Abstract
Metabolites, substrates or products of metabolic processes, are involved in many biological functions, such as energy metabolism, signaling, stimulatory and inhibitory effects on enzymes and immunological defense. Metabolomic phenotypes are influenced by combination of genetic and environmental effects allowing for metabolome-genome-wide association studies (mGWAS) as a powerful tool to investigate the relationship between these phenotypes and genetic variants. The objectives of this study were to estimate genomic heritability and perform mGWAS and in silico functional enrichment analyses for a set of plasma metabolites in Canadian crossbred beef cattle. Thirty-three plasma metabolites and 45,266 single nucleotide polymorphisms (SNPs) were available for 475 animals. Genomic heritability for all metabolites was estimated using genomic best linear unbiased prediction (GBLUP) including genomic breed composition as covariates in the model. A single-step GBLUP implemented in BLUPF90 programs was used to determine SNP P values and the proportion of genetic variance explained by SNP windows containing 10 consecutive SNPs. The top 10 SNP windows that explained the largest genetic variation for each metabolite were identified and mapped to detect corresponding candidate genes. Functional enrichment analyses were performed on metabolites and their candidate genes using the Ingenuity Pathway Analysis software. Eleven metabolites showed low to moderate heritability that ranged from 0.09 ± 0.15 to 0.36 ± 0.15, while heritability estimates for 22 metabolites were zero or negligible. This result indicates that while variations in 11 metabolites were due to genetic variants, the majority are largely influenced by environment. Three significant SNP associations were detected for betaine (rs109862186), L-alanine (rs81117935), and L-lactic acid (rs42009425) based on Bonferroni correction for multiple testing (family wise error rate <0.05). The SNP rs81117935 was found to be located within the Catenin Alpha 2 gene (CTNNA2) showing a possible association with the regulation of L-alanine concentration. Other candidate genes were identified based on additive genetic variance explained by SNP windows of 10 consecutive SNPs. The observed heritability estimates and the candidate genes and networks identified in this study will serve as baseline information for research into the utilization of plasma metabolites for genetic improvement of crossbred beef cattle.
Highlights
The metabolic phenotype is a characteristic metabolite profile that depends on the interactions between genetic and environmental effects
The objectives of this study were to estimate genomic heritability of 33 plasma metabolites in crossbred beef cattle, to identify genetic variants, genomic regions and candidate genes associated with metabolite variation, and to understand the biological functions and gene networks linked to these associations
The single nucleotide polymorphisms (SNPs) rs81117935 was found within the catenin alpha 2 gene (CTNNA2), while the other two SNPs were not mapped to any known candidate gene (Table 4)
Summary
The metabolic phenotype (or “metabotype”) is a characteristic metabolite profile that depends on the interactions between genetic and environmental effects. Blood metabolites have been shown to be powerful tools for the indication of the nutritional and health status of humans and animals. Metabolome-genome-wide association study (mGWAS) is a powerful tool for identifying genetic variants underlying metabolic phenotypes and provides new opportunities to decipher the genetic basis of metabolic phenotypes. MGWAS detect genetic variants that are functionally associated with metabolic phenotype variation. Previous studies have reported that single nucleotide polymorphisms (SNPs) in the glutamine synthase 2 gene (GLS2) were associated with glutamine in human serum, which provides a potential biological association, as the enzyme GLS2 catalyzes the hydrolysis of glutamine (Suhre et al, 2011; Kettunen et al, 2012). Genome-wide hits with unknown gene function offer an opportunity to infer novel biological mechanisms of the SNP-metabolite association. Recent application of GWAS have successfully uncovered genetic variants that contribute to variation in both the external phenotype (e.g., type 2 diabetes) and the metabolic phenotype (e.g., fasting glucose levels) (Stranger et al, 2011)
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