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European Team Combines GWAS and Metabolomics to ID Genetic Variants Influencing Human Metabolism

NEW YORK (GenomeWeb News) – Common genetic variants can influence individuals’ metabolism, predisposing them to — or protecting them from — certain diseases, new research suggests.
In a paper appearing online today in PLoS Genetics, German and Austrian researchers used a combination genome-wide association/metabolomics study to identify genes linked to metabolic homeostasis in 284 German men. After assessing more than 360 metabolites in individuals’ blood samples, the team found several variants affecting these metabolites, including SNPs in four enzymes governing lipid metabolism. And, researchers noted, assessing such “metabotypes” may eventually improve health care.
“Our results suggest that common genetic polymorphisms induce major differentiations in the metabolic make-up of human populations,” senior author Karsten Suhre, a bioinformatics researcher at the German Research Center for Environmental Health, and his colleagues wrote. “These genetically determined metabotypes may subscribe the risk for a certain medical phenotype, the response to a given drug treatment, or the reaction to a nutritional intervention or environmental challenge.”
In an effort to uncover genetic associations that could inform their understanding of human metabolic function and disease biology, Suhre and his team combined traditional genome-wide association studies with metabolomics. Their goal: to discover genes and pathways bringing together biochemical measurements and disease etiology.
“Genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, or amino acids are not only expected to display much larger effect sizes due to their direct involvement in metabolite conversion modification, but may also provide access to the underlying molecular disease-causing mechanisms,” they reasoned.
The team first genotyped 1,644 individuals from Germany’s KORA population study using the Affymetrix 500K array set. Of these, they randomly selected 284 men between the ages of 55 and 79 years old for metabolite characterization. For these individuals, researchers took blood samples between eight and 10 in the morning (after overnight fasting) and eventually used electrospray ionization tandem mass spectrometry to assess 363 small molecule metabolites in the blood.
The team uncovered a handful of SNPs with relatively strong metabolic associations. By integrating information about metabolite concentrations, they also gained additional power to detect associations.
In particular, the researchers focused on SNPs in four genes coding for the enzymes FADS1, LIPC, SCAD, and MCAD, which function in biochemical pathways governing various aspects of lipid metabolism.
For example, FADS1 codes for an enzyme involved in fatty acid unsaturation and the FADS1 SNP rs174548 seems to explain as much as ten percent of the variance in the metabolism of some glycerophospholipids.
Consistent with its role in cholesterol metabolism, the authors noted, the same FADS1 SNP has also been linked to cholesterol levels in two large GWAS studies — though the associations did not reach the level of statistical significance.
The researchers subsequently screened their strongest associations against three previous GWAS studies, looking for associations between the SNPs of interest and clinical measurements influencing cardiovascular disease. Using this approach, they found overlap between several SNPs that seem to affect both metabolite biochemistry and clinical outcomes.
“In interactions with environmental factors such as nutrition of lifestyle, these metabotypes may influence the susceptibility of an individual for certain phenotypes,” the authors noted. For instance, the team cited potential links between long-chain fatty acid metabolism and attention deficit hyperactivity syndrome, ties between LIPC enzyme function and HDL-cholesterol related diseases, and associations between SCAD and MCAD polymorphisms and several systemic disorders.
And, they argued, such studies provide functionally relevant information about the genes and pathways underlying normal metabolism, gene-environment interactions, and related human diseases. Understanding these connections, in turn, may eventually lead to more targeted nutrition or therapies and more refined disease risk stratification.
“The identification of genetic variants that alter the homeostasis of key metabolites in the human body will eventually lead to a functional understanding of the genetics of complex diseases,” Suhre and his team wrote. “We argue that progress towards individualized medication lies in a combination of genotyping and metabotyping, based on evidence provided in part by GWA studies combined with metabolomics like the one presented here.”

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