High-Throughput Phenotyping Methods for Quantifying Hair Fiber Morphology

The study developed high-throughput methods for quantifying hair fiber morphology, focusing on curvature and cross-sectional shape, using a new Python package called fibermorph. It involved 140 individuals of mixed African-European ancestry and found that correlations between hair curvature and cross-sectional shape were not significant when accounting for ancestry, suggesting these were due to population structure rather than genetic linkage. The research introduced a novel method using polycaprolactone plastic for embedding hair, improving scalability and accuracy. The study emphasized the importance of quantitative methods to avoid racial stereotyping in hair morphology studies and provided open-source tools for further research.