Integrative factorial methods to explore the relationships between genotypes, phenotypes and climate in Holstein cows

International audience

English. Understanding the complex relationship between animal genotypes, phenotypes and climate is a topic of increasing relevance in animal breeding, given the rapidly changing climatic conditions. In this work, we used 15,545 first lactation daily records on Italian Holstein dairy cows from ANAFIBJ. Recorded traits were milk production, fat and protein content, as well as climate data such as the Temperature Humidity Index (THI), maximum daily temperature (MaxTemp) and average daily percent humidity (%Hum). In addition, herd characteristics such as geographical coordinates, were available. Moreover, pedigree and genomic (Bovine 50k SNP chip) data were available for each recorded cow. Since THI is highly correlated with MaxTemp (r=0.99), THI was not further considered in the study.A first step of the study consisted in the analysis of the lactation curves for milk, fat and protein through a functional Principal Component Analysis (fPCA). The aim of fPCA is to summarize longitudinal data in a few synthetic variables (the eigenfunctions). For the three traits, three eigenfunctions were retained by the fPCA. The first eigenfunction describes the average curve, and the following ones are related to the features of the curve. For instance, for the milk production, the three eigenfunctions describe the production level, the production persistency and the peak production. They explain, respectively: 89.6%, 8% and 2.4% of the total variation for milk. Similar results were observed for protein content (85.2%, 11.7 % and 3.1 %), and fat content (89.2 %, 7.4%, 3.4 %). Eigenfunctions scores were then analysed in relationship with the average climate variables MaxTemp and %hum, with a model that also includes a Herd effect. For instance, climate variables are significant for the milk production level (First eigenfunction of milk production level) Following steps of the study will consist in the joint analysis of climate, production traits and genotypes by combining milk production and climate records with the SNP genotypes of dairy AI sires that have lactating daughters across different regions of Italy. This will add a genetic and a geographic component to the analysis of the relationships between phenotypes, climate and genotypes.