RT Journal Article
SR Electronic
T1 High-throughput phenotyping methods for quantifying hair fiber morphology
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.11.24.392191
DO 10.1101/2020.11.24.392191
A1 Tina Lasisi
A1 Arslan A. Zaidi
A1 Timothy Harding Webster
A1 Nicholas Bradley Stephens
A1 Kendall Routch
A1 Nina Grace Jablonski
A1 Mark David Shriver
YR 2020
UL http://biorxiv.org/content/early/2020/11/24/2020.11.24.392191.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.