Back

Multi-biobank genome-wide association study of dermatochalasis implicates genes involved in skin biology and morphology

Rajueni, K.; Koskimaki, F.; Salo, V.; Pasanen, A.; Sliz, E.; Vanhala, S.; Reis, K.; Reigo, A.; FinnGen, ; Estonian Biobank Research Team, ; Palta, P.; Tasanen, K.; Liinamaa, J.; Kettunen, J.; Saarela, V.; Karjalainen, M. K.

2026-08-06 ophthalmology
10.64898/2026.08.04.26359692 medRxiv
Show abstract

Objective: The objective of this study was to detect genetic factors associated with dermatochalasis using a genome-wide association study (GWAS) across three large cohorts. Design: GWAS meta-analysis Participants: A total of 13,200 dermatochalasis cases and 962,513 controls were included. Methods: A GWAS meta-analysis of dermatochalasis combining data from the FinnGen, the Estonian Biobank and the UK Biobank was conducted. We also performed colocalization analyses, a phenome-wide association study and age-at-onset analysis, and assessed genetic correlations with various diseases and traits. Main outcome measures: Identification of genetic variants associated with dermatochalasis. Results: We identified 18 loci associated with dermatochalasis at genome-wide significance, 16 of which were novel. Most of these loci had genes involved in skin biology and cutaneous diseases, such as the genes encoding elastin (ELN) and Latent TGF-{beta} binding protein 1 (LTBP1). Phenome-wide association study revealed previous associations with morphology-related traits, while genetic correlation analysis highlighted multiple genetic correlations, especially with smoking and pain. Conclusions: We detected 18 genetic loci associated with dermatochalasis, characterized these loci in detail and demonstrated their relevance in skin biology and related processes. These findings give novel information on the genetic background of dermatochalasis and provide a solid basis for further research.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.