Journal of Animal Breeding and Genetics最新文献

The present study examined correlated responses in commercial traits of economic importance resulting from selection for high growth over eight years in a population of red tilapia (Oreochromis spp.). During this period, data on total length, body colour and survival were recorded for 75,950 individual fish, which were progeny of 1203 dams and 608 sires. Our restricted maximum likelihood-mixed model analysis showed that selection for increased body weight produced concomitant positive changes in animal length by 3.1%. A correlated increase of 4.9% per generation was also observed for the survival rate in this population. Body hue colour, a trait of commercial interest, displayed a slight increase of 1.8% per generation. The substantial improvement in total length is consistent with the high genetic correlation (r_g) between body weight and length (r_g = 0.84). Body weight was weakly but significantly correlated genetically with the survival rate during the grow-out period from stocking to harvest. However, the genetic correlations between weight (or length) and body hue colour were not significant. Furthermore, there were substantial heritable genetic variations in these traits, with heritability estimates ranging from 0.12 to 0.36. Maternal and common full-sib effects accounted for 2.5%-17% of the total phenotypic variation. It is concluded that selection for increased body weight resulted in desirable correlated responses in complex quantitative traits of red tilapia, and these genetic characters will continue to respond to selection, given the substantial genetic variation in this red tilapia population.

In order to investigate the applicability and efficiency of genomic selection for growth and carcass traits in Nordic beef cattle, single-step genomic BLUP (ssGBLUP) was applied in 4321 Charolais and 4532 Hereford animals with information on approximately 43,000 SNPs each. Statistics including dispersion value (b1), accuracy ratio and the relative accuracy improvement were estimated for genotyped female animals in the validation set. For estimating dispersion, accuracy ratio and relative accuracy improvement, the Legarra-Reverter linear regression (LR) method was used by truncating the phenotypes after 2018, and the validation set comprised females born from 2019 to 2021. Moreover, for ssGBLUP, different alpha values of 0.95 and 0.70 were utilised as weights on the genomic information when the H matrix was blended for the genomic relationship matrix G and the pedigree relationship matrix A. In general, implementing ssGBLUP led to higher accuracy ratios and improved dispersion values (b1 value closer to the optimum value of one), compared to when using pedigree-based BLUP (PBLUP). Using an alpha value of 0.70 gave a dispersion value closer to one compared with when using an alpha value of 0.95. Additionally, the relative accuracy estimation was improved substantially for several traits by using ssGBLUP instead of PBLUP, with the highest (30%) relative improvement for carcass conformation in Swedish Hereford cattle. In conclusion, ssGBLUP would be beneficial to implement in the future Nordic beef cattle breeding programs.

The Montana Tropical cattle, a Taurine and Indicine composite, were developed in Brazil since 1994 and were based on crossing four biological types of cattle: zebu (mainly Nelore), tropical adapted taurine (mainly Senepol and Romosinuano), British taurine (mainly Angus) and continental taurine (as Charolais, Simental and Limousin). This study aimed to characterise the genetic ancestry of this composite breed at the genomic level. Principal component analysis revealed the composite in intermediate space between indicine and taurine but closer to the Taurine cluster, which is consistent with its multi-breed origin. The ADMIXTURE analysis indicated the Montana Tropical to be composed of several progenitor breeds without an indication of a dominant breed. Local ancestry analysis showed the Montana animals to have an average of 24% (standard deviation of ±5.41) Zebu ancestry. The total taurine ancestry was 62%, consisting of 19.6% (±6.96) from tropical adapted taurine, 20.1% (±5.99) from British taurine, 21.92% (±7.79) from continental taurine and 14% (±5.26) of the genome was undetermined. Based on the pedigree, these animals would have 20.8% ± 8.5% Zebu, 50.5% ± 14.4% tropical adapted taurine, 21.1% ± 13.5% British taurine and 7.6% ± 5.1% of continental European taurine in their composition. The genomic regions in the composite originating from each biological type highlight the trait complementarity each genetic group contributes. For example, the genomic region of high tropical adapted taurine ancestry was shown to harbour the slick hair locus, and the regions of high indicine ancestry are associated with high length of productive life. This study unravels the complex genetic ancestry of the Montana Tropical composite, highlighting the effective blend of ancestral gene pools that enhance key production and adaptation traits.

Microbiota composition represents a promising tool in precision farming, simultaneously serving as a benchmark of environmental challenge, a predictor of animal physiological status, and a direct target for host selection. In this paper, we compared the ability of microbiota composition and genomic information to predict swine performance in two production settings, namely a purebred nucleus (NU) and a terminal cross commercial population (TE). Microbiota consistently predicted all traits in both scenarios (NU-TE: training on NU to predict TE; TE-NU: training on TE to predict NU) and at two time points: mid-test and off-test. The highest correlation (i.e., prediction accuracy) was achieved for back fat, with values of 0.08 and 0.04, and 0.30 and 0.23 for mid and off-tests, predicting from nucleus to terminal, and vice versa. Similarly, daily gains correlations were 0.05 and 0.04, and 0.18 and 0.15 for the same time points and scenario combinations. Including genomic information yielded correlations ranging from low for loin area to moderate for back fat (0.19 nucleus to terminal, 0.16 for the opposite). Microbiota had higher prediction accuracies than genomic for back fat both from nucleus to terminal and vice versa (+0.11, +0.07) and daily gain (+0.08, +0.02) at off-test. Lower accuracies were obtained for the IMF. Including genomic and microbial information produced higher accuracies than microbiota or genomic alone for back fat (0.37 and 0.29 for nucleus to terminal and opposite) and daily gain (0.19 and 0.21 for nucleus to terminal and opposite). Results for other traits differed for different scenarios. Results show that microbiota composition effectively predicted most growth and carcass traits, particularly growth and fat deposition, across production systems, prediction scenarios (NU-TE and TE-NU), and time points (mid-test and off-test). These findings highlight the potential of microbiota profiles to predict phenotypes across production systems and support their use as a tool for selecting animals in environments they have not been exposed to.

The aim of this study was to assess the accuracy, bias and dispersion of genomic predictions for accumulated profitability (APF) and profit per kilogram of liveweight gain (PFT) in Nelore cattle using different prediction approaches. The dataset consisted of 3969 phenotypic records for each trait. The pedigree harboured information from 38,930 animals born between 1998 and 2016, including 2691 sires and 19,884 dams. A total of 2449 animals were genotyped using the Clarifide Nelore 3.0 SNP panel. Nine models for genomic prediction were evaluated: a linear animal model was applied to estimate genetic parameters and perform the genomic single-trait best linear unbiased prediction (ST_ss-default). Additionally, a two-trait (ssGBLUP TT_W450 and TT_DMI), three-trait (TTT_CAR) and multi-trait ssGBLUP (MT_ss) were tested. Finally, two models employing the weighted linear (ST_sswl1 and ST_sswl2) and non-linear (ST_sswnl1 and ST_sswnl2) single-step genomic approach (WssGBLUP) were used to predict genomic breeding values (GEBV). The ability to predict future performance was assessed by calculating the correlation between GEBV and adjusted phenotypes. The average prediction accuracy of the GEBV models ranged from 0.345 to 0.665 for PFT and from 0.425 to 0.603 for APF. The predictive capability of the MT_ss model (0.665) was significantly higher than that of the other models for PFT, except for the TTT_CAR model (0.604), which also showed an improvement in predictive performance. For APF, the MT_ss (0.561) and TT_W450 (0.556) models demonstrated improved genomic prediction accuracy compared to the other models. In general, the single trait ssGBLUP (ST_ss-default) models and the non-linear weighting approach did not enhance prediction accuracy for either trait. For the phenotypic prediction ability of PFT, the linear WssGBLUP models ST_sswl1 (0.65) and ST_sswl2 (0.70), TT_W450 (0.64) and ssGBLUP-M (0.66) demonstrated the highest prediction accuracies. Similar results were observed for the phenotypic prediction ability of APF for both models. However, the linear WssGBLUP models ST_sswl1 (0.84) and ST_sswl2 (0.94) provided higher prediction performance compared to the two-, three- and multi-trait models. The results indicate that the multi-trait model achieved better predictive ability for the novel traits PFT and APF. Multi-trait genomic selection may yield greater genetic gains than other models for these forthcoming economically important traits in breeding programmes.

This study aimed to evaluate the impact of different genomic prediction approaches on the predictive ability for meat tenderness in Nellore cattle. Phenotypic (n = 73,286), pedigree (n = 4,141,892) and genomic (n = 15,300) data from animals belonging to the genetic improvement program of the National Association of Breeders and Researchers (ANCP) were used. Six models were tested: (1) standard ssGBLUP (Single-step Genomic Best Linear Unbiased Prediction), considering direct additive genetic and residual effects as random, contemporary group (CG) as a fixed effect, and slaughter age as a linear and quadratic covariate; (2) Model 1 + ssGBLUP weighted with SNP effects from the first WssGWAS iteration; (3) Model 1 + ssGBLUP weighted with SNP effects from the second WssGWAS iteration; (4) Model 1 + body weight as a covariate; (5) Model 1 as a bi-trait model with body weight at 450 days (W450); (6) Model 1 as a multi-trait model with carcass traits: ribeye area (REA), backfat thickness (BFT) and rump fat thickness (RFT). Predictive ability was evaluated using linear regression, in which the dataset was divided into a complete and a partial subset (n = 374) dataset. Accuracy ranged from 0.04 (Models 2 and 3) to 0.37 (Model 6). Bias was low for all models, with Models 2 and 3 showing the least bias (-0.001). Model 6 showed the best performance in terms of accuracy and correlation (0.897), suggesting it was more effective in capturing genetic variability of meat tenderness, while reducing bias and increasing the precision of the estimates. Multi-trait models may offer more robust genomic predictions by leveraging trait correlations to increase prediction accuracy.

Runs of Homozygosity (ROH) are commonly used to quantify autozygosity/identity-by-descent (IBD) in an individual or population. However, the method's accuracy at the segment level in livestock populations has only been evaluated in a few studies. Thus, the aim of this study was to determine to what extent ROH are truly IBD and estimate the proportion of IBD segments that go undetected in a simulated livestock population. We simulated a population of randomly mating animals for 100 generations. The genome consisted of a single chromosome with a SNP density of either 46 or 92 SNPs per mega base (Mb). In addition, a set of founder markers tracing IBD was recorded. ROH were detected using four different parameter combinations. Using the two sets of markers, we calculated the true positive rate, power, and overall correlation between true (F_IBD) and estimated inbreeding (F_ROH). Additionally, a new measure for within-ROH inbreeding (F|ROH) was introduced and calculated the level of homozygosity within a ROH compared to the general expectation in the genome. The results indicate that ROH longer than 2 Mb are a reliable indicator of IBD, with the F|ROH being over 0.9 for all ROH lengths and parameter combinations. True positive rates only exceeded 0.9 consistently for ROH over 9 Mb, indicating that many of the identified ROH may be associated with common ancestors more ancient than the base population. The power was mainly controlled by the parameter stringency, that is, allowing for shorter ROH increased the power. The ROH-based individual measure of inbreeding F_ROH was highly correlated to F_IBD while also having regression coefficients close to 1 (i.e., a 1% variation in F_ROH corresponded to a 1% variation in F_IBD). Using stringent ROH parameters resulted in underestimation of the rate of inbreeding in the population. Increasing marker density improved predictions, including a higher true positive rate, power, higher correlations, and less underestimation of inbreeding rates.

Twin births in dairy cattle are rare but present significant challenges for animal welfare, as both the health of the cow and the calves are affected. This causes economic losses, which prompts breeders to select against twin births and identify associated risk factors. This study examines the phenotypic relationship between milk yield, fertility traits and twin births in German Holstein cattle using a large, population-wide dataset. GEBV correlations for twin births, milk production and fertility traits were estimated. Genome-wide association studies (GWAS) were conducted for calving numbers 1-3 in order to explore the genetic background in more detail. The twin birth rate showed a strong phenotypic association with milk production and a moderate phenotypic association with the timing of successful insemination. However, GEBV correlations were low: 0.04 with milk yield and -0.10 to 0.01 with fertility traits. GWAS revealed two potential candidate genes on BTA11: LHCGR and FSHR, which encode receptors for LH and FSH, two hormones crucial to estrus. In contrast to the first calving, significantly associated regions on BTA5 and BTA25 were found in calving numbers 2 and 3. This study demonstrates the interaction between genotype and environment, concluding that a genetic predisposition for twin births, in combination with a favourable endocrine state (environment), increases the likelihood of twin births.