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Predicting breeding values and genetic components using generalized linear mixed models for categorical and continuous traits in walnut (Juglans regia)

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Abstract

Genetic components for economically important traits in walnut (Juglans regia) were estimated for the first time using historical pedigree and heirloom phenotypic data from the walnut breeding program at the University of California, Davis. The constructed pedigree is composed of ~ 15,000 individuals and is derived from current and historic phenotypic records dating back > 50 years and located across California. To predict the additive genetic values of individuals under selection, generalized linear mixed models (GLMM), implemented with MCMCglmm, were developed. Several repeatability models were established to obtain the best model and predict the genetic parameters for each trait. Repeatability for yield, harvest date, extra-light kernel color (ELKC), and lateral bearing were predicted at 0.82, 0.98, 0.63, and 0.96, respectively, and average narrow-sense heritabilities were 0.54, 0.77, 0.49, and 0.75, respectively. Each individual in the pedigree was ranked by its estimated breeding value (EBV). The genetic trend showed specific patterns for each trait, and real genetic improvement was found over time. The completed pedigree built here, the estimated breeding values, and the ranking of individuals according to their breeding values, can be used to guide future crossing designs in the walnut breeding program and future implementation of genomic selection methods in walnut.

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Acknowledgments

We would especially like to thank the California Walnut Board for supporting this study.

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Correspondence to Pedro J. Martínez-García.

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The authors declare that they have no conflict of interest.

Data archiving statement

All relevant data are presented in the main paper and in the Supplementary Materials. Supplemental file 1 presents EBV for all traits and inbreeding coefficients. Appendix 1 presents the R code for our model.

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Communicated by M. Wirthensohn

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ESM 1

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Appendix1 R code

Appendix1 R code

General mixed model used for yield, lateral bearing, and ELKC

Prior

R = list (V = 30, nu = 0.002), G = list (G1 = list (V = 10, n = 0.002), G2 = list (V = 5, n = 0.002))

Model (all random effects)

Traitmodel < − MCMCglmm (TRAIT, random = ~ animal + ID + location + year + age, pedigree = ped, family = “ordinal”, nitt = 500,000, burnin = 5000, thin = 20, verbose = F, pr = T, data = input, prior = prior).

General mixed model used for harvest date

Prior

R = list (V = 30, nu = 0.002), G = list (G1 = list (V = 10, n = 0.002), G2 = list (V = 5, n = 0.002))

Model (all random effects)

Traitmodel < − MCMCglmm (TRAIT, random = ~ animal + ID + location + year + age, pedigree = ped, family = “gaussian,” nitt = 500,000, burnin = 5000, thin = 20, verbose = F, pr = T, data = input, prior = prior)

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Martínez-García, P.J., Famula, R.A., Leslie, C. et al. Predicting breeding values and genetic components using generalized linear mixed models for categorical and continuous traits in walnut (Juglans regia). Tree Genetics & Genomes 13, 109 (2017). https://doi.org/10.1007/s11295-017-1187-z

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  • DOI: https://doi.org/10.1007/s11295-017-1187-z

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