Journal of Animal Breeding and Genetics最新文献

This study employed participatory methods to identify breeding objectives and define desired genetic gains for economically important traits in the Russian sturgeon (Acipenser gueldenstaedtii). Two structured questionnaires were distributed to all Russian sturgeon farmers in Iran. The first questionnaire collected farm management information and asked farmers to prioritise five important traits from a list of thirteen. The top-ranked traits were ovarian fat lobe weight (OFW), total caviar weight (TCW), body weight of broodstock (BWB), larval body area at hatching (LBA), and yolk sac area (YSA). In the second questionnaire, pairwise comparisons were applied to derive individual trait preferences through the Analytical Hierarchy Process (AHP). Social group preference (Soc-p) values were computed for each social group using the weighted goal programming (WGP) model implemented in LINGO software. The greatest disagreement in Soc-p values emerged between the commercial product and water temperature categories. Subsequently, the extended WGP models were employed to derive consensus preference (Con-p) values for these categories. The average of the Con-p values was 0.28 (OFW), 0.22 (BWB), 0.14 (TCW), 0.13 (LBA), and 0.05 (YSA). These Con-p values were then used to determine the desired genetic gains, which were highest for TCW (1.39%) and lowest for YSA (0.34%). The use of AHP and WGP, rather than economic indices, was justified by the limited availability of reliable economic data in Iranian sturgeon aquaculture and the need for farmer-driven, consensus-based breeding goals. This research demonstrates that participatory approaches can successfully define genetic priorities, improve consensus among diverse farmer groups, and guide sustainable breeding strategies for Russian sturgeon in Iran.

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.

General resilience in livestock can be estimated from the variability in longitudinal data and may support balanced breeding objectives by helping animals better cope with environmental stress. However, its economic value and inclusion within multi-trait selection indexes remain largely unexplored. Current genetic improvement programs for sheep primarily focus on wool growth, reproduction and lean meat production. This study developed a bioeconomic model to estimate the economic value of general resilience, using the natural logarithm of variance of wool fibre diameter as a resilience indicator trait in sheep. A commercial Merino sheep enterprise was considered, and the economic value of resilience was derived from its association with health, mortality, and energy allocation for production. The breeding objective included key traits related to meat production, reproduction, and wool quality and quantity, to compare the economic impact of resilience with these traits. Among 13 traits, resilience contributed 2.02% to the total value of the index and ranked 11th in economic importance. After 10 years of simulated selection, resilience was expected to improved by 0.07 genetic standard deviations, translating to a modest economic gain of $0.08 per ewe. A sensitivity analysis was conducted to assess the impacts of increasing the relative contribution of resilience within the index on selection responses under four scenarios: (i) base genetic correlations, (ii) stronger correlations, (iii) weaker correlations, and (iv) adding genomic information. Resilience responded 50%-75% more effectively to selection when it had stronger genetic correlations with production traits or when genomic information was incorporated. However, placing greater emphasis on resilience (> 10% of the index) reduced progress in key production traits. Future research should explore these trade-offs in industry selection indexes to integrate resilience effectively without compromising productivity.

This study estimated genetic parameters for pregnancy loss (PL) in Brahman cattle and evaluated the genetic correlation of PL with growth and reproductive traits using both the pedigree relationship matrix (A) and pedigree plus genomic relationship matrix (H). Data were collected from two herds in Bolivia, focusing on three age groups: heifers, primiparous and multiparous cows. Threshold animal models were fitted to each group. Multitrait models were fitted between the PL at different age group and between PL and the following traits: adjusted weights at 450 (W450) and 550 (W550) days, scrotal circumference adjusted at 450 (SC450) and 550 (SC550) days, accumulated cow productivity (ACP), age at first calving (AFC) and stayability (STAY). The H matrix increased the heritability for PL in heifers from 0.06 to 0.11. The genetic correlation between PL in heifers and primiparous cows changed using H (from 0.18 to 0.7), and it was high between heifers and multiparous cows. Moderate-high negative genetic correlation was observed between PL and STAY, with changes in heifers when using H (-0.17 to -0.57). AFC and PL presented medium-high positive genetic correlations. Negative correlations between PL and SC450 or SC550 were found in primiparous and multiparous cows. Using H, the correlation between PL in heifers and ACP shifted from 0.08 to -0.31, showing medium-high negative correlations for the other two age groups. Genetic correlations were low between PL and W450 or W550. Genomic information allows the use of PL as a selection criterion in heifers. Selection for major sexual precocity, longevity and productivity would enable the reduction of pregnancy loss.

In the metafounder approach, the relationship matrix of metafounders, $\Gamma$ is used to transfer information on relationships between pedigree founders into the numerator relationship matrix $A$ , creating matrix $A^{\Gamma }$ . Commonly metafounders are defined based on the available information of the animal (e.g., country, sex, breed) similar to unknown parent groups (UPG). This limits the ability of metafounders to correctly reflect the population structure. In Single-Step Models, hidden stratification in the population may cause inconsistencies between matrix $A$ and the genomic relationship matrix $G$ when they are combined into matrix $H$ . Reliable information on the true structure in a population can be obtained from genotypes. In this study, we investigate an approach to transfer information on population structure from the genotyped animals to the ungenotyped ancestors. We used an unsupervised clustering approach to assign pedigree founders to metafounders and performed Single-Step genomic evaluation for an increasing number of metafounders (nMF) assumed. The optimum nMF to model was determined by harmonising the trend in inbreeding in $G$ and $A^{\Gamma }$ and by monitoring of elements in $\Gamma$ . A semi-stochastic simulation based on real genotypes from Fleckvieh was used to investigate two scenarios: a trait with a strong genetic trend and a trait with no genetic trend. The quality of the prediction was determined by a regression of true breeding value as obtained from the simulation on estimated breeding value. The modelling of metafounders defined by population structure analysis led to a slight reduction in prediction quality in a trait with no trend, but was still stable in the range of the optimum nMF. In a trait with a strong genetic trend, prediction qualtity was improved compared to a common Single-Step model. The largest improvement was achieved in the range of the proposed optimum nMF.

The economic benefits of genetic improvement were estimated based on genetic trends observed for economically important traits in stud herds participating in Argentina's genetic evaluation scheme. The analysis included Horned Merino, Polled Merino, Dohne Merino, Corriedale and Polwarth sheep born between 2014 and 2023. This benefit was calculated as the difference between the additional income generated by the increased value of meat and wool in multiplier and commercial herds, and the costs associated with genetic improvement at the stud tier and additional cost of improved rams at multiplier and commercial tiers. The benefits of 10 years of genetic improvement and their residual effect for another 10 years were computed, updating the annual results with a discount rate of 5%. The benefit obtained in the five breeds reached USD 4.95 million considering only breeding program costs at the stud tier. Including additional ram buying costs, the benefit reached USD 3.75 million, the difference being captured by the ram selling tiers. At the breed level (ignoring ram buying costs) the return to investment (ROI) amounted to 33.6. At multiplier and commercial herd tiers (including ram buying costs) the income to cost ratios were 5.5 and 4.0, respectively. The Corriedale breed was responsible for 39% of the total benefit. Altogether, genetic improvement in the stud tier reached 1.47 million lambs annually or about 54% of the five wool sheep populations of the country. Thus, conventional genetic improvement efforts of economically important traits of wool sheep breeds proved to have been highly profitable.

In autochthonous livestock breeds with small populations, such as the Rubia Galega from Galicia (Spain), mating between relatives is common and can lead to inbreeding depression. Genomic inbreeding coefficients were estimated for 4984 animals using ~63,000 SNPs to assess inbreeding depression in four key traits: age at first calving (AFC) with 3503 records, calving interval (CI) with 3315 records, birth weight (BW) with 4878 records and weight at 210 days (W210) with 3285 records. Runs of homozygosity were sorted by length ([1,2], (2,4], (4,8], (8,16], > 16 Mb), and the corresponding inbreeding coefficients (F_ROH>1, F_ROH>2, F_ROH>4, F_ROH>8, F_ROH>16) were calculated using the consecutiveRUNs R package. A Genomic BLUP (GBLUP) was conducted for each F_ROH estimate using the BLUPF90+ programs. The results revealed significant inbreeding depression for AFC and CI, whereas W210 and BW exhibited similar inbreeding trends, but the effects of inbreeding on these traits were not statistically significant. To further explore the genetic basis of inbreeding depression, SNPs located within ROHs were tested, though a t-test, for their association with phenotypic traits. Genes located in significant regions (-log(p-value) > 3 from t-test) were annotated using Ensembl BioMart within a ± 0.5 Mb window. Recent inbreeding (ROH > 8 Mb) showed significant negative effects on reproductive traits, and key genomic regions-particularly on chromosome 2 involving MSTN, NAB1, and COL5A2-were linked to increased AFC and reduced BW and W210; ROH-based inbreeding estimates proved effective in detecting inbreeding depression in this native breed. Overall, ROH-based analyses revealed genomic regions and candidate genes, notably MSTN, contributing to inbreeding depression and key production traits in Rubia Galega cattle.

Climate change has intensified the need to reduce greenhouse gas emissions, particularly methane (CH₄) from enteric fermentation. Genetic selection has emerged as a promising mitigation strategy; however, studies on Bos taurus indicus , especially Nellore cattle, remain limited. This study aimed to estimate heritabilities and genetic correlations for CH₄ emission traits and their relationships with feeding behaviour, feed efficiency, and performance, as well as to evaluate the direct and correlated responses to selection for lower CH₄ emissions. Data were from 2418 Nellore cattle evaluated in feed efficiency trials. Traits included dry matter intake (DMI), feeding time per day (FTd), feed events per day (FEd), and feeding rate (FR), residual feed intake (RFI), average daily gain (ADG), and mid-test body weight (MBW). Methane emissions were measured in 1153 animals using the SF₆ tracer technique, providing daily CH₄ emission (g/day), CH₄ per unit of DMI (CH₄DMI, g/day), and residual CH₄ (CH₄res). Variance components were estimated using the single-step genomic BLUP (ssGBLUP) method through Bayesian inference. Heritability estimates were moderate for CH₄ (0.25), CH₄DMI (0.14), CH₄res (0.14), and performance traits such as DMI (0.35), ADG (0.36), and MBW (0.40). Higher estimates were observed for feeding behaviour traits FTd (0.49) and FR (0.42). Genetic correlations between CH₄ and production traits were high, particularly with DMI (0.79), ADG (0.90), and MBW (0.91), indicating that selection for reduced CH₄ emissions may affect growth. Direct selection for CH₄ led to a modest annual reduction in emissions but also a correlated decline in MBW. These results demonstrate that while CH₄ emissions are heritable, their strong genetic association with productivity traits indicates that isolated selection for reduced emissions may lead to undesirable outcomes in feed intake and performance. Therefore, strategies aiming to reduce CH₄ emissions should consider the genetic relationships with growth and efficiency traits to avoid compromising animal productivity.

The objective was to evaluate the genetic relationship between the surface temperature of regions of interest, measured using infrared images of young horses and functional longevity in jumping. This relationship was assessed by comparing the temperatures measured in the offspring of two groups of sires, one favourable and one unfavourable, to longevity. The study used a specific data collection protocol on a sample of 921 young progeny, before they began competing, of 141 extreme stallions, comprising 61 favourable and 80 unfavourable sires. These stallions had been selected based on estimated breeding values for functional longevity derived from official competition data of 202,320 horses. Infrared imaging provided 49 temperature variables, including average and maximum values for regions of interest such as temperature differences from the body for eyes, hocks, fetlocks, feet, carpi and back. It also included differentials between these regions, asymmetry between right and left sides and variability within each area. Heritability was estimated using a mixed model with fixed effects, of age, sex, coat colour, weight and visit, along with random genetic effects (considering a pedigree of 8002 horses). The effect of temperature on the group of sires was assessed using multivariate partial least squares logistic regression, adjusting temperature for fixed effects. Results indicated high heritability for the temperature of regions of interest: body (0.53 ± 0.14), carpi (0.55 ± 0.19), fetlocks (0.47 ± 0.12), feet (0.46 ± 0.12 and 0.38 ± 0.12). Lower heritability was observed for differences between regions (around 0.20) and even lower for asymmetry and variability. Lower average and maximum eye temperatures, lateral asymmetry in hind feet temperature and temperature variability in the back were associated with a higher probability of belonging to the favourable group of sires for functional longevity. Infrared imaging may be a tool for identifying easily measurable selection criteria associated with longevity. Given the limited number of horses, the limited number of significant variables associated with the group of sires and the specificity of the protocol, verification and validation studies are necessary before its use.

The identification of quantitative trait locus (QTL) or genes responsible for key agronomic traits has significantly enhanced genetic improvement through marker-assisted selection (MAS). However, the impacts of MAS on genetic parameters and subsequent selection processes have not been thoroughly characterised. Here, through genome-wide selective sweep analysis, we identified a diverse set of genes involved in oocyte meiosis, including PPP3CA, AR, PPP1CB, SPDYA, MAD1L1, and BMPR1B. The genome-wide association study (GWAS) further identified three genes UNC5C, BMPR1B, and PDLIM5 as being associated with lambing rate in Hu sheep. From these analyses, the FecB loci emerged as a potential molecular marker for lambing rate. with an increase of 0.5 lambs per G allele. The heritability of the lambing rate was estimated to be 0.19 (±0.02). Moreover, based on 10-fold cross-validation, the accuracy of genomic selection (GS) was found to be 0.30. Simulated MAS resulted in a reduction of the additive genetic variance components, with estimated heritability dropping to 0.14 (±0.02) and GS accuracy decreasing to 0.18-representing a decline of 26.42% and 34.81%, respectively. To address the reduced GS accuracy, we performed GWAS on the reference set to identify weighted single nucleotide polymorphisms (SNPs). This method has the potential to increase accuracy by 13.8%. Our study found that MAS has a negative impact on GS. To address this issue, we integrated prior information on SNPs from GWAS, which exhibit pleiotropic genetic architecture. This integration enables us to utilise genetic markers for complex traits more effectively, thereby improving the accuracy and efficiency of GS.