Aggressive behavior in cattle is associated with a polymorphism in the MAOA gene promoter.

Molecular mechanisms underlying aggressive behavior are primitive and similar among the subphylum Vertebrata. In humans, a primary goal in the study of aggression is to determine the neurobehavioral molecular factors triggering violence. Although several species have been used to study agonistic responses, researchers are limited by the difficulty of artificially inducing aggression in animals not selected for it. Conversely, the Lidia cattle breed has been selected since the eighteenth century to display agonistic responses based on traits such as aggressiveness, ferocity and mobility, all of them showing significant heritability values. This intensive selection may have driven shifts in specific allele frequencies. In a previous analysis across the autosomes, we revealed long-term selection regions including genes involved in behavioral development. In the present study, we focus on mapping recent signatures of selection associated with aggressiveness at chromosome X, by comparing Lidia cattle samples with two non-specialized Spanish breeds showing tamed behavior. The most significant markers peaked around the monoamine oxidase A (MAOA) gene, and thus the associations of three functionally important regions located near the promoter of this gene were further investigated. A polymorphism consisting of a variable number of tandem repeats of the nucleotide 'C' (BTX:105,462,494) and displaying lower number of repetitions in the Lidia breed when compared with the tamed breeds was detected. In silico analyses predicted that the g.105,462,494delsinsC variant may code for the Sp1 binding motif, one of the major transcription factors controlling the core promoter and expression of the MAOA gene in humans.

Rapid identification and interpretation of gene-environment associations using the new R.SamBada landscape genomics pipeline.

samßada is a genome-environment association software, designed to search for signatures of local adaptation. However, pre- and postprocessing of data can be labour-intensive, preventing wider uptake of the method. We have now developed R.SamBada, an r-package providing a pipeline for landscape genomic analysis based on samßada, spanning from the retrieval of environmental conditions at sampling locations to gene annotation using the Ensembl genome browser. As a result, R.SamBada standardizes the landscape genomics pipeline and eases the search for candidate genes of local adaptation, enhancing reproducibility of landscape genomic studies. The efficiency and power of the pipeline is illustrated using two examples: sheep populations from Morocco with no evident population structure and Lidia cattle from Spain displaying population substructuring. In both cases, R.SamBada enabled rapid identification and interpretation of candidate genes, which are further discussed in the light of local adaptation. The package is available in the r CRAN package repository and on GitHub (github.com/SolangeD/R.SamBada).