Thousands of years ago, archaic humans such as Neanderthals and Denisovans went extinct. But before that, they interbred with the ancestors of present-day humans, who still to this day carry genetic mutations from the extinct species.
Over 40 percent of the Neanderthal genome is thought to have survived in different present-day humans of non-African descent but spread out so that any individual genome is only composed of up to two percent Neanderthal material. Some human populations also carry genetic material from Denisovans a mysterious group of archaic humans that may have lived in Eastern Eurasia and Oceania thousands of years ago.
The introduction of beneficial genetic material into our gene pool, a process known as adaptive introgression, often happened because it was advantageous to humans after they expanded across the globe. To name a few examples, scientists believe some of the mutations affected skin development and metabolism. But many mutations are yet still undiscovered.
Now, researchers from the GLOBE Institute at the University of Copenhagen have developed a new method using deep learning techniques to search the human genome for undiscovered mutations.
Associate Professor Fernando Racimo, GLOBE Institute, corresponding author of the new study said, we developed a deep learning method called 'genomatnn' that jointly models introgression, which is the transfer of genetic information between species and natural selection. The model was developed in order to identify regions in the human genome where this introgression could have happened. Our method is highly accurate and outcompetes previous approaches in power. We applied it to various human genomic datasets and found several candidate beneficial gene variants that were introduced into the human gene pool.
The new method is based on a so-called convolutional neural network (CNN), which is a type of deep learning framework commonly used in image and video recognition.
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