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posted by janrinok on Sunday January 21 2018, @11:38AM   Printer-friendly
from the it-all-adds-up dept.

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

Bayes Theorem


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  • (Score: 2) by sbgen on Monday January 22 2018, @03:36AM

    by sbgen (1302) on Monday January 22 2018, @03:36AM (#625934)

    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.

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