Crossbred Bull Fertility Prediction Using a Transcriptome based SNP Methodology

Abstract

Although various methods have been advanced and introduced to comprehend the most suitable approach for predicting fertility, extensive analysis has been undertaken. The utilization of high-throughput data sequencing has been instrumental in the generation of extensive datasets derived from diverse studies, with a primary focus on elements such as epigenetics, genomic variations, genome-wide associations, and differential gene expression in the context of bull fertility. However, the task of achieving effective prediction remains a formidable challenge. The current investigation aims to examine variations identified through RNA-Seq, with a particular emphasis on coding region alterations, and subsequently, map these variations to the region of genes associated with fertility functions and proximity. In this study, transcriptome analysis was carried over sperm samples obtained from 12 bulls, half of which exhibited high fertility (n = 6) and the other half low fertility (n = 6) based on their conception rates. Bioinformatics tools align the data, genetic variants were identified and mapped subsequently to known fertility quantitative trait loci (QTL). After filtering for fertility-specific single nucleotide polymorphisms (SNPs) with missense mutations, we identified a total of six significant biomarkers. This research demonstrates the efficacy of our innovative approach for screening genetic variants, ultimately leading to the identification of fertility markers within bovine spermatozoa.