Integrative genomics analysis highlights functionally relevant genes for equine behaviour.
Amy R HoltbyThomas J HallBeatrice A McGivneyHaige HanKeith J MurphyDavid E MacHughLisa M KatzEmmeline W HillPublished in: Animal genetics (2023)
Behavioural plasticity enables horses entering an exercise training programme to adapt with reduced stress. We characterised SNPs associated with behaviour in yearling Thoroughbred horses using genomics analyses for two phenotypes: (1) handler-assessed coping with early training events [coping] (n = 96); and (2) variation in salivary cortisol concentration at the first backing event [cortisol] (n = 34). Using RNA-seq derived gene expression data for amygdala and hippocampus tissues from n = 2 Thoroughbred stallions, we refined the SNPs to those with functional relevance to behaviour by cross-referencing to the 500 most highly expressed genes in each tissue. The SNPs of high significance (q < 0.01) were in proximity to genes (coping - GABARAP, NDM, OAZ1, RPS15A, SPARCL1, VAMP2; cortisol - CEBPA, COA3, DUSP1, HNRNPH1, RACK1) with biological functions in social behaviour, autism spectrum disorder, suicide, stress-induced anxiety and depression, Alzheimer's disease, neurodevelopmental disorders, neuroinflammatory disease, fear-induced behaviours and alcohol and cocaine addiction. The strongest association (q = 0.0002) was with NDN, a gene previously associated with temperament in cattle. This approach highlights functionally relevant genes in the behavioural adaptation of Thoroughbred horses that will contribute to the development of genetic markers to improve racehorse welfare.
Keyphrases
- genome wide
- dna methylation
- stress induced
- gene expression
- single cell
- rna seq
- genome wide identification
- autism spectrum disorder
- copy number
- depressive symptoms
- social support
- prefrontal cortex
- randomized controlled trial
- genome wide analysis
- bioinformatics analysis
- mental health
- cognitive decline
- blood brain barrier
- big data
- functional connectivity
- skeletal muscle
- machine learning
- diabetic rats
- high glucose
- resting state
- intellectual disability
- working memory
- genome wide association
- congenital heart disease
- virtual reality
- drug induced