Genetic analysis of perceived youthfulness reveals differences in how men's and women's age is assessed.

Canadian Longitudinal Study of Aging (CLSA) Genetic correlation Perceived Youthfulness QSkin Sun and health study Sex-stratified Genome-wide Association Study (GWAS)

Journal

The Journal of investigative dermatology
ISSN: 1523-1747
Titre abrégé: J Invest Dermatol
Pays: United States
ID NLM: 0426720

Informations de publication

Date de publication:
07 Mar 2024
Historique:
received: 07 06 2023
revised: 29 01 2024
accepted: 06 02 2024
medline: 10 3 2024
pubmed: 10 3 2024
entrez: 9 3 2024
Statut: aheadofprint

Résumé

Skin aging is a natural process that occurs over time, but can be accelerated by sun exposure. Measuring skin age in a large population can provide insight into the extent of skin damage from sun exposure and skin cancer risk. Understanding the genetics of skin aging, within and across sexes, could improve our understanding of the genetic drivers of both skin aging and skin cancer. We used UK Biobank data to examine the genetic overlap between perceived youthfulness and traits relevant to actinic photoaging. Our GWAS identified 22 genome-wide significant loci for women and 43 for men. The genetic correlation between perceived youthfulness in men and women was significantly less than unity (rg=0.75, 95% CI=0.69-0.80), suggesting a gene-by-sex interaction. In women, perceived youthfulness was modestly correlated with keratinocyte cancer (rg = -0.19) and skin tanning (rg = 0.18). In men, perceived youthfulness was correlated with male pattern baldness (rg = -0.23). This suggests that the genetic architecture of perceived youthfulness may differ, with genes influencing skin tanning and skin cancer susceptibility driving the difference in women, while genes influencing male pattern baldness and other puberty-related traits drive the difference in men. We recommend future genetic analysis of skin aging include a sex-stratified component.

Identifiants

pubmed: 38460809
pii: S0022-202X(24)00180-5
doi: 10.1016/j.jid.2024.02.019
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.

Auteurs

Nathan Ingold (N)

Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia. Electronic address: nathan.ingold@qimrberghofer.edu.au.

Mathias Seviiri (M)

Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia.

Jue-Sheng Ong (JS)

Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.

Scott Gordon (S)

Genetic Epidemiology, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.

Rachel E Neale (RE)

Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; Faculty of Medicine, The University of Queensland, Herston, Queensland, Australia.

David C Whiteman (DC)

Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; School of Public Health, University of Queensland, Queensland, Australia.

Catherine M Olsen (CM)

Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; Faculty of Medicine, The University of Queensland, Herston, Queensland, Australia.

Stuart MacGregor (S)

Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia.

Matthew H Law (MH)

Statistical Genetics, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, Queensland, Australia.

Classifications MeSH