Quantifying the continuous variation in human scalp hair morphology is of interest to anthropologists, geneticists, dermatologists and forensic scientists, but existing methods for studying hair form are time-consuming and not widely used. Here, we present a high-throughput sample preparation protocol for the imaging of both longitudinal (curvature) and cross-sectional scalp hair morphology. Additionally, we describe and validate a new Python package designed to process longitudinal and cross-sectional hair images, segment them, and provide measurements of interest. Lastly, we apply our methods to an admixed African-European sample (n = 140), demonstrating the benefit of quantifying hair morphology over classification, and providing evidence that the relationship between cross-sectional morphology and curvature may be an artefact of population stratification rather than a causal link.

Renewed focus on African populations confirms the complexity of skin pigmentation genetics, and suggests future directions for pigmentation research.

Traditional clustering and visualization approaches in human genetics often operate under frameworks that assume inherent, discrete groupings ^1,2 . These methods can inadvertently simplify multifaceted relationships, functioning to entrench the idea of typological groups ³ . We introduce a network-based pipeline and visualization tool grounded in relational thinking ⁴ , 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 Project ⁵ , 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.