A deep learning classifier for local ancestry inference
This provides a new method for local ancestry inference in medical and population genetics, but it is incremental as it matches rather than surpasses existing tools.
The authors tackled local ancestry inference by formulating it as an image segmentation problem and developed a deep convolutional neural network tool, which achieved ancestry assignments nearly as accurate as the gold standard RFMix on simulated admixed data.
Local ancestry inference (LAI) identifies the ancestry of each segment of an individual's genome and is an important step in medical and population genetic studies of diverse cohorts. Several techniques have been used for LAI, including Hidden Markov Models and Random Forests. Here, we formulate the LAI task as an image segmentation problem and develop a new LAI tool using a deep convolutional neural network with an encoder-decoder architecture. We train our model using complete genome sequences from 982 unadmixed individuals from each of five continental ancestry groups, and we evaluate it using simulated admixed data derived from an additional 279 individuals selected from the same populations. We show that our model is able to learn admixture as a zero-shot task, yielding ancestry assignments that are nearly as accurate as those from the existing gold standard tool, RFMix.