G.E. Shook

Genetic Improvement of Mastitis Through Selection on Somatic Cell Count

Heredity influences both clinical mastitis and somatic cell score. Intramammary infection is the major cause of elevated somatic cell score. A nationwide program of genetic evaluation of dairy cattle for somatic cell score is being developed. Proper selection of artificial insemination sires, considering their genetic merit for both milk production and somatic cell score, will reduce the genetic increase in mastitis susceptibility that accompanies selection for high production.

Evaluation of models for somatic cell score lactation patterns in Holsteins

Abstract Milk somatic cell score (SCS) typically reaches a minimum early in lactation and then rises. Nonlinear mixed effects models were used to describe this trajectory while accounting for between and within-cow variation. A total of 2387 SCS records from 217 Holsteins were analyzed. Four nonlinear and two linear functions were studied. Approximate maximum likelihood estimates indicated that cows free of intramammary infection had sharper SCS decreases after calving, and lower overall levels. Lactations starting between October and December had the highest fall of SCS levels at the beginning of lactation, and the smallest increases thereafter. In general, there was significant variation between cows’ individual trajectories. For some parameters and models, however, this variation was small. A four-parameter model suggested by Morant and Gnanasakthy was supported better by the data than the other five functional forms.

Whole-Genome association analysis of susceptibility to paratuberculosis in Holstein cattle.

The objective of this study was to identify genetic markers and genomic regions associated with susceptibility to Mycobacterium avium ssp. paratuberculosis (MAP) infection in Holstein cattle. Associated single nucleotide polymorphisms (SNPs) were identified by genotyping 521 MAP-infected Holstein cows and comparing SNP allele frequencies of these infected cows with allele frequencies estimated from specific reference populations. Reference population allele frequency estimates used Holstein sire genotype data and were weighted estimates based on sire usage within the population in question. The 521 infected cows were 233 and 288 cows from two resource populations of approximately 5000 cows each, collected independently. Population 1 was comprised primarily of daughters of twelve Holstein artificial insemination sires used heavily within the US dairy cattle population. Samples were obtained from 300 co-operating commercial dairy herds throughout the US and were tested by both MAP faecal culture and blood-enzyme-linked immunosorbent assay (ELISA). Population 2 consisted of dairy cattle from six co-operating dairy herds in Wisconsin, with all animals in the herds tested by blood enzyme-linked immunosorbent assay (ELISA) for MAP infection. Genotyping was performed with the Illumina Bovine SNP50 Bead Chip, providing genotypes for 35,772 informative SNPs. Data from the two resource populations were analysed both in separate and combined analyses. The most significant autosomal markers from the individual and combined analyses (n=197, nominal P<0.001) were used in a stepwise logistic regression analysis to identify a set of 51 SNPs that could be used as a predictor of genetics for Holstein cattle susceptibility to MAP infection.

Genetic parameters for anovulation and pregnancy loss in dairy cattle.

The objectives were to estimate heritabilities and genetic variances for anovulation at ~50 d in milk and pregnancy loss occurring between first and second pregnancy diagnoses after artificial insemination. Data were originally collected for trials on reproductive management. Anovulation data consisted of 5,818 records from 13 studies in 8 herds with an overall prevalence of 23.3%. A Bayesian approach using Markov Chain Monte Carlo methods was used in mixed threshold models for both traits. The statistical model for anovulation included fixed effects [parity, herd-study-treatment, and body condition score (BCS)], covariates (inbreeding and milk yield), and random effects (sire and residual). A second statistical model included all terms in the first model except BCS. In addition, 2 bivariate, mixed sire models were used to analyze anovulation with BCS and anovulation with milk yield. The posterior mean heritability estimate for anovulation was 0.171 [posterior standard deviation (PSD) = 0.052]. Correlations of anovulation with milk yield were as follows: genetic = 0.168, PSD = 0.187; residual = -0.046, PSD = 0.022; and phenotypic = -0.036. Bivariate analysis of BCS with anovulation showed a genetic correlation (-0.301, PSD = 0.177) and phenotypic correlation (-0.192, PSD = 0.019). Pregnancy-loss data consisted of 3,775 records from 14 studies in 8 herds with an overall prevalence of 14.4%. Analysis of pregnancy loss used a sire-maternal grandsire threshold model with embryo survival as the subject of analysis. Independent variables consisted of fixed effects (parity and herd-study), covariates (embryo and maternal inbreeding), and random effects (sire of embryo, maternal grandsire of embryo, and residual). In addition, separate sire models were analyzed using embryo as the subject and cow as the subject of analysis. The sire-maternal grandsire model yielded a heritability for direct effect of 0.489 (PSD = 0.221) and for maternal effects of 0.166 (PSD = 0.113). In this study, the breeding value variance for embryo effects was 3 times the breeding value variance for maternal effects, indicating that, at the level of breeding values, the embryos ability to survive has a greater effect on pregnancy loss than does the cows ability to maintain the pregnancy. These results suggest that genetic improvement of reproductive performance could be enhanced by selection for fundamental measures such as abnormally long periods of postpartum anovulation and pregnancy loss. Larger studies of these traits are needed to obtain parameter estimates with greater precision.

Genome-wide association analysis and genomic prediction of Mycobacterium avium subspecies paratuberculosis infection in US Jersey cattle.

Paratuberculosis (Johne’s disease), an enteric disorder in ruminants caused by Mycobacterium avium subspecies paratuberculosis (MAP), causes economic losses in excess of

Genome-Wide Association Study of Susceptibility to Infection by Mycobacterium avium Subspecies paratuberculosis in Holstein Cattle

200 million annually to the US dairy industry. To identify genomic regions underlying susceptibility to MAP infection in Jersey cattle, a case-control genome-wide association study (GWAS) was performed. Blood and fecal samples were collected from ∼5,000 mature cows in 30 commercial Jersey herds from across the US. Discovery data consisted of 450 cases and 439 controls genotyped with the Illumina BovineSNP50 BeadChip. Cases were animals with positive ELISA and fecal culture (FC) results. Controls were animals negative to both ELISA and FC tests that matched cases on birth date and herd. Validation data consisted of 180 animals including 90 cases (positive to FC) and 90 controls (negative to ELISA and FC), selected from discovery herds and genotyped by Illumina BovineLD BeadChip (∼7K SNPs). Two analytical approaches were used: single-marker GWAS using the GRAMMAR-GC method and Bayesian variable selection (Bayes C) using GenSel software. GRAMMAR-GC identified one SNP on BTA7 at 68 megabases (Mb) surpassing a significance threshold of 5×10−5. ARS-BFGL-NGS-11887 on BTA23 (27.7 Mb) accounted for the highest percentage of genetic variance (3.3%) in the Bayes C analysis. SNPs identified in common by GRAMMAR-GC and Bayes C in both discovery and combined data were mapped to BTA23 (27, 29 and 44 Mb), 3 (100, 101, 106 and 107 Mb) and 17 (57 Mb). Correspondence between results of GRAMMAR-GC and Bayes C was high (70–80% of most significant SNPs in common). These SNPs could potentially be associated with causal variants underlying susceptibility to MAP infection in Jersey cattle. Predictive performance of the model developed by Bayes C for prediction of infection status of animals in validation set was low (55% probability of correct ranking of paired case and control samples).

Genetic susceptibility to paratuberculosis.

Paratuberculosis, or Johnes disease, is a chronic, granulomatous, gastrointestinal tract disease of cattle and other ruminants caused by the bacterium Mycobacterium avium, subspecies paratuberculosis (MAP). Control of Johnes disease is based on programs of testing and culling animals positive for infection with MAP while concurrently modifying management to reduce the likelihood of infection. The current study is motivated by the hypothesis that genetic variation in host susceptibility to MAP infection can be dissected and quantifiable associations with genetic markers identified. For this purpose, a case-control, genome-wide association study was conducted using US Holstein cattle phenotyped for MAP infection using a serum ELISA and/or fecal culture test. Cases included cows positive for either serum ELISA, fecal culture or both. Controls consisted of animals negative for the serum ELISA test or both serum ELISA and fecal culture when both were available. Controls were matched by herd and proximal birth date with cases. A total of 856 cows (451 cases and 405 controls) were used in initial discovery analyses, and an additional 263 cows (159 cases and 104 controls) from the same herds were used as a validation data set. Data were analyzed in a single marker analysis controlling for relatedness of individuals (GRAMMAR-GC) and also in a Bayesian analysis in which multiple marker effects were estimated simultaneously (GenSel). For the latter, effects of non-overlapping 1 Mb marker windows across the genome were estimated. Results from the two discovery analyses were generally concordant; however, discovery results were generally not well supported in analysis of the validation data set. A combined analysis of discovery and validation data sets provided strongest support for SNPs and 1 Mb windows on chromosomes 1, 2, 6, 7, 17 and 29.

Genetic parameters for paratuberculosis infection and effect of infection on production traits in Israeli Holsteins.

Multiple studies indicate that host animal genetics play a role in susceptibility to Mycobacterium avium subsp. paratuberculosis (MAP) infection. However, due to differences in methods used to define MAP-infected animals and controls and differences in methods of genetic analysis, there is as yet no clear consensus on the genes or markers to reliably define the MAP infection susceptibility of any animal species. Meta-analysis of combined studies and larger studies will help resolve the situation in the coming years.

An approximate bayesian analysis of somatic cell score curves in holsteins

Paratuberculosis (Johnes disease) is an infectious enteric disease in dairy cattle and other species that causes substantial economic loss worldwide. In this study, two recursive Gaussian-threshold models were employed in order to infer the effects of Johnes disease on milk yield, fat yield, and protein yield while simultaneously estimating genetic parameters (i.e. heritability and genetic correlation) in an Israeli Holstein population. Disease diagnosis was based on ELISA serum antibody tests. Data were available for 4694 daughters of 361 sires; 3.5% were positive; and 1.6% were suspect for the disease test. Disease status was coded either as a binary character (negative vs. positive) or as an ordered categorical trait (negative, suspect, and positive) in the two recursive models and as a binary trait in a linear model. Among sires with ≥ 50 daughters, predicted probability of Mycobacterium avium ssp. paratuberculosis (MAP) infection in future daughters ranged from <1% to 16.5%. Heritability estimates for Johnes disease were near 0.15, confirming a genetic contribution to disease susceptibility. Genetic correlation estimates for Johnes disease with the three yield traits were 0.15-0.22. Residual correlations for Johnes disease with the yield traits were between -0.01 and -0.10. For the linear regression model, yield losses associated with a positive disease diagnosis during 305 days of lactation were 300 kg milk and around 10 kg for fat and protein. Yield loss estimates from the recursive models were 25-50% less than linear model estimates. Recursive modeling has theoretical advantages over linear models for these phenotypes, but the estimated genetic parameters in this study did not differ significantly between the two types of models.

An across-breed genome wide association analysis of susceptibility to paratuberculosis in dairy cattle

Somatic cell score (SCS) decreases to a minimum at around 60 days postpartum and then increases until the end of lactation. This pattern was described with three non-linear mixed effects models. Parameters of the models were linear in fixed and random (cow) effects. Data were 1705 SCS test-day records (155 primiparous Holsteins) taken from 14 to 300 days in milk. Point estimates were the mode of the joint posterior distribution of fixed and random effects (given dispersion parameters) and approximate marginal maximum likelihood estimates of covariance components. Intramammary infection during lactation, season and period of calving affected SCS patterns. Estimates were similar with or without a relationship matrix. There was between-cow variation of trajectories, suggesting that SCS curve parameters can be modified by selection in some advantageous manner.