No abstract

The breeding goal in dairy cattle is to increase lifetime profit per animal. Profit is a function of production and the time that a cow remains in herd. Thus, profit can only be recorded when a cow is culled, and breeding value of more profitable aniUnauthenticated Download Date | 5/13/18 1:11 AM

Holstein Friesian cow training sets were created according to disease incidences. The different datasets were used to investigate the impact of random forest (RF) and genomic BLUP (GBLUP) methodology on genomic prediction accuracies. In addition, for further verifications of some specific scenarios, single-step genomic BLUP was applied. Disease traits included the overall trait categories of (i) claw disorders, (ii) clinical mastitis and (iii) infertility from 80 741 first lactation Holstein cows kept in 58 large-scale herds. A subset of 6744 cows was genotyped (50K SNP panel). Response variables for all scenarios were de-regressed proofs (DRPs) and pre-corrected phenotypes (PCPs). Initially, all sick cows were allocated to the testing set, and healthy cows represented the training set. For the ongoing cow allocation schemes, the number of sick cows in the training set increased stepwise by moving 10% of the sick cows from the testing to the training set in each step. The size of training and testing sets was kept constant by replacing the same number of cows in the testing set with (randomly selected) healthy cows from the training set. For both the RF and GBLUP methods, prediction accuracies were larger for DRPs compared to PCPs. For PCPs as a response variable, the largest prediction accuracies were observed when the disease incidences in training sets reflected the disease incidence in the whole population. A further increase in prediction accuracies for some selected cow allocation schemes (i.e. larger prediction accuracies compared to corresponding scenarios with RF or GBLUB) was achieved via single-step GBLUP applications. Correlations between genome-wide association study SNP effects and RF importance criteria for single SNPs were in a moderate range, from 0.42 to 0.57, when considering SNPs from all chromosomes or from specific chromosome segments. RF identified significant SNPs close to potential positional candidate genes: GAS1, GPAT3 and CYP2R1 for clinical mastitis; SPINK5 and SLC26A2 for laminitis; and FGF12 for endometritis.

The aim of this study was to compare genetic (co)variance components and prediction accuracies of breeding values from genomic random regression models (gRRM) and pedigree-based random regression models (pRRM), both defined with or without an additional environmental gradient. The used gradient was a temperaturehumidity index (THI), considered in statistical models to investigate possible genotype by environment (G×E) interactions. Data included 106,505 test-day records for milk yield (MY) and 106,274 test-day records for somatic cell score (SCS) from 12,331 genotyped Holstein Friesian daughters of 522 genotyped sires. After single nucleotide polymorphism quality control, all genotyped animals had 40,468 single nucleotide polymorphism markers. Test-day traits from recording years 2010 to 2015 were merged with temperature and humidity data from the nearest weather station. In this regard, 58 large-scale farms from the German federal states of Berlin-Brandenburg and Mecklenburg-West Pomerania were allocated to 31 weather stations. For models with a THI gradient, additive genetic variances and heritabilities for MY showed larger fluctuations in dependency of DIM and THI than for SCS. For both traits, heritabilities were smaller from the gRRM compared with estimates from the pRRM. Milk yield showed considerably larger G×E interactions than SCS. In genomic models including a THI gradient, genetic correlations between different DIM × THI combinations ranged from 0.26 to 0.94 for MY. For SCS, the lowest genetic correlation was 0.78, estimated between SCS from the last DIM class and the highest THI class. In addition, for THI × THI combinations, genetic correlations were smaller for MY compared with SCS. A 5-fold cross-validation was used to assess prediction accuracies from 4 different models. The 4 different models were gRRM and pRRM, both modeled with or without G×E interactions. Prediction accuracy was the correlation between breeding values for the prediction data set (i.e., excluding the phenotypic records from this data set) with respective breeding values considering all phenotypic information. Prediction accuracies for sires and for their daughters were largest for the gRRM considering G×E interactions. Such modeling with 2 covariates, DIM and THI, also allowed accurate predictions of genetic values at specific DIM. In comparison with a pRRM, the effect of a gRRM with G×E interactions on gain in prediction accuracies was stronger for daughters than for sires. In conclusion, we found stronger effect of THI alterations on genetic parameter estimates for MY than for SCS. Hence, gRRM considering THI especially contributed to gain in prediction accuracies for MY.

No abstract

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

The aim of this study was the evaluation of climate sensitivity via genomic reaction norm models [i.e., to infer cow milk production and milk fatty acid (FA) responses on temperature-humidity index (THI) alterations]. Test-day milk traits were recorded between 2010 and 2016 from 5,257 first-lactation genotyped Holstein dairy cows. The cows were kept in 16 largescale cooperator herds, being daughters of 344 genotyped sires. The longitudinal data consisted of 47,789 test-day records for the production traits milk yield (MY), fat yield (FY), and protein yield (PY), and of 20,742 test-day records for 6 FA including C16:0, C18:0, saturated fatty acids (SFA), unsaturated fatty acids (UFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA). After quality control of the genotypic data, 41,057 SNP markers remained for genomic analyses. Meteorological data from the weather station in closest herd distance were used for the calculation of maximum hourly daily THI. Genomic reaction norm models were applied to estimate genetic parameters in a single-step approach for production traits and FA in dependency of THI at different lactation stages, and to evaluate the model stability. In a first evaluation strategy (New_sire), all phenotypic records from daughters of genotyped sires born after 2010 were masked, to mimic a validation population. In the second strategy (New_env), only daughter records of the new sires recorded in the most extreme THI classes were masked, aiming at predicting sire genomic estimated breeding values (GEBV) under heat stress conditions. Model stability was the correlation between GEBV of the new sires in the reduced data set with respective GEBV estimated from all phenotypic data. Among all test-day production traits, PY responded as the most sensitive to heat stress. As observed for the remaining production traits, genetic variances were quite stable across THI, but genetic correlations between PY from temperate climates with PY from extreme THI classes dropped to 0.68. Genetic variances in dependency of THI were very similar for C16:0 and SFA, indicating marginal climatic sensitivity. In the early lactation stage, genetic variances for C18:0, MUFA, PUFA, and UFA were significantly larger in the extreme THI classes compared with the estimates under thermoneutral conditions. For C18:0 and MUFA, PUFA, and UFA in the middle THI classes, genetic correlations in same traits from the early and the later lactation stages were lower than 0.50, indicating strong days in milk influence. Interestingly, within lactation stages, genetic correlations for C18:0 and UFA recorded at low and high THI were quite large, indicating similar genetic mechanisms under stress conditions. The model stability was improved when applying the New_env instead of New_sire strategy, especially for FA in the first stage of lactation. Results indicate moderately accurate genomic predictions for milk traits in extreme THI classes when considering phenotypic data from a broad range of remaining THI. Phenotypically, ...

No abstract