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Integration of genomic data with gene network analysis can be a relevant strategy for unraveling genetic mechanisms. It can be used to explore shared biological processes between genes, as well as highlighting transcription factors (TFs) related to phenotypes of interest. Unlike other species, gene-TF network analyses have not yet been well applied to horse traits. We aimed to (1) identify candidate genes associated with horse performance via systematic review, and (2) build biological processes and gene-TF networks from the identified genes aiming to highlight the most candidate genes for horse performance. Our systematic review considered peer-reviewed articles using 20 combinations of keywords. Nine articles were selected and placed into groups for functional analysis via gene networks. A total of 669 candidate genes were identified. From that, gene networks of biological processes from each group were constructed, highlighting processes associated with horse performance (e.g., regulation of systemic arterial blood pressure by vasopressin and regulation of actin polymerization and depolymerization). Transcription factors associated with candidate genes were also identified. Based on their biological processes and evidence from the literature, we identified the main TFs related to horse performance traits, which allowed us to construct a gene-TF network highlighting TFs and the most candidate genes for horse performance.

Identification and Functional Annotation of Genes Related to Horses’ Performance: From GWAS to Post-GWAS