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High-throughput phenotyping methods for quantifying hair fiber morphology

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 protoc...

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Autores principales: Lasisi, Tina, Zaidi, Arslan A., Webster, Timothy H., Stephens, Nicholas B., Routch, Kendall, Jablonski, Nina G., Shriver, Mark D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8169905/
https://www.ncbi.nlm.nih.gov/pubmed/34075066
http://dx.doi.org/10.1038/s41598-021-90409-x
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author Lasisi, Tina
Zaidi, Arslan A.
Webster, Timothy H.
Stephens, Nicholas B.
Routch, Kendall
Jablonski, Nina G.
Shriver, Mark D.
author_facet Lasisi, Tina
Zaidi, Arslan A.
Webster, Timothy H.
Stephens, Nicholas B.
Routch, Kendall
Jablonski, Nina G.
Shriver, Mark D.
author_sort Lasisi, Tina
collection PubMed
description 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.
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spelling pubmed-81699052021-06-03 High-throughput phenotyping methods for quantifying hair fiber morphology Lasisi, Tina Zaidi, Arslan A. Webster, Timothy H. Stephens, Nicholas B. Routch, Kendall Jablonski, Nina G. Shriver, Mark D. Sci Rep Article 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. Nature Publishing Group UK 2021-06-01 /pmc/articles/PMC8169905/ /pubmed/34075066 http://dx.doi.org/10.1038/s41598-021-90409-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Lasisi, Tina
Zaidi, Arslan A.
Webster, Timothy H.
Stephens, Nicholas B.
Routch, Kendall
Jablonski, Nina G.
Shriver, Mark D.
High-throughput phenotyping methods for quantifying hair fiber morphology
title High-throughput phenotyping methods for quantifying hair fiber morphology
title_full High-throughput phenotyping methods for quantifying hair fiber morphology
title_fullStr High-throughput phenotyping methods for quantifying hair fiber morphology
title_full_unstemmed High-throughput phenotyping methods for quantifying hair fiber morphology
title_short High-throughput phenotyping methods for quantifying hair fiber morphology
title_sort high-throughput phenotyping methods for quantifying hair fiber morphology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8169905/
https://www.ncbi.nlm.nih.gov/pubmed/34075066
http://dx.doi.org/10.1038/s41598-021-90409-x
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