Understanding the genetic relationship between fertility and feed efficiency traits and its pleiotropic effect in cattle

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Reduced fertility is one of the main concerns regarding decreased profitability and animal welfare in the livestock sector. Similarly, reduced feed efficiency (FE) also has a great impact of the profitability of dairy herds and over the animal welfare. Feed costs can represent up to 75% of the production costs in cattle herds. Interestingly, a relevant, but unfavourable, genetic correlation is observed between FE and fertility in both beef and dairy cattle herds. The genetic basis of this unfavourable genetic correlation is still poorly understood. Therefore, the better understanding of the biological processes associated with genetic correlation between fertility and FE traits would help to develop new models and strategies to improve the simultaneous genetic selection. The main biological process responsible for the genetic correlation between traits is the pleiotropy. The decomposition of the biological processes associated with the observed genetic correlations between traits can bring great advantages for elucidating the development process of the phenotypes of interest, as well as increase the efficiency of selection for these traits. The interactions between markers and phenotypes can be modulate as causality networks as well as the interactions between genes and their production, such as mRNAs, can be modulate as co-expression networks. The analysis and interpretation of this biological information in a network structure allows the application of graph theory concepts which can help to extract meaningful information regarding putative causal variants and prioritization of functional candidate genes. In the proposed innovative research program I aim to identify functional candidate genes and genetic variants associated with the pleiotropic effect that might be the cause of the unfavourable genetic correlation observed between fertility and FE in dairy herds. This objective will be reach through calculation of pleiotropic scores from the summary statistics of genome wide association studies performed for fertility and FE traits. State-of-the-art multi-OMICS technologies will be used to identify differentially co-expressed gene networks from the endometrium tissue of pregnant and non-pregnant Holstein cows, as well as from milk samples from high- and low-FE Holstein cows. This information will be integrated using a bioinformatic and statistical pipeline, based on network representation learning, to prioritize functional candidate genes for the pleiotropic effect. The identification of this functional candidate genes has the potential to allow, for the first time, a fine mapping for the identification of causal variants simultaneously responsible for fertility and FE in Holstein cattle. Consequently, helping to reduce selection bias, unfavourable simultaneous selection, appropriately accounts the correlation between the traits, while helping to increase prediction accuracy, statistical power, and parameter estimation accuracy.