Genetic analysis and models for semen production and artificial insemination result in sheep

In sheep, more than 800,000 articial inseminations (AI) are performed each year in France. In order to improve their eciency, French AI centres would like to increase the number of doses produced per ram and the probability of AI success. We analyzed semen production and AI results from six AI centre members of the ANIO (association nationale des centres d'insemination ovine). Our objectives were (1) the identication of the main environmental eects aecting semen production (volume, concentration, number of spermatozoa and motility) and AI success, (2) the estimation of the corresponding genetic parameters. Each ram in an AI centre is collected repeatedly and frequently during several breeding seasons. To take into account repeated measurements, we analyzed the semen production using two models : a simple repeatability model and a character process model with three environmental eects (long-term environmental eect, short-term environmental eect and classical measurement error). Separate analysis within breed and centre were performed. For all traits and in nearly all centres, a model with a spatial power correlation structure for the short term environmental eect and a rst order autoregressive process for the long term environmental eect tted the data the best. Results obtained for xed eects and genetic parameters were in accordance with the literature. The main factors aecting semen production were year, season, number of ejaculations, daily variation, interval from previous to current collection and age. Heritability estimates were moderate for volume, concentration and number of spermatozoa (0.12 to 0.33) and lower for motility (0.02 to 0.14). Each on-farm recorded insemination was matched to the corresponding ejaculate produced at the AI centre and to the corresponding outcome which is a binary response of either success at insemination (1) or failure (0). Separate analyses within breed were performed using a model which estimates jointly male and female fertility. This model linked the AI result to a purely additive combination (on the underlying scale) of environmental and genetic eects of the two subjects (additive model). Results obtained were in accordance across breeds and with the literature. Main environmental eects were the combination year*season, age of the female, time interval between previous lambing and insemination, motility of the semen, inseminator and the combination herd*year. Heritability estimates varied from 0.001 to 0.005 for male fecundancy and from 0.040 to 0.078 for female fertility. Repeatability estimates varied from 0.007 to 0.015 for male fecundancy and from 0.104 to 0.136 for female fertility. To be more in accordance with the biology, we propose another model which supposes that the observed phenotype is the product of 2 unobserved phenotypes (male success, female success), one for each subject aecting the observation (product model). We developed an algorithm tting the product model and showed, with simulations, that it is workable and provides good estimations of the parameters. We showed that tting an additive model whereas data are simulated with product model gave biased genetic estimates especially for high EBV and that the product model is able to estimate separately xed eects for each of the 2 subjects while additive model is not. We estimated the genetic correlation between female fertility and milk yield in Lacaune breed and obtain similar antagonistic genetic correlation than in cattle (-0.23).