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bioRxiv · 10.1101/2020.11.24.392191

High-throughput phenotyping methods for quantifying hair fiber morphology

Lasisi, T.·Zaidi, A. A.·Webster, T. H.·Stephens, N. B.·Routch, K.·Jablonski, N. G.·Shriver, M. D.
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Abstract

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 qualitative classification or racial categories, and providing evidence against the long-held belief that cross-sectional morphology predicts curvature.

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Lasisi, T., Zaidi, A. A., Webster, T. H., Stephens, N. B., Routch, K., Jablonski, N. G., Shriver, M. D.. 2020-11-24. High-throughput phenotyping methods for quantifying hair fiber morphology. https://doi.org/10.1101/2020.11.24.392191

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