Journal of Animal Breeding and Genetics最新文献

As the swine industry continues to explore pork quality traits alongside growth, feed efficiency and carcass leanness traits, it becomes imperative to understand their underlying genetic relationships. Due to this increase in the number of desirable traits, animal breeders must also consider methods to efficiently perform direct genetic changes for each trait and evaluate alternative selection indexes with different sets of phenotypic measurements. Principal component analysis (PCA) and genome-wide association studies (GWAS) can be combined to understand the genetic architecture and biological mechanisms by defining biological types (biotypes) that relate these valuable traits. Therefore, the main objectives of this study were to: (1) estimate genomic-based genetic parameters; (2) define animal biotypes utilizing PCA; and (3) utilize GWAS to link the biotypes to candidate genes and quantitative trait loci (QTL). The phenotypic dataset included 2583 phenotypic records from female Duroc pigs from a terminal sire line. The pedigree file contained 193,764 animals and the genotype file included 21,309 animals with 35,651 single nucleotide polymorphisms (SNPs). Eight principal components (PCs), accounting for a total of 99.7% of the population variation, were defined for three growth, eight conventional carcass, 10 pork quality and 18 novel carcass traits. The eight biotypes defined from the PCs were found to be related to growth rate, maturity, meat quality and body structure, which were then related to candidate genes. Of the 175 candidate genes found, six of them [LDHA (SSC1), PIK3C3 (SSC6), PRKAG3 (SSC15), VRTN (SSC7), DLST (SSC7) and PAPPA (SSC1)] related to four PCs were found to be associated with previously defined QTL, linking the biotypes with biological processes involved with muscle growth, fat deposition, glycogen levels and skeletal development. Further functional analyses helped to make connections between biotypes, relating them through common KEGG pathways and gene ontology (GO) terms. These findings contribute to a better understanding of the genetic relationships between growth, carcass and meat quality traits in Duroc pigs, enabling breeders to better understand the biological mechanisms underlying the phenotypic expression of these traits.

Crossbreeding plays a pivotal role within pig breeding programmes, aiming to maximize heterosis and improve reproductive traits in crossbred maternal lines. Nevertheless, there is evidence indicating that the performance of reciprocal crosses between two genetic lines might exhibit variability. These variations in performance can be attributed to differences in the correlations between gametic effects, acting as either sire or dam, within purebred and crossbred populations. To address this issue, we propose a multivariate gametic model that incorporates up to four correlated gametic effects for each parental population. The model is employed on a data set comprising litter size data (total number of piglets born—TNB- and number of piglets born alive—NBA-) derived from a reciprocal cross involving two Iberian pig populations: Entrepelado and Retinto. The data set comprises 6933 records from 1564 purebred Entrepelado (EE) sows, 4995 records from 1015 Entrepelado × Retinto (ER) crosses, 2977 records from 756 Retinto × Entrepelado (RE) crosses and 7497 records from 1577 purebred Retinto (RR) sows. The data set is further supplemented by a pedigree encompassing 6007 individual-sire-dam entries. The statistical model also included the order of parity (with six levels), the breed of the service sire (five levels) and the herd-year-season effects (141 levels). Additionally, the model integrates random dominant and permanent environmental sow effects. The analysis employed a Bayesian approach, and the results revealed all the posterior estimates of the gametic correlations to be positive. The range of the posterior mean estimates of the correlations varied across different gametic effects and traits, with a range between 0.04 (gametic correlation between the paternal effects for purebred and the maternal for crossbred in Retinto) and 0.53 (gametic correlation between the paternal effects for purebred and the paternal for crossbred in Entrepelado). Furthermore, the posterior mean variance estimates of the maternal gametic effects were consistently surpassed those for paternal effects within all four populations. The results suggest the possible influence of imprinting effects on the genetic control of litter size, and underscore the importance of incorporating crossbred data into the breeding value predictions for purebred individuals.

The main goal of the Belgian Warmblood horse studbook (BWP) is to breed successful competition horses, with emphasis on show jumping. However, competition results are only available later in life and competition traits are lowly heritable. Hence, the use of phenotypes that record performance-related traits at an early life stage could help increase genetic progress. In this study, we evaluated the potential of eleven linear scored early life jumping traits assessed during jumping in freedom (2–5 years old) or under the saddle (4–6 years old) as proxies for later success in show jumping competitions. To this end, we estimated their heritabilities and genetic correlations with the competition trait, adjusted fence height, by using 2170 free jumping records, 1588 jumping under saddle records, 674,527 show jumping competition records and almost 81,000 informative horses in the pedigree. As participation of young horses in these contests is on a voluntary basis, a pre-selection most probably exists. To verify this hypothesis, we investigated the association between participation to young horse contests and participation to show jumping competitions later on (called here start status phenotype). We also estimated heritabilities for “start status in free jumping contest”, “start status in jumping under saddle contest” and “start status in free jumping or jumping under saddle contest” by fitting threshold models. Furthermore, we calculated genetic correlations between these traits and adjusted fence height and calculated the correlations between EBVs for start status in young horse contests and EBVs for success in competitions. Estimated heritabilities of early life jumping traits ranged between 0.05 and 0.30. Their genetic correlations with adjusted fence height were moderate to high (r_g = 0.37–0.63). Relatively more horses that participated in young horse contests competed later on compared to horses that did not participate in young horse contests (p-value < 0.001). They were also significantly more successful in show jumping competitions. Furthermore, start status in young horse contests was moderately heritable in BWP horses (h² = 0.56–0.65) and moderately to highly correlated with later success in competitions (r_g = 0.30–0.77). Hence, we showed that ELJ traits are good proxies for later success in competitions and that a pre-selection of horses occurs in young horse contests. It is suggested to stimulate participation to young horse contests to achieve a more representative sample of the population. Early life jumping traits can therefore optimize the genetic progress for show jumping performance.

Body composition traits are complex traits controlled by minor genes and, in hybrid populations, are impacted by additive and nonadditive effects. We aimed to identify candidate genes and increase the accuracy of genomic prediction of body composition traits in crossbred pigs by including dominance genetic effects. Genomic selection (GS) and genome-wide association studies were performed on seven body composition traits in 807 Yunong-black pigs using additive genomic models (AM) and additive-dominance genomic models (ADM) with an imputed high-density single nucleotide polymorphism (SNP) array and the Illumina Porcine SNP50 BeadChip. The results revealed that the additive heritabilities estimated for AM and ADM using the 50 K SNP data ranged from 0.20 to 0.34 and 0.11 to 0.30, respectively. However, the ranges of additive heritability for AM and ADM in the imputed data ranged from 0.20 to 0.36 and 0.12 to 0.30, respectively. The dominance variance accounted for 23% and 27% of the total variance for the 50 K and imputed data, respectively. The accuracy of genomic prediction improved by 5% on average for 50 K and imputed data when dominance effect were considered. Without the dominance effect, the accuracies for 50 K and imputed data were 0.35 and 0.38, respectively, and 0.41 and 0.43, respectively, upon considering it. A total of 12 significant SNP and 16 genomic regions were identified in the AM, and 14 significant SNP and 21 genomic regions were identified in the ADM for both the 50 K and imputed data. There were five overlapping SNP in the 50 K and imputed data. In the AM, a significant SNP (CNC10041568) was found in both body length and backfat thickness traits, which was in the PLAG1 gene strongly and significantly associated with body length and backfat thickness in pigs. Moreover, a significant SNP (CNC10031356) with a heterozygous dominant genotype was present in the ADM. Furthermore, several functionally related genes were associated with body composition traits, including MOS, RPS20, LYN, TGS1, TMEM68, XKR4, SEMA4D and ARNT2. These findings provide insights into molecular markers and GS breeding for the Yunong-black pigs.

Gestation length (GL) can potentially affect health and performance of both the dam and the newborn calf, and it is controlled by two genetic components, direct and maternal. This means that both the calf (direct effect) and the cow (maternal effect) genotypes contribute to determine GL and its variability. The aims of the present study were to estimate direct and maternal variance components of GL, develop a routine genetic evaluation of GL in Italian Holstein and evaluate potential (un)favourable associations with traits for which selection is undertaken in this population. A multiple-trait repeatability linear animal model was employed for the estimation of variance components considering GL in first and later parities as different traits. The posterior mean (PM) of heritability of the direct effect was 0.43 for first parity and 0.35 for later parities. The PM of heritability of the maternal effect was lower, being 0.08 for primiparae and 0.06 for pluriparae. The posterior standard deviation (PSD) of the heritability estimates was small, ranging from 0.001 to 0.005. The relationship of direct and maternal effects with important traits such as milk yield and fertility indicated that selecting for extreme GL, longer or shorter, may have negative consequences on several traits, suggesting that GL has an intermediate optimum in dairy cattle. In conclusion, this study reveals that selecting an intermediate GL in the Italian Holstein population is advisable. Although scarcely variable compared to other conventional traits for which Italian Holstein is selected, GL is heritable and a deeper knowledge can be useful for decision-making at the farm level.

Celtic-Iberian pig breeds were majority in Spain and Portugal until the first half of the 20th century. In the 1990s, they were nearly extinct as a result of the introduction of foreign improved pig breeds. Despite its historical importance, the genetic background of the Celtic-Iberian pig strain is poorly documented. In this study, we have identified genomic regions that might contain signatures of selection peculiar of the Celtic-Iberian genetic lineage. A total of 153 DNA samples of Celtic-Iberian pigs (Spanish Gochu Asturcelta and Portuguese Bísara breeds), Iberian pigs (Spanish Iberian and Portuguese Alentejano breeds), Cinta Senese pig, Korean local pig and Cosmopolitan pig (Hampshire, Landrace and Large White individuals) were analysed. A pairwise-comparison approach was applied: the Gochu Asturcelta and the Bísara samples as test populations and the five other pig populations as reference populations. Three different statistics (XP-EHH, F_ST and ΔDAF) were computed on each comparison. Strict criteria were used to identify selection sweeps in order to reduce the noise brought on by the Gochu Asturcelta and Bísara breeds' severe population bottlenecks. Within test population, SNPs used to construct potential candidate genomic areas under selection were only considered if they were identified in four of ten two-by-two pairwise comparisons and in at least two of three statistics. Genomic regions under selection constructed within test population were subsequently overlapped to construct candidate regions under selection putatively unique to the Celtic-Iberian pig strain. These genomic regions were finally used for enrichment analyses. A total of 39 candidate regions, mainly located on SSC5 and SSC9 and covering 3130.5 kb, were identified and could be considered representative of the ancient genomic background of the Celtic-Iberian strain. Enrichment analysis allowed to identify a total of seven candidate genes (NOL12, LGALS1, PDXP, SH3BP1, GGA1, WIF1, and LYPD6). Other studies reported that the WIF1 gene is associated with ear size, one of the characteristic traits of the Celtic-Iberian pig strain. The function of the other candidate genes could be related to reproduction, adaptation and immunity traits, indirectly fitting with the rusticity of a non-improved pig strain traditionally exploited under semi-extensive conditions.

Stayability (STAY) is a way to evaluate the productive longevity of females. Measuring the STAY at each cow calving allows earlier indicators of longevity to be obtained. Our objective with this study was to verify the association between STAY and consecutive calvings and traits potentially used as selection criteria in beef cattle, such as age at first calving (AFC), days to calving (DC), weaning weight (WW), and yearling weight (YW). Data from the Nelore, Angus/Brangus, and Hereford/Braford breeds were used. The estimation of variance components and subsequent prediction of breeding values were performed for all traits. The estimated breeding values (EBV) were used to analyse the association between STAY and the other traits. The Pearson's correlation estimated between the EBV for the intercept coefficient for STAY to consecutive calvings and those of AFC, DC, WW (direct and maternal effects), and YW was favourable and of low magnitude (<0.25) depending on the breed studied. The influence of the genetic merit of AFC on the chance of selection for STAY was favourable and relevant regardless of the intensity of selection and breed. DC and WW (maternal effect) traits were favourably influenced by the chance of selection for STAY, irrespective of breed. The WW (direct effect) did not affect the chance of selection for STAY for the Nelore and Hereford/Braford breeds and negatively influenced, but to a small extent, the Angus/Brangus breed. For YW, an increase in genetic merit affected the chances of selection for STAY, depending on the breed and selection intensity evaluated. The influence of the genetic merit for AFC, DC, and WW (maternal effect) on the chance of selection for STAY to consecutive calvings was favourable and relevant regardless of the selection intensity scenario evaluated. The WW (direct effect) did not influence the chance of selection for STAY. For the scenario with high selection intensity, the selection for YW favourably influenced the chance of selection for STAY in Angus/Brangus and Hereford/Braford breeds but not in Nelore.

Genomic selection (GS) has become common in sheep breeding programmes in Australia, New Zealand, France and Ireland but requires validation in South Africa (SA). This study aimed to compare the predictive ability, bias and dispersion of pedigree BLUP (ABLUP) and single-step genomic BLUP (ssGBLUP) for production and reproduction traits in South African Merinos. Animals in this study originated from five research and five commercial Merino flocks and included between 54,072 and 79,100 production records for weaning weight (WW), yearling weight (YW), fibre diameter (FD), clean fleece weight (CFW) and staple length (SL). For reproduction traits, the dataset included 58,744 repeated records from 17,268 ewes for the number of lambs born (NLB), number of lambs weaned (NLW) and the total weight weaned (TWW). The single-step relationship matrix, H, was calculated using PreGS90 software combining 2811 animals with medium density (~50 K) genotypes and the pedigree of 88,600 animals. Assessment was based on single-trait analysis using ASREML V4.2 software. The accuracy of prediction was evaluated according to the ‘LR-method’ by a cross-validation design. Validation candidates were assigned according to Scenario I: born after a certain time point; and Scenario II: born in a particular flock. In Scenario I, the genotyping rate for the reference population between 2006 and the 2013 cut-off point approached 7% of animals with phenotypes and 20% of their sires. For reproduction traits, about 20% of ewes born between 2006 and 2011 cut-off were genotyped, along with 15% of their sires. Genotyping rates in the validation set of Scenario I were 3.7% (production) and 11.4% (reproduction) for candidates and 35% of their sires. Sires were allowed to have progeny in both the reference and validation set. In Scenario I, ssGBLUP increased the accuracy of prediction for all traits except NLB, ranging between +8% (0.62–0.67) for FD and +44% (0.36–0.52) for WW. This showed a promising gain in accuracy despite a modestly sized reference population. In the ‘across flock validation’ (Scenario II), overall accuracy was lower, but with greater differences between ABLUP and ssGBLUP ranging between +17% (0.12–0.14) for TWW and +117% (0.18–0.39) for WW. There was little indication of severe bias, but some traits were prone to over dispersion and the use of genomic information did not improve this. These results were the first to validate the benefit of genomic information in South African Merinos. However, because production traits are moderately heritable and easy to measure at an early age, future research should be aimed at best exploiting GS methods towards genetic prediction of sex-limited and/or lowly heritable traits such as NLW. GS methods should be combined with dedicated efforts to increase genetic links between sectors and improved phenotyping by commercial flocks.

Herd health is one of the key problems influencing the efficiency of the dairy industry. Genetic selection, with a focus on animal health, is important for herd improvement. This study aimed to estimate genetic parameters for health traits and their correlations with fertility and milk production traits in dairy cattle. Based on records from 58,549 lactating cows calved between 2015 and 2021, a total of 24 health traits (six composite health traits and 18 independent health traits), four fertility traits and five milk production traits were analysed. First, linear and threshold animal models were used to estimate the variance components and heritabilities of the health traits. Second, a bivariate linear animal model was used to estimate genetic correlations among all 24 health traits. Finally, a bivariate linear animal model based on records from the first lactation was used to estimate the correlations between health traits and fertility or milk production traits. The results showed that all health traits had low heritabilities, ranging from 0.002 (0.001) to 0.048 (0.004) in the linear model and from <0.001 (0.021) to 0.226 (0.035) in the threshold model. Genetic correlations between health traits across categories were generally low, whereas the relatively high genetic correlations were found between health traits within the same category. In this study, only a few significant and moderate genetic correlations were observed between health traits and fertility or milk production traits. Clinical mastitis showed relatively moderate correlations with fertility traits, ranging from 0.277 (0.113) (interval from first to last insemination) to 0.401 (0.104) (calving interval). Moreover, there were moderate genetic correlations between hoof health and milk production traits. The results from the current study will support balanced dairy breeding to genetically improve disease resistance in dairy cows.

Multiple trait animal models (MTM) allow to estimate the breeding values (BV) of several traits simultaneously while accounting for genetic and environmental correlations among them. However, relationships among traits may not be reciprocal but rather causal in nature. In these cases, and given a causal network, structural equations models (SEM) arise as a more appropriate methodology. Although MTM and SEM have been shown to be parametrically equivalent, the estimated breeding value (EBV) obtained from either one or the other should be interpreted differently. In this study, we investigated the impact of using these estimates on the response to selection for a causal network comprising five different traits through a stochastic simulation experiment. Three different selection targets were assayed, involving traits located upstream, midstream and downstream this causal network. We first considered the case in which traits were causally related but not genetically correlated. The current results support our hypothesis that MTM will absorb causal relationships as genetic correlations and, consequently, change the response to selection achieved as compared with SEM. We found no differences on the response to selection when the target trait was located at the top of the causal network, but noticeable differences were detected on upstream traits when selection pressure was placed on midstream or downstream traits. We also assayed a scenario in which causal effects and genetic correlations act simultaneously and found that selection based on BVs estimated using SEM diminished the indirect response in traits upstream the causal network.