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Journal of Animal Science|May 30, 2023
Efficient ways to combine data from broiler and layer chickens to account for sequential genomic selectionJorge Hidalgo, Daniela Lourenco, Shogo Tsuruta, et al.Journal of Animal Science|May 7, 2020
Indirect predictions with a large number of genotyped animals using the algorithm for proven and youngAndre L S Garcia, Yutaka Masuda, Shogo Tsuruta, et al.Veterinary World|February 14, 2022
Validation of single-step genomic predictions using the linear regression method for milk yield and heat tolerance in a Thai-Holstein populationPiriyaporn Sungkhapreecha, Ignacy Misztal, Jorge Hidalgo, et al.Genetics, Selection, Evolution : GSE|April 16, 2024
Estimating genetic parameters of digital behavior traits and their relationship with production traits in purebred pigsMary Kate Hollifield, Ching-Yi Chen, Eric Psota, et al.Journal of Animal Science|January 31, 2020
Changes in genetic parameters for fitness and growth traits in pigs under genomic selectionJorge Hidalgo, Shogo Tsuruta, Daniela Lourenco, et al.Journal of Animal Science|August 16, 2023
Boundaries for genotype, phenotype, and pedigree truncation in genomic evaluations in pigsFernando Bussiman, Ching-Yi Chen, Justin Holl, et al.JDS Communications|March 6, 2026
Decreasing the computing time of approximated reliabilities of genomic estimated breeding values in the single-step genomic best linear unbiased predictor using different core sizes for the algorithm for proven and youngS N Sanchez-Sierra, Matias Bermann, Natascha Vukasinovic, et al.Journal of Animal Science|August 11, 2021
Investigating the persistence of accuracy of genomic predictions over time in broilersJorge Hidalgo, Daniela Lourenco, Shogo Tsuruta, et al.Journal of Animal Science|July 31, 2020
Beef trait genetic parameters based on old and recent data and its implications for genomic predictions in Italian Simmental cattleAlberto Cesarani, Jorge Hidalgo, Andre Garcia, et al.Journal of Animal Science|October 9, 2024
Transforming estimated breeding values from observed to probability scale: how to make categorical data analyses more efficientJorge Hidalgo, Ignacy Misztal, Shogo Tsuruta, et al.Pageof 13