Journal of Animal Breeding and Genetics最新文献

In chickens, economically important traits are commonly controlled by multiple genes and are often correlated. The genetic mechanisms underlying the correlated phenotypes likely involve pleiotropy or linkage disequilibrium, which is not handled properly in single-trait genome-wide association studies (GWAS). We employed factor analytical models to estimate the value of latent traits to reduce the dimensionality of the adjusted phenotypes. The dataset included phenotypes from 369 F2 chickens, categorised into six observable classes, namely body weight (BW), feed intake (FI), feed efficiency (FE), immunity (IMU), blood metabolites (BMB), and carcass (CC) traits. All birds were genotyped using a 60K SNP Beadchip. A Bayesian network (BN) algorithm was used to discern the recursive causal relationships among the inferred latent traits. Multi-Trait (MT) and Structural Equation Model (SEM) were applied for association analysis. Several candidate genes were detected across six phenotypic classes, namely the IPMK gene for BW and FI, and, the MTERF2 gene for BW and FE. The rs14565514 SNP, close to genes IPMK, UBE2D1, and CISD1, was recognised as a pleiotropic marker by both models. The NRG3 gene, located on chromosome 6, was associated with FI. CRISP2, RHAG, CYP2AC1, and CENPQ genes, located on chromosome 3, were detected for BMB through both MT- and SEM-GWAS. In general, the results indicated that the SEM-GWAS is superior to MT-GWAS due to considering the causal relationships among the traits, correcting the effects of the traits on each other, and also leading to the identification of pleiotropic SNP markers.

For developing beef cattle breeding programmes, it is essential to understand the genetic basis of economically relevant traits, such as carcass, meat quality and female sexual precocity. However, the direct selection of most of these traits is a challenge for producers because of the high cost and measurement difficulty. Genetic correlation estimates between carcass and meat quality traits obtained after slaughter and sexual precocity indicator traits in Nellore are limited in the literature. Thus, this study aimed to estimate genetic parameters for longissimus muscle area (LMA), backfat thickness (BF), hot carcass weight (HCW), shear force tenderness (SF), marbling score (MARB), intramuscular fat content (IMF), age at first calving (AFC), heifer pregnancy (HP) and scrotal circumference (SC) in Nellore cattle, using pedigree and genomic information. For this, data from 6910 young bulls with phenotypic information for carcass and meat traits, 230,682 for sexual precocity indicator traits, and 17,850 animals genotyped with or imputed to the Illumina Bovine HD BeadChip were used. The (co)variance components and genetic parameters were estimated considering BLUP and single-step GBLUP (ssGBLUP) models via Bayesian inference using the GIBBSF90+ software. The multi-trait animal model included additive and residual genetic effects as random; the fixed effects of contemporary group (for all traits) and date of analysis as classes (for BF, SF and MARB); and the linear effects of age at slaughter (all carcass and meat traits) and age at yearling (YW and SC) as covariates. Heritability estimates ranged from 0.13 to 0.34 for carcass and meat quality traits, and for SC, AFC and HP, were 0.33, 0.07 and 0.29, respectively. Favourable genetic correlations were estimated between YW-HCW (0.79 ± 0.03), YW-LMA (0.28 ± 0.05), YW-SC (0.35 ± 0.03), HCW-LMA (0.44 ± 0.05), HCW-SF (-0.22 ± 0.09), HCW-SC (0.19 ± 0.05), MARB-IMF (0.90 ± 0.07), SF-IMF (-0.20 ± 0.11), BF-MARB (0.29 ± 0.08), BF-IMF (0.22 ± 0.09), BF-AFC (-0.21 ± 0.07) and BF-HP (0.24 ± 0.10). In general, the correlations between carcass traits and those of meat quality were low to moderate. Additionally, carcass and meat quality traits did not exhibit strong genetic correlations with female precocity indicators. So, to achieve significant genetic advances in female sexual indicator traits, carcass composition and meat quality, these traits must compose selection indices for Nellore cattle.

The current study investigated the application of structural equation models for genetic analysis of lifetime reproductive traits and latent variable modelling in the Murciano-Granadina goat breed. In the current investigation, data collected between 2016 and 2023 in a private dairy farm of the Murciano-Granadina goat breed in Ghale-Ganj city, located in the southern area of Kerman Iranian province were used. The investigated lifetime reproductive traits included overall litter size at birth (OLSB), overall litter size at weaning (OLSW), overall litter weight at birth (OLWB), and overall litter weight at weaning (OLWW). Four multivariate animal models, including standard (SMM), Inductive Causation algorithm-based structural equation (ICM), ICM with biological modification (ICM-BM), and fully recursive (FRM) models were fitted on the data and compared in terms of predictive ability measures including mean squared prediction error (MSE) and Pearson's correlation coefficient between the observed and predicted values (r(y, $\hat{y}$ )) of records. ICM-BM performed better than other models in terms of the lowest MSE and the highest r(y, $\hat{y}$ ). Under ICM-BM, heritability estimates were low values of 0.08, 0.08, 0.11, and 0.10 for OLSB, OLSW, OLWB, and OLWW, respectively. Genetic correlations among lifetime reproductive traits were positive and varied from 0.72 (OLSB-OLWW) to 0.95 (OLSB-OLWB). The confirmatory factor analysis technique was used to construct a latent variable named reproductive performance (RP) from the investigated lifetime reproductive traits. The posterior mean for heritability of RP was estimated at 0.06. The genetic correlations between RP and the investigated lifetime reproductive traits were high and positive, ranging from 0.92 (RP-OLSB) to 0.99 (RP-OLSW). The corresponding phenotypic correlations were also high and positive, ranging from 0.81 (RP-OLWB) to 0.95 (RP-OLSW). Considering causal structure among the traits detected via ICM-BM had more advantages for genetic evaluation of the lifetime reproductive traits in the Murciano-Granadina goat compared with SMM. The low heritability estimates implied that the studied lifetime reproductive traits and RP were mainly controlled by non-additive genetic and environmental effects which limits the efficiency of direct genetic selection for improving these traits. Furthermore, positive genetic and phenotypic correlations favoured using RP latent variable for breeding purposes.

The aims of the present study were to perform single-step genomic predictions in the dual-purpose German Black Pied cattle (DSN) breed considering a DSN specific SNP chip (DSN_200 K), and to use the corresponding estimated breeding values (EBV) in ongoing optimum genetic contribution (OGC) selection. All results were compared with the application of the commercial Illumina BovineSNP50 BeadChip (50 K). The traits of interest in the present study (due to the differing breeding history of these traits in the past) included 305-day lactation protein percentage (Pro%) of 9029 DSN cows, fat-to-protein ratio (FPR) from the first test-day of 8773 DSN cows, and stature (STAT) measured in cm of 4409 DSN cows. The DSN cows represented the calving years 2008-2019. Genotyping of 2797 DSN animals was conducted using both the DSN_200 K and the 50 K. From the genotyped animals, a subset of 1800 cows had phenotypic records for all three traits FPR, Pro% and STAT. Heritabilities from the single-step genetic parameter estimations were quite large for Pro% (0.69) and STAT (0.78), but small for FPR (0.11). The choice of the SNP chip only had minor effects on variance components, heritabilities and EBVs. Furthermore, genetic parameters were very similar from genetic-statistical models additionally considering a linear regression on pedigree-based inbreeding coefficients. OGC selection was applied to a pool of 1125 pre-selected bull sires (BS) and bull dams (BD). A more relaxed genetic relationship constraint was associated with favourable effects on the average EBVs for Pro%, FPR and STAT, and a declining number of selected BS. The gains in genetic merit were marginal when relaxing the constraint at 0.06 for the genetic relationships or higher. The same associations were found for an overall breeding index (I-DSN), considering the three traits with equal weights. Consequently, we suggested OGC applications with a genetic relationship constraint of 0.06, which contributed to genetic gain in I-DSN of 17.9%, and to increased diversity due to an increased number of BS, when compared to the current practical elite animal selection scheme. A large number of finally selected BS and BD was identical when either using EBV from the DSN_200 K or from the 50 K. From such perspective, we only see marginal extra value for the specific DSN SNP-chip application.

The advancement of epigenetics has highlighted DNA methylation as an intermediate-omic influencing gene regulation and phenotypic expression. With emerging technologies enabling the large-scale and affordable capture of methylation data, there is growing interest in integrating this information into genetic evaluation models for animal breeding. This study used methylome information from six dairy cows to simulate the methylation profile of 13,183 genotyped animals. The liability to methylation was treated as an additive trait, while a trait moderated by methylation effects was also simulated. A multiomic model (GOBLUP) was adapted to incorporate methylation data in genomic and genetic evaluations, using the traditional BLUP method as a benchmark. The GOBLUP accurately recovered heritability estimates for the liability to methylation in all low, medium and high heritability scenarios and was consistent at estimating the heritability for the epigenetics-moderated trait of interest at a low-medium heritability of 0.14. The genetic variance recovered by the BLUP model was influenced by the h² of the liability to methylation, and a part of the methylation variance for the phenotypic trait was captured as additive. The h² of the phenotypic trait partially relies on the h² value for the methylation windows in the traditional model. A newly proposed estimated epigenetic value (EEV) combines the traditional additive genetic information from genotyping arrays with epigenetic information. The correlation between the traditional estimated breeding value (EBV) and EEV was high (0.92-0.99 depending on the scenario), but the correlation of the EEV with the true breeding value was higher than the correlation between the traditional EBV and the TBV (0.85 vs. 0.75, 0.71 vs. 0.66 and 0.61 vs. 0.62 depending on the scenario). This study demonstrates that the GOBLUP multiomic recursive model can effectively separates additive and epigenetic variances, enabling improved breeding decisions by accounting for genetic liability to DNA methylation. This enables more informed breeding decisions, optimising selection for desired traits. Emerging sequencing techniques offer new opportunities for cost-effective simultaneous acquisition of genetic and epigenetic data, further enhancing breeding accuracy.

We addressed genomic prediction accounting for partial correlation of marker effects, which entails the estimation of the partial correlation network/graph (PCN) and the precision matrix of an unobservable m-dimensional random variable. To this end, we developed a set of statistical models and methods by extending the canonical model selection problem in Gaussian concentration, and directed acyclic graph models. Our frequentist formulations combined existing methods with the EM algorithm and were termed Glasso-EM, Concord-EM and CSCS-EM, whereas our Bayesian formulations corresponded to hierarchical models termed Bayes G-Sel and Bayes DAG-Sel. We implemented our methods in a real bull fertility dataset and then carried out gene annotation of seven markers having the highest degrees in the estimated PCN. Our findings brought biological evidence supporting the usefulness of identifying genomic regions that are highly connected in the inferred PCN. Moreover, a simulation study showed that some of our methods can accurately recover the PCN (accuracy up to 0.98 using Concord-EM), estimate the precision matrix (Concord-EM yielded the best results) and predict breeding values (the best reliability was 0.85 for a trait with heritability of 0.5 using Glasso-EM).

Social behaviour traits and their impact on feed efficiency are of particular interest in pig farming. The integration of automatic feeders enables the collection of multiple phenotypes for breeding purposes. The additive genetic and social genetic effect can be estimated considering all the visits to the feeder by modelling each visit independently in a 'visit-based approach'. This study aimed to determine the impact of the social genetic effect on individual feed intake and duration per visit in Pietrain pigs and Iberian pigs separately. The dataset comprised 883,906 visits from 1608 Pietrain pigs and 775,054 visits from 856 Iberian pigs. In the Pietrain population, the social genetic effects did not explain a substantial percentage of the phenotypic variance (~1%). In contrast, the Iberian population exhibited more substantial contributions, with social genetic effects accounting for 6.2% of the variance in duration per visit and 5.5% in feed intake per visit. The correlations between additive direct genetic and additive social genetic effects were slightly positive for feed intake across all analyses, and around zero for duration per visit with most of them including the zero in the highest posterior density interval (HPD95%). These weak correlations suggest that both effects could be selected independently. The visit-based approach successfully identified social genetic effects in the studied populations. Models incorporating social genetic effects demonstrated lower residual variance, enhancing the accuracy of additive values and, consequently, the potential for an improved response to selection.

Holstein cattle account for the largest proportion of dairy cattle in China. The current China Performance Index (CPI) consists mainly of production traits. To derive economic values (EV) of additional traits for balanced breeding programs, a bio-economic model is necessary. Landless and intensive dairy farms are dominant in China, wherein all feed is purchased, and in-farm technicians and veterinarians are employed. Therefore, in the present study, a tailored bio-economic model was developed using the parameters of a typical dairy farm in North China. The typical farm had 1500 cows and 1400 youngstock, with a replacement rate of 33.5% per year and a productive life of 1090 days. The bio-economic model was on a per cow per year basis and described the revenues and costs from different animal categories. The EVs of 17 traits, including production, calving, fertility, longevity, and health traits, were derived and used to develop a more balanced selection index. Results showed that the bio-economic model can represent the typical dairy farm system in North China. The EVs of production traits were 2.39 Chinese Yuan (CNY), 32.85 CNY, and 89.60 CNY per kg milk yield, fat yield and protein yield, respectively. The EVs of production traits were two to three times higher than those in some European countries, due to the higher prices on milk volume and milk solids in China. The EVs of health traits ranged from -0.45 CNY to -11.95 CNY and were nearly half of those in other countries, due to the lower in-farm veterinarian labour costs. The EVs of most other functional traits were in line with the published values of other countries with similar economic assumptions in the model. Using the calculated EVs, a more balanced selection index was derived by including functional traits. This index had higher relative weight (46.8%) on functional traits than the current CPI (12.5%). With the high milk prices, it is still most profitable to allow for a decline in functional traits, although the decline is considerably smaller with the developed balanced index than with the CPI. Collectively, the bio-economic model and EVs provided the foundations for implementing balanced breeding programs in the Chinese Holstein population.

Swedish Warmblood horses (SWB) are bred for show jumping and/or dressage with young horse test scores as indicator traits. This study aimed to investigate possible candidate genes and regions of importance for evaluated and linearly scored young horse test traits. A single-step genome-wide association study (ssGWAS) was done using the BLUPF90 suite of programs for factors scores from factor analysis of traits assessed at young horse tests together with height at withers. The ssGWAS included 20,814 SWB with factors scores for four factors for evaluated traits. A total of 6436 of these horses also had factor scores for 13 factors for linearly scored traits. Genotypes from a 670K SNP array were available for 380 of the horses in this study. All genotyped horses had factor scores for evaluated traits, and 379 also had factors scores for linearly scored traits. Significant SNPs associated with three factors related to size were located on ECA3 within or nearby a well-known region, including the genes ligand dependent nuclear receptor corepressor like (LCORL), non-SMC condensin I complex subunit G (NCAPG), DDB1 and CUL4 Associated Factor 16 (DCAF16), and the Family with Sequence Similarity 184 Member B (FAM184B). Significant SNPs were also detected for two factors for evaluated traits representing conformation and jumping, and four factors for linearly scored traits related to body length, neck conformation, walk and trot (hindleg position and activity), respectively. Among nearby genes, calcium/calmodulin-dependent protein kinase type 1D (CAMK1D) for the factor for linearly scored traits related to neck conformation and GLI Family Zinc Finger 2 (GLI2) for the factor for evaluated jumping traits, were most promising. For these, top associated SNPs were detected within the genes, and the known gene functions seems to be related to the phenotypes. In conclusion, ssGWAS is beneficial to detect plausible candidate genes/regions for desired traits in warmblood horses.

The need for producing in environmentally resilient system drives new research to achieve sustainable beef production. Water footprint of the beef supply chain is a concern that must be addressed, aiming to improve water use within the production chain. One approach is genetic selection of beef cattle for water efficiency. However, it is essential to understand the genetic architecture and mechanisms involved in the expression of this phenotype to choose the best selection criteria. Thus, our study aimed to estimate genetic parameters for water efficiency traits, conduct a genome-wide association study (GWAS) and identify the genetic networks and biological processes involved. A population of 1762 purebred Senepol cattle was phenotyped for the following water efficiency traits: water intake (WI), gross water efficiency (GWE), water conversion ratio (WCR), residual water intake based on average daily gain (RWI_ADG) and residual water intake based on dry matter intake (RWI_DMI). A subset of 1342 animals was genotyped using GGP Bovine 50 K SNP Chip with (734 animals) or 100 K (508 animals), and imputation from 50 K to 100 K was performed with Beagle software. The heritability estimates were 0.36 ± 0.06, 0.26 ± 0.05, 0.22 ± 0.05, 0.24 ± 0.05 and 0.20 ± 0.05 for WI, GWE, WCR, RWI_ADG and RWI_DMI, respectively. Unlike the raw measures of WI, the phenotypic correlations between average daily gain (ADG) and the residuals (RWI_DMI and RWI_ADG) were zero. All water efficiency traits were moderately to highly correlated with each other. GWAS were used to estimate the effect of 79,860 single nucleotide polymorphisms (SNPs), and significant SNPs were only observed for WCR. Enrichment analysis of genes in the significant regions revealed the involvement of different biological processes, such as saliva production, water transport, renal system and immune system. Genetic selection of Senepol cattle for water efficiency traits is feasible and can reduce water requirements for meat production. Water efficiency measures are polygenic traits, and different biological processes act simultaneously on the expression of related phenotypes.