Scientists learn to predict disease by the genetic code of a person

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<pre>Scientists learn to predict disease by the genetic code of a person

The combined group of American mathematicians and biologists has developed algorithms for processing polygenic models. For broad-minded people they are known as “genetically caused diseases” or ailments provoked by the activity of specific genes. Only in practice there is an incredibly huge number of combinations of these genes and only now scientists have approximately understood how to take into account the influence of all of them when making a diagnosis.

One gene, for whatever function it responds, rarely causes the disease, almost all diseases by its essence is polygenic – the provoked activity of groups or subsets of genes, often not connected at all. American scientists have developed a model on which to place a very large number of different combinations of genes. And applied to it the evaluation algorithm using the “big data” analysis methods, having received a frighteningly accurate instrument.

As an example, the study of the probabilities of coronary artery disease, which depend on 6.6 million positions in the genome. Based on data on patients from the British medical base Biobank, the authors of the methodology concluded that for 8% of people the probability of such a disease is three times higher than for all others. The lower risk level for the new system leaves you only 1 chance out of 100 to get sick, but here the highest index guarantees the disease in 11% of cases.

It seems that with such a spread about the accuracy of the forecast and speech does not go, but look at the situation from the other side. The data can be obtained on the basis of DNA analysis, for any person, starting with the first minutes of life. And make a plan – in what conditions does he have to live better, what to eat and drink, what drugs to take to never get sick with something specific. For a businessman, sports coach, astronaut recruiter, the difference of 10% is already an argument to make the choice between candidates not to spend tens of years and efforts to prepare a less promising person. And how happy insurance companies will be happy with their dynamic tariffs … that's why the technology does not yet think to be implemented. Let it remain within the laboratory.

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