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Interactions between single nucleotide polymorphisms (SNPs) associated with milk yield, fat and protein percentage in a Holstein cattle population

Laiza Helena de Souza Iung ; Juliana Petrini ; Luke Kramer ; Paulo Fernando Machado ; Luiz Lehmann Coutinho ; Gerson Barreto Mourão
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Genome wide association studies (GWAS) emerged with the advent of high-throughput genotyping, and it has focused mainly in additive genetic effects. However, genotypic information can be used to explore non-additive effects in order to expand the knowledge about the genetic mechanisms behind complex phenotypes. Thus, the aim of this study was to identify interaction effects between single nucleotide polymorphisms (SNPs) associated with milk yield (MY), fat percentage (FP) and protein percentage (PP) in a Holstein cattle population. Imputed genotypes (57,368 SNPs) from 666 Holstein cows were used. Phenotypes were pre-corrected for contemporary group (CG; formed by calving year, calving season, farm and month of analysis), lactation order, and days in milk (DIM, cubic effect). EpiSNPmpi (Ma et al. 2008) software was used to conduct the interaction analysis. Only interactions involving SNPs located on different chromosomes and a minimum of 5 animals per genotype combination were maintained. In addition, a false discovery rate (FDR) was applied considering 5% as a significance level. Pathway analysis was developed by using the Reactome database. A total of 74 interactions, all dominance x dominance, were obtained. SNPs of several chromosomes were involved in interactions in all traits analyzed, however, the stronger interaction signals were seen on BTA14/BTA26, BTA1/BTA23 and BTA9/BTA16 for MY, BTA5/BTA18, BTA3/BTA6 and BTA2/BTA11 for FP and BTA11/BTA14, BTA6/BTA19 and BTA7/BTA16 for PP. Most of these genomic regions are well known to harbor candidate genes or quantitative trait loci (QTL) that greatly affect milk production traits. Many of the genes related to the interactions found here are involved in cellular processes, protein and lipid metabolism, signal transduction and immune system. Such processes are directly or indirectly related to milk production traits. These findings highlight the need to continue studying interactions between genomic regions in order to help us to reveal the complex architecture underlying quantitative traits.