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SUMMARYDue to reduced fertility, cryopreserved semen is seldom used for commercial porcine artificial insemination (AI). Predicting the fertility of individual frozen ejaculates for selection of higher quality semen prior to AI would increase overall success. Our objective was to test novel and traditional laboratory analyses to identify characteristics of cryopreserved spermatozoa that are related to boar fertility. Traditional post-thaw analyses of motility, viability, and acrosome integrity were performed on each ejaculate. In vitro fertilization, cleavage, and blastocyst development were also determined. Finally, spermatozoa-oviduct binding and competitive zonabinding assays were applied to assess sperm adhesion to these two matrices. Fertility of the same ejaculates subjected to laboratory assays was determined for each boar by multi-sire AI and defined as (i) the mean percentage of the litter sired and (ii) the mean number of piglets sired in each litter. Means of each laboratory evaluation were calculated for each boar and those values were applied to multiple linear regression analyses to determine which sperm traits could collectively estimate fertility in the simplest model. The regression model to predict the percent of litter sired by each boar was highly effective (p < 0.001, r 2 = 0.87) and included five traits;acrosome-compromised spermatozoa, percent live spermatozoa (0 and 60 min post-thaw), percent total motility, and the number of zona-bound spermatozoa. A second model to predict the number of piglets sired by boar was also effective (p < 0.05, r 2 = 0.57).These models indicate that the fertility of cryopreserved boar spermatozoa can be predicted effectively by including traditional and novel laboratory assays that consider functions of spermatozoa.

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Cryopreserved boar sperm is seldom used for AI because fertility is reduced. Despite many potential advantages of frozen-thawed sperm for AI, lack of reliable fertility estimation of frozen ejaculates before AI limits the application of frozen sperm. Conventional post-thaw evaluation of sperm does not accurately estimate fertility. Identifying sperm traits that predict fertility would help select ejaculates that produce adequate litter sizes. Our objective was to identify traits of cryopreserved sperm that are related to boar fertility for AI through the use of novel and traditional laboratory analyses. Semen from 14 boars of several breeds was cooled to 15°C for shipping before freezing. Post-thaw motility was evaluated using a microscope and confirmed with computer-automated sperm analysis. Sperm viability and acrosome integrity were measured at 0, 30, and 60 min post-thaw. In addition to traditional analyses, each sperm sample was tested by IVF to record fertilization, cleavage, and blastocyst development. A sperm-oviduct binding assay was used to compare the number of sperm bound to epithelial aggregates harvested from the isthmus. Additionally, a competitive zona binding assay using 2 distinct fluorophores for boar identification was used to count the number of sperm from each boar bound to the zona. Frozen sperm from the same ejaculates subjected to laboratory analyses were used to determine actual boar fertility. Fertility was measured by AI of mature gilts using 4.0 × 109 total sperm from one boar at 24 h and a second boar at 36 h after the onset of oestrus, and AI order was reversed in consecutive replicates. Fertility was expressed as the percentage of the litter sired by each boar. Reproductive tracts were harvested at 32 days after AI, and fetal paternity was identified using microsatellite markers. The actual boar fertility was regressed against the mean of each laboratory evaluation by boar, and the assays that best predicted fertility were identified using stepwise logistic regression. The model generated was highly predictive of fertility (P < 0.001, r2 = 0.87) and included 5 traits: acrosome compromised sperm (0 and 30 min), percent live sperm (0 min), percent total motility (30 min), and the number of zona bound sperm. An additional model in which fertility was assessed by the number of piglets sired by boar also predicted fertility (P < 0.05, r2 = 0.57) and shared many of the same traits. These models were highly accurate when used to predict actual fertility of cryopreserved boar sperm. This approach may be used to screen ejaculates before AI and advance the use of frozen boar sperm by the swine industry.Research was supported by Agriculture and Food Research Initiative Competitive Grant no. 2010-85112-20620 from the USDA National Institute of Food and Agriculture.