Keeping dairy cows in grassland systems relies on detailed analyses of genetic resistance against endoparasite infections, including between- and within-breed genetic evaluations. The objectives of this study were (1) to compare different Black and White dairy cattle selection lines for endoparasite infections and (2) the estimation of genetic (co)variance components for endoparasite and test-day milk production traits within the Black and White cattle population. A total of 2,006 fecal samples were taken during 2 farm visits in summer and autumn 2015 from 1,166 cows kept in 17 small- and medium-scale organic and conventional German grassland farms. Fecal egg counts were determined for gastrointestinal nematodes (FEC-GIN) and flukes (FEC-FLU), and fecal larvae counts for the bovine lungworm Dictyocaulus viviparus (FLC-DV). The lowest values for gastrointestinal nematode infections were identified for genetic lines adopted to pasture-based production systems, especially selection lines from New Zealand. Heritabilities were low for FEC-GIN (0.05-0.06 ± 0.04) and FLC-DV (0.05 ± 0.04), but moderate for FEC-FLU (0.33 ± 0.06). Almost identical heritabilities were estimated for different endoparasite trait transformations (log-transformation, square root). The genetic correlation between FEC-GIN and FLC-DV was 1.00 ± 0.60, slightly negative between FEC-GIN and FEC-FLU (-0.10 ± 0.27), and close to zero between FLC-DV and FEC-FLU (0.03 ± 0.30). Random regression test-day models on a continuous time scale [days in milk (DIM)] were applied to estimate genetic relationships between endoparasite and longitudinal test-day production traits. Genetic correlations were negative between FEC-GIN and milk yield (MY) until DIM 85, and between FEC-FLU and MY until DIM 215. Genetic correlations between FLC-DV and MY were negative throughout lactation, indicating improved disease resistance for high-productivity cows. Genetic relationships between FEC-GIN and FEC-FLU with milk protein content were negative for all DIM. Apart from the very early and very late lactation stage, genetic correlations between FEC-GIN and milk fat content were negative, whereas they were positive for FEC-FLU. Genetic correlations between FEC-GIN and somatic cell score were positive, indicating similar genetic mechanisms for susceptibility to udder and endoparasite infections. The moderate heritabilities for FEC-FLU suggest inclusion of FEC-FLU into overall organic dairy cattle breeding goals to achieve long-term selection response for disease resistance.

A total of 31,396 females born from 2010 to 2013 in 43 large-scale Holstein-Friesian herds were phenotyped for calf and cow disease traits using a veterinarian diagnosis key. Calf diseases were general disease status (cGDS), calf diarrhea (cDIA), and calf respiratory disease (cRD) recorded from birth to 2 mo of age. Incidences were 0.48 for cGDS, 0.28 for cRD, and 0.21 for cDIA. Cow disease trait recording focused on the early period directly after calving in first parity, including the interval from 10 d before calving to 200 d in lactation. For cows, at least one entry for the respective disease implied a score = 1 (sick); otherwise, score = 0 (healthy). Corresponding cow diseases were first-lactation general disease status (flGDS), first-lactation diarrhea (flDIA), and first-lactation respiratory disease (flRD). Additional cow disease categories included mastitis (flMAST), claw disorders (flCLAW), female fertility disorders (flFF), and metabolic disorders (flMET). A further cow trait category considered first-lactation test-day production traits from official test-days 1 and 2 after calving. The genotype data set included 41,256 single nucleotide polymorphisms (SNP) from 9,388 females with phenotypes. Linear and generalized linear mixed models with a logit link-function were applied to Gaussian and categorical cow traits, respectively, considering the calf disease as a fixed effect. Most of the calf diseases were not significantly associated with the occurrence of any cow disease. By trend, increasing risks for the occurrence of cow diseases were observed for healthy calves, indicating mechanisms of disease resistance with aging. Also by trend, occurrence of calf diseases was associated with decreasing milk, protein, and fat yields. Univariate linear and threshold animal models were used to estimate heritabilities and breeding values (EBV) for all calf and cow traits. Heritabilities for cGDS and cRD were 0.06 and 0.07 for cDIA. Genetic correlations among all traits were estimated using linear-linear animal models in a series of bivariate runs. The genetic correlation between cDIA and cRD was 0.29. Apart from the genetic correlation between flRD with cGDS (-0.38), EBV correlations and genetic correlations between calf diseases with all cow traits were close to zero. Genome-wide association studies were applied to estimate SNP effects for cRD and cDIA, and for the corresponding traits observed in cows (flRD and flDIA). Different significant SNP markers contributed to cDIA and flDIA, or to cRD and flRD. The average correlation coefficient between cRD and flRD considering SNP effects from all chromosomes was 0.01, and between cDIA and flDIA was -0.04. In conclusion, calf diseases are not appropriate early predictors for cow traits during the early lactation stage in parity 1.

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 infer phenotypic and genetic effects of health disorders on longevity traits, considering Holstein dairy cow records from large-scale co-operator herds. In this regard, we focused on 13 different disease traits and on 2 longevity definitions: length of productive life (LPL) and stayability (STAY). The LPL was defined as the interval in days from first calving to culling. For LPL, we considered 90,215 cows with known culling dates. For binary STAY, we defined 3 survival stages in the first 3 lactations: from calving to DIM 59, from DIM 60 to DIM 299, and from DIM 300 to the next calving date. Due to the earlier trait recording possibilities, 129,386 cows were considered for the STAY analysis. Accordingly, the presence or absence of diseases in lactation stages were defined as binary traits. A further data set for the 90,215 cows with a culling date included the subjective culling reasons defined by farmers. Comparison of culling reasons, as defined by farmers, with diagnoses from the disease data set indicated some disagreements. For example, only 18.71% of the cows with the farmer culling reason "metabolic diseases" were diagnosed with a metabolic disorder. Better agreements were identified for mastitis (84.09%). Phenotypically, in most cases, occurrence of diseases at different lactation stages had negative influence on LPL and STAY. In this regard, we identified strong detrimental effects of clinical mastitis and of metabolic disorders from early lactation stages on longevity traits. For example, the presence of clinical mastitis in the first stage of first lactation was associated with LPL decrease of 95.35 d. Using generalized linear mixed models for binary health disorders, heritabilities ranged from <0.01 (±0.079 standard error) for ruminal acidosis early in first, second, and third lactation to 0.24 (±0.039) for interdigital hyperplasia from the last stage in third lactation. Heritabilities from single-trait and bivariate animal models ranged from 0.03 (±0.003) to 0.10 (±0.007) for LPL, and from 0.01 (±0.002) to 0.06 (±0.007) for STAY. Genetic correlations between longevity traits and health disorders were mostly negative (i.e., favorable in a breeding sense). For improvements to longevity genetic evaluations for young bulls with a limited number of daughter culling dates, we suggest consideration of health traits from a well-organized co-operator herd monitoring system as early longevity predictors, especially for censored data. Genetic correlations between mastitis from different lactation stages with LPL and STAY ranged from −0.28 (±0.07) to −0.69 (±0.05), and from −0.26 (±0.08) to −0.77 (±0.08), respectively. Interestingly, only diagnoses for dermatitis digitalis showed opposite results phenotypically and genetically. Strong genetic associations between ruminal acidosis and STAY were observed (genetic correlations: −0.48 ± 0.18 to −0.98 ± 0.31), supporting the inferred phenotypic associations. Genetic correlations between longevity traits LPL and STAY were quite large, betw...

A data set including 57,868 records for calf birth weight (CABW) and 9,462 records for weight at first insemination (IBW) were used for the estimation of direct and maternal genetic effects in Holstein Friesian dairy cattle. Furthermore, CABW and IBW were correlated with test-day production records and health traits in first-lactation cows, and with nonreturn rates in heifers. Health traits considered overall disease categories from the International Committee for Animal Recording diagnosis key, including the general disease status, diarrhea, respiratory diseases, mastitis, claw disorders, female fertility disorders, and metabolic disorders. For single-trait measurements of CABW and IBW, animal models with maternal genetic effects were applied. The direct heritability was 0.47 for CABW and 0.20 for IBW, and the direct genetic correlation between CABW and IBW was 0.31. A moderate maternal heritability (0.19) was identified for CABW, but the maternal genetic effect was close to zero for IBW. The correlation between direct and maternal genetic effects was antagonistic for CABW (-0.39) and for IBW (-0.24). In bivariate animal models, only weak genetic and phenotypic correlations were identified between CABW and IBW with either test-day production or health traits in early lactation. Apart from metabolic disorders, there was a general tendency for increasing disease susceptibilities with increasing CABW. The genetic correlation between IBW and nonreturn rates in heifers after 56 d and after 90 d was slightly positive (0.18), but close to zero when correlating nonreturn rates with CABW. For the longitudinal BW structure from birth to the age of 24 mo, random regression models with the time-dependent covariate "age in months" were applied. Evaluation criteria (Bayesian information criterion and residual variances) suggested Legendre polynomials of order 3 to modeling the longitudinal body weight (BW) structure. Direct heritabilities around birth and insemination dates were slightly larger than estimates for CABW and IBW from the single-trait models, but maternal heritabilities were exactly the same from both models. Genetic correlations between BW were close to 1 for neighboring age classes, but decreased with increasing time spans. The genetic correlation between BW at d 0 (birth date) and at 24 mo was even negative (-0.20). Random regression model estimates confirmed the antagonistic relationship between direct and maternal genetic effects, especially during calfhood. This study based on a large data set in dairy cattle confirmed genetic parameters and (co)variance components for BW as identified in beef cattle populations. However, BW records from an early stage of life were inappropriate early predictors for dairy cow health and productivity.

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.

Background Gastrointestinal nematodes (GIN), liver flukes ( Fasciola hepatica ) and bovine lungworms ( Dictyocaulus viviparus ) are the most important parasitic agents in pastured dairy cattle. Endoparasite infections are associated with reduced milk production and detrimental impacts on female fertility, contributing to economic losses in affected farms. In quantitative-genetic studies, the heritabilities for GIN and F. hepatica were moderate, encouraging studies on genomic scales. Genome-wide association studies (GWAS) based on dense single nucleotide polymorphism (SNP) marker panels allow exploration of the underlying genomic architecture of complex disease traits. The current GWAS combined the identification of potential candidate genes with pathway analyses to obtain deeper insights into bovine immune response and the mechanisms of resistance against endoparasite infections. Results A 2-step approach was applied to infer genome-wide associations in an endangered dual-purpose cattle subpopulation [Deutsches Schwarzbuntes Niederungsrind (DSN)] with a limited number of phenotypic records. First, endoparasite traits from a population of 1166 Black and White dairy cows [including Holstein Friesian (HF) and DSN] naturally infected with GIN, F. hepatica and D. viviparus were precorrected for fixed effects using linear mixed models. Afterwards, the precorrected phenotypes were the dependent traits (rFEC-GIN, rFEC-FH, and rFLC-DV) in GWAS based on 423,654 SNPs from 148 DSN cows. We identified 44 SNPs above the genome-wide significance threshold ( p Bonf = 4.47 × 10 − 7 ), and 145 associations surpassed the chromosome-wide significance threshold (range: 7.47 × 10 − 6 on BTA 1 to 2.18 × 10 − 5 on BTA 28). The associated SNPs identified were annotated to 23 candidate genes. The DAVID analysis inferred four pathways as being related to immune response mechanisms or involved in host-parasite interactions. SNP effect correlations considering specific chromosome segments indicate that breeding for resistance to GIN or F. hepatica as measured by fecal egg counts is genetically associated with a higher risk for udder infections. Conclusions We detected a large number of loci with small to moderate effects for endoparasite resistance. The potential candidate genes regulating resistance identified were pathogen-specific. Genetic antagonistic associations between disease resistance and productivity were specific for specific chromosome segments. The 2-step approach was a valid methodological approach to infer genetic mechanisms in an endangered breed with a limited number of phenotypic records. Electronic supplementary material The online version...

Background Specific adaptive features including disease resistance and growth abilities in harsh environments are attributed to indigenous cattle breeds of Benin, but these breeds are endangered due to crossbreeding. So far, there is a lack of systematic trait recording, being the basis for breed characterizations, and for structured breeding program designs aiming on conservation. Bridging this gap, own phenotyping for morphological traits considered measurements for height at withers (HAW), sacrum height (SH), heart girth (HG), hip width (HW), body length (BL) and ear length (EL), including 449 cattle from the four indigenous Benin breeds Lagune, Somba, Borgou and Pabli. In order to utilize recent genomic tools for breed characterizations and genetic evaluations, phenotypes for novel traits were merged with high-density SNP marker data. Multi-breed genetic parameter estimations and genome-wide association studies (GWAS) for the six morphometric traits were carried out. Continuatively, we aimed on inferring genomic regions and functional loci potentially associated with conformation, carcass and adaptive traits. Results SNP-based heritability estimates for the morphometric traits ranged between 0.46 ± 0.14 (HG) and 0.74 ± 0.13 (HW). Phenotypic and genetic correlations ranged from 0.25 ± 0.05 (HW-BL) to 0.89 ± 0.01 (HAW-SH), and from 0.14 ± 0.10 (HW-BL) to 0.85 ± 0.02 (HAW-SH), respectively. Three genome-wide and 25 chromosome-wide significant SNP positioned on different chromosomes were detected, located in very close chromosomal distance (±25 kb) to 15 genes (or located within the genes). The genes PIK3R6 and PIK3R1 showed direct functional associations with height and body size. We inferred the potential candidate genes VEPH1, CNTNAP5, GYPC for conformation, growth and carcass traits including body weight and body fat deposition. According to their functional annotations, detected potential candidate genes were associated with stress or immune response (genes PTAFR, PBRM1, ADAMTS12) and with feed efficiency (genes MEGF11 SLC16A4, CCDC117). Conclusions Accurate measurements contributed to large SNP heritabilities for some morphological traits, even for a small mixed-breed sample size. Multi-breed GWAS detected different loci associated with conformation or carcass traits. The identified potential candidate genes for immune response or feed efficiency indicators reflect the evolutionary development and adaptability features of the breeds.