DOI: 10.5061/dryad.qjq2bvqgz
title: Data from: Multi-generation genomic prediction of maize yield using parametric and non-parametric sparse selection indices

description: Genomic prediction models are often calibrated using multi-generation
 data. Over time, as data accumulates, training data sets become
 increasingly heterogeneous. Differences in allele frequency and linkage
 disequilibrium patterns between the training and prediction genotypes may
 limit prediction accuracy. This leads to the question of whether all
 available data or a subset of it should be used to calibrate genomic
 prediction models. Previous research on training set optimization has
 focused on identifying a subset of the available data that is optimal for
 a given prediction set. However, this approach does not contemplate the
 possibility that different training sets may be optimal for different
 prediction genotypes. To address this problem, we recently introduced a
 sparse selection index (SSI) that identifies an optimal training set for
 each individual in a prediction set. Using additive genomic relationships,
 the SSI can provide increased accuracy relative to genomic-BLUP (GBLUP).
 Non-parametric genomic models using Gaussian kernels (KBLUP) have, in some
 cases, yielded higher prediction accuracies than standard additive models.
 Therefore, here we studied whether combining SSIs and kernel methods could
 further improve prediction accuracy when training genomic models using
 multi-generation data. Using four years of doubled haploid maize data from
 the International Maize and Wheat Improvement Center (CIMMYT), we found
 that when predicting grain yield the KBLUP outperformed the GBLUP, and
 that using SSI with additive relationships (GSSI) lead to 5-17% increases
 in accuracy, relative to the GBLUP. However, differences in prediction
 accuracy between the KBLUP and the kernel-based SSI were smaller and not
 always significant.
description: Data consist of 3722 Doubled-Haploid (DH) lines derived from
 biparental families developed at CIMMYT’s Maize DH facility at the
 Agricultural & Livestock Research Organization (KALRO) in Kiboko,
 Kenya. The biparental families were obtained by crossing elite inbred
 lines with drought-tolerant lines. The DH lines were selected from a
 larger population (based on the results of evaluating germination, good
 stand, plant type, low ear placement, and well-filled ears) for stage I
 multi-location yield trials conducted from 2017 to 2020.
 Each year, the selected DH lines were crossed with a single-cross
 tester from the complementary heterotic group to generate tree-way hybrids
 that were evaluated under well-watered (denoted as optimal) and drought
 conditions. Trial were planted in an alpha-lattice design with two
 replications and evaluated in two well-watered locations and one managed
 drought stress location during the 2017, 2018, 2019, and 2020 growing
 seasons. Grain yield (GY, tons/ha), anthesis date (AD, days) and plant
 height (PH, cm) traits were recorded. Plots were manually harvested and GY
 was corrected to a moisture of 12.5%. AD was measured from planting to the
 moment in which 50% of the plants shed pollen, and PH was measured between
 the soil surface and the flag leaf collar on five representative plants in
 each plot. DNA samples from leaves were sent to the
 Institute for Genomic Diversity, Cornell University, Ithaca, NY, USA, for
 genotyping with repetitive sequences (rAmpSeq). A distortion segregation
 analysis was performed to a total of 5465 dominant markers coded as 0
 (absence) and 1 (presence) from where a total of 61 markers were discarded
 at a 5% FDR. The remaining markers were filtered by minor allele frequency
 (MAF&lt;0.05), leading 4612 filtered markers that were used for
 analyses. The adjusted means of GY, AD and PH were
 obtained using mixed-effects models fitted separately for each
 trait-environmental-condition-year combination. The Best Linear Unbiased
 Estimates (BLUE) of genotypes for the optimal experiments were estimated
 within year across the two locations by fitting models including
 <em>Genotype</em>, <em>Location</em>,
 <em>Replicate</em>, <em>Block</em>, and
 <em>Genotype</em>-by-<em>Location</em>
 interaction. Likewise, within each year, the BLUE for each trait for the
 single-location drought experiment was obtained through a linear model
 including <em>Genotype</em>, <em>Replicate</em>,
 and <em>Block</em> only. 
 A total of ? = 3527 lines containing marker information and phenotypic information remained after quality control. The final number of lines in 2017, 2018, 2019, and 2020 are <em>n</em> = 901, <em>n</em> = 1418, <em>n</em> = 722, and <em>n</em> = 486, respectively. Data from the 2017 and 2018 cycles have been previously described and analyzed by Beyene <em>et al</em>. (2019) and Atanda <em>et al</em>. (2020).
description: Data contains phenotypic observations on 3528 genotypes from
 which 901, 1419, 722, and 486 are from 2017, 2018, 2019, and 2020,
 respectively. Missing values: In 2018, genotype with
 GID '973680' was not observed in Optimal experiments and
 genotype '976304' was not observed in Drought experiments.
 Genotype '1132699' from 2019 was not recorded in Drought
 experiments. Phenotypic data: File
 'Pheno_data.csv' is a matrix containing the adjusted means for
 all the 3528 genotypes (in rows) for each trait-environmental-condition
 combination (in columns). Column 'GID' contains the Genotype ID
 and column 'Year' contains the cycle to which each genotype
 belongs to. Genotypic data: File
 'Geno_data.csv' contains, for each genotype (in rows),
 presence-absence marker information on 4612 markers (in columns). Column
 'GID' contains the Genotype ID and matches GID column in
 'Pheno_data.csv' file.