Hypoplasia and ovarian cysts are the most common ovarian pathologies in cattle. In this genome-wide study we analyzed the signal intensity of 648,315 Single Nucleotide Polymorphisms (SNPs) and identified 1338 genes differentiating cows with ovarian pathologies from healthy cows. The sample consisted of six cows presenting an ovarian pathology and six healthy cows. SNP signal intensities were measured with a genotyping process using the Axiom Genome-Wide BOS 1 SNPchip. Statistical tests for equality of variance and mean were applied to SNP intensities, and significance p-values were obtained. A Benjamini-Hochberg multiple testing correction reveled significant SNPs. Corresponding genes were identified using the Bovine Genome UMD 3.1 annotation. Principal Components Analysis (PCA) confirmed differentiation. An analysis of Copy Number Variations (CNVs), obtained from signal intensities, revealed no evidence of association between ovarian pathologies and CNVs. In addition, a haplotype frequency analysis showed no association with ovarian pathologies. Results show that SNP signal intensity, which captures not only information for base-pair genotypes elucidation, but the amount of fluorescence nucleotide synthetization produced in an enzymatic reaction, is a rich source of information that, by itself or in combination with base-pair genotypes, might be used to implement differentiation, prediction and diagnostic procedures, increasing the scope of applications for Genotyping Microarrays.
Keywords: Axiom Genome-Wide Bos 1 array; Holstein cattle; SNP; bioinformatics; ovarian cysts.