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D4.2 Predictive models for livestock sector (including animal production and meat quality) - Executive Summary

GLOMICAVE aims to exploit the information hidden in the existent scientific literature and large-scale omics datasets for a better understanding of genotype-phenotype relationships. In this deliverable, we will focus on the Livestock sector (Task 6.1 and 6.2) and will present predictive models for those two cases, respectively the prediction of pregnancy and calving outcomes in cattle, and the prediction of meat quality, both from metabolomics data.

Tenderness is a complex trait and a primary driver of consumer preferences in beef products. Although many of the factors affecting the variation in meat tenderness are not well understood, it is known that skeletal muscle metabolites contribute to meat quality traits such as tenderness [MOOW19]. Metabolomic techniques can be very useful for the rapid analysis of many samples and provide highly sensitive results that can be applied to characterise and quantify the component changes in muscles, increasing the genetic gain by improving the accuracy of selection [WaKa20].

In general, greater efficiency in reproduction procedures leads to requiring fewer effective cattle to reach the same objectives and shorten the productive periods (fewer foods produced for cattle, also). Reducing individuals is not only “3R”, but also less methane-producing and, therefore eco-responsible. Discrimination of recipients for embryo transfer (ET) is usually based on embryo-recipient synchrony, followed by detection of a functional corpus luteum by ultrasonography, progesterone measurement, or simply rectal palpation. Such recipient selection methods fail to include all receptive animals and discards, sometimes erroneously, high percentages of synchronised females. Therefore, developing systematic and reliable methodologies for recipient selection is a major objective within bovine embryo transfer. Embryo recipients able to sustain healthy pregnancies show distinctive and identifiable metabolomic profiles in blood plasma.

However, the knowledge of metabolic pathways dealing with cattle fertility must be enhanced to identify new predictive biomarkers for pregnancy and the context into which such biomarkers are predictive. So, GLOMICAVE tools will allow a better selection of ET recipients and, in fine, to an increase in pregnancy rate. According to AETE and IETS statistics, more than 1,100,000 bovine embryos have been transferred in the world, including more than 150,000 ET in Europe. The use of embryo technologies is continuously increasing worldwide in the last 20 years, particularly since genomic selection emergence in cattle from the 2010th. Average pregnancy rates are not known precisely, varying from 40 to 60% depending on various factors (embryo type, recipient’s characteristics, and management….). From a techno-economical perspective, increasing the pregnancy rate by 10% (lower estimation) could save, for the same number of pregnant animals, around 110,000 and 15,000 ET recipients per year, respectively, for the world and Europe. Using less ET recipients without affecting the number of calves born will allow to save costs of ET failures (260 € per embryo transfer failure on average). Without considering the impact on genetic gain (fewer loss of high genetic value embryos), these recipient’s economies will lead to significant savings around 3,9 M€ per year for Europe and 28,6 M€ for the world.

Regarding the meat quality case, 24 samples have been analysed, which corresponds to strategy A. A bit less samples were collected in Strategy A (222 instead of 300, justification in Milestone MS14) so descriptive statistical analysis of the data will be presented.

Regarding the cattle reproduction case, 282 embryo transfer cases have been analysed in the Starting study. This Starting study contains samples coming from previous background studies from SERIDA and ELIANCE. The analysis of these data will lead to the establishment of a model for prediction of embryo transfer outcome and identification of biomarkers of pregnancy and birth success depending on the embryo transfer factors. On a second time, samples of the Validation study will be used to validate the prediction model thus designed.