Nutrigenomics – another step closer to an unprecedented level of health and nutrition
A few months ago I reported about an exciting concept call Nutrigenomics; the futuristic capability to consume a “designer diet” based on a person’s individual genotype that will prevent disease and/or possibly enhance athletic performance. A team of researchers at the Harvard School of Public Health (HSPH) has just brought us that little bit closer to this reality.
In this ground-breaking research the team of scientists led by statisticians at HSPH figured out how to analyze huge quantities of genetic data, surpassing the capabilities of traditional techniques and speeding the quest for understanding the genetic basis of complex diseases. In doing so, the technique gives other researchers an easier and more accurate way to determine how multiple genes maybe involved in complicated physical traits such as heart disease, fat gain or muscle growth.
As I’ve mentioned in a previous article, the Human Genome Project was completed in 2003 and as a result ongoing studies have made available unparalleled amounts of information regarding human genes. We now know there are now 8 million genetic markers called SNPs (single nucleotide polymorphisms) available for analysis. These are key sites within our genome that can be manipulated by the foods we eat, the exercise we perform and the environment in which we live.
New research projects have been fueled by a technology revolution within the past two years. To give you an idea of the importance of the information obtained by the team at HSPH; in a single study scientists can now utilize a gene chip that can scan up to 100,000 SNPs, whereas, previously, a single study would have considered only 10 to 15 SNPs. Basically, this new methodology can process huge amounts of data. This is a giant step towards making genetic-based nutrition a reality in our life time.
Typically, a biostatistical study in gene research would demand a two-step process of 1) culling the number of SNP candidates, and 2) testing the survivors for associations to specific traits, such as risk for high body mass index. The process requires the use of two completely separate datasets.
This process also invokes something called the “multiple comparison problem.” Every candidate SNP must be tested. The more SNPs a study has, the more likelihood of false-positive signals, the fewer SNPs end up surviving the initial culling process. The result is that some true SNP candidates may never make it to the second testing phase. When dealing with tens of thousands of SNPs, that culling could mean numerous viable candidates slip through the cracks.
The new analysis method introduced by the HSPH researchers uses just one dataset. It bypasses the multiple comparison problem altogether by first estimating how much genetics can explain a specific trait within a population and then tracing the roots of the trait back to candidate SNPs that would explain that “genetic effect size.” To test their methodology, the research team ran simulation studies using data from a childhood asthma management program. The results of the simulation studies suggested that the new approach outperformed the traditional approach by 100%!
Besides dealing away with the multiple comparison problem, the HSPH technique offers another feature that maybe even more important to health–conscious people like you and me. This new technology appears to be able to find multiple SNPs involved in a single disease or trait. This means that scientists will be able to identify complex phenotypes such as molecular signals to accelerate body fat reduction, muscle growth, reduce high blood pressure or prevent heart disease. Until now, no statistical tool existed that would allow researchers to look at several thousand disease genes and successfully identify those small number of genes that influence such complex traits. We do now. And rest assured, very cool things are going to follow.