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Supplemental Material for Rogers et al., 2020

dataset
posted on 11.11.2020, 18:22 by Anna R. Rogers, Jeffrey C. Dunne, Cinta Romay, Martin Bohn, Edward S. Buckler, Ignacio A. Ciampitti, Jode Edwards, David Ertl, Sherry Flint-Garcia, Michael A. Gore, Christopher Graham, Candice N. Hirsch, Elizabeth Hood, David C. Hooker, Joseph Knoll, Elizabeth C. Lee, Aaron Lorenz, Jonathan P. Lynch, John McKay, Stephen P. Moose, Seth C. Murray, Rebecca Nelson, Torbert Rocheford, James C Schnable, Patrick S Schnable, Rajandeep Sekhon, Maninder Singh, Margaret Smith, Nathan Springer, Kurt Thelen, Peter Thomison, Addie Thompson, Mitch Tuinstra, Jason Wallace, Randall J. Wisser, Wenwei Xu, A.R. Gilmour, Shawn M. Kaeppler, Natalia De Leon, James B. Holland
Figure S1 Heatmap of the A Matrix demonstrating relatedness between individuals.

Figure S2. Scatterplot of the A and D Matrices off-diagonal values

Figure S3. Comparison of entry sharing to mean kinship between Environments.

Figure S4 (a-b). Biplot of first two factors of the FA model colored by environmental cluster. Dendrogram of environmental clustering using same color schematic.

Figure S5. Scree plot for within-between variance ratio from clustering environments based on weather factors

Figure S6. First ten weather variables selected in stepwise regression model relating D×FA(1) environment loadings to weather variables summarized over 5-day windows. Each variable and mean environment yield are standardized.

Figure S7. Workflow for processing genomic marker data from inbred parents to generate hybrid genotypes.

Figure S8. Boxplot of stand for each environment. Outliers detected using IQR and mean-percentile criteria colored pink.




Supplementary Tables:
Table S1. Table matching each Environment name used for analysis to its site and year.

Table S2. Covariates of experimental design and which year each could be fitted for/Blocking factors and residual variance models tested for each year in stage 1 analysis.

Table S3. Model chosen for each trait-environment combination including the fixed effects, random effects, and residual variance structure of the model selected using BIC. In addition, error variance from the model and both mean and plot basis heritabilities are given.

Table S4. Pairwise genetic correlations between environments from Yield models fit in Echidna.

Table S5. ANOVA model for Yield to using weather-based environment clusters demonstrate relationship between environmental data and environment main effect and G×E variances.

Table S6. Model fit information for stepwise regression models using 5, 10, 15, and 30-day windowed environmental covariates as a predictor of parameter estimates from respective AxFA(1) and DxFA(1) models. Best fitting model for each parameter by BIC are in bold.

Table S7. Loadings terms for the model with best BIC from stepwise regression analysis.


File S1. Supplemental Methods. Description of the marker, phenotypic, and weather data cleaning and exploration done. approximately 10 pages of comprehensive data cleaning information.

File S2. Stage 1 R Code. This script processes through Stage 1 modeling including multiple model types to account for experimental design in a given environment. The best model is output using BIC as a metric.

File S3. Hybrid best linear unbiased estimators BLUEs for all traits within each environment based on selected model

File S4. Sample Echidna code for models utilizing IDVG Variance Structures

File S5. Sample Echidna code for models utilizing the A Matrix.

File S6. Sample Echidna code for models utilizing the D Matrix

File S7. Sample Echidna code for models utilizing both the A and D matrices in tandem.

File S8. Environment BLUEs (averaged across all hybrids) for all traits

File S9. Hybrid BLUEs (averaged across all environments) for all traits

File S10. Hybrid Cluster assignments and A and D diagonal values, along with their diagonal values from the A and D matrices.

File S11. Tables of Variance components for each trait and the model fit. For Yield, an expanded set of models were fit while all other traits fit only the IDVG models and most simple model using both the A and D realized relationship matrices.

File S12. Factor loadings of each environment variable from the FA(10) model applied to 30-day period weather data.

File S13. List of Hybrid names in order of rows and columns of the realized genomic relationship matrices

File S14. Realized additive relationship matrix, TASSEL output format

File S15. Realized dominance relationship matrix, TASSEL output format

File S16. R code for processing G2F weather data from cleaned files. The first function, plant, computes days since planting for each location. The second function, DailyMeans computes daily values for covariates from data taken throughout a given day at a location using Julian date format. The third function uses a while loop to allow the user to specify the sliding window size for their environmental data output. Between steps assume that the user has filled in any missing values using an appropriate source.

File S17. This files contains correlations between data obtained from the in-field Spectrum Watchdog weather stations and data scraped from the Iowa Environmental Mesonet database.


File S18. Weather variables averaged over 5-day periods for each environment. These values are in original units before scaling.


History

Article title

The Importance of Dominance and Genotype-by-Environment Interactions on Grain Yield Variation in a Large-Scale Public Cooperative Maize Experiment

Manuscript #

GENETICS/2020/303514

Licence

Exports