The Starset SocietyWhen a patient’s DNA is read, it is compared with a reference version of the human genome. This allows geneticists and rare disease experts to look at a list of places where the patient’s DNA differs. Most variations will be harmless and shared with millions of other people, but some can cause illness.
Computer software usually helps make that judgment. The program works through the list and scores each difference for how dangerous it appears. Doctors can use those scores to guide treatment options. Researchers in laboratories around the world can use the knowledge to study potential mechanisms of action.
A research team led by Dr. Donate Weghorn at the Center for Genomic Regulation (CRG) in Barcelona has now tested 50 of the world’s top prediction tools used for this purpose against 13.5 million mutations spread across 6,659 human genes.
Where the software goes wrong
The work, published in the American Journal of Human Genetics, shows that almost all programs overestimate the potential harm of mutations that are less likely to occur than average and underestimate the effect of more likely mutations.
The research team found this happens because almost all computer programs look at a region of DNA and ask whether that spot has stayed the same over millions of years of evolution. If it has, the software decides it has been conserved for a reason, must matter and that changing it must be bad.
That includes programs such as AlphaMissense, built by Google DeepMind, along with EVE and popEVE, co-developed by other research groups at the CRG. However, the developers of some of the programs tested expect the effect to be modest in a diagnostic setting.
In their view, it is unlikely to change the interpretation of clearly damaging variants and would more plausibly reshuffle the ranking of variants with moderate predicted effects.
Mutation hotspots distort the picture
The new study finds the models are biased because some regions in the human genome can be much more mutation-prone than other regions. For example, Weghorn has previously shown that the starting points of genes are 35% more prone to mutations than other regions.
Research groups around the world have also found other types of mutation hotspots in other regions, or discovered regions that almost never change because nothing much happens there in the first place.
“For some variants, it could be that if you take the biology of mutation rates into account, they could flip from harmless to harmful,” says Weghorn, group leader at the CRG, who led the study. “For most, however, it will be a more gradual shift,” she adds.
The researchers found that mutations in genes for DNA repair, cilia and sperm function are likely being overcalled as dangerous, while genes linked to intellectual disability and to conditions passed down from a single parent are likely being undercalled.
“Both would be bad,” says Weghorn.
Lab tests reveal a smaller effect
To test the predictive tools, the team used data created by experiments carried out in the laboratory of Ben Lehner, also at the CRG. Lehner’s group physically made hundreds of thousands of mutations across 500 human protein fragments and measured the damage each one caused.
The measurements showed that mutations that occur more often do tend to be slightly less damaging. Life appears to carry some built-in tolerance for its own most common errors, an idea proposed decades ago in theory but never before demonstrated with measurements of protein function.
“The finding that genomes appear to have evolved robustness against their most common mutations opens a new way of thinking about genomic evolution itself,” says Hossameldin Ali, first author of the study.
The Weghorn research group showed this effect is too small to account for what the programs are doing, which remained skewed even after the researchers corrected for it.
A practical fix for future tools
However, the researchers stress that the findings of their study cannot say what this does to any single person’s result, as the software used is just one of the tools clinical experts use to guide diagnostic and treatment decisions.
According to the authors of the study, the fix would involve modifying computer software to include information from maps showing where the human genome is more or less mutation-prone.
read more at phys.org
