The marker effects of a single-step random regression model for four test-day traits in German Holstein.

Single-step genomic model has become the golden standard for routine evaluation in livestock species, like Holstein dairy cattle. The single-step genomic model with direct estimation of marker effects has been proven to be efficient in accurately accounting for millions of genotype records. For diverse applications including frequent genomic evaluation updates on a weekly basis, estimates of the marker effects from the single-step evaluations play a central role in genomic prediction. In this study we focused on exploring the marker effect estimates from the single-step evaluation. Phenotypic, genotypic and pedigree data were taken from the official evaluation for German dairy breeds in April 2021. A multi-lactation random regression test-day model was applied to more than 242 million test-day records separately for 4 traits: milk, fat and protein yields and somatic cell scores. Nearly one million genotyped Holstein animals were considered in the single-step genomic evaluations including about 21 million animals in pedigree. Deregressed multiple across country breeding values of Holstein bulls having daughters outside Germany were integrated into the national test-day data to increase the reliability of genomic breeding values. To assess the stability and bias of the marker effects of the single-step model, test-day records of the last 4 years were deleted and the integrated bulls born in the last 4 years were truncated from the complete phenotypic data set. Estimates of the marker effects were shown to be highly correlated, with correlations around 0.9, between the full and truncated evaluations. Regression slope values of the marker effect estimates from the full on the truncated evaluations were all close to their expected value, being about 1.03. Calculated using random regression coefficients of the marker effect estimates, drastically different shapes of genetic lactation curve were seen for 2 markers on chromosome 14 for the 4 test-day traits. The contribution of individual chromosomes to the total additive genetic variances seemed to follow the polygenic inheritance mode for protein yield and somatic cell scores. However, chromosome 14 was found to make an exceptionally large contribution to the total additive genetic variance for milk and fat yields because of markers near the major gene DGAT1. For the first lactation test-day traits, we obtained nearly zero correlations of chromosomal direct genomic values between any pair of the chromosomes; no spurious correlations were found in our analysis, thanks to the large reference population. For trait milk yield, chromosomal direct genomic values appeared to have a large variation in the between-lactation correlations among the chromosomes, especially between first and second / third lactations. The optimal features of the random regression test-day model and the single-step marker model allowed us to track the differences in the shapes of genetic lactation curves down to the individual markers. Furthermore, the single-step random regression test-day model enabled us to better understand the inheritance mode of the yield traits and somatic cell scores, e.g., variable chromosomal contributions to the total additive genetic variance and to the genetic correlations between lactations.

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© 2023, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).