A genome-wide association study for somatic cell score using the Illumina high-density bovine beadchip identifies several novel QTL potentially related to mastitis susceptibility

Abstract

Mastitis is an inflammation-driven disease of the bovine mammary gland that occurs in response to physical damage or infection and is one of the most costly production-related diseases in the dairy industry worldwide. We performed a genome-wide association study (GWAS) to identify genetic loci associated with somatic cell score (SCS), an indicator trait of mammary gland inflammation. A total of 702 Holstein-Friesian bulls were genotyped for 777,962 single nucleotide polymorphisms (SNPs) and associated with SCS phenotypes. The SCS phenotypes were expressed as daughter yield deviations (DYD) based on a large number of progeny performance records. A total of 138 SNPs on 15 different chromosomes reached genome-wide significance (corrected p-value ≤ 0.05) for association with SCS (after correction for multiple testing). We defined 28 distinct QTL regions and a number of candidate genes located in these QTL regions were identified. The most significant association (p-value = 1.70 × 10−7) was observed on chromosome 6. This QTL had no known genes annotated within it, however, the Ensembl Genome Browser predicted the presence of a small non-coding RNA (a Y RNA gene) in this genomic region. This Y RNA gene was 99% identical to human RNY4. Y RNAs are a rare type of non-coding RNA that were originally discovered due to their association with the autoimmune disease, systemic lupus erythematosus. Examining small-RNA sequencing (RNAseq) data being generated by us in multiple different mastitis-pathogen challenged cell-types has revealed that this Y RNA is expressed (but not differentially expressed) in these cells. Other QTL regions identified in this study also encoded strong candidate genes for mastitis susceptibility. A QTL region on chromosome 13, for example, was found to contain a cluster of β-defensin genes, a gene family with known roles in innate immunity. Due to the increased SNP density, this study also refined the boundaries for several known QTL for SCS and mastitis.

Genome-wide association study using high-density single nucleotide polymorphism arrays and whole-genome sequences for clinical mastitis traits in dairy cattle

“…Several studies have reported associations with SCC/CM with genomic region on BTA6 similar to those in the present study; for example, in US Holstein (Cole et al 2011), German Holstein (Abdel-Shafy et al, 2014, and Norwegian Red cattle (Nilsen et al, 2009;Sodeland et al, 2011). Meredith et al (2013) identified associations on BTA 13 and BTA 20, similar to the current study. In addition, Sodeland et al (2011) reported a wide region on BTA 20 in Norwegian Red cattle, supporting the results of the current study.…”

“…Previous mapping studies have identified QTL that affect clinical mastitis (CM) and SCC in cattle (e.g., Lund et al, 2007Lund et al, , 2008Sahana et al, 2008Sahana et al, , 2013Cole et al, 2011;Meredith et al, 2013;Abdel-Shafy et al, 2014). These studies, however, were not able to pinpoint specific candidate gene variants.…”

Genome-wide detection of copy-number variations in local cattle breeds

“…The SEC24D gene encodes a protein associated with various pathways, including in the immune system and transport to the Golgi, and is involved in transcriptional regulation in cattle (Salleh et al 2017). SDF2 L1, MAPK1 and mir301b are involved in the processing of a wide range of defensins (Meredith et al 2013). We also detected MEGF10, which encodes a protein that regulates the myogenesis of satellite cells in skeletal muscles (Park et al 2014), and CRIM1, which is located close to a QTL on Chr11 and encodes an insulin-like growth factor-binding domain (Kolle et al 2000).…”

Genomic response to natural selection within alpine cattle breeds

“…Approaches that integrate quantitative trait loci (QTLs) mapping studies, genome-wide association studies (GWAS), and selection signatures have led the way to fine-map and localize functional mutations of many genes contributing to phenotypic diversity (Randhawa et al 2016). In cattle, various GWAS have been carried out to analyse genetic differences for a variety of traits like production and reproduction and for susceptibility or resistance to certain diseases such as Johne's disease, bovine tuberculosis, mastitis or somatic cell score (Meredith et al 2013).…”