Genome-wide scans for selective sweeps using convolutional neural networks

Motivation: Recent methods for selective sweep detection cast the problem as a classification task and use summary statistics as features to capture region characteristics that are indicative of a selective sweep, thereby being sensitive to confounding factors. Furthermore, they are not designed to perform whole-genome scans or to estimate the extent of the genomic region that was affected by positive selection; both are required for identifying candidate genes and the time and strength of selection.Results: We present ASDEC, a neural-network-based framework that can scan whole genomes for selective sweeps. ASDEC achieves similar classification performance to other convolutional neural network-based classifiers that rely on summary statistics, but it is trained 10x faster and classifies genomic regions 5x faster by inferring region characteristics from the raw sequence data directly. Deploying ASDEC for genomic scans achieved up to 15.2x higher sensitivity, 19.4x higher success rates, and 4x higher detection accuracy than state-of-the-art methods. We used ASDEC to scan human chromosome 1 of the Yoruba population (1000Genomes project), identifying nine known candidate genes.

Automated summary

ASDEC is a neural network-based framework that can scan whole genomes for selective sweeps. It achieves similar classification performance to other convolutional neural network-based classifiers, but is trained and classifies genomic regions much faster by inferring region characteristics from raw sequence data. Using ASDEC for genomic scans resulted in higher sensitivity, success rates, and detection accuracy compared to state-of-the-art methods.