RNA sequencing to study gene expression and single nucleotide polymorphism variation associated with citrate content in cow milk

Abstract

The technological properties of milk have significant importance for the dairy industry. Citrate, a normal constituent of milk, forms one of the main buffer systems that regulate the equilibrium between Ca(2+) and H(+) ions. Higher-than-normal citrate content is associated with poor coagulation properties of milk. To identify the genes responsible for the variation of citrate content in milk in dairy cattle, the metabolic steps involved in citrate and fatty acid synthesis pathways in ruminant mammary tissue using RNA sequencing were studied. Genetic markers that could influence milk citrate content in Holstein cows were used in a marker-trait association study to establish the relationship between 74 single nucleotide polymorphisms (SNP) in 20 candidate genes and citrate content in 250 Holstein cows. This analysis revealed 6 SNP in key metabolic pathway genes [isocitrate dehydrogenase 1 (NADP+), soluble (IDH1); pyruvate dehydrogenase (lipoamide) β (PDHB); pyruvate kinase (PKM2); and solute carrier family 25 (mitochondrial carrier; citrate transporter), member 1 (SLC25A1)] significantly associated with increased milk citrate content. The amount of the phenotypic variation explained by the 6 SNP ranged from 10.1 to 13.7%. Also, genotype-combination analysis revealed the highest phenotypic variation was explained combining IDH1_23211, PDHB_5562, and SLC25A1_4446 genotypes. This specific genotype combination explained 21.3% of the phenotypic variation. The largest citrate associated effect was in the 3' untranslated region of the SLC25A1 gene, which is responsible for the transport of citrate across the mitochondrial inner membrane. This study provides an approach using RNA sequencing, metabolic pathway analysis, and association studies to identify genetic variation in functional target genes determining complex trait phenotypes.

Multi-Tissue Omics Analyses Reveal Molecular Regulatory Networks for Puberty in Composite Beef Cattle

“…Sequence reads were assembled to the annotated UMD3.1 bovine reference genome (release 74; ftp://ftp.ensembl.org/pub/release-74/genbank/bos_taurus/). Quality control and RNA-Seq expression analysis was performed using procedures described by Cánovas et al [84] and using the CLC Genomics workbench software (CLC Bio, Aarhus, Denmark). Transcript levels were quantified in reads per kilobase of exon per million reads (RPKM).…”

Genetic Influences on Cattle Grazing Distribution: Association of Genetic Markers with Terrain Use in Cattle

“…3 The fixed additive effect regression model was compared with a random model to determine P values of the associations between genotype and phenotype (Canovas et al, 2013; t3:12 Rincon et al, 2009).…”

“…Both indexes are normalized for each deployment of tracking collars at each ranch.t4:133 Gene name or type of marker. ACN9 indicates ACN9 homolog (gene located 30,000 bp upstream from SNP BovineHD0400004308); FAM48A, family with sequence similarity 48, t4:14 member A (gene located 70,000 bp upstream from SNP BovineHD1200007410); GRM5, glutamate receptor, metabotropic 5; MAML3, mastermind-like 3; and RUSC2, RUN and SH3 domain t4:15 containing 2.t4:16 4 The minor allele frequency (MAF) refers to the allele shown in the previous column.t4:17 5 The fixed-additive effect regression model was compared with a random model to determine P values of the associations between genotype and phenotype(Canovas et al, 2013; t4:18 Rincon et al, 2009).…”

Global differential gene expression in the pituitary gland and the ovaries of pre- and postpubertal Brahman heifers

“…The bovine reference genome, also known as the UMD3.1 assembly (release 77; ftp://ftp.ensembl.org/pub/release-77/genbank/bos_taurus; accessed May 15, 2015), was used as the reference genome for sequence assembly, which was performed using the CLC workbench software (CLC bio, Aarhus, Denmark). This software was also used for quality control of sequence data and calculation of reads per kilobase per million mapped reads (RPKM) per gene (Mortazavi et al, 2008;Cánovas et al, 2013). The CLC workbench software assesses sequence quality indicators based on the protocols of the FastQC project (http://www.bioinformatics.babraham.…”