The multi-scale complexity of human genetic variation beyond continental groups

Abstract

Traditional clustering and visualization approaches in human genetics often operate under frameworks that assume inherent, discrete groupings1,2. These methods can inadvertently simplify multifaceted relationships, functioning to entrench the idea of typological groups3. We introduce a network-based pipeline and visualization tool grounded in relational thinking4, which constructs networks from a variety of genetic similarity metrics. We identify communities at multiple resolutions, departing from typological models of analysis and interpretation that categorize individuals into a (predefined) number of sets. We applied our pipeline to a dataset merged from the 1000 Genomes and Human Genome Diversity Project5, revealing the limitations of traditional groupings and capturing the complexities introduced by demographic events and evolutionary processes. This method embraces the context-specificity of genetic similarities that are salient depending on the question, markers of interest, and study individuals. Different numbers of communities are revealed depending on the resolution chosen and metric used, underscoring a fluid spectrum of genetic relationships and challenging the notion of universal categorization. We provide a web application (https://sohail-lab.shinyapps.io/GG-NC/) for interactive visualization and engagement with these intricate genetic landscapes.