Old school molecular biology and biochemistry techniques are being pushed to new levels made possible by the nanotechnology and computer revolutions. One such implementation is the GWAS, genome wide association study.
The idea is based on the fact that there are genetic variations in the human population. Thus, population X (without disease condition Y) is compared against population Z (with disease condition Y). Do the genetic variants distribute the same in population X and population Z?
For example, let's say there is a gene variant P that exits in 75% of the study population (X + Z). If P is NOT associated with disease condition Y then P should show up in population X and Z at 75%. But, in an extreme example, variant P shows up in 55% of population X and 95% of population Z then you would say variant P is associated with disease Y.
Of course, that is an extreme example and real life the data is much less clear cut. And of course, correlation (or association) is not causation.
Came across this article with the provocative title, Have We Wasted 7 Years and $100 Million Dollars on GWAS Studies?
The author rightly points out the technical challenges of conducting such experiments and the limits of the interpretation of the data. And certainly, it looked bleak for whether it has been worthwhile. But in the end, he concludes, that is science, you got to try and see what happens and that these efforts lay a foundation for future experiments. He summarizes:
So has it been worth it to spend over $100 million dollars in research funding on these studies over the past seven years?
Yes.
But not because we discovered lots of actionable genetic markers. We haven’t.
And not because we have achieved a genetic understanding of common (and costly) diseases as we promised in our grants. We haven’t.
But science isn’t about delivering on a business plan.
Science is about discovery; breaking ground on venues of research that were previously entirely uncharted or unknown.
Already, follow-up studies are taking a deeper look at the genomic regions associated with certain traits.
Some of these studies are looking to close the gap of missing heritability by using Next-Generation Sequencing and new hypothesis about the biological architecture of common and chronic diseases.
With the expectation that genetics will play a large role in how clinical practice of medicine approaches preventative and personal care, there is an enormous amount of research left to make an individual’s genome actionable.
I’ll be watching closely.