Adaptation of Mediterranean local cattle breeds to climate changes: a preliminary study on genome-wide diversity

In recent decades, changes in climate have caused impacts on natural and human systems on all continents. Mediterranean countries will be particularly affected by this phenomenon, with growing temperatures and reduced rainfall during summer months. Understanding how species and ecosystems respond to climate change has become a crucial focus in ecology, biodiversity conservation and management. The recent availability of genome-wide SNP panels allows providing background information concerning genome structure in domestic animals, opening new perspectives to livestock genetics. Several approaches have been performed in landscape genomics to detect adaptation to different climate pressure by correlations between genomic data and climate variables. The International Project GALIMED "Genetic Adaptation of Bovine Livestock and production systems in MEDiterranean region", funded by INRA (metaprogram ACCAF), develops an integrated approach that combines the analysis of SNP markers, bioclimatic variables and farming system information to identify genotypes and breeding practices able to respond to climate change. The general aim of the research is to identify genomic regions related to adaptation to climate change in these local cattle. In this work in particular, the genetic variability within breed and the genetic relationships between Italian and Corse local cattle breeds, that are part of this project, will be presented. Individuals of Cinisara (71), Modicana (72), Maremmana (25), Piemontese (21), Romagnola (21), Sarda (30), Sardo-Modicana (28) and Corse (31) cattle breeds, were genotyped using Illumina Bovine SNP 50k BeadChip. Piemontese e Romagnola genotyping data were public available, whereas the other breeds are part of the GALIMED project. Farming systems data were collected by interviewing breeders. Geographic coordinates and 19 bioclimatic variables were also available. A Principal Components Analysis (PCA) was performed either on SNPs data and climatic variables. Afterwards a Co-inertia (CIA) analysis was realized to detect a possible common structure between such different information. We present here first results of genetic variability and climatic data. After edits, 43,625 SNPs were retained for the analysis. Breeds are clearly differentiated according to geography and climate. The first axis of PCA on individual genotypes differentiates Modicana from other breeds, and shows that Sardo-Modicana is close to Modicana, but is clearly admixed to another breed. The second axis differentiates Cinisara from a cluster "Romagnola/Maremmana" and shows a large variability of the Modicana breed. The CIA coefficient between molecular data and both geographic and climatic information is equal to 0.38, significantly greater than 0 (p-value=0.001), suggesting evidence of genetic adaptation to different climatic pressures of the studied breeds. Further analyses are on going to identify the genetic regions with a potential adaptive role. The results will provide to farmers, technicians and industrial partners, a solid scientific foundation to reconsider their objectives and selection criteria and to improve their farming practices to prepare their livestock to the new environmental conditions.