Enhancing evaluation of bull fertility through multivariate analysis of sperm

Authors: AZEVEDO, H - Brazilian Agricultural Research Corporation (EMBRAPA); Blackburn, Harvey; Lozada-Soto, Emmanuel; Spiller, Scott; Purdy, Phil

Submitted to: Journal of Dairy Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/16/2024
Publication Date: 9/27/2024
Citation: Azevedo, H.C., Blackburn, H.D., Lozada-Soto, E.A., Spiller, S.F., Purdy, P.H. 2024. Enhancing evaluation of bull fertility through multivariate analysis of sperm. Journal of Dairy Science. https://doi.org/10.3168/jds.2024-25163.
DOI: https://doi.org/10.3168/jds.2024-25163

Interpretive Summary: Computer-assisted sperm analysis (CASA) has long held the promise providing improved post-thaw sperm evaluations. Toward this goal a multivariate analysis of sperm measured by CASA identified groups of bulls with similar sperm kinematic and morphometric characteristics. Importantly significant fertility differences between bull groups was found. This study's outcome and approach serve as a basis to integrate response variables (e.g., speed, cell size, and beat cross frequency) and improve CASA's analytical utility, resulting in increasing the prediction of bull fertility and assist artificial insemination companies and livestock gene bank management.

Technical Abstract: Computer-assisted sperm analysis (CASA) has become the predominant tool for assessing bull semen in artificial insemination programs. Despite such popularity CASA’s ability to project fertility has been limited, especially when emphasis is based upon single motion characteristics. Our alternative hypothesis is that numerical sets of CASA measures provide a more effective method to differentiate the potential fertilization capacity of bulls and that bulls can be clustered based upon sets of CASA measures. Therefore, we used CASA to measure frozen-thawed semen samples from 307 Holstein and 152 Jersey bulls sourced from USDA-ARS’s National Animal Germplasm Program gene bank. Sperm was evaluated immediately after thawing and 30 minutes later. We evaluated sperm kinetic and morphometric means and variances to capture the structure of CASA data in relation to various sources of variation. These data were subjected to univariate and multivariate statistical methods to investigate animal and management factors affecting sperm characteristics measured by CASA. Clustering with K-means identified four clusters of bulls based upon each cluster’s set of CASA parameters after thawing. There was little overlap among clusters for sets of CASA measures. At the extremes, bull cluster 1 (BC1, n = 180) and BC3 (n = 101) had different sire conception rates (SCR) -0.07 vs -1.29, respectively and sets of CASA measures. Interestingly, BC2 had CASA measures that could be perceived as negative, e.g., cell size at 8.18'm2 vs 6.37'm2 for BC4 and total motility of 29.7% vs 48.7% for BC3, but SCR for BC2 was higher (-0.79) than BC3 (-1.29). Despite such discrepancies for some BC2 CASA values it appears the potentially negative effects were offset by the levels of other CASA values. Our findings suggest improved approaches for using CASA could lie in evaluating multiple CASA measures as sets within specific numerical ranges rather than as independent measures.