Critical factors for non-surgical artificial insemination in sheep

Exploring Endogenous and Exogenous Factors for Successful Artificial Insemination in Sheep: A Global Overview

Artificial insemination (AI) plays a vital role in animal breeding programs. AI is applied to enhance animal genetics and facilitate the widespread integration of desirable characteristics with a high potential for productivity. However, in sheep, this biotechnology is not commonly practicable due to multi-factorial challenges, resulting in inconsistent outcomes and unpredictable results. Thoughtful selection of semen donors and recipients based on genetic merit deeply impacts ovine AI outcomes. Additionally, endogenous factors such as breed, age, fertility traits, genetic disorders, and cervical anatomy in ewes contribute to ovine AI success. Extensive research has studied exogenous influences on sexual behavior, reproductive health, and hormonal regulation, all impacting ovine AI success. These exogenous factors include techniques like estrus induction, synchronization, semen handling methods (fresh/chilled/frozen), and insemination methods (cervical/laparoscopic), as well as nutritional factors and climatic conditions. This overview of the literature highlights the endogenous and exogenous challenges facing successful ovine AI and proposes strategies and best practices for improvement. This paper will serve as a guide for understanding and optimizing the success of ovine AI.

The Need for a Framework Addressing the Temporal Aspects of Fish Sperm Motility Leading to Community-Level Standardization

Motility is a widely available parameter that can be used to assess sperm quality of aquatic species. Sperm from fishes with external fertilization usually undergo a dynamic and short-lived period of motility after activation. The common practice of assigning a single value at an arbitrary peak of motility presents challenges for reproducibility, community-level standardization, and comparisons across studies. This study aimed to explore statistical approaches to standardize motility reporting, and to develop an initial framework for community-level standards. Sperm samples from 14 zebrafish (Danio rerio) with a total of 21,705 cells were analyzed by use of computer-assisted sperm analysis with data collection starting at 10 s after activation at 5-s intervals for 50 s. Four common motility variables were selected for analyses: curvilinear velocity, straight-line velocity, beat cross frequency, and amplitude of lateral head displacement. Cluster analysis was used to evaluate sperm subpopulations within and among males over time, least-square means was used to explore temporal aspects, and the first derivative of the regression equations was used to calculate the rate of change for the motility parameters. Cluster analysis proved informative, but overlapping ephemeral clusters were not valuable for providing standardization options. Analysis of temporal aspects and rate of change indicated opportunities for standardization by reporting the overall motility-time functions or reporting during stable time windows instead of peak motility or at random times. These approaches could minimize the inconsistencies caused by male-to-male variation and dynamic changes of subpopulations while providing comparable information. An overall temporal framework was identified for motility reporting along the collection-processing-cryopreservation-thawing sequence to provide a basis to support efforts of community-level standardization.

Assessing Population Structure and Genetic Diversity in U.S. Suffolk Sheep to Define a Framework for Genomic Selection

Long-term sustainability of breeds depends on having sufficient genetic diversity for adaptability to change, whether driven by climatic conditions or by priorities in breeding programs. Genetic diversity in Suffolk sheep in the U.S. was evaluated in four ways: 1) using genetic relationships from pedigree data [(n=64,310 animals recorded in the U.S. National Sheep Improvement Program (NSIP)]; 2) using molecular data (n=304 Suffolk genotyped with the OvineHD BeadChip); 3) comparing Australian (n=109) and Irish (n=55) Suffolk sheep to those in the U.S. using molecular data; and 4) assessing genetic relationships (connectedness) among active Suffolk flocks (n=18) in NSIP. By characterizing genetic diversity, a goal was to define the structure of a reference population for use for genomic selection strategies in this breed. Pedigree-based mean inbreeding level for the most recent year of available data was 5.5%. Ten animals defined 22.8% of the current gene pool. The effective population size (Ne) ranged from 27.5 to 244.2 based on pedigree and was 79.5 based on molecular data. Expected (HE) and observed (HO) heterozygosity were 0.317 and 0.306, respectively. Model-based population structure included 7 subpopulations. From Principal Component Analysis, countries separated into distinct populations. Within the U.S. population, flocks formed genetically disconnected clusters. A decline in genetic diversity over time was observed from both pedigree and genomic-based derived measures with evidence of population substructure as measured by FST. Using these measures of genetic diversity, a framework for establishing a genomic reference population in U.S. Suffolk sheep engaged in NSIP was proposed.