Genome wide association study identifies novel potential candidate genes for bovine milk cholesterol content

Abstract

This study aimed to identify single nucleotide polymorphisms (SNPs) associated with milk cholesterol (CHL) content via a genome wide association study (GWAS). Milk CHL content was determined by gas chromatography and expressed as mg of CHL in 100 g of fat (CHL_fat) or in 100 mg of milk (CHL_milk). GWAS was performed with 1,183 cows and 40,196 SNPs using a univariate linear mixed model. Two and 20 SNPs were significantly associated with CHL_fat and CHL_milk, respectively. The important regions for CHL_fat and CHL_milk were at 41.9 Mb on chromosome (BTA) 17 and 1.6–3.2 Mb on BTA 14, respectively. DGAT1, PTPN1, INSIG1, HEXIM1, SDS, and HTR5A genes, also known to be associated with human plasma CHL phenotypes, were identified as potential candidate genes for bovine milk CHL. Additional new potential candidate genes for milk CHL were RXFP1, FAM198B, TMEM144, CXXC4, MAML2 and CDH13. Enrichment analyses suggested that identified candidate genes participated in cell-cell signaling processes and are key members in tight junction, focal adhesion, Notch signaling and glycerolipid metabolism pathways. Furthermore, identified transcription factors such as PPARD, LXR, and NOTCH1 might be important in the regulation of bovine milk CHL content. The expression of several positional candidate genes (such as DGAT1, INSIG1 and FAM198B) and their correlation with milk CHL content were further confirmed with RNA sequence data from mammary gland tissues. This is the first GWAS on bovine milk CHL. The identified markers and candidate genes need further validation in a larger cohort for use in the selection of cows with desired milk CHL content.

Selection signatures in two oldest Russian native cattle breeds revealed using high-density single nucleotide polymorphism analysis

“…Additional large set of candidate genes (LRRC24, LRRC14, RECQL4, HEATR7A, MFSD3, GPT, PPP1R16A, FOXH1, CYHR1, SLC39A4, CPSF1, ADCK5, FBXL6, BOP1, SCRT1, DGAT1, GPAA1, EXOSC4, PARP10, HSF1, OPLAH, and GRINA), associated with milk production traits in Kholmogor cattle was found on BTA 14. These genes were shown to be involved in regulation of milk fatty acid composition [113][114][115][116], milk yield [117,118], milk fat yield, and milk fat percentage [119][120][121][122][123]. In contrast to Kholmogor cattle, we identified only two candidate genes on BTA 5, which were associated with milk production traits in Yaroslavl cattle, including TMCC3 and DDX10 [124,125].…”

Targeted genotyping to identify potential functional variants associated with cholesterol content in bovine milk

“…SNP rs109959260 in TM6SF2 explained the lowest variance in fat yield among five significantly associated SNPs but might be an important SNP for the trait given the important role of TM6SF2 in regulating liver fat metabolism by influencing triglyceride secretion and hepatic lipid droplet content (Kozlitina et al ; Liu et al ). In our previous GWAS (Do et al ), FAM198B was identified as one of the most promising candidate genes for CHL_fat as it was located in the flanking region of the most significant SNPs (rs41600454 and rs42640895) and also its expression in the mammary gland was significantly correlated with CHL_fat concentration. None of three SNPs (rs42781651, rs109593679 and rs109747327) in FAM198B was significantly associated with CHL_fat but SNP rs42781651 had a significant association with fat yield based on a dominance effect (Table ).…”

“…Recently, we performed a genome wide association study (GWAS) and identified several positional candidate genes including DGAT1 , PTPN1 , INSIG1 , HEXIM1 , SDS , HTR5A , RXFP1 , FAM198B , TMEM144 , CXXC4 , MAML2 and CDH13 for milk cholesterol content (Do et al ) . However, some candidate genes reported as important for cholesterol metabolism in human (Parini et al ; Ferraz‐de‐Souza et al ; Abdo et al ; Liu et al ) were not significantly associated with cholesterol content in this study (Do et al ). This could be due to the fact that variants in these genes are not included in the BovineSNP50 Genotyping BeadChip (Illumina Inc., USA).…”

Genomic analysis of divergently selected experimental lines in rabbit

“…Hence, the number of independent testing in livestock is commonly much lower than in human . Thus, this suggested the inclusion of a lower "suggestive" threshold, 1 x 10 -4 , for livestock GWASs Bertolini et al, 2018;Do et al, 2018). (Li and Zhu, 2013).…”

“…When its application is needed, the regression factor lambda (λ) corrects the observed p-values leading to new p-values for every assessed SNP (Aulchenko et al, 2007). In this research, we used two thresholds; a LD-adjusted Bonferroni (8.12x10 -6 ) calculated for 10-Mb LD blocks according to LD analysis implemented in PLINK, and also, a suggestive threshold of 1x10 -4 due to the high relatedness of the samples (Lander & Kruglyak, 1995;Do et al, 2018). As Bonferroni is a very conservative method, we also implemented the suggestive threshold because it is less stringent as the samples from animals with high relatedness would have genomic segments of LD larger than in human (Wang et al, 2016b;.…”