Localizing post-admixture adaptive variants with object detection on ancestry-painted chromosomes

Abstract

Gene flow between previously isolated populations during the founding of an admixed or hybrid population has the potential to introduce adaptive alleles into the new population. If the adaptive allele is common in one source population, but not the other, then as the adaptive allele rises in frequency in the admixed population, genetic ancestry from the source containing the adaptive allele will increase nearby as well. Patterns of genetic ancestry have therefore been used to identify post-admixture positive selection in humans and other animals, including examples in immunity, metabolism, and animal coloration. A common method identifies regions of the genome that have local ancestry 'outliers' compared to the distribution across the rest of the genome, considering each locus independently. However, we lack theoretical models for expected distributions of ancestry under various demographic scenarios, resulting in potential false positives and false negatives in ancestry outlier approaches. Further, ancestry patterns between distant sites are often not independent. As a result, current methods tend to infer wide genomic regions containing many genes as under selection, limiting biological interpretation. Instead, we develop a deep learning object detection method applied to images generated from local ancestry-painted genomes. This approach preserves information from the surrounding genomic context and avoids potential pitfalls of user-defined summary statistics. We find the method is robust to a variety of demographic misspecifications using simulated data. Applied to human genotype data from the admixed population of Cabo Verde, we localize a known adaptive locus to a single narrow region compared to multiple or long windows obtained using two other ancestry-based methods.

Deep Learning in Population Genetics

“…One key aspect to make deep learning a popular framework in population genetics, is to ensure reproducible analyses and avoid repeating training of highly parameterized networks from scratch. In this context, recent efforts to provide users with documented workflows ( Whitehouse and Schrider 2022 ) and pre-trained networks ( Hamid et al 2022 ) will both reduce carbon footprint ( Grealey et al 2022 ) and facilitate the application of deep learning to a wider range of data sets, allowing users to modify the network’s parameters according to the specific requirements of the biological system under examination.…”

“…sample allele frequencies from pooled-sequencing experiments ( Anand et al 2016 )], and therefore appropriate considerations should be made in terms of additional statistical uncertainty associated with such output. Approaches based on using trees or local ancestry tracts as input ( Hamid et al 2022 ) may be more prone to input data uncertainty.…”

The genomic footprint of social stratification in admixing American populations

“…However, this step likely implies a shift from a population-based continuous ancestry tract length profile to an individual-based one, which needs to be linked to a redesign of the architecture of the neural network. Other architectures such as convolutional neural networks and generative models have been recently deployed to infer introgression, structure, admixture proportions, and post-admixture selection from population genomic data ( Gower et al, 2021 ; Wang et al, 2021 ; Meisner and Albrechtsen, 2022 ; Hamid et al, 2022 ). We caution that local ancestry analysis is a sensitive up-stream step to our method.…”

The genomic footprint of social stratification in admixing American populations

“…However, this step likely implies a shift from a population-based continuous ancestry tract length profile to an individual-based one, which needs to be linked to a redesign of the architecture of the neural network. Other architectures such as convolutional neural networks and generative models have been recently deployed to infer introgression, structure, admixture proportions, and post-admixture selection from population genomic data Gower et al (2021); Wang et al (2021); Meisner and Albrechtsen (2022); Hamid et al (2022 ) .…”