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Researchers developed a new mathematical tool to validate and improve methods used by medical professionals to interpret results from clinical genetic tests. The work was published this month in Genetics in Medicine.
The research was led by Sean Tavtigian, PhD, a cancer researcher at Huntsman Cancer Institute (HCI) and professor of oncological sciences at the University of Utah, in collaboration with genetics experts from around the United States.
Tavtigian utilized Bayes' Theorem, a math equation first published in 1763, as the basis of a computational tool he and the team developed to assess the rigor of the current, widely-used approach to evaluate the results of a clinical genetic test.
Clinical genetic testing is used in a variety of medical fields, including cancer care, obstetrics, and neurosciences, among others. Results of a genetic test may help to provide a definitive medical diagnosis, or assess the likelihood of a person to develop a particular disease before symptoms appear. The range of approaches employed to provide health care based on the results of the test can vary significantly. Patients may be at negligible risk for disease with no medical management required, or they may pursue costly, invasive medical treatment in an effort to stave off disease or manage and minimize symptoms.
With millions and millions of changes possible in genes that control health in any given person, the challenge of discerning which gene changes are likely to cause disease is vast. In the past few years, human genetic researchers have identified thousands of Variants of Uncertain Significance (VUS), that is, genetic changes without a known understanding of how they may impact a person's health. "A large fraction of VUS are believed to be generally harmless," describes Tavtigian. "One only wants to change the medical management of patients when the genetic testing identifies a variant that is likely to be disease-causing. Against a huge population of harmless VUS, how do you identify the small subset that are likely to require medical management?"
Source: https://huntsmancancer.org/newsroom/2018/01/centuries-old-math-equation.php
Sean V Tavtigian, Marc S Greenblatt, Steven M Harrison, Robert L Nussbaum, Snehit A Prabhu, Kenneth M Boucher, Leslie G Biesecker. Modeling the ACMG/AMP variant classification guidelines as a Bayesian classification framework. FENETICS in MEDICINE, 2018; DOI: 10.1038/gim.2017.210
(Score: 2) by AthanasiusKircher on Sunday January 21 2018, @01:52PM (9 children)
Yeah, this is really basic stats/probability.
I hoped there might be more about Bayes' Theorem in TFA or something to justify the excitement about math... But alas no. It's basically what you see in the summary, plus more vague description of validating their approach. I have no idea why the author of the headline thought the history was noteworthy. A lot of basic math is centuries or millennia old... I guess this person just hadn't heard of Bayes before??
(If so, that's an education issue now. Given all the applicatons in modern data and probabilistic analysis, Bayes' Theorem should be better known than Pythagoras. It's not like it's significantly more difficult to understand, but it's essential to understand why intuitive estimates of probability are often so off...)
(Score: 3, Informative) by janrinok on Sunday January 21 2018, @03:54PM (7 children)
I suspect that you are concentrating on the wrong part of the summary - the use of Bayesian Theory is only the tool used to solve the following breakthrough:
The breakthrough is, that by using a mathematical theory that has been know about for years (and yet had not be used up to now to solve this problem) scientists are now able to better identify which genes are linked to which to specific problems and thus help genetic testing to treat those who require it with a higher degree of certainty that was the case before. Often, it is not the tool itself that is the ground breaking step, but that somebody thought to use that tool to solve a problem in a way that had not been done before.
[nostyle RIP 06 May 2025]
(Score: 0) by Anonymous Coward on Sunday January 21 2018, @05:56PM
What theory?
(Score: 2) by AthanasiusKircher on Sunday January 21 2018, @06:30PM (4 children)
I don't mean this to sound aggressive or insulting, but do you know what Bayes' Theorem is? It's an incredibly basic concept in probability. And any sort of problem that is even vaguely like the one you quote would be an obvious application for Bayes' Theorem. Maybe there's something interesting about the details of how they employed Bayes in a complex way or something -- but the concept of applying the basic theory is so utterly simplistic that all geneticists would be idiots if they didn't think of using it in some form. Any problem involving multiple probabilities that may be dependent on each other is a potential application.
In fact, when you click on the link to the actual study (which only gives a limited summary), you see the actual authors explain what they did:
In other words, they had some guidelines that were only qualitative, and then they employed the most obvious probabilistic tool to those guidelines to quantify them in their first attempt at a mathematical analysis. Now -- there may be more to this, but I can't tell without reading the complete study. On the surface, this all sounds like a really basic idea, though...
Regardless of whether there's more to the actual research, I still maintain that whoever wrote the headline doesn't understand how utterly common and obvious applying Bayesian reasoning to such a problem would be.
(Score: 3, Insightful) by MichaelDavidCrawford on Sunday January 21 2018, @08:35PM
Very few computer scientists know about Noether's theorem despite its fundamental importance to physics
Yes I Have No Bananas. [gofundme.com]
(Score: 2) by sbgen on Monday January 22 2018, @03:36AM
You sound like you have training in mathematics. However, do you know the data analysis paradigm in use in the clinical and molecular genetics field from which the paper is? The most prevailing way to analyze data is Null Hypothesis Significance Testing (NHST). This is a major problem because the basic assumptions of NHST are patently unmet in most cases in clinical genetics yet the paradigm persists. Without some sort of significance test your data will not be published in most journals. People have only started to talk about it recently and use of Bayesian analysis is is beginning. You might rightfully be disappointed with the headline or the fact that Baye's theorem is not widely used in estimating the probabilities in medical genetics; but next time you get a prescription remember the decision may be based on NHST.
I havent read the paper and I am not commenting on the headline you are unhappy with. Thought I should elaborate on what janinrok said above.
Warning: Not a computer expert, but got to use it. Yes, my kind does exist.
(Score: 2) by janrinok on Monday January 22 2018, @07:59AM (1 child)
And I repeat - until now, nobody had thought of using this very obvious analysis approach. That is why this is news. It might be obvious to you, me, and to many others, but none of us suggested doing it before.
[nostyle RIP 06 May 2025]
(Score: 0) by Anonymous Coward on Monday January 22 2018, @03:42PM
Yes, moving past ignorance is progress. But not all progress is worthy of attention by non-specialists.
(Score: 0) by Anonymous Coward on Monday January 22 2018, @01:33AM
A computer working tirelessly would try all available approaches to solving a problem - why can't we automate this kind of discovery?
(Score: 2) by Dr Spin on Sunday January 21 2018, @07:03PM
Bayes' Theorem should be better known than Pythagoras.
Are you suggesting that the average doctor understands Pythagoras?
Next, you will be suggesting they are capable of understanding the odds on horses!
You, here, probably can understand most first year degree level maths. The average person has a problem
with percentages. People often go into medicine because they cannot understand maths at all!
Warning: Opening your mouth may invalidate your brain!