Genome-wide association study of age at puberty and its (co)variances with fertility and stature in growing and lactating Holstein-Friesian dairy cattle.

Reproductive performance is a key determinant of cow longevity in a pasture-based, seasonal dairy system. Unfortunately, direct fertility phenotypes such as intercalving interval or pregnancy rate tend to have low heritabilities and occur relatively late in an animal's life. In contrast, age at puberty (AGEP) is a moderately heritable, early-in-life trait that may be estimated using an animal's age at first measured elevation in blood plasma progesterone (AGEP4) concentrations. Understanding the genetic architecture of AGEP4 in addition to genetic relationships between AGEP4 and fertility traits in lactating cows is important, as is its relationship with body size in the growing animal. Thus, the objectives of this research were 3-fold. First, to estimate the genetic and phenotypic (co)variances between AGEP4 and subsequent fertility during first and second lactations. Second, to quantify the associations between AGEP4 and height, length, and BW measured when animals were approximately 11 mo old (standard deviation = 0.5). Third, to identify genomic regions that are likely to be associated with variation in AGEP4. We measured AGEP4, height, length, and BW in approximately 5,000 Holstein-Friesian or Holstein-Friesian × Jersey crossbred yearling heifers across 54 pasture-based herds managed in seasonal calving farm systems. We also obtained calving rate (CR42, success or failure to calve within the first 42 d of the seasonal calving period), breeding rate (PB21, success or failure to be presented for breeding within the first 21 d of the seasonal breeding period) and pregnancy rate (PR42, success or failure to become pregnant within the first 42 d of the seasonal breeding period) phenotypes from their first and second lactations. The animals were genotyped using the Weatherby's Versa 50K SNP array (Illumina, San Diego, CA). The estimated heritabilities of AGEP4, height, length, and BW were 0.34 (90% credibility interval [CRI]: 0.30, 0.37), 0.28 (90% CRI: 0.25, 0.31), 0.21 (90% CRI: 0.18, 0.23), and 0.33 (90% CRI: 0.30, 0.36), respectively. In contrast, the heritabilities of CR42, PB21 and PR42 were all <0.05 in both first and second lactations. The genetic correlations between AGEP4 and these fertility traits were generally moderate, ranging from 0.11 to 0.60, whereas genetic correlations between AGEP4 and yearling body-conformation traits ranged from 0.02 to 0.28. Our GWAS highlighted a genomic window on chromosome 5 that was strongly associated with variation in AGEP4. We also identified 4 regions, located on chromosomes 14, 6, 1, and 11 (in order of decreasing importance), that exhibited suggestive associations with AGEP4. Our results show that AGEP4 is a reasonable predictor of estimated breeding values for fertility traits in lactating cows. Although the GWAS provided insights into genetic mechanisms underpinning AGEP4, further work is required to test genomic predictions of fertility that use this information.

Genome-wide association study of anogenital distance and its (co)variances with fertility in growing and lactating Holstein-Friesian dairy cattle.

Anogenital distance (AGD) is a moderately heritable trait that can be measured at a young age that may provide an opportunity to indirectly select for improved fertility in dairy cattle. In this study, we characterized AGD and its genetic and phenotypic relationships with a range of body stature and fertility traits. We measured AGD, shoulder height, body length, and body weight in a population of 5,010 Holstein-Friesian and Holstein-Friesian × Jersey crossbred heifers at approximately 11 mo of age (AGD1). These animals were born in 2018 across 54 seasonal calving, pasture-based dairy herds. A second measure of AGD was collected in a subset of herds (n = 17; 1,956 animals) when the animals averaged 29 mo of age (AGD2). Fertility measures included age at puberty (AGEP), then time of calving, breeding, and pregnancy during the first and second lactations. We constructed binary traits reflecting the animal's ability to calve during the first 42 d of their herd's seasonal calving period (CR42), be presented for breeding during the first 21 d of the seasonal breeding period (PB21) and become pregnant during the first 42 d of the seasonal breeding period (PR42). The posterior mean of sampled heritabilities for AGD1 was 0.23, with 90% of samples falling within a credibility interval (90% CRI) of 0.20 to 0.26, whereas the heritability of AGD2 was 0.29 (90% CRI 0.24 to 0.34). The relationship between AGD1 and AGD2 was highly positive, with a genetic correlation of 0.89 (90% CRI 0.82 to 0.94). Using a GWAS analysis of 2,460 genomic windows based on 50k genotype data, we detected a region on chromosome 20 that was highly associated with variation in AGD1, and a second region on chromosome 13 that was moderately associated with variation in AGD1. We did not detect any genomic regions associated with AGD2 which was measured in fewer animals. The genetic correlation between AGD1 and AGEP was 0.10 (90% CRI 0.00 to 0.19), whereas the genetic correlation between AGD2 and AGEP was 0.30 (90% CRI 0.15 to 0.44). The timing of calving, breeding, and pregnancy (CR42, PB21, and PR42) during first or second lactations exhibited moderate genetic relationships with AGD1 (0.19 to 0.52) and AGD2 (0.46 to 0.63). Genetic correlations between AGD and body stature traits were weak (≤0.16). We conclude that AGD is a moderately heritable trait, which may have value as an early-in-life genetic predictor for reproductive success during lactation.

A novel fasting regimen revealed protein reservation and complement C3 down-regulation after 14-day's continual dietary deprivation.

Objectives: The aim is to evaluate the effect of a novel 14-day fasting regimen on the balance between skeletal muscle and adipose tissue composition which might associate with inflammatory factors. Our analysis includes basic physical examinations, clinical laboratory analysis, bioelectrical impedance and biochemical analytic assessments of healthy volunteers. Methods: Eight healthy subjects were randomly selected from a pool of volunteers to undergo a continual dietary deprivation (CDD) regimen. Individuals were assigned to take Flexible Abrosia (FA, prebiotic combination) plus appropriate mineral supplement of potassium and magnesium at 3 mealtime every day to prevent potential injury from starved intestinal flora and avoid spasms of smooth muscle due to hunger. Physical and medical examinations were conducted and blood samples were collected at following timepoints: before CDD as self-control (0D), day 7 and day 14 during fasting, and 7-21days and/or 2~3mo after refeeding. Results: The combination of FA and mineral supplements significantly decreased self-reported physical response of starvation, with tolerable hunger-mediated sensations experienced during CDD. Bioelectrical and biochemical results indicated significant reduction in both muscle lean and fat mass on day 7. Meanwhile, markers related to fat composition consistently decreased during and after CDD. In addition, most biochemical marker levels, including serum proteins, reached their inflection points at the 7th day of CDD as compared to the control measurements. Levels of these factors started to show a relative plateau, or reversed direction upon the 14th day of CDD. The exceptions of above factors were myostatin and complement protein C3, which remained at lower concentrations in the blood throughout CDD, and were unable to fully recover toward baseline levels even after 3 months' refeeding. Conclusion: Our results indicated that human subjects undergoing prolonged dietary restriction were well protected by FA and mineral ions from gut injury or physical discomfort of starvation. Most factors showed a relative plateau response at the end of 14D-CDD. The muscle tissues were well preserved during prolonged fasting, and an improved protein/lipid ratio was observed. Upon refeeding, constant lower levels of myostatin and complement C3 were maintained after CDD implies a long-term beneficial effect in dealing with anti-aging and inflammation.

Effect of Head Position and Magnetic Resonance Sequence on Cochlear Implant-Related Artifact Size and Internal Auditory Canal Visibility.

HYPOTHESIS: To assess whether cochlear implant (CI)-related magnetic resonance imaging (MRI) artifact and visibility of the internal auditory canal (IAC) can be improved with head positioning and select MRI sequences. BACKGROUND: CI-related MRI artifact limits the use of CIs in otherwise good candidates because of the need for serial MRIs for monitoring of pathology. This most notably impacts patients with tumors of the cerebellopontine angle and IAC. METHODS: Two cadaver heads were implanted with either a Med-El CONCERT (fixed magnet) or SYNCHRONY (rotating magnet) device. Each head was imaged in a 1.5T scanner in 11 different positions. The SYNCHRONY-implanted head was also imaged in a 3.0T scanner in seven positions. Artifact size and IAC visibility (graded on a Likert scale) were measured for each head position by a neuroradiologist. RESULTS: The CONCERT CI produced significantly smaller artifact than the SYNCHRONY CI (effect size, 14.65 mm; p < 0.001). There was no significant difference between CI models in regard to IAC visibility. No head positions were statistically significantly better than neutral position for minimizing artifact size or IAC visibility, although some positions resulted in significantly larger artifact (effect sizes, 2.1-14.3 mm; p < 0.05) or significantly poorer IAC visibility (effect size, 1.4; p < 0.01). The T2 three-dimensional (CISS/FIESTA) sequence demonstrated significantly smaller artifact than T1 sequences, whereas T1 sequences demonstrated significantly better IAC visibility than T2 sequences. CONCLUSION: Head positioning and magnetic resonance sequence selection impact CI-related artifact size and IAC visibility.

Erratum to "Cumulative dairy cow genetic change from selection and crossbreeding over the last 2 decades in New Zealand closely aligns to model-based predictions published in 2000" (JDS Commun. 2:51-54).

[This corrects the article DOI: 10.3168/jdsc.2020-0043.].

Variance parameter estimation for age at puberty phenotypes under 2 levels of phenotype censorship.

Age at puberty (AGEP) is a moderately heritable trait in cattle that may be predictive of an animal's genetic merit for reproductive success later in life. In addition, under some mating strategies (for example, where mating begins before all animals have attained puberty) animals that attain puberty at a relatively young age will also likely conceive earlier than their herd mates, and thus begin their productive life earlier. Unfortunately, AGEP is challenging to measure because animals must be observed over a period of several months. Our objectives for this study were twofold. The first objective was to produce variance components for AGEP. The second objective was to investigate the implications of a simplified phenotyping strategy for AGEP, when the interval between repeated blood plasma progesterone measures was extended from weekly to monthly, increasing the extent of left, interval, and right censoring. We measured AGEP in a closely monitored population of around 500 Holstein-Friesian heifers, born in 2015 and managed under a seasonal, pasture-based dairy system. Animals were blood tested weekly from approximately 240 to 440 d of age and were deemed to have reached puberty when blood plasma progesterone elevation (>1 ng/mL) was detected in 2 of 3 consecutive blood tests (AGEP_Weekly). To simulate a simplified phenotyping strategy based on monthly herd visits (AGEP_Monthly), we selectively disregarded data from all but 3 blood test events, when animals were around 300, 330, and 360 d of age (standard deviation = 14.5 d). The posterior mean of estimated heritabilities for AGEP_Weekly was 0.54, with a 90% credibility interval (90% CRI) of 0.41 to 0.66, whereas it was 0.44 (90% CRI 0.32 to 0.57) for AGEP_Monthly. The correlation between EBVs for AGEP_Weekly and AGEP_Monthly was 0.87 (90% CRI, 0.84 to 0.89). We conclude that in this population, AGEP is a moderately heritable trait. Further, increasing phenotype censorship from weekly to monthly observations would not have altered the main conclusions of this analysis. Our results support the strategic use of censoring to reduce costs and animal ethics considerations associated with collection of puberty phenotypes.

Observation of the Berezinskii-Kosterlitz-Thouless Transition in a Two-Dimensional Bose Gas via Matter-Wave Interferometry.

We probe local phase fluctuations of trapped two-dimensional Bose gases using matter-wave interferometry. This enables us to measure the phase correlation function, which changes from an algebraic to an exponential decay when the system crosses the Berezinskii-Kosterlitz-Thouless (BKT) transition. We determine the temperature dependence of the BKT exponent η and find the critical value η_{c}=0.17(3) for our trapped system. Furthermore, we measure the local vortex density as a function of the local phase-space density, which shows a scale-invariant behavior across the transition. Our experimental investigation is supported by Monte Carlo simulations and provides a comprehensive understanding of the BKT transition in a trapped system.

Pregnancy status predicted using milk mid-infrared spectra from dairy cattle.

Accurate and timely pregnancy diagnosis is an important component of effective herd management in dairy cattle. Predicting pregnancy from Fourier-transform mid-infrared (FT-MIR) spectroscopy data is of particular interest because the data are often already available from routine milk testing. The purpose of this study was to evaluate how well pregnancy status could be predicted in a large data set of 1,161,436 FT-MIR milk spectra records from 863,982 mixed-breed pasture-based New Zealand dairy cattle managed within seasonal calving systems. Three strategies were assessed for defining the nonpregnant cows when partitioning the records according to pregnancy status in the training population. Two of these used records for cows with a subsequent calving only, whereas the third also included records for cows without a subsequent calving. For each partitioning strategy, partial least squares discriminant analysis models were developed, whereby spectra from all the cows in 80% of herds were used to train the models, and predictions on cows in the remaining herds were used for validation. A separate data set was also used as a secondary validation, whereby pregnancy diagnosis had been assigned according to the presence of pregnancy-associated glycoproteins (PAG) in the milk samples. We examined different ways of accounting for stage of lactation in the prediction models, either by including it as an effect in the prediction model, or by pre-adjusting spectra before fitting the model. For a subset of strategies, we also assessed prediction accuracies from deep learning approaches, utilizing either the raw spectra or images of spectra. Across all strategies, prediction accuracies were highest for models using the unadjusted spectra as model predictors. Strategies for cows with a subsequent calving performed well in herd-independent validation with sensitivities above 0.79, specificities above 0.91 and area under the receiver operating characteristic curve (AUC) values over 0.91. However, for these strategies, the specificity to predict nonpregnant cows in the external PAG data set was poor (0.002-0.04). The best performing models were those that included records for cows without a subsequent calving, and used unadjusted spectra and days in milk as predictors, with consistent results observed across the training, herd-independent validation and PAG data sets. For the partial least squares discriminant analysis model, sensitivity was 0.71, specificity was 0.54 and AUC values were 0.68 in the PAG data set; and for an image-based deep learning model, the sensitivity was 0.74, specificity was 0.52 and the AUC value was 0.69. Our results demonstrate that in pasture-based seasonal calving herds, confounding between pregnancy status and spectral changes associated with stage of lactation can inflate prediction accuracies. When the effect of this confounding was reduced, prediction accuracies were not sufficiently high enough to use as a sole indicator of pregnancy status.

Animal medical genetics: a historical perspective on more than 50 years of research into genetic disorders of animals at Massey University.

Over the last 50 years, there have been major advances in knowledge and technology regarding genetic diseases, and the subsequent ability to control them in a cost-effective manner. This review traces these advances through research into genetic diseases of animals at Massey University (Palmerston North, NZ), and briefly discusses the disorders investigated during that time, with additional detail for disorders of major importance such as bovine α-mannosidosis, ovine ceroid-lipofuscinosis, canine mucopolysaccharidosis IIIA and feline hyperchylomicronaemia. The overall research has made a significant contribution to veterinary medicine, has provided new biological knowledge and advanced our understanding of similar disorders in human patients, including testing various specific therapies prior to human clinical trials.

Cumulative dairy cow genetic change from selection and crossbreeding over the last 2 decades in New Zealand closely aligns to model-based predictions published in 2000.

A deterministic model was developed in 1998 to evaluate the concurrent effects of selection and crossbreeding on the rate of genetic gain and increases in productivity of New Zealand dairy cattle over the ensuing 25-yr period. The predictions of today's breed composition of the national dairy herd and genetic trends for body weight and lactation yields of milk, fat, and protein are compared with today's actual values. Selection was assumed to use an index that included live weight and lactation yields of milk, fat, and protein. Mating strategies involving the Holstein-Friesian (F), Jersey (J), and Ayrshire (A) breeds were evaluated. Effects of heterosis and age were included to calculate phenotypic live weight and yields of milk, fat, and protein per cow. At the time the model was developed, New Zealand had an across-breed evaluation system, but only straightbred bulls were used after progeny testing based on records of straightbred and crossbred daughters. The model predicted that if crossbred cows and bulls could be considered as bull parents, faster rates of genetic gain may result because of increased selection intensities in the cow to breed bull selection pathway. This scenario transpired, and the best bulls and cows for farm profit were used regardless of breed. Under that mating strategy for the 2018 birthyear, the model predicted the national breed composition would be 11% F, 34% J, 52% F×J, and 2% A; the actual breed composition was 36% F, 9% J, 53% F×J, and 1% A. The model-predicted annual genetic gains would be 16.7 L of milk, 1.2 kg of fat, 1.5 kg of protein, and -0.7 kg of body weight; the realized annual improvements were 13.6 L of milk, 1.31 kg of fat, 1.17 kg of protein, and -0.36 kg of body weight. Predicted long-term responses to selection can closely mirror realized improvements, confirming the value of modeling to inform animal breeding decision-making.

Micro-ribonucleic acid-23a-3p prevents the onset of type 2 diabetes mellitus by suppressing the activation of nucleotide-binding oligomerization-like receptor family pyrin domain containing 3 inflammatory bodies-caused pyroptosis through negatively regulating NIMA-related kinase 7.

AIMS/INTRODUCTION: Micro-ribonucleic acids (miRNAs) possess crucial functions in governing metabolisms associated with type 2 diabetes mellitus. This study aimed to investigate the role of miR-23a-3p in pyroptosis caused by nucleotide-binding oligomerization-like receptor family pyrin domain containing 3 (NLRP3) inflammatory body activation, thereby reducing the occurrence of type 2 diabetes mellitus. MATERIALS AND METHODS: miR-23a-3p and NIMA-related kinase 7 (NEK7) expression in type 2 diabetes mellitus patients and rat models was examined. Dual-luciferase reporter gene experiments were used to verify the targeting relationship between miR-23a-3p and NEK7. Bone marrow-derived macrophages were transfected with miR-23a-3p mimic, miR-23a-3p inhibitor or short hairpin NEK7 and were treated with a specific activator of NLRP3 inflammatory body (lipopolysaccharide + adenosine-5'-triphosphate) to evaluate expression of NEK7, miR-23a-3p, gasdermin D p30, pro-caspase-1 and caspase-1 in cells, and interleukin-1ß and tumor necrosis factor-α in supernatant. Type 2 diabetes mellitus rat models were used to observe the influences of miR-23a-3p, NEK7 and NLRP3 inflammatory body on pyroptosis and type 2 diabetes mellitus in vivo. RESULTS: NEK7 was overexpressed, whereas miR-23a-3p was underexpressed in patients and rat models with type 2 diabetes mellitus. NEK7 was a target gene of miR-23a-3p. After the addition of lipopolysaccharide + adenosine-5'-triphosphate in bone marrow-derived macrophages, the expression of miR-23a-3p subsequently declined. Furthermore, the addition of lipopolysaccharide + adenosine-5'-triphosphate elevated NEK7, NLRP3, pro-caspase-1, cle-caspase-1 and gasdermin D p30 expressions in bone marrow-derived macrophages, and enhanced levels of interleukin-1ß and tumor necrosis factor-α in the supernatant, accompanied with conspicuous cell pyroptosis, which was reversed after miR-23a-3p overexpression and NEK7 silencing. miR-23a-3p overexpression alleviated liver and kidney damage in type 2 diabetes mellitus rats, and reduced NLRP3-induced pyroptosis. CONCLUSIONS: Targeting NEK7 by miR-23a-3p could reduce NLRP3-induced pyroptosis, and assuage liver and kidney injuries in type 2 diabetes mellitus rats.

Accuracy of genomic prediction of shell quality in a White Leghorn line.

Several genomic methods were applied for predicting shell quality traits recorded at 4 different hen ages in a White Leghorn line. The accuracies of genomic prediction of single-step GBLUP and single-trait Bayes B were compared with predictions of breeding values based on pedigree-BLUP under single-trait or multitrait models. Breaking strength (BS) and dynamic stiffness (Kdyn) measurements were collected on 18,524 birds from 3 consecutive generations, of which 4,164 animals also had genotypes from an Affymetrix 50K panel containing 49,591 SNPs after quality control edits. All traits had low to moderate heritability, ranging from 0.17 for BS to 0.34 for Kdyn. The highest accuracies of prediction were obtained for the multitrait single-step model. The use of marker information resulted in higher prediction accuracies than pedigree-based models for almost all traits. A genome-wide association study based on a Bayes B model was conducted to detect regions explaining the largest proportion of genetic variance. Across all 8 shell quality traits analyzed, 7 regions each explaining over 2% of genetic variance and 54 regions each explaining over 1% of genetic variance were identified. The windows explaining a large proportion of genetic variance overlapped with several potential candidate genes with biological functions linked to shell formation. A multitrait repeatability model using a single-step method is recommended for genomic evaluation of shell quality in layer chickens.

A Novel 7-Days Prolonged Dietary Deprivation Regimen Improves ALT and UA After 3-6 Months Refeeding, Indicating Therapeutic Potential.

Objectives: The aim of this study was to evaluate a total fasting regimen assisted by a novel prebiotic, Flexible Abrosia (FA), in more than 7 days of continual dietary deprivation (7D-CDD). Our analysis included basic physical examinations, bioelectrical impedance analysis, and clinical lab and ELISA analysis in normal volunteers. Methods: Seven healthy subjects with normal body weight participated in 7D-CDD with the assistance of a specially designed probiotic. Individuals were assigned to take FA (113.4 KJ/10 g) at each mealtime to avoid possible injuries to intestinal flora and smooth the hunger sensation. During 7D-CDD, the subjects were advised to avoid any food intake, especially carbohydrates, except for drinking plentiful amounts of water. The examination samples were collected before CDD as self-control, at 7 days fasting, and after 7~14 days of refeeding. Three subjects were also tested after 6-m refeeding. Results: The FA-CDD regimen significantly decreased suffering from starvation, with tolerable hunger sensations during the treatment. With the addition of daily mineral electrolytes, the subjects not only passed through the entire 7D-CDD regimen but also succeed in 12~13 days total fasting in two subjects. There was a significant reduction in blood glucose, insulin, and high-density lipoprotein levels during fasting, and the blood concentrations of uric acid (UA), alanine aminotransferase (ALT), and creatine kinase (CK) were increased. However, after more than 2 months of refeeding, the disease markers ALT, GOT, and CK either remained stable or were slightly downregulated compared to their initial D0 control level. Conclusion: Our experiment has supplied the first positive evidence that, with the assistance of a daily nutritional supply of around 100 kcal total calories to their intestinal flora, human subjects were able to tolerate hunger sensations. We have found that, although 7D-CDD induced increases in UA, CK, and transferases during fasting, refeeding led the markers to become either down-regulated or unchanged compared to their initial levels. This phenomenon was further confirmed in longer-term (6 m) recovery. Our results failed to support the hypothesis that fasting induced liver damage, since ALT, GOT, and CK remained low after longer-term refeeding. Our findings indicate that the 7D-CDD regimen might be practical and that it might be valuable to design larger clinical fasting trials for improvement of health strategy-targeting in metabolic disorders.

The evolving role of Fourier-transform mid-infrared spectroscopy in genetic improvement of dairy cattle.

Over the last 100 years, significant advances have been made in the characterisation of milk composition for dairy cattle improvement programs. Technological progress has enabled a shift from labour intensive, on-farm collection and processing of samples that assess yield and fat levels in milk, to large-scale processing of samples through centralised laboratories, with the scope extended to include quantification of other traits. Fourier-transform mid-infrared (FT-MIR) spectroscopy has had a significant role in the transformation of milk composition phenotyping, with spectral-based predictions of major milk components already being widely used in milk payment and animal evaluation systems globally. Increasingly, there is interest in analysing the individual FT-MIR wavenumbers, and in utilising the FT-MIR data to predict other novel traits of importance to breeding programs. This includes traits related to the nutritional value of milk, the processability of milk into products such as cheese, and traits relevant to animal health and the environment. The ability to successfully incorporate these traits into breeding programs is dependent on the heritability of the FT-MIR predicted traits, and the genetic correlations between the FT-MIR predicted and actual trait values. Linking FT-MIR predicted traits to the underlying mutations responsible for their variation can be difficult because the phenotypic expression of these traits are a function of a diverse range of molecular and biological mechanisms that can obscure their genetic basis. The individual FT-MIR wavenumbers give insights into the chemical composition of milk and provide an additional layer of granularity that may assist with establishing causal links between the genome and observed phenotypes. Additionally, there are other molecular phenotypes such as those related to the metabolome, chromatin accessibility, and RNA editing that could improve our understanding of the underlying biological systems controlling traits of interest. Here we review topics of importance to phenotyping and genetic applications of FT-MIR spectra datasets, and discuss opportunities for consolidating FT-MIR datasets with other genomic and molecular data sources to improve future dairy cattle breeding programs.

Sire selection and genetic improvement of dairy cattle assuming pure market competition.

Breeding companies and farmers rely on selection indices to identify sires they expect to improve production system profitability. Such indices combine estimates of genetic merit for individual traits with corresponding economic values that are fixed based on circumstances at a particular time. Perfect market competition has also been proposed as an economic basis to value alternative sires. The objective of this study was to propose an economic model and develop it for the evaluation of dairy sires. The pure competition model (PUC) was used to evaluate the relationship between a profitability index based on the PUC approach versus the traditional selection index approach for 330 dairy sires comprising Holstein-Friesians, Jerseys, and Ayrshires. The correlation between these 2 selection indices was only 0.56, indicating that the conventional selection index did not correlate well with an index based on the PUC model. In particular, the higher ranking bulls were overvalued using the conventional selection index. Our study concluded that the use of fixed economic values is problematic for the delivery of consistent rankings in selection indices. In contrast, sire rankings based on PUC are more reliable because the sires are evaluated on the basis of efficiency gains rather than production while accounting for market prices and marginal values of dairy outputs over time.

Increasing the accuracy of genomic prediction in pure-bred Limousin beef cattle by including cross-bred Limousin data and accounting for an F94L variant in MSTN.

Explicitly fitting effects for major genes or QTL that account for a large percentage of variation in a whole genomic prediction model may increase prediction accuracy. This study compared approaches to account for a major effect of an F94L variant in the MSTN gene within the genomic prediction using bovine whole-genomic SNP markers. Among the beef cattle breeds, Limousin have been known to have an F94L variant that is not present in Angus. The reference population in this study consisted of 3060 beef cattle including pure-bred Limousin (PL), cross-bred Limousin with Angus (LF) and pure-bred Angus, genotyped using a BovineSNP50 BeadChip and directly for the MSTN-F94L variant. We compared prediction accuracies in PL animals using the three datasets from only the PL population, admixed PL and LF (AL) or multibreed analysis using all of the PL, LF and Angus (MB) population according to four-fold cross-validation after K-means clustering. The MSTN-F94L variant was the most strongly associated with five traits (birth weight, calving ease direct, milk, weaning weight and yield grade) among the 13 measured traits in PL and AL populations. Fitting the MSTN-F94L variant as a random effect, the genomic prediction accuracies for birth weight increased by 2.7% in PL, by 2.2% in AL and by 3.2% in MB. Prediction accuracies for five traits increased in the MB analysis. Fitting MSTN-F94L as a fixed effect in PL, AL and MB analyses resulted in increased prediction accuracy in PL for eight traits. Prediction accuracies can be improved by including a causal variant in genomic evaluation compared with simply using whole-genome SNP markers. Fitting the causal variant as a fixed effect along with markers fitted as random effects resulted in greater prediction accuracies for most traits. Causal variants should be genotyped along with SNP markers.

Strategies for noise reduction and standardization of milk mid-infrared spectra from dairy cattle.

The use of Fourier-transform mid-infrared (FTIR) spectroscopy is of interest to the dairy industry worldwide for predicting milk composition and other novel traits that are difficult or expensive to measure directly. Although there are many valuable applications for FTIR spectra, noise from differences in spectral responses between instruments is problematic because it reduces prediction accuracy if ignored. The purpose of this study was to develop strategies to reduce the impact of noise and to compare methods for standardizing FTIR spectra in order to reduce between-instrument variability in multiple-instrument networks. Noise levels in bands of the infrared spectrum caused by the water content of milk were characterized, and a method for identifying and removing outliers was developed. Two standardization methods were assessed and compared: piecewise direct standardization (PDS), which related spectra on a primary instrument to spectra on 5 other (secondary) instruments using identical milk-based reference samples (n = 918) analyzed across the 6 instruments; and retroactive percentile standardization (RPS), whereby percentiles of observed spectra from routine milk test samples (n = 2,044,094) were used to map and exploit primary- and secondary-instrument relationships. Different applications of each method were studied to determine the optimal way to implement each method across time. Industry-standard predictions of milk components from 2,044,094 spectra records were regressed against predictions from spectra before and after standardization using PDS or RPS. The PDS approach resulted in an overall decrease in root mean square error between industry-standard predictions and predictions from spectra from 0.190 to 0.071 g/100 mL for fat, from 0.129 to 0.055 g/100 mL for protein, and from 0.143 to 0.088 g/100 mL for lactose. Reductions in prediction error for RPS were similar but less consistent than those for PDS across time, but similar reductions were achieved when PDS coefficients were updated monthly and separate primary instruments were assigned for the North and South Islands of New Zealand. We demonstrated that the PDS approach is the most consistent method to reduce prediction errors across time. We also showed that the RPS approach is sensitive to shifts in milk composition but can be used to reduce prediction errors, provided that secondary-instrument spectra are standardized to a primary instrument with samples of broadly equivalent milk composition. Appropriate implementation of either of these approaches will improve the quality of predictions based on FTIR spectra for various downstream applications.

Variance component estimates for post-thaw sperm variables measured by computer assisted semen analyzer for inbred and non-inbred Hereford bulls.

Inbreeding has been shown to have negative effects on animal reproductive performance. It, however, has not been documented as to the extent that inbreeding impacts sperm cell characteristics especially after cells have undergone cryopreservation. In the present study, therefore, there was evaluation of sperm motion and size characteristics from two inbred lines and non-inbred Hereford bulls using Computer Assisted Sperm Analysis (CASA). Pre-frozen semen samples were obtained from Line 1, Prospector, and a non-inbred Hereford bulls. Average inbreeding of each group was 27%, 45%, and 0%, for Line 1, Prospector, and non-inbred groups, respectively. For each bull, two straws (0.5 ml) were evaluated, within each straw two slides with ten fields per slide analyzed with the CASA. Cells were analyzed by mixed model analysis using a model that contained line as fixed effect and individual bull, straw, slide, and fields are random effects. No significant differences between lines of bulls were found for motility or progressive motility. For the head elongation (ELON), and head area (AREA) significant differences existed between lines. Mean separation indicated that Line 1 group had a larger head area and the heads were not as elongated as the Prospector and non-inbred group cells. The lack of significant differences for motility and progressive motility across lines was surprising and indicates there was no inbreeding depression for post-thaw semen characteristics. One explanation for this could be that indirect selection pressure was placed on these characteristics as inbreeding was increased.

Familial episodic ataxia in lambs is potentially associated with a mutation in the fibroblast growth factor 14 (FGF14) gene.

Familial episodic ataxia of lambs is a congenital transient autosomal dominant disorder of newborn lambs, with varying expressivity. Affected lambs show episodes of an asymmetric ataxic gait, base-wide extensor hypertonia of the thoracic limbs and flexor hypertonia of the pelvic limbs. The aim of the study was to determine the genetic variant causing familial episodic ataxia in lambs. Using whole genome sequencing of two half-sib affected lambs, their sire, and their two normal dams, a heterozygous C>T transition at OAR10:77593415 (Oar_v3.1) in exon 1 of the fibroblast growth factor 14 (FGF14) gene (c.46C>T) was identified. The c.46C>T transition resulted in a premature stop codon at position 16 of the 247 amino acid FGF14 protein (p.Q16*). PCR and Sanger sequencing was used to genotype an additional 20 clinically affected animals, demonstrating all lambs carried the c.46C>T variant but 1 clinically more severely affected inbred lamb was homozygous (TT). A further 11 unrelated normal ewes were positionally sequenced, none of which had the variant, while in 18 lambs of unknown status born over 2 years of breeding trials six lambs were found to have the c.46C>T variant, likely clinically unidentified heterozygotes due to the variable expressivity, while 12 did not. In conclusion, familial episodic ataxia of lambs is potentially associated with a c.46C>T variant in the FGF14 gene. Further research is required into the mechanism behind the apparent recovery of lambs.