GWAS Looks at Metabolites; May Shed Light on Risk Mechanisms
BioWorld Today Science Editor
By looking at how single-nucleotide polymorphisms (SNP) relate to the level of key metabolites in the blood, a multinational team of scientists identified nearly 40 variants that are associated with various metabolic traits.
Some of those variants have previously been shown to affect the risk for specific diseases, and the current work, which was published in the Sept. 1, 2011, issue of Nature, shows possible biological mechanisms that may underlie those risks.
And the study has effect sizes that most GWAS researchers – who more typically look at how SNPs affect the risk of developing certain diseases – can only dream of.
While a typical variant uncovered in a genomewide association study might raise the risk of a disease by a few percentage points at best, two-thirds of the SNPs that the scientists uncovered accounted for between 10 percent and 60 percent differences in the metabolite levels they looked at – per copy.
Those effect sizes, co-corresponding author Nicole Soranzo told BioWorld Today, were no longer surprising to the team; they were in line with effect sizes they had seen in earlier smaller studies.
What was surprising, she added, was "how robust this analysis is to possible nongenetic influences," given that metabolite levels can change very rapidly in response to environmental stimuli.
But the team's finding that a large part of the variance in many metabolite levels is genetic suggested that "these metabolites can indeed be very useful to explore key metabolic changes not only in healthy individuals, but also in people with disease."
Soranzo said that in her opinion the reason for those large effect sizes is that "we are looking at things that are measured quite accurately. But also, the effect is quite close to the primary effect of the gene."
GWAS studies on diseases such as diabetes look at a net outcome, that is, whether a person has the disease or not. But that outcome is a joint result of many different SNPs, as well as environmental factors. And so any given SNP can show no more than what co-corresponding author Karsten Suhre termed "the tip of the iceberg" of overall risk.
Scientifically, Suhre told BioWorld Today, the main advance of the study is that it gives life to the concept of "metabolic individuality."
That concept itself, he added, is not new; it was proposed a century ago by Archibald Garrod, who first developed the concept of inborn errors of metabolism, and also postulated that that metabolic individuality, which he termed chemical individuality according to the nomenclature of his day, "gives us predispositions for, or protection against, many diseases."
Garrod did not have the tools to actually identify what such chemical individuality might consist of. But with the advent of metabolomics, scientists are now able to demonstrate such metabolic individuality – and tie it to its genetic underpinnings.
In the work now published in Nature, Soranzo, Suhre and their team did so by screening about 600,000 SNPs and correlating them to levels of more than 37,000 metabolic traits. Such traits are either metabolite concentrations, or the ratios of different metabolites, in the blood. They identified 37 SNPs that were correlated with differences in the levels of specific metabolic traits.
Some of those associations are new; in other cases, the study has uncovered possible mechanisms that may underlie known disease SNPs. For example, variants in the GKCR gene affect the risk for both diabetes and cardiovascular disease. In the current study, the authors found that they are associated with the ratio of the sugars, mannose to glucose, suggesting that mannose may be a useful diabetes biomarker, or even a therapeutic target.
The method is, of course, most useful in diseases that have a strong metabolic component, such as diabetes and kidney disease. In other disease indications, such as neurological disorders, "you could catch the part which is due to metabolic factors," but not, for example, risk that is due to variations in regulatory SNPs, Suhre said.
More generally, Suhre said that associating the SNPs his team has identified with more specific endpoints will lead to further useful insights into the relationship between genes, metabolism and disease.
The study paves the way for overcoming a conundrum of genomewide association studies. Their sheer size makes them susceptible to "discovering" associations that are due to nothing but chance. Scientists prevent such false positives by setting the cutoff for statistical significance very high – but that, in turn, brings the risk of missing real but weak associations.
"You get away from the problem of statistical power and false positives," he said. The SNPs he and his team have identified "clearly do something – the question is what."
Published: September 6, 2011
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