information and one with the BLUPs of genotypic values.
DOI: 10.15454/pnqquq/3anov4
title: genom-pred_inter-pop_S2_LASSO.Rmd

description: This script takes as inputs `geno-p279_formated.tsv`, `BLUPs-p279_formated-scaled.tsv`, `geno-diallel_formated.tsv` and `BLUPs-diallel_formated-scaled.tsv`. It applies genomic prediction for scenario 2 with RR method. For each trait, it saves predicted individual genotypic values (`GP_S2_LASSO_pred.tsv`), predictive ability within each half-diallel cross (`GP_S2_LASSO_per-cross-summary.tsv`) and estimated marker effects (`GP_S2_LASSO_mrk-effects.tsv`).
DOI: 10.15454/pnqquq/gfswwh
title: prepare_files_inter-pop_pred_diall_p279.Rmd

description: This script combines, within each population, marker data and estimated genotypic values (BLUPs) (e.g., `diallel_blup_geno-wide_chapitre.tsv` and `blups_p279_chapitre_all-traits_Flutre2020.tsv` tables), keeping only common individuals and scaling BLUPs. It also keeps only markers that are common between the two populations.
DOI: 10.15454/pnqquq/i1zad7
title: diallel_blups_geno-wide_chapitre.tab

description: This table contains the sum of genotypic of cross BLUP values for half-diallel genotypes and for the 15 traits studied.
DOI: 10.15454/pnqquq/ocraov
title: BLUPs-diallel_formated-scaled.tsv

description: BLUPs of cross + genotypic values (15 traits) in the half-diallel, with 627 genotypes, comprising the five half-diallel parents and offspring of the ten crosses.
DOI: 10.15454/pnqquq/sytbpe
title: pheno_lme4_diallel_samplday.Rmd

description: This script takes as input "pheno-diallel_with-outlier-columns.tsv". It performs mixed model selection (after raw data transformation when necessary), checks underluying assumptions, and estimates variance components, heritability values and BLUPs of genotypic values. This script generates two tables: one with mixed model fitting information and one with the BLUPs of genotypic values.
DOI: 10.15454/pnqquq/0jit1t
title: genom-pred_intra-pop_S1a_LASSO.Rmd

description: This script takes as input `geno-diallel_formated` and `BLUPs_diallel_formated-scaled`. It applies genomic prediction for scenario 1a and LASSO method for all traits within the half-diallel population. For each cross-validation replicate and each trait, it saves predicted individual genotypic values (`GP_S1a_LASSO_pred-obs.Rdata`), predictive ability within each half-diallel cross (`GP_S1a_LASSO_per-cross-summary.tsv`) and estimated marker effects (`GP_S1a_LASSO_mrk-effects.tsv`).
DOI: 10.15454/pnqquq/cayiyr
title: genom-pred_intra-pop_S1b_LASSO.Rmd

description: This script takes as input `geno-diallel_formated` and `BLUPs_diallel_formated-scaled`. It applies genomic prediction for scenario 1a and LASSO method for all traits acros half-sib families of the half-diallel. For each cross-validation replicate and each trait, it saves predicted individual genotypic values (`GP_S1b_LASSO_pred-obs.Rdata`), predictive ability within each half-diallel cross (`GP_S1b_LASSO_per-cross-summary.tsv`) and estimated marker effects (`GP_S1b_LASSO_mrk-effects.tsv`).
DOI: 10.15454/pnqquq/efeedh
title: genom-pred_intra-pop_S1a_RR.Rmd

description: This script takes as input `geno-diallel_formated` and `BLUPs_diallel_formated-scaled`. It applies genomic prediction for scenario 1a and RR method for all traits within the half-diallel population. For each cross-validation replicate and each trait, it saves predicted individual genotypic values (`GP_S1a_RR_pred-obs.Rdata`), predictive ability within each half-diallel cross (`GP_S1a_RR_per-cross-summary.tsv`) and estimated marker effects (`GP_S1a_RR_mrk-effects.tsv`).
DOI: 10.15454/pnqquq/g5uece
title: genom-pred_inter-pop_S2_LASSO_optTRS.Rmd

description: This script takes as inputs files generated in `prepare_files_inter-pop_pred_diallel.Rmd` and in `Training-pop-p279_opti_diallel_STPGA.Rmd`.It applies genomic prediction with LASSO method on the optimized training sets previously created and plots results. Results from each selected training set (i.e., each cross, whole half-diallel and random sampling) are respectively in `GP_S2_LASSO_optTRS_per-cross-summary.tsv`, `GP_S2_LASSO_optTRS_all-cross-summary.tsv` and `GP_S2_LASSO_randTRS_per-cross-summary.Rdata`.
DOI: 10.15454/pnqquq/qp5wsx
title: genom-pred_inter-pop_S2_RR.Rmd

description: This script takes as inputs `geno-p279_formated.tsv`, `BLUPs-p279_formated-scaled.tsv`, `geno-diallel_formated.tsv` and `BLUPs-diallel_formated-scaled.tsv`. It applies genomic prediction for scenario 2 with RR method. For each trait, it saves predicted individual genotypic values (`GP_S2_RR_pred.tsv`), predictive ability within each half-diallel cross (`GP_S2_RR_per-cross-summary.tsv`) and estimated marker effects (`GP_S2_RR_mrk-effects.tsv`).
DOI: 10.15454/pnqquq/wem2fi
title: genom-pred_intra-pop_S1b_RR.Rmd

description: This script takes as input `geno-diallel_formated` and `BLUPs_diallel_formated-scaled`. It applies genomic prediction for scenario 1a and RR method for all traits acros half-sib families of the half-diallel. For each cross-validation replicate and each trait, it saves predicted individual genotypic values (`GP_S1b_RR_pred-obs.Rdata`), predictive ability within each half-diallel cross (`GP_S1b_RR_per-cross-summary.tsv`) and estimated marker effects (`GP_S1b_RR_mrk-effects.tsv`).
DOI: 10.15454/pnqquq/fbmxsk
title: estim_H2_per-cross.Rmd

description: This script takes as input `pheno-diallel_with-outlier-columns.tsv` and model fitting information `diallel_fit-info_blups_chapitre.tsv`, estimates heritability per cross (`diallel_H2-per-cross_all-traits.tsv`) and generates Figure S2.
DOI: 10.15454/pnqquq/lwofvc
title: genom-pred_inter-pop_S2WW_RR.Rmd

description: This script takes as inputs `geno-p279_formated.tsv`, `BLUPs-p279_formated-scaled.tsv`, `geno-diallel_formated.tsv`, `BLUPs-diallel_formated-scaled.tsv` and `origin_CC279_UTF8.tsv`. It applies genomic prediction for scenario 2 with RR method, with only individuals from WW subpopulation as training set. For each trait, it saves predicted individual genotypic values (`GP_S2WW_RR_pred.tsv`), predictive ability within each half-diallel cross (`GP_S2WW_RR_per-cross-summary.tsv`) and estimated marker effects (`GP_S2WW_RR_mrk-effects.tsv`).
DOI: 10.15454/pnqquq/tzwljy
title: genom-pred_inter-pop_S2WW_LASSO.Rmd

description: This script takes as inputs `geno-p279_formated.tsv`, `BLUPs-p279_formated-scaled.tsv`, `geno-diallel_formated.tsv` and `BLUPs-diallel_formated-scaled.tsv`. It applies genomic prediction for scenario 2 with RR method. For each trait, it saves predicted individual genotypic values (`GP_S2_LASSO_pred.tsv`), predictive ability within each half-diallel cross (`GP_S2_LASSO_per-cross-summary.tsv`) and estimated marker effects (`GP_S2_LASSO_mrk-effects.tsv`).
DOI: 10.15454/pnqquq/zbkurs
title: compile_BLUPs-info_diallel-p279.Rmd

description: This script compiles mixed model fitting information and BLUPs of genotypic values for each trait and for both populations. Fitting information from the diversity panel were retrieved from Table S2 from Flutre et al. 2020. This script generates one table with all BLUPs of genotypic values (`diallel_blup_geno-wide_chapitre.tsv`), as well as fit information `diallel_fit-info_blups_chapitre.tsv` (Table S1 and S5).
DOI: 10.15454/pnqquq/zqtavs
title: distribution_raw-pheno_diallel-p279_pops.Rmd

description: This scripts aims at visualizing distribution of raw phenotypic data for both population (Figure S14).
DOI: 10.15454/pnqquq/3jcweg
title: compute_ensemble-genom-pred_diallel.Rmd

description: This script loads marker effects estimated in the whole diversity panel and computes parental average genotypes for all possible crosses among cultivars of this population. It generates predicted genotypic values of cross means for all possible crosses. Then, it performs a PCA on all these values on which half-diallel parents predicted values are projected. This script generates Figure S11.
DOI: 10.15454/pnqquq/f5eegq
title: genom-pred_inter-pop_S2_RR_optTRS.Rmd

description: This script takes as inputs files generated in `prepare_files_inter-pop_pred_diallel.Rmd` and in `Training-pop-p279_opti_diallel_STPGA.Rmd`.It applies genomic prediction with RR method on the optimized training sets previously created and plots results. Results from each selected training set (i.e., each cross, whole half-diallel and random sampling) are respectively in `GP_S2_RR_optTRS_per-cross-summary.tsv`, `GP_S2_RR_optTRS_all-cross-summary.tsv` and `GP_S2_RR_randTRS_per-cross-summary.Rdata`.