Searching new signals for production traits through gene‐based association analysis in three Italian cattle breeds. (22nd May 2015)

• Record Type: Journal Article
• Authors: Capomaccio, Stefano; Milanesi, Marco; Bomba, Lorenzo; Cappelli, Katia; Nicolazzi, Ezequiel L.; Williams, John L.; Ajmone‐Marsan, Paolo; Stefanon, Bruno
• Is Part Of: Animal genetics. Volume 46:Number 4(2015:Aug.)
• Page Start: 361
• Page End: 370
• Publication Date: 2015-05-22
• Subjects: Animal genetics -- Periodicals; 572.8
• DOI: 10.1111/age.12303
• Languages: English
• ISSNs: 0268-9146

Abstract

<abstract abstract-type="main" id="age12303-abs-0001"> <title>Summary</title> <p>Genome‐wide association studies (GWAS) have been widely applied to disentangle the genetic basis of complex traits. In cattle breeds, classical GWAS approaches with medium‐density marker panels are far from conclusive, especially for complex traits. This is due to the intrinsic limitations of GWAS and the assumptions that are made to step from the association signals to the functional variations. Here, we applied a gene‐based strategy to prioritize genotype–phenotype associations found for milk production and quality traits with classical approaches in three Italian dairy cattle breeds with different sample sizes (Italian Brown <italic>n </italic>=<italic> </italic>745; Italian Holstein <italic>n </italic>=<italic> </italic>2058; Italian Simmental <italic>n </italic>=<italic> </italic>477). Although classical regression on single markers revealed only a single genome‐wide significant genotype–phenotype association, for Italian Holstein, the gene‐based approach identified specific genes in each breed that are associated with milk physiology and mammary gland development. As no standard method has yet been established to step from variation to functional units (i.e., genes), the strategy proposed here may contribute to revealing new genes that play significant roles in complex traits, such as those investigated here, amplifying low association signals using a gene‐centric approach.</p> </abstract>