DOI: 10.5061/dryad.vx0k6dk3p
title: Data for: Multi-trait/environment sparse genomic prediction using the SFSI R-package

description: This data set is from CIMMYT’s Global Wheat Program and includes
 adjusted phenotypic records of grain yield (ton/ha) from n=3,731 wheat
 (Triticum aestivum) lines evaluated at four environmental conditions (B2I,
 B5I, MEL, and LHT), and marker data for 9,045 SNPs. This dataset is a
 subset, corresponding to the lines that have data in the four
 environments, from a full dataset containing 29,484 lines evaluated at six
 environments.
description: The original data set includes phenotype data from 29,484 wheat
 lines that were derived from years 2009 through 2016 evaluated at the
 CIMMYT’s experimental station in Ciudad Obregon, Mexico. Lines were
 evaluated under six environmental conditions representing a combination of
 planting system (bed vs. flat, the later referred to as melgas), number of
 irrigations (2, 5 irrigations or drip irrigation), and sowing date
 (optimum, late or early planting). Each year, grain yield trials were
 established in an α-lattice design with three replicates into incomplete
 blocks. Moisture-standardized grain yield (ton/ha) was measured at each
 plot. Least-square means by line and environmental condition were obtained
 using mixed-effects models with a fixed intercept and the random effects
 of trial, block (within trial), and replicate (within trial).
 Lines were genotyped using GBS (Genotyping-by-sequencing) for
 42,706 SNP markers. SNPs with more than 70% of missing values and those
 with minor allele frequency lower than 5% were removed, leading to 9,045
 filtered SNPs. The remaining markers that were missing were imputed with
 the sample mean of lines at the corresponding loci.
description: # Data for: Multi-Trait/Environment Sparse Genomic Prediction using the
 SFSI R-package Dataset DOI:
 [10.5061/dryad.vx0k6dk3p](10.5061/dryad.vx0k6dk3p) ## Data set overview
 This data set is from CIMMYTs Global Wheat Program and includes adjusted
 phenotypic records of grain yield (ton/ha) from n=3,731 wheat (*Triticum
 aestivum*) lines evaluated at four environmental conditions (B2I, B5I,
 MEL, and LHT), and marker data for 9,045 SNPs. This data set is a subset,
 corresponding to the lines that have data in the four environments, from
 the full data set described and analyzed by Perez-Rodriguez *et al.*
 (2017) and Lopez-Cruz & de los Campos (2021). ### Data structure The
 data set consists of two `.csv` files: * `pheno.csv`: Phenotypic data file
 consists of a matrix with grain yield records for 3,731 wheat lines (in
 rows) evaluated at four environments (B2I, B5I, MEL, and LHT) in columns.
 * `geno.csv`: Genotypic data (geno.csv) is a matrix with 9,045 SNP markers
 (in columns) for all the 3,731 wheat lines (in rows) ## Full data
 description ### Experimental data The original data set comprised a total
 of 29,484 lines derived from years 2009 through 2016 evaluated at the
 CIMMYTs experimental station in Ciudad Obregon, Mexico. Lines were
 evaluated under six environmental conditions representing a combination of
 planting system (bed vs. flat, the later referred to as melgas), number of
 irrigations (2, 5 irrigations or drip irrigation), and sowing date
 (optimum, late or early planting): * B2I: bed planting and two irrigations
 * B5I: bed planting and five irrigations * MEL: melgas flat planting and
 five irrigations * LHT: late heat * DRB: bed planting and drip irrigation
 * EHT: early heat Grain yield trials were established in an alpha-lattice
 design with three replicates into incomplete block. Moisture-standardized
 grain yield (ton/ha) was measured as the total yield plot. **Adjusted
 means**. Least-square means for each line within environmental condition
 were obtained using mixed-effect models that include a fixed intercept and
 the random effects of trial, block (within trial), and replicate (within
 trial). ### Genotypic data Lines were were genotyped using GBS
 (Genotyping-by-sequencing) technology which produced 42,706 SNP markers.
 SNPs were filtered by removing those with more than 70% of missing values
 and those with minor allele frequency (MAF) lower than 5%. A total of
 9,045 SNPs were retained after applying these filters. Markers scores that
 were missing were imputed with the sample mean of lines at the
 corresponding loci. ### References Perez-Rodriguez, P., Crossa, J.,
 Rutkoski, J., Poland, J., Singh, R., Legarra, A., Autrique, E., Campos, G.
 de los, Burgueño, J., & Dreisigacker, S. (2017). Single-Step Genomic
 and Pedigree Genotype x Environment Interaction Models for Predicting
 Wheat Lines in International Environments. *The Plant Genome*, 10(2), 115.
 Lopez-Cruz, M., & de los Campos, G. (2021). Optimal breeding-value
 prediction using a Sparse Selection Index. *Genetics*, 218(1), 110.