Diagnosis of placenta accreta spectrum in high-risk women using ultrasonography or magnetic resonance imaging: systematic review and meta-analysis.

OBJECTIVES: To perform a systematic review and meta-analysis of the diagnostic test accuracy of ultrasound and magnetic resonance imaging (MRI) and compare the performance of the two modalities in the diagnosis of placenta accreta spectrum (PAS). METHODS: This was a systematic review conducted following the Cochrane Diagnostic Test Accuracy Reviews guideline. A literature search was performed in five databases: PubMed, EMBASE, PMC, The Cochrane Library and BVS-Bireme between 27 July and 4 August 2020. The search was updated on 18 August 2021. We included observational studies evaluating diagnostic accuracy in women with risk factors for PAS who had undergone both ultrasound and MRI examinations, published in English between 2011 and 2021. Quality Assessment of Diagnostic Accuracy Studies-2 was used to evaluate the quality of the studies. Forest plots for sensitivity and specificity with 95% CIs and receiver-operating-characteristics curves for ultrasound and MRI were constructed. RESULTS: The literature search identified 266 studies. After reviewing the titles and abstracts of the articles, 51 were selected for full-text review and 17 studies including 1301 women with MRI and ultrasound data available were selected for the meta-analysis. The study population included 457 cases with PAS diagnosed using the gold standard method (intraoperative or histopathological analysis). The overall quality of the evaluated studies was considered satisfactory according to QUADAS-2. The meta-analysis revealed a sensitivity of 0.833 (95% CI, 0.776-0.878) and specificity of 0.834 (95% CI, 0.746-0.897) for ultrasound. For MRI, sensitivity was 0.838 (95% CI, 0.786-0.879) and specificity was 0.831 (95% CI, 0.770-0.878). There was no statistically significant difference between the two modalities. The Cochran's Q values indicated a high level of heterogeneity of sensitivity and specificity of ultrasound and MRI across studies. CONCLUSIONS: Ultrasound and MRI have similar accuracy in the diagnosis of PAS. These results suggest that, in a setting with a high prevalence of risk factors, the choice of imaging modality for initial screening for PAS should depend on the availability of equipment and the examiner's expertise. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.

Genetic analyses of blood ß-hydroxybutyrate predicted from milk infrared spectra and its association with longevity and female reproductive traits in Holstein cattle.

Ketosis is one of the most prevalent and complex metabolic disorders in high-producing dairy cows and usually detected through analyses of ß-hydroxybutyrate (BHB) concentration in blood. Our main objectives were to evaluate genetic parameters for blood BHB predicted based on Fourier-transform mid-infrared spectra from 5 to 305 d in milk, and estimate the genetic relationships of blood BHB with 7 reproduction traits and 6 longevity traits in Holstein cattle. Predicted blood BHB records of 11,609 Holstein cows (after quality control) were collected from 2016 to 2019 and used to derive 4 traits based on parity number, including predicted blood BHB in all parities (BHBp), parity 1 (BHB1), parity 2 (BHB2), and parity 3+ (BHB3). Single- and multitrait repeatability models were used for estimating genetic parameters for the 4 BHB traits. Random regression test-day models implemented via Bayesian inference were used to evaluate the daily genetic feature of BHB variability. In addition, genetic correlations were calculated for the 4 BHB traits with reproduction and longevity traits. The heritability estimates of BHBp, BHB1, BHB2, and BHB3 ranged from 0.100 ± 0.026 (± standard error) to 0.131 ± 0.023. The BHB in parities 1 to 3+ were highly genetically correlated and ranged from 0.788 (BHB1 and BHB2) to 0.911 (BHB1 and BHB3). The daily heritability of BHBp ranged from 0.069 to 0.195, higher for the early and lower for the later lactation periods. A similar trend was observed for BHB1, BHB2, and BHB3. There are low direct genetic correlations between BHBp and selected reproductive performance and longevity traits, which ranged from -0.168 ± 0.019 (BHBp and production life) to 0.157 ± 0.019 (BHBp and age at first calving) for the early lactation stage (5 to 65 d). These direct genetic correlations indicate that cows with higher BHBp (greater likelihood of having ketosis) in blood usually have shorter production life (-0.168 ± 0.019). Cows with higher fertility and postpartum recovery, such as younger age at first calving (0.157 ± 0.019) and shorter interval from calving to first insemination in heifer (0.111 ± 0.006), usually have lower BHB concentration in the blood. Furthermore, the direct genetic correlations change across parity and lactation stage. In general, our results suggest that selection for lower predicted BHB in early lactation could be an efficient strategy for reducing the incidence of ketosis as well as indirectly improving reproductive and longevity performance in Holstein cattle.

Non-invasive and invasive brain stimulation in alcohol use disorders: A critical review of selected human evidence and methodological considerations to guide future research.

INTRODUCTION: Alcohol use disorder (AUD) ranks among the leading causes of decrements in disability-adjusted life-years. Long-term exposure to alcohol leads to an imbalance of activity between frontal cortical systems and the striatum, thereby enhancing impulsive behaviours and weakening inhibitory control. Alternative therapeutic approaches such as non-invasive and invasive brain stimulation have gained some momentum in the field of addictology by capitalizing on their ability to target specific anatomical structures and correct abnormalities in dysfunctional brain circuits. MATERIALS AND METHODS: The current review, covers original peer-reviewed published research on the use of brain stimulation methods for the rehabilitation of AUD. A broad and systematic search was carried out on four electronic databases: NCBI PubMed, Web of Science, Handbooks and the Cochrane Library. Any original article in English or French language, without restrictions of patient age or gender, article type and publication outlet, were included in the final pool of selected studies. RESULTS: The outcomes of this systematic review suggest that the dorsolateral prefrontral cortex (DLPFC) is a promising target for treating AUD with high frequency repetitive transcranial magnetic stimulation. Such effect would reduce feelings of craving by enhancing cognitive control and modulating striatal function. Existing literature also supports the notion that changes of DLPFC activity driven by transcranial direct current stimulation, could decrease alcohol craving and consumption. However, to date, no major differences have been found between the efficacy of these two non-invasive brain-stimulation approaches, which require further confirmation. In contrast, beneficial stronger evidence supports an impact of deep brain stimulation reducing craving and improving quality of life in AUD, effects that would be mediated by an impact on the nucleus accumbens, a central structure of the brain's reward circuitry. Overall, neurostimulation shows promise contributing to the treatment of AUD. Nonetheless, progress has been limited by a number of factors such as the low number of controlled randomized trials, small sample sizes, variety of stimulation parameters precluding comparability and incomplete or questionable sham-conditions. Additionally, a lack of data concerning clinical impact on the severity of AUD or craving and the short follow up periods precluding and accurate estimation of effect duration after discontinuing the treatment, has also limited the clinical relevance of final outcomes. CONCLUSION: Brain stimulation remains a promising approach to contribute to AUD therapy, co-adjuvant of more conventional procedures. However, a stronger therapeutic rational based on solid physio-pathological evidence and accurate estimates of efficacy, are still required to achieve further therapeutic success and expand clinical use.

Genome-wide association study and pathway analysis for carcass fatness in Nellore cattle measured by ultrasound.

Identifying genes or genomic regions influencing carcass-quality traits such as fatness (FTN) is essential to optimize the genetic selection processes in beef cattle. The aim of this study was to identify genomic regions associated with FTN in Nellore cattle as well as to elucidate the metabolic pathways related to the phenotypic expression. Ultrasound-based measurements of FTN were collected in 11 750 animals, with 39 903 animals in the pedigree file. Additionally, 1440 animals were genotyped using the GGP-indicus 35K SNP panel, which contained 33 623 SNPs after quality control. Twenty genes related to FTN were found on 11 chromosomes, explaining 12.96% of the total additive genetic variance. Gene ontology revealed seven genes: NR1L2, PKD2, GSK3ß, EXT1, RAD51B, SORCS1 and DPH6, associated with important processes related to FTN. In addition, novel candidate genes (MAATS1, LYPD1, CDK5RAP2, RAD51B, c13H2Oorf96 and TRAPPC11) were detected and could provide further knowledge to uncover genetic regions associated to carcass fatness in beef cattle.

A pig slurry feast/famine feeding regime strategy to improve mesophilic anaerobic digestion efficiency and digestate hygienisation.

The increasing concentration of livestock farms results in large amounts of waste production and the need for their management. The study of anaerobic digestion (AD) technology, under mesophilic conditions, applied to pig slurry is of the upmost importance for biogas recovery and sanitised digestate, contributing to a circular economy. The assessment of the effects of a feast/famine regime on biogas and biomethane (bio-CH4) yield with different feeding frequencies was performed. The evaluation was made in regards to three scenarios: the first is based on daily feeding (FR1); in the second, the feeding occurs once every two days (FR2); and in the third, the feeding happens once every three days (FR3). The results demonstrate that the biogas and methane yield increased by 34% and 37% between FR1 and FR3. The stability inside the reactor was maintained since specific loading energetic rate values did not exceed the recommended limit (0.4 d-1). It was also possible to conclude that the AD technology was efficient to sanitise the pig slurry, with the count of Escherichia coli going from 1 × 105 colony-forming units (CFU) g-1 to less than 100 CFU g-1, meeting the legal requirements for agricultural valorisation. The total anaerobic mesophile plate counts were significantly (p < 0.1) reduced from feeding to digestate, and the plate counts of Clostridia were significantly (p < 0.05) increased, reflecting the changes in the composition of the microbiota. The increasing yield in bio-CH4 in accordance with Clostridium counts suggests this genus as a positive microbiological key indicator of the AD performance.

Short communication: Time-dependent genetic parameters and single-step genome-wide association analyses for predicted milk fatty acid composition in Ayrshire and Jersey dairy cattle.

Milk fat content and fatty acid (FA) composition have great economic value to the dairy industry as they are directly associated with taste and chemical-physical characteristics of milk and dairy products. In addition, consumers' choices are not only based on the nutritional aspects of food, but also on products known to promote better health. Milk FA composition is also related to the metabolic status and physiological stages of cows and thus can also be used as indicator for other novel traits of interest (e.g., metabolic diseases and methane yield). Genetic selection is a promising alternative to manipulate milk FA composition. In this study, we aimed to (1) estimate time-dependent genetic parameters for 5 milk FA groups (i.e., short-chain, medium-chain, long-chain, saturated, and unsaturated) predicted based on milk mid-infrared spectroscopy, for Canadian Ayrshire and Jersey breeds, and (2) conduct a time-dependent, single-step genome-wide association study to identify genomic regions, candidate genes, and metabolic pathways associated with milk FA. We analyzed 31,709 test-day records of 9,648 Ayrshire cows from 268 herds, and 34,341 records of 11,479 Jersey cows from 883 herds. The genomic database contained a total of 2,330 Ayrshire and 1,019 Jersey animals. The average daily heritability ranged from 0.18 (long-chain FA) to 0.34 (medium-chain FA) in Ayrshire, and from 0.25 (long-chain and unsaturated FA) to 0.52 (medium-chain and saturated FA) in Jersey. Important genomic regions were identified in Bos taurus autosomes BTA3, BTA5, BTA12, BTA13, BTA14, BTA16, BTA18, BTA20, and BTA21. The proportion of the variance explained by 20 adjacent SNP ranged from 0.71% (saturated FA) to 1.11% (long-chain FA) in Ayrshire, and from 0.70% (unsaturated FA) to 3.09% (medium-chain FA) in Jersey cattle. Important candidate genes and pathways were also identified, such as the PTK2 and TRAPPC9 genes, associated with milk fat percentage, and HMGCS, FGF10, and C6 genes, associated with fertility traits and immune response. Our findings on the genetic parameters and candidate genes contribute to a better understanding of the genetic architecture of milk FA composition in Ayrshire and Jersey dairy cattle.

Genomic analyses for predicted milk fatty acid composition throughout lactation in North American Holstein cattle.

Milk fat composition has important implications in the nutritional and processing properties of milk. Additionally, milk fat composition is associated with cow physiological and health status. The main objectives of this study were (1) to estimate genetic parameters for 5 milk fatty acid (FA) groups (i.e., short-chain, medium-chain, long-chain, saturated, and unsaturated) predicted from milk infrared spectra using a large data set; (2) to predict genomic breeding values using a longitudinal single-step genomic BLUP approach; and (3) to conduct a single-step GWAS aiming to identify genomic regions, candidate genes, and metabolic pathways associated with milk FA, and consequently, to understand the underlying biology of these traits. We used 629,769 test-day records of 201,465 first-parity Holstein cows from 6,105 herds. A total of 8,865 genotyped (Illumina BovineSNP50K BeadChip, Illumina, San Diego, CA) animals were considered for the genomic analyses. The average daily heritability ranged from 0.24 (unsaturated FA) to 0.47 (medium-chain and saturated FA). The reliability of the genomic breeding values ranged from 0.56 (long-chain fatty acid) to 0.74 (medium-chain fatty acid) when using the default τ and ω scaling parameters, whereas it ranged from 0.58 (long-chain fatty acid) to 0.73 (short-chain fatty acid) when using the optimal τ and ω values (i.e., τ = 1.5 and ω = 0.6), as defined in a previous study in the same population. Relevant chromosomal regions were identified in Bos taurus autosomes 5 and 14. The proportion of the variance explained by 20 adjacent single nucleotide polymorphisms ranged from 0.71% (saturated FA) to 15.12% (long-chain FA). Important candidate genes and pathways were also identified. In summary, our results contribute to a better understanding of the genetic architecture of predicted milk FA in dairy cattle and reinforce the relevance of using genomic information for genetic analyses of these traits.

Short communication: Genetic parameter estimates for caprine arthritis encephalitis in dairy goats.

Caprine arthritis encephalitis (CAE) is a chronic disease caused by a retrovirus from the Lentivirus genus. No effective vaccines or treatments exist, and therefore genetic selection for CAE resistance might be a feasible alternative. To our best knowledge, no other studies have investigated the genetic architecture of CAE resistance in dairy goats. In this context, this study was designed to estimate genetic parameters for CAE infection in Alpine and Saanen goats using a Bayesian threshold model. A total of 542 adult goats (and >3-generation pedigree), which were group-housed in a population with high CAE prevalence, were tested based on a serological infection assessment test (negative = 1 or positive = 2) and used for this study. Genetic parameters were estimated using the BLUPF90 family programs. There was considerable genetic variability for CAE resistance, and pedigree-based heritability was significantly different from zero (0.026 < heritability < 0.128). Our findings indicate that the prevalence of CAE in goat herds can be reduced or eliminated through direct genetic selection for CAE resistance in addition to proper management strategies.

Pyrethroid and organophosphate pesticide resistance in field populations of horn fly in Brazil.

Pesticides are used worldwide to control arthropod parasites in cattle herds. The indiscriminate and/or inappropriate use of pesticides without veterinary guidance is a reality in several countries of South America. Improper pesticide use increases the chances of contamination of food and the environment with chemical pesticides and their metabolites. Reduction of these contamination events is an increasing challenge for those involved in livestock production. The horn fly, Haematobia irritans (Linnaeus) (Diptera: Muscidae), is one of the most economically important parasites affecting cattle herds around the world. As such, horn fly control efforts are often required to promote the best productive performance of herds. Pesticide susceptibility bioassays revealed that pyrethroid resistance was widespread and reached high levels in horn fly populations in the Brazilian state of Rondônia. The knockdown resistance (kdr) sodium channel gene mutation was detected in all horn fly populations studied (n = 48), and the super kdr sodium channel gene mutation was found in all homozygous resistant kdr individuals (n = 204). Organophosphate resistance was not identified in any of the fly populations evaluated.

Symposium review: The choice and collection of new relevant phenotypes for fertility selection.

In dairy production, high fertility contributes to herd profitability by achieving greater production and maintaining short calving intervals. Improved management practices and genetic selection have contributed to reversing negative trends in dairy cow fertility, but further progress is still required. Phenotypes included in current genetic evaluations are largely interval and binary traits calculated from insemination and calving date records. Several indicator traits such as calving, health, variation in body condition score, and longevity traits also apply to genetic improvement of fertility. Several fertility traits are included in the selection indices of many countries, but for improved selection, the development of novel phenotypes that more closely describe the physiology of reproduction and limit management bias could be more effective. Progesterone-based phenotypes can be determined from milk samples to describe the heritable interval from calving to corpus luteum activity, as well as additional measures of cow cyclicity. A fundamental component of artificial insemination practices is the observation of estrus. Novel phenotypes collected on estrous activity could be used to select for cows clearly displaying heat, as those cows are more likely to be inseminated at the right time and therefore have greater fertility performance. On-farm technologies, including in-line milk testing and activity monitors, may allow for phenotyping novel traits on large numbers of animals. Additionally, selection for improved fertility using traditional traits could benefit from refined and accurate recording and implementation of parameters such as pregnancy confirmation and reproductive management strategy, to differentiate embryonic or fetal loss, and to ensure selection for reproductive capability without producer intervention. Opportunities exist to achieve genetic improvement of reproductive efficiency in cattle using novel phenotypes, which is required for long-term sustainability of the dairy cattle population and industry.

Genomic prediction of lactation curves for milk, fat, protein, and somatic cell score in Holstein cattle.

Application of random regression models (RRM) in a 2-step genomic prediction might be a feasible way to select young animals based on the complete pattern of the lactation curve. In this context, the prediction reliability and bias of genomic estimated breeding value (GEBV) for milk, fat, and protein yields and somatic cell score over days in milk (DIM) using a 2-step genomic approach were investigated. In addition, the effect of including cows in the training and validation populations was investigated. Estimated breeding values for each DIM (from 5 to 305 d) from the first 3 lactations of Holstein animals were deregressed and used as pseudophenotypes in the second step. Individual additive genomic random regression coefficients for each trait were predicted using RRM and genomic best linear unbiased prediction and further used to derive GEBV for each DIM. Theoretical reliabilities of GEBV obtained by the RRM were slightly higher than theoretical reliabilities obtained by the accumulated yield up to 305 d (P305). However, validation reliabilities estimated for GEBV using P305 were higher than for GEBV using RRM. For all traits, higher theoretical and validation reliabilities were estimated when incorporating genomic information. Less biased GEBV estimates were found when using RRM compared with P305, and different validation reliability and bias patterns for GEBV over time were observed across traits and lactations. Including cows in the training population increased the theoretical reliabilities and bias of GEBV; nonetheless, the inclusion of cows in the validation population does not seem to affect the regression coefficients and the theoretical reliabilities. In summary, the use of RRM in 2-step genomic prediction produced fairly accurate GEBV over the entire lactation curve for all analyzed traits. Thus, selecting young animals based on the pattern of lactation curves seems to be a feasible alternative in genomic selection of Holstein cattle for milk production traits.

Invited review: Advances and applications of random regression models: From quantitative genetics to genomics.

An important goal in animal breeding is to improve longitudinal traits; that is, traits recorded multiple times during an individual's lifetime or physiological cycle. Longitudinal traits were first genetically evaluated based on accumulated phenotypic expression, phenotypic expression at specific time points, or repeatability models. Until now, the genetic evaluation of longitudinal traits has mainly focused on using random regression models (RRM). Random regression models enable fitting random genetic and environmental effects over time, which results in higher accuracy of estimated breeding values compared with other statistical approaches. In addition, RRM provide insights about temporal variation of biological processes and the physiological implications underlying the studied traits. Despite the fact that genomic information has substantially contributed to increase the rates of genetic progress for a variety of economically important traits in several livestock species, less attention has been given to longitudinal traits in recent years. However, including genomic information to evaluate longitudinal traits using RRM is a feasible alternative to yield more accurate selection and culling decisions, because selection of young animals may be based on the complete pattern of the production curve with higher accuracy compared with the use of traditional parent average (i.e., without genomic information). Moreover, RRM can be used to estimate SNP effects over time in genome-wide association studies. Thus, by analyzing marker associations over time, regions with higher effects at specific points in time are more likely to be identified. Despite the advances in applications of RRM in genetic evaluations, more research is needed to successfully combine RRM and genomic information. Future research should provide a better understanding of the temporal variation of biological processes and their physiological implications underlying the longitudinal traits.

Application of single-step genomic evaluation using multiple-trait random regression test-day models in dairy cattle.

Test-day traits are important for genetic evaluation in dairy cattle and are better modeled by multiple-trait random regression models (RRM). The reliability and bias of genomic estimated breeding values (GEBV) predicted using multiple-trait RRM via single-step genomic best linear unbiased prediction (ssGBLUP) were investigated in the 3 major dairy cattle breeds in Canada (i.e., Ayrshire, Holstein, and Jersey). Individual additive genomic random regression coefficients for the test-day traits were predicted using 2 multiple-trait RRM: (1) one for milk, fat, and protein yields in the first, second, and third lactations, and (2) one for somatic cell score in the first, second, and third lactations. The predicted coefficients were used to derive GEBV for each lactation day and, subsequently, the daily GEBV were compared with traditional daily parent averages obtained by BLUP. To ensure compatibility between pedigree and genomic information for genotyped animals, different scaling factors for combining the inverse of genomic (G-1) and pedigree (A-122) relationship matrices were tested. In addition, the inclusion of only genotypes from animals with accurate breeding values (defined in preliminary analysis) was compared with the inclusion of all available genotypes in the analyzes. The ssGBLUP model led to considerably larger validation reliabilities than the BLUP model without genomic information. In general, scaling factors used to combine the G-1 and A-122 matrices had small influence on the validation reliabilities. However, a greater effect was observed in the inflation of GEBV. Less inflated GEBV were obtained by the ssGBLUP compared with the parent average from traditional BLUP when using optimal scaling factors to combine the G-1 and A-122 matrices. Similar results were observed when including either all available genotypes or only genotypes from animals with accurate breeding values. These findings indicate that ssGBLUP using multiple-trait RRM increases reliability and reduces bias of breeding values of young animals when compared with parent average from traditional BLUP in the Canadian Ayrshire, Holstein, and Jersey breeds.

Estimating the effect of the deleterious recessive haplotypes AH1 and AH2 on reproduction performance of Ayrshire cattle.

The effects of 2 deleterious recessive haplotypes on reproduction performance of Ayrshire cattle, Ayrshire Haplotype 1 (AH1) and Ayrshire Haplotype 2 (AH2), were investigated in Canadian Ayrshire cattle. We calculated their phenotypic effects on stillbirth (SB) rate and 56-d nonreturn rate (NRR) by estimating the interaction of service sire carrier status with maternal grandsire carrier status using the official Canadian evaluation models for those 2 traits. The interaction term included 9 subclasses for the 3 possible statuses of each bull: haplotype carrier, noncarrier, or not genotyped. For AH1, 394 carriers and 1,433 noncarriers were available, whereas 313 carriers and 1,543 noncarriers were available for the AH2 haplotype. The number of matings considered for SB was 34,312 for heifers (first parity) and 115,935 for cows (later parities). For NRR, 49,479 matings for heifers and 160,528 for cows were used to estimate the haplotype effects. We observed a negative effect of AH1 on SB rates, which was 2.0% higher for matings of AH1-carrier sires to dams that had an AH1-carrier sire; this effect was found for both heifers and cows. However, AH1 had small, generally nonsignificant effects on NRR. The AH2 haplotype had a substantial negative effect on NRR, with 5.1% more heifers and 4.0% more cows returning to service, but the effects on SB rates were inconsistent and mostly small effects. Our results validate the harmful effects of AH1 and AH2 on reproduction traits in the Canadian Ayrshire population. This information will be of great interest for the dairy industry, allowing producers to make mating decisions that would reduce reproductive losses.

Genetics and genomics of reproductive disorders in Canadian Holstein cattle.

In Canada, reproductive disorders known to affect the profitability of dairy cattle herds have been recorded by producers on a voluntary basis since 2007. Previous studies have shown the feasibility of using producer-recorded health data for genetic evaluations. Despite low heritability estimates and limited availability of phenotypic information, sufficient genetic variation has been observed for those traits to indicate that genetic progress, although slow, can be achieved. Pedigree- and genomic-based analyses were performed on producer-recorded health data of reproductive disorders, including retained placenta (RETP), metritis (METR), and cystic ovaries (CYST) using traditional BLUP and single-step genomic BLUP. Genome-wide association studies and functional analyses were carried out to unravel significant genomic regions and biological pathways, and to better understand the genetic mechanisms underlying RETP, METR, and CYST. Heritability estimates (posterior standard deviation in parentheses) were 0.02 (0.003), 0.01 (0.004), and 0.02 (0.003) for CYST, METR, and RETP, respectively. A moderate to strong genetic correlation of 0.69 (0.102) was found between METR and RETP. Averaged over all traits, sire proof reliabilities increased by approximately 11 percentage points with the incorporation of genomic data using a multiple-trait linear model. Biological pathways and associated genes underlying the studied traits were identified and will contribute to a better understanding of the biology of these 3 health disorders in dairy cattle.

Use of a single-step approach for integrating foreign information into national genomic evaluation in Holstein cattle.

The use of multi-trait across-country evaluation (MACE) and the exchange of genomic information among countries allows national breeding programs to combine foreign and national data to increase the size of the training populations and potentially increase accuracy of genomic prediction of breeding values. By including genotyped and nongenotyped animals simultaneously in the evaluation, the single-step genomic BLUP (GBLUP) approach has the potential to deliver more accurate and less biased genomic evaluations. A single-step genomic BLUP approach, which enables integration of data from MACE evaluations, can be used to obtain genomic predictions while avoiding double-counting of information. The objectives of this study were to apply a single-step approach that simultaneously includes domestic and MACE information for genomic evaluation of workability traits in Canadian Holstein cattle, and compare the results obtained with this methodology with those obtained using a multi-step approach (msGBLUP). By including MACE bulls in the training population, msGBLUP led to an increase in reliability of genomic predictions of 4.8 and 15.4% for milking temperament and milking speed, respectively, compared with a traditional evaluation using only pedigree and phenotypic information. Integration of MACE data through a single-step approach (ssGBLUPIM) yielded the highest reliabilities compared with other considered methods. Integration of MACE data also helped reduce bias of genomic predictions. When using ssGBLUPIM, the bias of genomic predictions decreased by half compared with msGBLUP using domestic and MACE information. Therefore, the reliability and bias of genomic predictions for both traits improved substantially when a single-step approach was used for evaluation compared with a multi-step approach. The use of a single-step approach with integration of MACE information provides an alternative to the current method used in Canadian genomic evaluations.

Single-step genome-wide association for longitudinal traits of Canadian Ayrshire, Holstein, and Jersey dairy cattle.

Estimating single nucleotide polymorphism (SNP) effects over time is essential to identify and validate candidate genes (or quantitative trait loci) associated with time-dependent variation of economically important traits and to better understand the underlying mechanisms of lactation biology. Therefore, in this study, we aimed to estimate time-dependent effects of SNP and identifying candidate genes associated with milk (MY), fat (FY), and protein (PY) yields, and somatic cell score (SCS) in the first 3 lactations of Canadian Ayrshire, Holstein, and Jersey breeds, as well as suggest their potential pattern of phenotypic effect over time. Random regression coefficients for the additive direct genetic effect were estimated for each animal using single-step genomic BLUP, based on 2 random regression models: one considering MY, FY, and PY in the first 3 lactations and the other considering SCS in the first 3 lactations. Thereafter, SNP solutions were obtained for random regression coefficients, which were used to estimate the SNP effects over time (from 5 to 305 d in lactation). The top 1% of SNP that showed a high magnitude of SNP effect in at least 1 d in lactation were selected as relevant SNP for further analyses of candidate genes, and clustered according to the trajectory of their SNP effects over time. The majority of SNP selected for MY, FY, and PY increased the magnitude of their effects over time, for all breeds. In contrast, for SCS, most selected SNP decreased the magnitude of their effects over time, especially for the Holstein and Jersey breeds. In general, we identified a different set of candidate genes for each breed, and similar genes were found across different lactations for the same trait in the same breed. For some of the candidate genes, the suggested pattern of phenotypic effect changed among lactations. Among the lactations, candidate genes (and their suggested phenotypic effect over time) identified for the second and third lactations were more similar to each other than for the first lactation. Well-known candidate genes with major effects on milk production traits presented different suggested patterns of phenotypic effect across breeds, traits, and lactations in which they were identified. The candidate genes identified in this study can be used as target genes in studies of gene expression.

Genome-wide association for milk production traits and somatic cell score in different lactation stages of Ayrshire, Holstein, and Jersey dairy cattle.

We performed genome-wide association analyses for milk, fat, and protein yields and somatic cell score based on lactation stages in the first 3 parities of Canadian Ayrshire, Holstein, and Jersey cattle. The genome-wide association analyses were performed considering 3 different lactation stages for each trait and parity: from 5 to 95, from 96 to 215, and from 216 to 305 d in milk. Effects of single nucleotide polymorphisms (SNP) for each lactation stage, trait, parity, and breed were estimated by back-solving the direct breeding values estimated using the genomic best linear unbiased predictor and single-trait random regression test-day models containing only the fixed population average curve and the random genomic curves. To identify important genomic regions related to the analyzed lactation stages, traits, parities and breeds, moving windows (SNP-by-SNP) of 20 adjacent SNP explaining more than 0.30% of total genetic variance were selected for further analyses of candidate genes. A lower number of genomic windows with a relatively higher proportion of the explained genetic variance was found in the Holstein breed compared with the Ayrshire and Jersey breeds. Genomic regions associated with the analyzed traits were located on 12, 8, and 15 chromosomes for the Ayrshire, Holstein, and Jersey breeds, respectively. Especially for the Holstein breed, many of the identified candidate genes supported previous reports in the literature. However, well-known genes with major effects on milk production traits (e.g., diacylglycerol O-acyltransferase 1) showed contrasting results among lactation stages, traits, and parities of different breeds. Therefore, our results suggest evidence of differential sets of candidate genes underlying the phenotypic expression of the analyzed traits across breeds, parities, and lactation stages. Further functional studies are needed to validate our findings in independent populations.

Invited review: Determination of large-scale individual dry matter intake phenotypes in dairy cattle.

Feed efficiency has been widely studied in many areas of dairy science and is currently seeing renewed interest in the field of breeding and genetics. A critical part of determining how efficiently an animal utilizes feed is accurately measuring individual dry matter (DM) intake. Currently, multiple methods are used to measure feed intake or determine the DM content of that feed, resulting in different levels of accuracy of measurement. Furthermore, the scale at which data need to be collected for use in genetic analyses makes some methodologies impractical. This systematic review aims to provide an overview of the current methodologies used to measure both feed intake in ruminants and DM content of feedstuffs, current methods to predict individual DM intake, and applications of large-scale intake measurements. Overall, advances in milk spectral data analysis present a promising method of estimating individual DM intake on a herd scale with further validation of prediction models. Although measurements of individual feed intake rely on the same underlying principle, the methods selected are largely dictated by the costs of capital, labor, and necessary analyses. Finally, DM methodologies were synthesized into a comprehensive protocol for use in a variety of feedstuffs.

Effect of ethylenediaminetetraacetic acid irrigation on immune-inflammatory response in teeth submitted to regenerative endodontic therapy.

AIM: To analyse longitudinally the immune-inflammatory response in teeth of mice that underwent a regenerative protocol with or without the use of ethylenediaminetetraacetic acid (EDTA) to irrigate the root canal system. METHODOLOGY: First maxillary molars of mice were devitalized using size 10 and 15 files. Teeth were divided into the following groups: Empty - the canals were left empty; Blood Clot (BC) - the canals were filled with a blood clot; and EDTA + Blood - the canals were irrigated with 0.06 mL of 17% EDTA for 1 min and filled with a blood clot. Access cavities were restored with Coltosol® . Animals were sacrificed at 7, 14 or 21 days after the operative procedures, and teeth were collected. RNA was extracted, mRNA expression of the cytokines IGF, NGF, IL-1α, IL-10, TGF and VEGF was assessed using real-time PCR, and the anova Kruskal-Wallis test was used. RESULTS: IL-1 mRNA expression was significantly higher in the EDTA + BC group than in the Empty and BC groups at the 7th and 14th days of evaluation (P < 0.05). IL-10 mRNA expression was similar across the three groups at all time periods. TGF-ß mRNA expression in the EDTA + BC group was significantly higher on the 7th and 21st days than on the 14th (P < 0.05); at day 21, TGF-ß mRNA expression was similar between the BC and EDTA + BC groups but significantly higher than in the Empty group (P < 0.05). IGF mRNA expression was significantly higher in the EDTA + BC group than in the other groups at all time periods. VEGF mRNA expression remained unchanged throughout the experimental period in all groups (P > 0.05). NGF mRNA expression was similar amongst all groups at the 7th and 21st days (P > 0.05). At the 14th day, however, there was a significant increase in NGF mRNA expression in the EDTA + Blood group (P < 0.05) when compared with the expression in the other groups. CONCLUSION: EDTA promoted increased expression of factors that have the potential to improve the outcome of regenerative endodontic treatment.