DOI: 10.5061/dryad.5hqbzkhhf
title: Environmental data provide marginal benefit for predicting climate adaptation

description: Populations of natural and cultivated plant and animal populations will be
 affected by more extreme climate events such as drought and flooding in
 the future. We explore whether characterization of the
 environment-of-origin of each accession in a large sample of traditional
 maize germplasm can be used to accelerate conservation and breeding
 efforts for adaptation. We compare the utility of genotype and
 environmental data for predicting fitness of individuals in a number of
 common garden trials. We find that environment-of-origin data and genome
 scans for loci that associate with abiotic environmental variables provide
 surprisingly little benefit to prioritizing accessions for improvement,
 despite clear evidence of environmental adaptation in these accessions.
 These results provide important practical insight into the use of gene
 banks for climate adaptation. Methods include prediction of environmental
 variables from genotyping-by-sequencing data, environmental GWAS (envGWAS)
 to identify loci associated with climatic gradients, and genomic
 prediction of yield component traits. Data includes metadata associated
 with phenotypic field trials, transformed climate-of-origin data
 associated with accessions used for study, output from MegaLMM for genomic
 prediction of environment (GPoE) and covariance matrices used for envGWAS,
 results from envGWAS and phenotypic GWAS, models run for transfer plots,
 and genomic prediction results. Additional raw data available via CIMMYT
 and reproducible code via Github can be found under Access information or
 at the associated publication "Environmental data provide marginal
 benefit for predicting climate adaptation."
description: # Environmental data provide marginal benefit for predicting climate
 adaptation Dataset DOI: [10.5061/dryad.5hqbzkhhf](10.5061/dryad.5hqbzkhhf)
 ## Description of the data and file structure One zip file including all
 analysis data is included to preserve structure. * envGWAS results *
 Includes all results from JointGWAS for multivariate environmental GWAS
 (envGWAS), including effect sizes, F-values and p-values. Also includes
 significant SNPs genome-wide and clumped lead SNPs for top peaks. * GPoE *
 Includes results for genomic prediction of environment analyses, including
 accessions in sampled folds and predicted environmental values. Includes
 both spatial and random 10-fold cross-validation (CV) analyses. * MegaLMM
 output * Includes results from MegaLMM output for generating genetic
 (G_hat) and residual (R_hat) covariance matrices used in JointGWAS for
 envGWAS. * Phenotypic GWAS * Includes results for GWAS for phenotypic
 traits, such as anthesis silking interval (ASI), bare cob weight, days to
 flowering, field weight, grain weight per hectare (corrected for
 moisture), and plant height. Includes effect sizes and p-values. *
 Phenotypic Prediction * Includes model accuracies for phenotypic trait
 prediction for above traits. Also includes model prediction using SNPs
 obtained from envGWAS done without population structure correction. *
 Tables * Includes metadata for phenotypic field trials, such as accessions
 tested, trial coordinates, and testers used. Also includes
 inverse-normal-transformed climate data for all accessions used in this
 dataset. ### Files and variables #### File:
 environmental\_data\_marginal\_benefit\_dryad\_data.zip **Description:**
 Note: Units for effect sizes and climate variables are ranked
 inverse-normal transformed (INT), which are unitless and are best
 represented as ranks along a normal distribution. This is done for
 comparison of accessions' environmental data along comparable
 distributions. ## envGWAS results * `envGWAS_results_chr_NN.csv` - results
 from JointGWAS for multivariate envGWAS, separated by chromosome (10 files
 corresponding to each chromosome `NN`) * Chr: Chromosome number where the
 SNP is located * pos: SNP position on the chromosome (v4 assembly). * snp:
 SNP identifier * maf: minor allele frequency, or frequency of less
 commonly seen allele at this SNP * MSE: mean squared error of this SNP for
 this model * X..Df: Degrees of freedom * X..Fvalue: F-statistic value for
 envGWAS association to any climate variable for the SNP. * X..Pvalue:
 P-value significance calculated from the envGWAS F-statistic *
 `envGWAS_beta_hats_chr_NN.csv` - beta hat effect sizes for individual
 ranked inverse-normal transformed (INT) climatic variables from
 multivariate envGWAS, separated by chromosome (10 files corresponding to
 each chromosome `NN`) * tmin..X: effect size for INT-transformed minimum
 temperature in growing season * tmax..X: effect size for INT-transformed
 maximum temperature in growing season * trange..X: effect size for
 INT-transformed temperature range in growing season * precipTot..X: effect
 size for INT-transformed total precipitation in growing season *
 aridityMean..X: effect size for INT-transformed mean aridity in growing
 season * rhMean..X: effect size for INT-transformed mean humidity in
 growing season * elevation..X: effect size for INT-transformed elevation
 of collected maize * `GEA_lead_SNPs_list_with_coord.csv` - top 32 hits
 from envGWAS after LD clumping (custom script) * CHR: Chromosome number
 where the SNP is located * BP: SNP position on the chromosome (v4
 assembly). * SNP: SNP identifier * STRAND: SNP strand orientation, either
 "+" (forward) or "-" (reverse). * v4_SNP: SNP
 identifier for assembly v4 * `GEA_significant_SNPs_list_with_coord.csv` -
 all significant hits from envGWAS above a p-value threshold of 1e-5 * CHR:
 Chromosome number where the SNP is located * BP: SNP position on the
 chromosome (v4 assembly). * SNP: SNP identifier * STRAND: SNP strand
 orientation, either "+" (forward) or "-" (reverse). *
 v4_SNP: SNP identifier for assembly v4 ## GPoE *
 `dataset_clim-spatial_prediction-rep_02` - directory with results from
 spatially sampled leave-one-out 10-fold cross-validation *
 `dataset_clim-spatial_prediction-rep_03` - directory with results from
 randomly sampled 10-fold cross-validation For each of these directories,
 they include: * `testing_folds_assignment.csv` (1 file listing all
 accessions' fold assignment) * accession: ID of accession used in
 analysis * fold: training/testing fold assigned for accession *
 `U_hat-rep_XX-fold_NN.csv` - GPoE predicted environmental variable results
 for fold `NN` in rep `XX` (02 for spatial sample or 03 for random sample),
 out of 10 total folds (10 files corresponding to each testing fold `NN`) *
 tmin..X: predicted INT-transformed minimum temperature in growing season *
 tmax..X: predicted INT-transformed maximum temperature in growing season *
 trange..X: predicted INT-transformed temperature range * precipTot..X:
 predicted INT-transformed total precipitation in growing season *
 aridityMean..X: predicted INT-transformed mean aridity in growing season *
 rhMean..X: predicted INT-transformed mean humidity in growing season *
 elevation..X: predicted INT-transformed elevation of collected maize *
 `withheld-accessions-rep_XX-fold_NN.csv` (10 files corresponding to each
 testing fold `NN` for each analysis `XX`, 02 for spatial sample or 03 for
 random sample) * accessions not used in training for fold `NN` (including
 buffer zone) ## MegaLMM output * `dataset_clim-rep_02` * `G_hat.csv` -
 genetic covariance matrix between climate traits, used in JointGWAS for
 envGWAS * `P_hat.csv` - full covariance matrix between climate traits.
 Generally unused for downstream analysis. * `R_hat.csv` - residual
 covariance matrix between climate traits, used in JointGWAS for envGWAS ##
 Phenotypic GWAS GWAS results for phenotypic traits, including effect size
 for each trait in each trial, as well as F-values and p-values across
 trials for the following traits: * ASI * BareCobWeight * DaysToFlowering *
 FieldWeight * GrainWeightPerHectareCorrected * PlantHeight Each trait
 contains a directory containing a single sub-directory `dataset_01_INT`,
 in which contains the analysis files as follows, separated by chromosome
 number. * `GWAS_results_chr_NN.csv` (10 files, corresponding to chromosome
 number `NN`) * CHROM: Chromosome number where the SNP is located * POS:
 SNP position on the chromosome (v4 assembly). * ID: SNP identifier * V4:
 SNP identifier for assembly v4 * MAF: minor allele frequency * MSE: mean
 squared error of model * Experimento.X..Df: degrees of freedom *
 Experimento.X..Fvalue: F-value across all trials * Experimento.X..Pvalue:
 p-value significance for F-value * `GWAS_beta_hats_chr_NN.csv` (10 files,
 corresponding to chromosome number `NN`) * Rows correspond to SNP beta hat
 effect size of given trait in each trial, with each column representing a
 different trial. ## Phenotypic Prediction *
 `deregressed_blups_nostress_filterTrial` - model accuracies for phenotype
 prediction with all models using deregressed BLUPs (best linear unbiased
 predictions), for non-stress trials. *
 `unstructured_deregressed_blups_nostress_filterTrial` - model accuracies
 for phenotype prediction, without including population structure, using
 deregressed BLUPs for non-stress trials. The format for both of these
 analysis results is the same. Files in each analysis are separated by
 trait. * `modelPrediction_nostress_NN_results_resid.csv` (5 files, with
 each trait `NN` corresponding to the following:) * BareCobWeight *
 DaysToFlowering * FieldWeight * GrainWeightPerHectareCorrected *
 PlantHeight Results in each file are described as following, with each row
 corresponding to a trial and tester combination that was cross-validated
 on. Model accuracies are Pearson's correlation r values ranging from
 -1 to 1. * Experimento: trial these models were tested on * fold: tester
 hybrid line these models were tested on * n_train: number of training
 testcrosses * n_test: number of testing testcrosses * n_total: total
 number of testcrosses * lm_PC5: model accuracy of rrBLUP linear model
 using top 5 PCs * lm_PC5_env: model accuracy of rrBLUP linear model using
 top 5 PCs + environmental data * lm_all_SNPs: model accuracy of rrBLUP
 linear model using kinship as gBLUP model * lm_all_SNPs_env: model
 accuracy of rrBLUP linear model using kinship as gBLUP model +
 environmental data * lm_enriched_SNPs: model accuracy of rrBLUP linear
 model using top 5 PCs + top 32 envGWAS SNPs * lm_matching_SNPs: model
 accuracy of rrBLUP linear model using top 5 PCs + random matching 32 SNPs
 * lm_env: model accuracy of rrBLUP linear model using only environmental
 data * rf_env: model accuracy of random forest using only environmental
 data * rf_env_PC5: model accuracy of random forest using top 5 PCs +
 environmental data ## Tables * `accessions_per_trial_per_tester.csv` -
 number of accessions tested in each trial (row) by elite tester (column)
 combination * `accessions_per_trial_per_tester_original.csv` - number of
 accessions tested in each trial (row) by elite tester (column)
 combination, but without initial filtering for low sample size, etc. See
 paper methods. * `accessions_per_trial_per_trait.csv` - number of
 accessions tested in each trial (row) by trait (column) combination *
 `GEA-climate-invnormtransformed.csv` - ranked inverse-normal transformed
 (INT) data for collection climate variables used in this analysis. Raw
 environmental variables are based on WorldClim 2, the Global Aridity Index
 and Potential Evapotranspiration Database, ERA5, and the SRTM 90m Digital
 Elevation Database v4.1, and 3-month growing season data is taken from the
 FAO (see paper methods). * tmin: INT-transformed minimum temperature in
 growing season based on WorldClim 2 * tmax: INT-transformed maximum
 temperature in growing season based on WorldClim 2 * trange:
 INT-transformed temperature range in growing season as defined by
 untransformed tmax - untransformed tmin * precipTot: INT-transformed total
 aggregate precipitation in growing season based on WorldClim 2 *
 aridityMean: INT-transformed mean aridity in growing season from ERA5 *
 rhMean: INT-transformed mean humidity in growing season based on the
 Global Aridity Index and Potential Evapotranspiration Database *
 elevation: INT-transformed elevation of collected maize based on the SRTM
 90m Digital Elevation Database v4.1 * `testers_in_trial.csv` - number of
 elite testers used in each trial (row) for each trait (column) *
 `trial_metadata.csv` - metadata of each trial * Trial ID: trial
 identifier, including year and location abbreviation * Year: year trial
 was held * Location: city near where trial were held * Elevation (m):
 elevation meters above sea level of trial location * Trial latitude:
 latitude coordinate of trial in WGS84 projection * Trial longitude:
 longitude coordinate of trial in WGS84 projection *
 `trials_per_tester.csv` - number of trials each tester (row) was used to
 test each trait (column) ## Access information The Github repo with
 analysis scripts is available at
 [github.com/liforrest6/SeeD](github.com/liforrest6/SeeD) . Data was
 derived from the following sources: * Imputed GBS SNP data used for
 genetic analysis is available at
 [https://data.cimmyt.org/dataset.xhtml?persistentId=hdl:11529/8702394](https://data.cimmyt.org/dataset.xhtml?persistentId=hdl:11529/8702394}). * Curated information of field trial metadata and phenotypic BLUP data is available at [https://data.cimmyt.org/dataset.xhtml?persistentId=hdl:11529/10548233](https://data.cimmyt.org/dataset.xhtml?persistentId=hdl:11529/10548233}) * Base layer map tiles for Figures 1 and S13 are by Stamen Design [https://stamen.com/](https://stamen.com/}{Stamen), under CC by 4.0 [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/}{CC). Data by OpenStreetMap [https://www.openstreetmap.org/](https://www.openstreetmap.org/}{OpenStreetMap}), under ODbL [https://www.openstreetmap.org/copyright](https://www.openstreetmap.org/copyright}{ODbL}).