DOI: 10.5061/dryad.2fqz6133v
title: Dataset to evaluate the impact of environmental kernels in genomic prediction models

description: Integrating genomic and environmental information holds the potential for
 enhancing the predictive power of genomic prediction models when
 accounting for the genotype-by-environment interactions. Hence,
 incorporating environmental covariates (EC) into these models can
 significantly influence their predictive accuracy. In this study, we
 utilized 1379 genotypes from the SoyNAM dataset, evaluated across four
 environments and genotyped with 4611 single-nucleotide polymorphism
 markers, to compare models incorporating genotype-by-environment and
 genotype-by-environmental covariate interactions using different
 covariance matrices. We evaluated four approaches: summarizing EC by
 averaging (AVG), filtering ECs based on a coefficient of determination
 criterion (FILT), segmenting ECs by crop phenology (STG), and a naïve
 approach that utilized all available information (ALL). Predictive ability
 was assessed as the Pearson correlation between the genomic estimated
 breeding values and the adjusted phenotypes, considering 10 replicates of
 three cross-validation scenarios (CV2: predicting tested genotypes in
 observed environments; CV1: untested genotypes in observed environments;
 CV0: tested genotypes in novel environments). Incorporating EC information
 into the models increased average predictive ability from 0.42 to 0.56 for
 CV1 and CV2. In these cases, the predictive ability was lower when EC
 information was averaged to compute the environmental kinship matrix, with
 slight differences observed with respect to the other approaches.
 Regarding the CV0 scheme, the model incorporating only
 genotype-by-environment information performed better (0.33). The naïve
 method, which utilized all available EC information (ALL), proved to be a
 promising approach, as it effectively improved the results in these
 scenarios while eliminating the need for additional steps in selecting
 variables.
description: # Dataset to evaluate the impact of environmental kernels in genomic
 prediction models Dataset DOI:
 [10.5061/dryad.2fqz6133v](https://doi.org/10.5061/dryad.2fqz6133v) ##
 Description of the data and file structure Material developed for the
 manuscript: Impact of environmental covariates summarization on predictive
 ability in genomic selection. This dataset supports the evaluation of how
 different approaches to obtain environmental kernels can impact predictive
 ability in genomic selection models. It includes a data composed by 1,379
 genotypes evaluated in four different environments across United States,
 that were genotyped with 4,611 SNP markers. The dataset is a subset from
 SoyNAM (Soybean Nested Association Mapping) and was initially loaded using
 the R package "SoyNAM" (Xavier et al., 2022). The file also
 includes all the R codes used for conducting data analyses, divided into
 different scripts for: extracting and handling environmental covariates,
 build the design matrices for fitting the models, assigning the phenotypic
 information for cross-validations, fitting the models and also getting the
 variance components. ### Files and variables #### File: impact-ec-main.zip
 **Description:**  Evaluating how different approaches to obtain
 environmental kernels can impact predictive ability. **data folder** W.csv
 or WP.csv: Contains the environmental covariates organized daily for the
 whole season period. Each number in variable's name corresponds to
 the day of the year that data was collected. * Location: The name of
 environment where the phenotypic data was collected. - T2M_MAX: The daily
 maximum temperature at 2 meters in graus Celsius. * T2M_MIN: The daily
 minimum temperature at 2 meters in graus Celsius. - RH2M: The ratio of
 actual partial pressure of water vapor to the partial pressure at
 saturation in percent. * T2M: The average air temperature at 2 meters
 above the surface of the earth in graus Celsius. - PRECTOTCORR: The total
 precipitation at the surface of the earth in water mass in milimeters. *
 WS2M: The average of wind speed at 2 meters above the surface of the earth
 in meters per second. - QV2M: The ratio of the mass of water vapor to the
 total mass of air at 2 meters in percent. WCONV.csv: Contains the
 environmental convariates summarised through the average across season
 period. * T2M_MAX: The daily maximum temperature at 2 meters in graus
 Celsius. - T2M_MIN: The daily minimum temperature at 2 meters in graus
 Celsius. * RH2M: The ratio of actual partial pressure of water vapor to
 the partial pressure at saturation in percent. - T2M: The average air
 temperature at 2 meters above the surface of the earth in graus Celsius. *
 PRECTOTCORR: The total precipitation at the surface of the earth in water
 mass in milimeters. - WS2M: The average of wind speed at 2 meters above
 the surface of the earth in meters per second. * QV2M: The ratio of the
 mass of water vapor to the total mass of air at 2 meters in percent.
 WFILT.csv: Contains the environmental covariates filtered by a coefficient
 of determination. Each number in variable's name corresponds to the
 day of the year that data was collected. * T2M_MAX: The daily maximum
 temperature at 2 meters in graus Celsius. - T2M_MIN: The daily minimum
 temperature at 2 meters in graus Celsius. * RH2M: The ratio of actual
 partial pressure of water vapor to the partial pressure at saturation in
 percent. - T2M: The average air temperature at 2 meters above the surface
 of the earth in graus Celsius. * PRECTOTCORR: The total precipitation at
 the surface of the earth in water mass in milimeters. - WS2M: The average
 of wind speed at 2 meters above the surface of the earth in meters per
 second. * QV2M: The ratio of the mass of water vapor to the total mass of
 air at 2 meters in percent. WSTG.csv and W_stages.csv:Contains the
 environmental covariates organized by soybean phenological stages. Each
 number in variable's name corresponds to the interval that data was
 summarised. * T2M_MAX: The daily maximum temperature at 2 meters in graus
 Celsius. - T2M_MIN: The daily minimum temperature at 2 meters in graus
 Celsius. * RH2M: The ratio of actual partial pressure of water vapor to
 the partial pressure at saturation in percent. - T2M: The average air
 temperature at 2 meters above the surface of the earth in graus Celsius. *
 PRECTOTCORR: The total precipitation at the surface of the earth in water
 mass in milimeters. - WS2M: The average of wind speed at 2 meters above
 the surface of the earth in meters per second. * QV2M: The ratio of the
 mass of water vapor to the total mass of air at 2 meters in percent.
 X.csv: Contains the molecular marker (Single nucleotide polymorphisms)
 information for the evaluated genotypes. Each column corresponds to the
 molecular marker coded as 0, 1 and 2. * Genotype:  The name of evaluated
 genotypes. Y.csv: Containts the phenotypic information of genotypes
 evaluated across environments. * year: The year which phenotypic data was
 collected. - location: The location where phenotypic data was collected. *
 environ: The combination of year x location. - strain: The name of the
 genotype that was evaluated. * yield: The grain yield in kg per hectare.
 env.cov.csv: Contains the raw environmental covariates data before
 different strategies for summarization. * YearFilt: The year in which the
 weather data were collected. - ENV: The name of the environment
 (combination of location × year). * YEAR: The year in which the weather
 data were collected. - MM: The month in which the weather data were
 collected. * DD: The day on which the weather data were collected. - DOY:
 The day of the year corresponding to the weather data. * YYYYMMDD: The
 complete date corresponding to the weather data. - T2M_MAX: The daily
 maximum temperature at 2 meters in graus Celsius. * T2M_MIN: The daily
 minimum temperature at 2 meters in graus Celsius. - RH2M: The ratio of
 actual partial pressure of water vapor to the partial pressure at
 saturation in percent. - T2M: The average air temperature at 2 meters
 above the surface of the earth in graus Celsius. * PRECTOTCORR: The total
 precipitation at the surface of the earth in water mass in milimeters. -
 WS2M: The average of wind speed at 2 meters above the surface of the earth
 in meters per second. * QV2M: The ratio of the mass of water vapor to the
 total mass of air at 2 meters in percent. **pipeline** *
 01.extracting_ec_data.R: R codes for extracting environmental covariates
 of locations. * 02.AdjustingW.R: R codes for adjusting different
 strategies to summarise the environmental covariates informations. *
 03.DesignMatrices.R: R codes for building design matrices for fitting
 genomic selection models. * 04.1.FittingFullDataModels.R: R Codes for
 fitting data with full data for estimating variance components using
 different models. * 04.FittingModelsParallel.R: R codes for conducting
 cross-validation in parallel for faster running time. * 05.VC.R: R codes
 for extracting variance components from full data models. *
 3.1.AssigningCV.R: R codes for assigning observations into folds for
 conducting cross-validation. * .gitignore: file generated automatically
 when creating github folder. Not needed for running analyses. -
 impact-ec.Rproj: file for openning R project. ## Code/software The
 materials uploaded in this file can be viewed through the softwares
 Microsoft Excel and the codes were written using the R Language. The
 original code can be accessed
 at: [https://github.com/vsagae/impact-ec](https://github.com/vsagae/impact-ec) ## Access information Data was derived from the following sources: * The dataset is a subset from SoyNAM (Soybean Nested Association Mapping) and was initially loaded using the R package "SoyNAM" (Xavier et al., 2022).