Whether we are predisposed to particular diseases depends to a large extent on the countless variants in our genome. However, particularly in the case of genetic variants that only rarely occur in the population, the influence on the presentation of certain pathological traits has so far been difficult to determine. Researchers from the German Cancer Research Center (DKFZ), the European Molecular Biology Laboratory (EMBL) and the Technical University of Munich have introduced an algorithm based on deep learning that can predict the effects of rare genetic variants.
The paper, "Integration of Variant annotations using deep set networks boosts rare variant testing," has been published in Nature Medicine . The method allows persons with high risk of disease to be distinguished more precisely and facilitates the identification of genes that are involved in the development of diseases. Every person's genome differs from that of their fellow human beings in millions of individual building blocks.
These differences in the genome are known as variants. Many of these variants are associated with particular biological traits and diseases. Such correlations are usually determined using so-called genome-wide association studies.
But the influence of rare variants, which occur with a frequency of only 0.1% or less in the population, is often statistically overlooked in association studies. "Rare variants in particular often have a significantly greater influence on the presentation of a b.