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Mastitis is one of the most prevalent and costly diseases in dairy cattle. It results in changes in milk composition and quality which are indicators of udder inflammation in absence of clinical signs. We applied structural equation modeling (SEM) - GWAS aiming to explore interrelated dependency relationships among phenotypes related to udder health, including milk yield (MY), somatic cell score (SCS), lactose (%, LACT), pH and non-casein N (NCN, % of total milk N), in a cohort of 1,158 Brown Swiss cows. The phenotypic network inferred via the Hill-Climbing algorithm was used to estimate SEM parameters. Integration of multi-trait models-GWAS and SEM-GWAS identified six significant SNPs for SCS, and quantified the contribution of MY and LACT acting as mediator traits to total SNP effects. Functional analyses revealed that overrepresented pathways were often shared among traits and were consistent with biological knowledge (e.g., membrane transport activity for pH and MY or Wnt signaling for SCS and NCN). In summary, SEM-GWAS offered new insights on the relationships among udder health phenotypes and on the path of SNP effects, providing useful information for genetic improvement and management strategies in dairy cattle.

The aim of this study was to identify genomic regions associated with 305‐day milk yield and lactation curve parameters on primiparous (n = 9,910) and multiparous (n = 11,158) Holstein cows. The SNP solutions were estimated using a weighted single‐step genomic BLUP approach and imputed high‐density panel (777k) genotypes. The proportion of genetic variance explained by windows of 50 consecutive SNP (with an average of 165 Kb) was calculated, and regions that accounted for more than 0.50% of the variance were used to search for candidate genes. Estimated heritabilities were 0.37, 0.34, 0.17, 0.12, 0.30 and 0.19, respectively, for 305‐day milk yield, peak yield, peak time, ramp, scale and decay for primiparous cows. Genetic correlations of 305‐day milk yield with peak yield, peak time, ramp, scale and decay in primiparous cows were 0.99, 0.63, 0.20, 0.97 and −0.52, respectively. The results identified three windows on BTA14 associated with 305‐day milk yield and the parameters of lactation curve in primi‐ and multiparous cows. Previously proposed candidate genes for milk yield supported by this work include GRINA, CYHR1, FOXH1, TONSL, PPP1R16A, ARHGAP39, MAF1, OPLAH and MROH1, whereas newly identified candidate genes are MIR2308, ZNF7, ZNF34, SLURP1, MAFA and KIFC2 (BTA14). The protein lipidation biological process term, which plays a key role in controlling protein localization and function, was identified as the most important term enriched by the identified genes.

Background Random regression models (RRM) are widely used to analyze longitudinal data in genetic evaluation systems because they can better account for time-course changes in environmental effects and additive genetic values of animals by fitting the test-day (TD) specific effects. Our objective was to implement a random regression model for the evaluation of dairy production traits in French goats. Results The data consisted of milk TD records from 30,186 and 32,256 first lactations of Saanen and Alpine goats. Milk yield, fat yield, protein yield, fat content and protein content were considered. Splines were used to model the environmental factors. The genetic and permanent environmental effects were modeled by the same Legendre polynomials. The goodness-of-fit and the genetic parameters derived from functions of the polynomials of orders 0 to 4 were tested. Results were also compared to those from a lactation model with total milk yield calculated over 250 days and to those of a multiple-trait model that considers performance in six periods throughout lactation as different traits. Genetic parameters were consistent between models. Models with fourth-order Legendre polynomials led to the best fit of the data. In order to reduce complexity, computing time, and interpretation, a rank reduction of the variance covariance matrix was performed using eigenvalue decomposition. With a reduction to rank 2, the first two principal components correctly summarized the genetic variability of milk yield level and persistency, with a correlation close to 0 between them. Conclusions A random regression model was implemented in France to evaluate and select goats for yield traits and persistency, which are independent i.e. no genetic correlation between them, in first lactation. Electronic supplementary material The online version of this article (10.1186/s12711-019-0485-3) contains supplementary material, which is available to authorized users.

The main objective of this study was to compare the performance of different ‘nonlinear quantile regression’ models evaluated at the τth quantile (0·25, 0·50, and 0·75) of milk production traits and somatic cell score (SCS) in Iranian Holstein dairy cows. Data were collected by the Animal Breeding Center of Iran from 1991 to 2011, comprising 101 051 monthly milk production traits and SCS records of 13 977 cows in 183 herds. Incomplete gamma (Wood), exponential (Wilmink), Dijkstra and polynomial (Ali & Schaeffer) functions were implemented in the quantile regression. Residual mean square, Akaike information criterion and log-likelihood from different models and quantiles indicated that in the same quantile, the best models were Wilmink for milk yield, Dijkstra for fat percentage and Ali & Schaeffer for protein percentage. Over all models the best model fit occurred at quantile 0·50 for milk yield, fat and protein percentage, whereas, for SCS the 0·25th quantile was best. The best model to describe SCS was Dijkstra at quantiles 0·25 and 0·50, and Ali & Schaeffer at quantile 0·75. Wood function had the worst performance amongst all traits. Quantile regression is specifically appropriate for SCS which has a mixed multimodal distribution. The main objective of this study was to compare the performance of different 'nonlinear quantile regression' models evaluated at the τth quantile (0·25, 0·50, and 0·75) of milk production traits and somatic cell score (SCS) in Iranian Holstein dairy cows. Data were collected by the Animal Breeding Center of Iran from 1991 to 2011, comprising 101 051 monthly milk production traits and SCS records of 13 977 cows in 183 herds. Incomplete gamma (Wood), exponential (Wilmink), Dijkstra and polynomial (Ali & Schaeffer) functions were implemented in the quantile regression. Residual mean square, Akaike information criterion and log-likelihood from different models and quantiles indicated that in the same quantile, the best models were Wilmink for milk yield, Dijkstra for fat percentage and Ali & Schaeffer for protein percentage. Over all models the best model fit occurred at quantile 0·50 for milk yield, fat and protein percentage, whereas, for SCS the 0·25th quantile was best. The best model to describe SCS was Dijkstra at quantiles 0·25 and 0·50, and Ali & Schaeffer at quantile 0·75. Wood function had the worst performance amongst all traits. Quantile regression is specifically appropriate for SCS which has a mixed multimodal distribution.

Estimates of genetic parameters for organic dairy farming have not been published previously, and neither is information available on the magnitude of genotype by environment interaction (GxE) between organic and conventional farming. However, organic farming is growing worldwide and basic information about genetic parameters is needed for future breeding strategies for organic dairy farming. The goal of this study was to estimate heritabilities of milk production traits under organic farming conditions and to estimate the magnitude of GxE between organic and conventional dairy farming. For this purpose, production records of first-parity Holstein heifers were used. Heritabilities of milk, fat and protein yield, and somatic cell score (SCS) were higher under organic farming conditions. For percentages of fat and protein, heritabilities of organic and conventional production were very similar. Genetic correlations between preorganic and organic, and organic and conventional milk production were 0.79 and 0.80, respectively. For fat yield, these correlations were 0.86 and 0.88, and for protein yield, these were 0.78 and 0.71, respectively. Our findings indicate that moderate GxE was present for yield traits. For percentage of fat and protein and SCS, genetic correlations between organic and conventional and preorganic production were close to unity, indicating that there was no GxE for these traits.

International audience; Background Knowledge regarding causal relationships among traits is important to understand complex biological systems. Structural equation models (SEM) can be used to quantify the causal relations between traits, which allow prediction of outcomes to interventions applied to such a network. Such models are fitted conditionally on a causal structure among traits, represented by a directed acyclic graph and an Inductive Causation (IC) algorithm can be used to search for causal structures. The aim of this study was to explore the space of causal structures involving bovine milk fatty acids and to select a network supported by data as the structure of a SEM.ResultsThe IC algorithm adapted to mixed models settings was applied to study 14 correlated bovine milk fatty acids, resulting in an undirected network. The undirected pathway from C4:0 to C12:0 resembled the de novo synthesis pathway of short and medium chain saturated fatty acids. By using prior knowledge, directions were assigned to that part of the network and the resulting structure was used to fit a SEM that led to structural coefficients ranging from 0.85 to 1.05. The deviance information criterion indicated that the SEM was more plausible than the multi-trait model.ConclusionsThe IC algorithm output pointed towards causal relations between the studied traits. This changed the focus from marginal associations between traits to direct relationships, thus towards relationships that may result in changes when external interventions are applied. The causal structure can give more insight into underlying mechanisms and the SEM can predict conditional changes due to such interventions.

Trait uniformity, or micro-environmental sensitivity, may be studied through individual differences in residual variance. These differences appear to be heritable, and the need exists, therefore, to fit models to predict breeding values explaining differences in residual variance. The aim of this paper is to estimate breeding values for micro-environmental sensitivity (vEBV) in milk yield and somatic cell score, and their associated variance components, on a large dairy cattle data set having more than 1.6million records. Estimation of variance components, ordinary breeding values, and vEBV was performed using standard variance component estimation software (ASReml), applying the methodology for double hierarchical generalized linear models. Estimation using ASReml took less than 7 d on a Linux server. The genetic standard deviations for residual variance were 0.21 and 0.22 for somatic cell score and milk yield, respectively, which indicate moderate genetic variance for residual variance and imply that a standard deviation change in vEBV for one of these traits would alter the residual variance by 20%. This study shows that estimation of variance components, estimated breeding values and vEBV, is feasible for large dairy cattle data sets using standard variance component estimation software. The possibility to select for uniformity in Holstein dairy cattle based on these estimates is discussed.

Clinical and subclinical mastitis directly affect mammary gland function and have a great economic impact on the sheep and goat dairy industries. The present study explores molecular diagnosis of ovine subclinical mastitis as a faster and more precise screening method compared with microbiology and biochemical techniques to assess the molecular and chemical properties of raw milk samples from healthy animals from 3 breeds of sheep raised in Portugal. Based on 16S ribosomal RNA screening by PCR, milk samples from all sheep were categorized as contaminated (n = 123) or noncontaminated (n = 104). For contaminated milk, different specific primers were used for pathogen identification (Staphylococcus aureus, Streptococcus agalactiae, Streptococcus dysgalactiae, and Streptococcus uberis). Streptococcus agalactiae was identified as the most frequent agent. We further assessed whether contaminated versus noncontaminated samples were chemically different in terms of fat, protein, lactose, pH, and solids-not-fat. This molecular screening method allowed rapid and efficient identification of contaminated raw sheep milk, including pathogen identification, before significant alterations in milk chemical properties could be detected. This methodology may lead to a specific and efficient animal treatment and consequently less expensive flock management.

Achieving and maintaining high herd health and welfare status and low veterinary medicine inputs are important aims in organic livestock farming. Therefore, an on-farm intervention study (CORE Organic ANIPLAN) was conducted on 128 organic dairy farms in seven European countries aiming at minimising medicine use through animal health and welfare planning (AHWP). Medicine use (excluding complementary treatments such as homeopathic remedies) was assessed as the total number of treatments and as the number of treatments of various disease categories (udder, fertility, metabolism, locomotion and others) generated from farm records and national databases, respectively. Health and production data were calculated at farm level from milk recording data: Somatic cell score (SCS) was used as an indicator for udder health, incidences of low (< 1.1) and high (> 1.5) fat–protein ratio as indicators of rumen acidosis and imbalanced energy supply, respectively. Calving interval was used as an indicator for fertility. Milk recording data and treatment data were retrospectively collected for a one year period before and after the first farm visit. Focus areas of animal health and welfare plans were either generated in Stable Schools (adapted Farmer Field Schools) or using face-to-face advice but following similar principles. Most frequently chosen focus areas were metabolic disorders (66% of farms), udder health (58%), lameness (47%), and fertility (39%). General linear models for repeated measures were used to analyse the development at the farm level. The total number of treatments, the number of udder treatments and the number of metabolic treatments were all significantly reduced during the one year study period, whilst the number of treatments of lame cows increased. With the exception of SCS, which improved significantly, the other health indicators remained stable. Milk yield and average lactation number also remained unchanged. Choice of different focus areas had no significant effects on the corresponding treatment and health variables except for indication of rumen acidosis; for the latter situation on farms with an AHWP focus on metabolic issues improved, but this was not the case across all farms. Overall, the implementation of AHWP reduced total treatment incidence and improved the udder health situation across all farms regardless of the focus areas in the AHWP. Hence, AHWP can be regarded as a feasible approach to minimising medicine use without the impairment of production and herd health under several organic dairy farming conditions in Europe.

Earlier studies identified large between-herd variation in estimated lactation curve parameters from test-day milk yield and milk composition records collected in Ragusa province, Italy. The objective of this study was to identify sources of variation able to explain these between-herd differences in milk production curves, by estimating associations of animal breed (Holstein Friesian vs. Brown Swiss), feeding system [separate feeding (SF) vs. total mixed ration (TMR)], and TMR chemical composition on milk and milk components herd curves. Data recorded from 1992 through 2007 for test-day (TD) milk, fat, and protein yields from 1,287,019 records of 148,951 lactations of 51,489 cows in 427 herds were processed using a random regression TD model. Random herd curves (HCUR) for milk, fat, and protein yields were estimated from the model per herd, year, and parity (1, 2, and 3+) using 4-order Legendre polynomials. From March 2006 through December 2007, samples of TMR were collected every 3 mo from 37 farms in Ragusa province. Samples were analyzed for dry matter, ash, crude protein, soluble nitrogen, acid detergent lignin, neutral detergent fiber, acid detergent fiber, and starch. Traits used to describe milk production curves were peak, days in milk at peak, persistency, and mean. Association of feeding system and animal breed with HCUR traits was investigated using a general mixed model procedure. Association of TMR chemical composition with HCUR traits was investigated using multivariate analysis with regression and stepwise model selection. Results were consistent for all traits and parities. Feeding system was significantly associated with HCUR peak and mean, with higher values for TMR. Animal breed was significantly associated with HCUR persistency, with higher values for Brown Swiss herds. Furthermore, animal breed influenced HCUR peak and mean, with higher values for Holstein Friesian herds. Crude protein had the largest effect on HCUR peak and mean, whereas the interaction between crude protein and dry matter mainly affected persistency. When provided by a national evaluation system, HCUR can be used as an indicator of herd feeding management.

Background Studies in mice and humans have shown that imprinted genes, whereby expression from one of the two parentally inherited alleles is attenuated or completely silenced, have a major effect on mammalian growth, metabolism and physiology. More recently, investigations in livestock species indicate that genes subject to this type of epigenetic regulation contribute to, or are associated with, several performance traits, most notably muscle mass and fat deposition. In the present study, a candidate gene approach was adopted to assess 17 validated single nucleotide polymorphisms (SNPs) and their association with a range of performance traits in 848 progeny-tested Irish Holstein-Friesian artificial insemination sires. These SNPs are located proximal to, or within, the bovine orthologs of eight genes (CALCR, GRB10, PEG3, PHLDA2, RASGRF1, TSPAN32, ZIM2 and ZNF215) that have been shown to be imprinted in cattle or in at least one other mammalian species (i.e. human/mouse/pig/sheep). Results Heterozygosities for all SNPs analysed ranged from 0.09 to 0.46 and significant deviations from Hardy-Weinberg proportions (P ≤ 0.01) were observed at four loci. Phenotypic associations (P ≤ 0.05) were observed between nine SNPs proximal to, or within, six of the eight analysed genes and a number of performance traits evaluated, including milk protein percentage, somatic cell count, culled cow and progeny carcass weight, angularity, body conditioning score, progeny carcass conformation, body depth, rump angle, rump width, animal stature, calving difficulty, gestation length and calf perinatal mortality. Notably, SNPs within the imprinted paternally expressed gene 3 (PEG3) gene cluster were associated (P ≤ 0.05) with calving, calf performance and fertility traits, while a single SNP in the zinc finger protein 215 gene (ZNF215) was associated with milk protein percentage (P ≤ 0.05), progeny carcass weight (P ≤ 0.05), culled cow carcass weight (P ≤ 0.01), angularity (P ≤ 0.01), body depth (P ≤ 0.01), rump width (P ≤ 0.01) and animal stature (P ≤ 0.01). Conclusions Of the eight candidate bovine imprinted genes assessed, DNA sequence polymorphisms in six of these genes (CALCR, GRB10, PEG3, RASGRF1, ZIM2 and ZNF215) displayed associations with several of the phenotypes included for analyses. The genotype-phenotype associations detected here are further supported by the biological function of these six genes, each of which plays important roles in mammalian growth, development and physiology. The associations between SNPs within the imprinted PEG3 gene cluster and traits related to calving, calf performance and gestation length suggest that this domain on chromosome 18 may play a role regulating pre-natal growth and development and fertility. SNPs within the bovine ZNF215 gene were associated with bovine growth and body conformation traits and studies in humans have revealed that the human ZNF215 ortholog belongs to the imprinted gene cluster associated with Beckwith-Wiedemann syndrome--a genetic disorder characterised by growth abnormalities. Similarly, the data presented here suggest that the ZNF215 gene may have an important role in regulating bovine growth. Collectively, our results support previous work showing that (candidate) imprinted genes/loci contribute to heritable variation in bovine performance traits and suggest that DNA sequence polymorphisms within these genes/loci represents an important reservoir of genomic markers for future genetic improvement of dairy and beef cattle populations.

Studies have reported genetic variation in milk urea nitrogen (MUN) between cows, suggesting genetic differences in nitrogen efficiency between cows. In this paper, the results of a genome-wide scan to identify quantitative trait loci (QTL) that contribute to genetic variation in MUN and MUN yield are presented. Two to 3 morning milk samples were taken from 1,926 cows, resulting in 5,502 test-day records. Test-day records were corrected for systematic environmental effects using a repeatability animal model. Averages of corrected phenotypes of 849 cows, belonging to 7 sire families, were used in an across-family multimarker regression approach to detect QTL. Animals were successfully genotyped for 1,341 single nucleotide polymorphisms. The QTL analysis resulted in 4 chromosomal regions with suggestive QTL: Bos taurus autosomes (BTA) 1, 6, 21, and 23. On BTA 1, 2 suggestive QTL affecting MUN were detected at 60 and 140 cM. On BTA 6, 1 suggestive QTL affecting both MUN and MUN yield was detected at 103 cM. On BTA 21, 1 suggestive QTL affecting MUN yield was detected at 83 cM. On BTA 23, 1 suggestive QTL affecting MUN was detected at 54 cM. Quantitative trait loci for MUN and MUN yield were suggestive and each explained between 2 and 3% of the phenotypic variance.