Journal of Animal Breeding and Genetics最新文献

The study design and analysis presented in the paper was extensively based previous work of Sae-lim et al. (2012) and this was not acknowledged in the paper. Therefore, the following comment should be added: ‘The study design and analysis were based on the previous work of Sae-Lim and co-workers (Sae-Lim et al., 2012)’.

Sae-Lim, P., Komen, H., Kause, A., Van Arendonk, J., Barfoot, A., Martin, K., & Parsons, J. (2012). Defining desired genetic gains for rainbow trout breeding objective using analytic hierarchy process. Journal of Animal Science, 90(6), 1766–1776.

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Commercial livestock producers need to prioritize genetic progress for health and efficiency traits to address productivity, welfare, and environmental concerns but face challenges due to limited pedigree information in extensive multi-sire breeding scenarios. Utilizing pooled DNA for genotyping and integrating seminal microbiome information into genomic models could enhance predictions of male fertility traits, thus addressing complexities in reproductive performance and inbreeding effects. Using the Angus Australia database comprising genotypes and pedigree data for 78,555 animals, we simulated percentage of normal sperm (PNS) and prolificacy of sires, resulting in 713 sires and 27,557 progeny in the final dataset. Publicly available microbiome data from 45 bulls was used to simulate data for the 713 sires. By incorporating both genomic and microbiome information our models were able to explain a larger proportion of phenotypic variation in both PNS (0.94) and prolificacy (0.56) compared to models using a single data source (e.g., 0.36 and 0.41, respectively, using only genomic information). Additionally, models containing both genomic and microbiome data revealed larger phenotypic differences between animals in the top and bottom quartile of predictions, indicating potential for improved productivity and sustainability in livestock farming systems. Inbreeding depression was observed to affect fertility traits, which makes the incorporation of microbiome information on the prediction of fertility traits even more actionable. Crucially, our inferences demonstrate the potential of the semen microbiome to contribute to the improvement of fertility traits in cattle and pave the way for the development of targeted microbiome interventions to improve reproductive performance in livestock.

The strategy of combining reference populations has been widely recognized as an effective way to enhance the accuracy of genomic prediction (GP). This study investigated the efficiency of genomic prediction using prior information and combined reference population. In total, prior information considering trait-associated single nucleotide polymorphisms (SNPs) obtained from meta-analysis of genome-wide association studies (GWAS meta-analysis) was incorporated into three models to assess the performance of GP using combined reference populations. Two different Yorkshire populations with imputed whole genome sequence (WGS) data (9,741,620 SNPs), named as P1 (1259 individuals) and P2 (1018 individuals), were used to predict genomic estimated breeding values for three live carcass traits, including backfat thickness, loin muscle area, and loin muscle depth. A 10 × 5 fold cross-validation was used to evaluate the prediction accuracy of 203 randomly selected candidate pigs from the P2 population and the reference population consisted of the remaining pigs from P2 and the stepwise added pigs from P1. By integrating SNPs with different p-value thresholds from GWAS meta-analysis downloaded from PigGTEx Project, the prediction accuracy of GBLUP, genomic feature BLUP (GFBLUP) and GBLUP given genetic architecture (BLUP|GA) were compared. Moreover, we explored effects of reference population size and heritability enrichment of genomic features on the prediction accuracy improvement of GFBLUP and BLUP|GA relative to GBLUP. The prediction accuracy of GBLUP using all WGS markers showed average improvement of 4.380% using the P1 + P2 reference population compared with the P2 reference population. Using the combined reference population, GFBLUP and BLUP|GA yielded 6.179% and 5.525% higher accuracies than GBLUP using all SNPs based on the single reference population, respectively. Positive regression coefficients were estimated in relation to the improvement in prediction accuracy (between GFBLUP/BLUP|GA and GBLUP) and the size of the reference as well as the heritability enrichment of genomic features. Compared to the classic GBLUP model, GFBLUP and BLUP|GA models integrating GWAS meta-analysis information increase the prediction accuracy and using combined populations with enlarged reference population size further enhances prediction accuracy of the two approaches. The heritability enrichment of genomic features can be used as an indicator to reflect weather prior information is accurately presented.

Regular monitoring of body condition score (BCS) changes during lactation is a crucial management tool in dairy cattle; however, the current BCS measurements are often discontinuous and unevenly spaced in time. The aim of this study was to investigate the ability of random regression test-day models (RR-TDM) to predict BCS for the entire lactation in dairy cows even if the actual scoring is limited to one BCS record. The data consisted of test-day records of milk yield (MY), fat percentage (FP), protein percentage (PP) and BCS (based on a 9-point scale with unit increments; 1–9) collected from 2014 to 2022 in 128 herds in the Walloon Region of Belgium. In total, 20,698 test-day records on 2166 first-parity Holstein cows (2–12 with an average of 9.42 test-day records per cow) were available for MY, FP and PP; and 7985 records on the same animals (2–12 with an average of 3.68 records per cow) were available for BCS. To estimate the solutions, only one randomly selected BCS record per animal along with all her MY, FP and PP records were used, which were then used to predict BCS data (calibration set). The remaining BCS (1–11 with an average 2.86 BCS records per animal) were used to evaluate the goodness of the predictions (validation set). Multiple-trait RR-TDM was used to estimate (co)variance components through the average information restricted maximum likelihood (AI-REML) algorithm. Predicted BCS were grouped into nine classes as the original observed BCS used for comparison. Pearson correlation between the predicted and observed BCS, prediction error (PE), absolute prediction error (APE) and root mean squared prediction error (RMSE) were calculated. Mean (standard deviation; SD) BCS was 4.97 (1.01), 4.95 (1.07) and 4.98 (1.00) BCS units in the full, calibration and validation datasets, respectively. Pearson correlation between the observed and predicted BCS was 0.71, mean (SD) PE was 0.04 (0.52) BCS units, mean (SD) APE was 0.48 (0.53) BCS units and RMSE was 0.72 BCS units. These findings demonstrate the ability of RR-TDM to predict BCS for the entire lactation using a single BCS record along with available test-day records of milk yield and composition in Holstein dairy cows.

Although Genome Wide Analysis (GWAS) have been widely used to understand the genetic architecture of complex quantitative traits, interpreting their results in terms of the biological processes that determine those traits has been difficult or even lacking, because of the variability in responses to the tests of hypotheses within a trait, species, and breed or cross, and the lack of follow-up studies. It is then essential employing appropriate statistical tests that point out to the causal genes responsible of the relevant fraction of the genetic variability observed. We briefly review the main theoretical aspects of the two schools of causal inference (Rubin's Causal Model, RCM, and Pearl's causal inference, PCI). RCM approachs the hypothesis testing from a randomization perspective by considering a wider space of the observation, i.e. the “potential outcomes”, rather than the narrower space that results from defining “treatment” effects after observing the data. Next, we discuss the assumptions involved to meet the requirements of randomization for RCM with observational data (non-designed experiments) with special emphasis on the Stable Unit Treatment Analysis (SUTVA). Due to the presence of “confounders” (i.e. systematic fixed effects, environmental permanent effects, interaction among genes, etc.), causal average treatment effects are viewed through the familiar lens of normal linear (or mixed) models. To overcome the difficulties of association analyses, a tests of causal effects is introduced using independent predicted residual breeding values from animal models of genetic evaluation that avoids the effects of population structure and confounder effects. An independent section discusses the issue of whether the additive effects defined at the “gene” level by R. A. Fisher and popularized in D. S. Falconer's textbook of quantitative genetics can be termed causal from either RCM or PCI.

Mitochondria are essential organelles in the regulation of cellular energetic metabolism. Mitochondrial DNA copy number (mtDNA_CN) can be used as a proxy for mitochondria number, size, and activity. The aims of our study are to evaluate the effect of mtDNA_CN and mitochondrial haploblocks on production traits in pigs, and to identify the genetic background of this cellular phenotype. We collected performance data of 234 pigs and extracted DNA from skeletal muscle. Whole-genome sequencing data was used to determine mtDNA_CN. We found positive correlations of muscle mtDNA_CN with backfat thickness at 207 d (+0.14; p-value = 0.07) and negative correlations with carcase loin thickness (−0.14; p-value = 0.03). Pigs with mtDNA_CN values below the lower quartile had greater loin thickness (+4.1 mm; p-value = 0.01) and lower backfat thickness (−1.1 mm; p-value = 0.08), which resulted in greater carcase lean percentage (+2.4%; p-value = 0.04), than pigs with mtDNA_CN values above the upper quartile. These results support the hypothesis that a reduction of mitochondrial activity is associated with greater feed efficiency. Higher mtDNA_CN was also positively correlated with higher meat ultimate pH (+0.19; p-value <0.01) but we did not observe significant difference for meat ultimate pH between the two groups with extreme mtDNA_CN. We found no association of the most frequent mitochondrial haploblocks with mtDNA_CN or the production traits, but several genomic regions that harbour potential candidate genes with functions related to mitochondrial biogenesis and homeostasis were associated with mtDNA_CN. These regions provide new insights into the genetic background of this cellular phenotype but it is still uncertain if such associations translate into noticeable effects on the production traits.

This study aimed to investigate functional variants in chromosome 14 (BTA14) and its impact in genomic selection for birth weight (BW), weaning weight (WW), and yearling weight (YW) in Nellore cattle. Genetic parameter estimation and the weighted single-step genomic best linear unbiased prediction (WssGBLUP) analyses were performed. Direct additive heritability estimates were high for WW and YW, and moderate for BW. Trait-associated variants distributed across multiple regions on BTA14 were observed in the weighted single-step genome-wide association studies (WssGWAS) results, implying a polygenic genetic architecture for weight in different ages. Several genes have been found in association with the weight traits, including the CUB And Sushi multiple domains 3 (CSMD3), thyroglobulin (TG), and diacylglycerol O-acyltransferase 1 (DGAT1) genes. The variance explained per SNP was higher in six functional classes of gene regulatory regions (5UTR, CpG islands, downstream, upstream, long non-coding RNA, and transcription factor binding sites (TFBS)), highlighting their importance for weight traits in Nellore cattle. A marginal increase in accuracy was observed when the selected functional variants (SV) information was considered in the WssGBLUP method, probably because of the small number of SV available on BTA14. The identified genes, pathways, and functions contribute to a better understanding of the genetic and physiological mechanisms regulating weight traits in the Nellore breed.

The objective of the study was to estimate genetic parameters of the growth traits under Bayesian inference in Harnali sheep. The information of pedigree and targeted traits of 2404 Harnali animals born to 159 sires and 695 dams was collected for the period from 1998 to 2021. The growth traits included weight at birth (BWT), 3 (WWT), 6 (6WT) and 12 (YWT) months of age. The genetic evaluation was carried out using six univariate animal models comprising direct and maternal effects using THRGIBBS1F90 and POSTGIBBSF90 programs. The fixed factors adjusted in the analysis were period of birth, sex of lamb and dam's weight at lambing. Bayesian estimates of direct heritability under best model for BWT, WWT, 6WT and YWT traits were 0.16 ± 0.04, 0.10 ± 0.04, 0.18 ± 0.04, and 0.05 ± 0.03, respectively. The significant maternal influences observed for BWT and WWT traits with 9% and 8% contribution to total phenotypic variances, respectively. Additionally, maternal permanent environmental influences were observed to BWT (4%) and YWT trait (3%). The genetic and phenotypic correlations among studied traits were high and positive. The genetic changes were positive and significant for WWT only. It was concluded that the weight at 6 months of age can be continued as selection criterion for further genetic improvement through selection. Also, maternal effects should be considered in breeding programme for enhancing early growth performance in Harnali sheep.

Genetic, environmental, technological and financial resources are used differently in cattle herds that participate in the same breeding programme. The percentages of calves sired by sires within their own herd or from external herds vary across herds, as do the intensities of use of reproductive biotechnologies. These divergences may be related to differences in the indicators of genetic performance for economic traits. The aim of this study was to determine the factors related to herd structure and genetic resource utilization that exert the greatest influence on the genetic merit of seedstock herds within a Nellore breeding programme. The database comprised 21 factors, along with genomic-enhanced expected progeny differences (GE-EPDs) for growth, reproductive and carcass traits, as well as a selection index of animals from 128 herds. By combining principal component analysis and cluster analysis, we were able to group the herds. We identified statistically significant differences (p < 0.05) in the mean values of the factors, GE-EPDs and genetic trends among the groups of herds. Differences in the percentage of sires from external herds and in sire age between the groups of herds were the factors most associated with differences in mean GE-EPDs and genetic trends. Using young sires from other herds or lineages is an effective strategy in animal breeding. By enhancing genetic variability, this approach does not only improve the genetic quality of herds but also accelerates genetic progress in desired traits over time. Therefore, to ensure the success of this strategy, it is crucial that seedstock herds undergo a thorough selection process aimed at maximizing the genetic potential of future generations of beef cattle.

This study investigated the impact of temperature and humidity on milk production traits in Tunisian dairy cows, analysing population-level trends and individual cow responses using various modelling techniques and heat stress (HS) indices. Two distinct datasets were used for this purpose: the first included 551,139; 331,654 and 302,396 test-day records for milk, fat and protein yields, respectively. The second supplemented the production information with daily average (THIavg) and maximum (THImax) temperature-humidity index (THI) data. Three main parts of analyses were conducted simultaneously: classical least squares, identification of HS thresholds and associated production losses and assessment of individual cow responses using random regression models (RRM) fitting various continuous functions that include/exclude individual effects. The best model, determined by goodness-of-fit measurements, was a cubic polynomial function that accounted for individual variation and THIavg as a heat load measure. HS thresholds were established at THIavg/THImax of 70/74 for milk yield, 50/55 for fat percentage, 59/66 for protein percentage, 54/63 for fat yield and 56/66 for protein yield. According to the fitted polynomial models, daily milk production traits showed a curvilinear decline with accelerated loss rates beyond the established thermal thresholds. However, for all models and thermal indices, maximum daily production losses remained below 164 g/day, 4.4 g/day and 6.1 g/day for milk, fat and protein yields, respectively. Despite these losses, the relatively high thermal thresholds and lower associated production losses suggest that Tunisian dairy cows can tolerate high heat loads. Moreover, observed variations in response patterns indicate potential for selecting heat-tolerant individuals within this population.