The use of milk Fourier-transform mid-infrared spectroscopy to diagnose pregnancy and determine spectral regional associations with pregnancy in US dairy cows.

Accurate early diagnosis of pregnancy is important for timely reproductive management of dairy farms. Fourier-transform mid-infrared (FT-MIR) milk spectral data are routinely used for determining milk components such as fat and protein, whereas milk composition is known to change with advancing stages of pregnancy. The objectives of this study were to compare partial least squares discriminant analysis (PLS-DA) and a Bayesian variable selection regression model (BayesC) for the diagnosis of pregnancy status (PS) from milk FT-MIR data and to infer any spectral regions that might be highly associated with PS at various stages of pregnancy. Conception dates on confirmed pregnant cows were obtained from Holstein cows within 123 herds in Michigan, Ohio, and Indiana during 2018 and 2019. Milk samples from these pregnant cows at 7 different stages of pregnancy were case-control matched to open contemporary herd mates to be within the same stage (±10 d for days in milk) of lactation for the same milk sample test date. The FT-MIR data were obtained for all of these milk samples. Ten-fold herd-independent cross-validation was used to compare PLS-DA versus BayesC using the area under the receiver operating characteristic curve (AUC). The BayesC model demonstrated higher mean AUC compared with PLS-DA at all stages exceeding 60 d of pregnancy. The mean BayesC AUC at stage 1 (1-30 d) was 0.58 ± 0.02, which was superior to a random guess (AUC = 0.50) yet too low to be of practical use. The mean BayesC AUC at stage 7 (≥180 d) was 0.13 greater compared with that of stage 1 (1-30 d) and 0.07 to 0.10 greater compared with stages 2, 3, 4, 5, and 6 (31-180 d in 30-d increments). The mean AUC of stages 2 to 6 were 0.03 to 0.06 greater compared with stage 1 yet again too low to be of practical use. Because of high multicollinearity between many adjacent wavenumbers, a spatially constrained clustering algorithm was used to adaptively partition wavenumbers into 68 windows before inferring associations of spectral regions with pregnancy. Pregnancy status was highly associated with wavenumber windows 1,063 to 1,134 cm-1, 1,201 to 1,257 cm-1, and 1,260 to 1,432 cm-1 based on an estimated BayesC posterior probability of association (PPA) approaching 100% for each of these windows at all pregnancy stages. Other windows ranging from 1,730 to 1,764 cm-1, 1,775 to 1,992 cm-1, 1,995 to 2,163 cm-1, and 2,167 to 2,316 cm-1 had varying medium to high PPA (30% to 100%) across stages. The estimated PPA in wavenumber regions from 1,477 to 1,507 cm-1, and 1,510 to 1,574 cm-1 was weaker in stages 1 and 2 compared with later stages, whereas for the regions 2,984 to 3,077 cm-1 and 3,081 to 3,133 cm-1 the effect of pregnancy was greater for stage 1 compared with other stages. Despite our conclusion that milk FT-MIR data poorly diagnose PS, our study provides new insights into spectral regions that are strongly associated with PS and warrant greater attention.

Microbiability of meat quality and carcass composition traits in swine.

The impact of gut microbiome composition was investigated at different stages of production (weaning, Mid-test and Off-test) on meat quality and carcass composition traits of 1,123 three-way crossbred pigs. Data were analysed using linear mixed models which included the fixed effects of dam line, contemporary group and gender as well as the random effects of pen, animal and microbiome information at different stages. The contribution of the microbiome to all traits was prominent although it varied over time, increasing from weaning to Off-test for most traits. Microbiability estimates of carcass composition traits were greater than that of meat quality traits. Among all of the traits analysed, belly weight (BEL) had a higher microbiability estimate (0.29 ± 0.04). Adding microbiome information did not affect the estimates of genomic heritability of meat quality traits but affected the estimates of carcass composition traits. Fat depth had a greater decrease (10%) in genomic heritability at Off-test. High microbial correlations were found among different traits, particularly with traits related to fat deposition with a decrease in the genomic correlation up to 20% for loin weight and BEL. This suggested that genomic correlation was partially contributed by genetic similarity of microbiome composition. The results indicated that better understanding of microbial composition could aid the improvement of complex traits, particularly the carcass composition traits in swine by inclusion of microbiome information in the genetic evaluation process.

Evaluation of Resistance to Fescue Toxicosis in Purebred Angus Cattle Utilizing Animal Performance and Cytokine Response.

Fescue toxicosis is a multifaceted syndrome common in cattle grazing endophyte-infected tall fescue; however, varying symptomatic responses potentially imply genetic tolerance to the syndrome. It was hypothesized that a subpopulation of animals within a herd would develop tolerance to ergot alkaloid toxicity. Therefore, the goals of this study were to develop selection criteria to identify tolerant and susceptible animals within a herd based on animal performance, and then examine responsive phenotypic and cytokine profiles to fescue toxicosis. Angus cows grazed endophyte-infected tall fescue at two locations for 13 weeks starting in mid-April 2016. Forage measurements were collected to evaluate ergot alkaloid exposure during the study. A post hoc analysis of animal performance was utilized to designate cattle into either tolerant or susceptible groups, and weekly physiological measurements and blood samples were collected to evaluate responses to chronic exposure to endophyte-infected tall fescue. Findings from this study support the proposed fescue toxicosis selection method formulated herein, could accurately distinguish between tolerant and susceptible animals based on the performance parameters in cattle chronically exposed to ergot alkaloids, and provides evidence to warrant additional analysis to examine the impact of ergot alkaloids on immune responsiveness in cattle experiencing fescue toxicosis.

Evaluation of a commercial genetic test for fescue toxicosis in pregnant Angus beef cattle.

Most tall fescue [Lolium arundinaceum (Schreb.) Darbysh] in the Southeastern United States contains an endophyte that causes fescue toxicosis (FT) in grazing animals, a serious disease that causes approximately $1 billion in economic losses to the animal industries in the United States. Recently, a genetic test called T-Snip (AgBotanica, LCC, Columbia, MO), was developed with the objective of identifying animals with genetic variation for FT tolerance. The aim of this study was to validate the use of this genetic test in mature, pregnant cows. Over 13 wk, weekly phenotypic data, including body weight, rectal temperatures, hair coat scores, hair shedding scores, and body condition scores, were collected on 148 pregnant purebred Angus cows at 2 locations in NC where infected fescue was the primary source of feed. Birth weights (cBW) and 205-d adjusted weaning weights (adjWW) from these cow's calves were recorded. All cows were genotyped for T-Snip. At the end of the trial, each phenotypic trait was calculated as the slope of the linear regression of performance on weeks. The effect of T-Snip rating genotypes (4 levels) on slope traits was tested using a linear model also including the fixed-effects of location, parity, and the initial measurement for each trait (covariate). For cBW and adjWW, the model also included the sex of the calf and the month of birth as categorical effects. Associations of T-Snip genotypes were observed for body weight gain (aBWd) of pregnant cows (P = 0.15; interaction with location), change in body condition score (aBCSd; P = 0.13), and adjWW (P = 0.06; interaction with location). For aBWd and adjWW, associations were found just within one location (P = 0.017 and 0.047, respectively), which was the location with higher endophyte infection rate. For all associations, the direction of the T-Snip genotypes was the same and as expected: the greater the genotype score, the better performance. No associations were found for the other traits (P > 0.10). These results indicate that the T-Snip test may be predictive of cow performance (aBWd, aBCSd, and adjWW) in an endophyte-infected tall fescue environment.

Modeling host-microbiome interactions for the prediction of meat quality and carcass composition traits in swine.

BACKGROUND: The objectives of this study were to evaluate genomic and microbial predictions of phenotypes for meat quality and carcass traits in swine, and to evaluate the contribution of host-microbiome interactions to the prediction. Data were collected from Duroc-sired three-way crossbred individuals (n = 1123) that were genotyped with a 60 k SNP chip. Phenotypic information and fecal 16S rRNA microbial sequences at three stages of growth (Wean, Mid-test, and Off-test) were available for all these individuals. We used fourfold cross-validation with animals grouped based on sire relatedness. Five models with three sets of predictors (full, informatively reduced, and randomly reduced) were evaluated. 'Full' included information from all genetic markers and all operational taxonomic units (OTU), while 'informatively reduced' and 'randomly reduced' represented a reduced number of markers and OTU based on significance preselection and random sampling, respectively. The baseline model included the fixed effects of dam line, sex and contemporary group and the random effect of pen. The other four models were constructed by including only genomic information, only microbiome information, both genomic and microbiome information, and microbiome and genomic information and their interaction. RESULTS: Inclusion of microbiome information increased predictive ability of phenotype for most traits, in particular when microbiome information collected at a later growth stage was used. Inclusion of microbiome information resulted in higher accuracies and lower mean squared errors for fat-related traits (fat depth, belly weight, intramuscular fat and subjective marbling), objective color measures (Minolta a*, Minolta b* and Minolta L*) and carcass daily gain. Informative selection of markers increased predictive ability but decreasing the number of informatively reduced OTU did not improve model performance. The proportion of variation explained by the host-genome-by-microbiome interaction was highest for fat depth (~ 20% at Mid-test and Off-test) and shearing force (~ 20% consistently at Wean, Mid-test and Off-test), although the inclusion of the interaction term did not increase the accuracy of predictions significantly. CONCLUSIONS: This study provides novel insight on the use of microbiome information for the phenotypic prediction of meat quality and carcass traits in swine. Inclusion of microbiome information in the model improved predictive ability of phenotypes for fat deposition and color traits whereas including a genome-by-microbiome term did not improve prediction accuracy significantly.

Genetic parameters of meat quality, carcass composition, and growth traits in commercial swine.

Swine industry breeding goals are mostly directed towards meat quality and carcass traits due to their high economic value. Yet, studies on meat quality and carcass traits including both phenotypic and genotypic information remain limited, particularly in commercial crossbred swine. The objectives of this study were to estimate the heritabilities for different carcass composition traits and meat quality traits and to estimate the genetic and phenotypic correlations between meat quality, carcass composition, and growth traits in 2 large commercial swine populations: The Maschhoffs LLC (TML) and Smithfield Premium Genetics (SPG), using genotypes and phenotypes data. The TML data set consists of 1,254 crossbred pigs genotyped with 60K SNP chip and phenotyped for meat quality, carcass composition, and growth traits. The SPG population included over 35,000 crossbred pigs phenotyped for meat quality, carcass composition, and growth traits. For TML data sets, the model included fixed effects of dam line, contemporary group (CG), gender, as well as random additive genetic effect and pen nested within CG. For the SPG data set, fixed effects included parity, gender, and CG, as well as random additive genetic effect and harvest group. Analyses were conducted using BLUPF90 suite of programs. Univariate and bivariate analyses were implemented to estimate heritabilities and correlations among traits. Primal yield traits were uniquely created in this study. Heritabilities [high posterior density interval] of meat quality traits ranged from 0.08 [0.03, 0.16] for pH and 0.08 [0.03, 0.1] for Minolta b* to 0.27 [0.22, 0.32] for marbling score, except intramuscular fat with the highest estimate of 0.52 [0.40, 0.62]. Heritabilities of primal yield traits were higher than that of primal weight traits and ranged from 0.17 [0.13, 0.25] for butt yield to 0.45 [0.36, 0.55] for ham yield. The genetic correlations of meat quality and carcass composition traits with growth traits ranged from moderate to high in both directions. High genetic correlations were observed for male and female for all traits except pH. The genetic parameter estimates of this study indicate that a multitrait approach should be considered for selection programs aimed at meat quality and carcass composition in commercial swine populations.

Influence of crossbreeding on meat goat doe fitness when comparing Boer F1 with base breeds in the Southeastern United States.

Understanding fitness level among various breeds is essential for sustainable meat goat production. Research on the relative fitness of Boer F1 does and straightbred base breed has been limited. Meat goat does of various genotypes (Boer, Kiko, Spanish, Boer × Kiko reciprocal F1 crosses, and Boer × Spanish reciprocal F1 crosses) were studied to evaluate breed effects on doe fitness traits and the expression of heterosis over 7 production years. The herd was semi-intensively managed under humid subtropical pasture. Doe age affected (P < 0.05) various traits. Boer × Kiko does were heavier (P < 0.05) than Boer does at fall breeding, but Boer × Spanish does did not differ (P > 0.05) from Boer does for breeding weight. The body weights of Boer × Spanish and Boer × Kiko crosses did not differ (P > 0.05) from the weights of their respective Kiko and Spanish base cohorts at breeding, kidding, or weaning. Boer does had lower (P < 0.05) kidding rate (KR) and weaning rate (WR) than the other breeds and crosses. Boer × Kiko and Kiko were similar for KR and WR. Boer × Spanish and Spanish were also similar for KR and WR. However, the combined group of Boer F1 does had lower (P < 0.01) KR and WR than the combined purebred biotype group of Kiko and Spanish does. Boer does weaned smaller (P < 0.05) litter sizes per doe exposed compared with Kiko, Spanish, Boer × Kiko, and Boer × Spanish does with the latter four doe breedtypes not differing from each other. The combined Boer F1 doe group weaned smaller (P < 0.05) litter sizes per doe exposed than the combined purebred group of Kiko and Spanish does. Boer × Kiko dams had higher (P < 0.05) fecal egg counts at parturition than Kiko dams. Significant heterosis was observed for reproductive traits within each of the 2-breed diallels. Boer F1 does exhibited reproductive output similar to or lower than Kiko and Spanish straightbred does and higher than Boer straightbred does.