Huijie He

The efficiency of the laparoscopic ovum pick-up and in vitro embryo production (LOPU-IVEP) system in sheep is governed by a complex interplay of genetic and environmental factors. This study comprehensively evaluated the effects of breed, season, and their interaction on oocyte retrieval, embryonic development, and pregnancy outcomes in a commercial setting. A 20-month longitudinal analysis of a large commercial cohort revealed that breed was the dominant factor determining oocyte quantity, whereas season exerted a stronger influence on developmental competence. Specifically, autumn and winter provided optimal conditions, resulting in significantly higher cleavage rate and blastocyst rate (p < 0.05). A significant breed-by-season interaction further identified synergistic combinations, such as Black-headed Suffolk in autumn or winter, which achieved superior embryo production outcomes. Importantly, correlation analysis revealed that oocyte quantity was not predictive of developmental potential or pregnancy success. These findings underscore the necessity of a precision management strategy that aligns LOPU schedules with breed-specific seasonal advantages to maximize annual embryo production efficiency.

The efficiency of the laparoscopic ovum pick-up and in vitro embryo production (LOPU-IVEP) system in sheep is influenced by a complex interplay of genetic and environmental factors. This study comprehensively evaluated the effects of breed, season, and their interaction on oocyte retrieval, embryonic development, and pregnancy outcomes. A 20-month longitudinal analysis was conducted on a large commercial cohort comprising five breeds: Black-headed Suffolk (n = 3,024), White-headed Suffolk (n = 699), Australian White (n = 760), Black-headed Dorper (n = 1,109), and East Friesian (n = 1,077). We found that breed was the predominant factor affecting oocyte quantity and initial quality, with East Friesian yielding the highest number of total and available COCs. In contrast, season exerted a stronger influence on developmental competence, with autumn and winter providing optimal conditions, resulting in the highest cleavage rates (64.05%), blastocyst rates (59.02%), and pregnancy rates for single embryo transfer (73.04-75.31%). A significant breed-by-season interaction further identified Black-headed Suffolk in autumn/winter as a superior combination for embryo production. Correlation analysis revealed that oocyte quantity parameters were not predictive of developmental potential or pregnancy success. Our findings advocate for a precision management strategy that aligns the LOPU schedule with breed-specific seasonal advantages to maximize annual embryo production efficiency.

Embryo genomic selection (EGS) is a contemporary breeding strategy that combines genomic selection (GS) methodology with embryo biotechnology. By conducting genotyping and genomic prediction at the pre-implantation stage, embryos with superior breeding value can be identified for transfer, markedly increasing breeding efficiency while reducing the uncertainty and temporal expenditure associated with conventional GS. This study establishes a reliable embryo biopsy-based GS pipeline for sheep, incorporating optimized whole-genome amplification and microcell genotyping techniques. We developed a high-efficiency in vitro sheep embryo production platform compatible with embryo biopsy. Systematic comparison of Multiple Displacement Amplification (MDA) and Multiple Annealing and Looping Based Amplification Cycles (MALBAC) whole-genome amplification systems yielded high-quality genotypes from biopsy samples of embryos containing as few as 10 cells. Imputation using 10× whole-genome sequencing data significantly increased both genotype call rates and accuracy. High concordance was observed between embryo and lamb genotypes, and genomic estimated breeding values (GEBVs) for key growth traits exhibited strong correlations (R²: 0.91–0.98). This system enables accurate preimplantation genomic evaluation and provides an efficient strategy to accelerate genetic improvement in sheep breeding programs.

This study aimed to estimate the genetic and non-genetic factors that affect the nine early growth traits of Dumeng sheep, as well as to estimate the variance components and genetic parameters associated with these traits. A dataset containing detailed information on 17,896 preweaning trait records of 4474 lambs was collected. In addition, 5015 postweaning trait records of 1003 lambs were documented. The effects of recipient dam age, sex, year, season, and herd on the early growth traits were assessed using the general linear model procedure of the statistical analysis system, revealing different levels of significance across different traits. To determine the most suitable model for estimating the genetic parameters, the likelihood ratio (LR) test was employed, fitting six animal models that either excluded or included maternal genetic and maternal permanent environmental effects within the average information restricted maximum likelihood (AIREML) framework using WOMBAT software (Version: 23/11/23). The model incorporating direct additive genetic effects, maternal genetic effects, and maternal permanent environment effects as random effects (model 6) provided the best fit for birth weight (BW) estimation. In contrast, the model combining direct additive genetic effects and maternal permanent environment effects as random effects (model 2) demonstrated a superior fit for estimating the genetic parameters of weaning weight (WW), average daily gain weight from birth to weaning (ADG1), and Kleiber ratio from birth to weaning (KR1). With regard to the genetic parameters of body weight at 6 months of age (6MW), average daily gain weight from weaning to 6 months (ADG2), average daily gain weight from birth to 6 months (ADG3), Kleiber ratio from weaning to 6 months (KR2), and Kleiber ratio from birth to 6 months (KR3), model 1, which incorporates only direct additive genetic effects, was identified as the optimal choice. With the optimal model, the heritability estimates ranged from 0.010 ± 0.033 for 6MW to 0.1837 ± 0.096 for KR3. The bivariate analysis method was employed to estimate the correlation between various traits using the most suitable model. The absolute values of genetic correlation coefficients among the traits spanned a range from 0.1460 to 0.9998, highlighting both weak and strong relationships among the studied traits. Specifically, the estimated genetic correlations between WW and ADG1, ADG3, KR1, and KR3 were 0.9859, 0.9953, 0.9911, and 0.9951, respectively, while the corresponding phenotypic correlations were 0.9752, 0.7836, 0.8262, and 0.5767. These findings identified that WW could serve as an effective selection criterion for enhancing early growth traits.