  The exploration of selected single nucleotide polymorphisms (SNPs) to
identify genetic diversity between different sequencing population pools
(Pool-seq) is a fundamental task in genetic research. As underlying sequence
reads and their alignment are error-prone and univariate statistical solutions
only take individual positions of the genome into account, the identification
of selected SNPs remains a challenging process. Deep learning models like
convolutional neural networks (CNNs) are able to consider large input areas in
their decisions. We suggest an unsupervised pipeline to be independent of a
rarely known ground truth. We train a supervised discriminator CNN to
distinguish alignments from different populations and utilize the model for
unsupervised SNP calling by applying explainable artificial intelligence
methods. Our proposed multivariate method is based on two main assumptions: We
assume (i) that instances having a high predictive certainty of being
distinguishable are likely to contain genetic variants, and (ii) that selected
SNPs are located at regions with input features having the highest influence on
the model's decision process. We directly compare our method with statistical
results on two different Pool-seq datasets and show that our solution is able
to extend statistical results.
