As we've discussed, many genetic changes occur every second in your body. These changes have the potential to lead to cancer down the line, but only if they occur in specific genes. When genetic tests are done by doctors, all these changes can be found but someone has to decide which changes could cause disease and which probably won't.

Geneticists have discovered thousands of genetic changes for which they have no idea how (or if) they might impact a person's health. Most of these are believed to be harmless, so doctors don't want to subject someone to treatments if just any genetic change is found. Doctors only want to pursue treatment if they suspect a genetic change occurred which could cause cancer.

In recent years, the American College of Genetics and Genomics (ACMG) released a set of 18 rules under which a genetic test can be reviewed, to evaluate whether a patient might be at risk of developing a disease like cancer due to genetic changes. 

In this week's news from Huntsman Cancer Institute, a researcher named Sean Tavtigian wanted to test this set of rules along with colleagues from other cancer centers, to see how strong they are. They turned to an 18th century equation to do so.

That equation is called the Bayes' Theorem, named after Thomas Bayes--the minister and statistician who originally conceived of it. Published in 1763, it was designed to find the probability an event would occur given some specified conditions.

Examples could be predicting the location of a specific star on a specific time and date or finding the probability of pulling a green ball from a hat given the number of green and yellow balls inside.

The HCI researchers wanted to look at was the probability a genetic change would lead to disease given the evidence considered by ACMG's rules.

The answers they got validated the rules, showing they are a strong way to understand whether a genetic change might cause disease. They also pointed out a few ways to improve the rules.

Those of us who ever have a genetic test can thank both Tavtigian and Bayes for how reliable the process is.

Sources:

 A Centuries-Old Math Equation Used to Solve a Modern-Day Genetics Challenge. (2018, January 18). Retrieved March 26, 2018, from https://huntsmancancer.org/newsroom/2018/01/centuries-old-math-equation.php

Bayes, T., & Price, R. (1764). A method of calculating the exact probability of all conclusions found on induction. London: S.n.

Tavtigian, S. V., Greenblatt, M. S., Harrison, S. M., Nussbaum, R. L., Prabhu, S. A., Boucher, K. M., & Biesecker, L. G. (2018). Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework. GENETICS in MEDICINE. doi:10.1038/gim.2017.210