Journal of Animal Breeding and Genetics最新文献

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.

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.