Literature DB >> 33941792

A web-based survey on various symptoms of computer vision syndrome and the genetic understanding based on a multi-trait genome-wide association study.

Keito Yoshimura1, Yuji Morita2, Kenji Konomi3, Sachiko Ishida4, Daisuke Fujiwara5, Keisuke Kobayashi6, Masami Tanaka4.   

Abstract

A variety of eye-related symptoms due to the overuse of digital devices is collectively referred to as computer vision syndrome (CVS). In this study, a web-based survey about mind and body functions, including eye strain, was conducted on 1998 Japanese volunteers. To investigate the biological mechanisms behind CVS, a multi-trait genome-wide association study (GWAS), a multivariate analysis on individual-level multivariate data, was performed based on the structural equation modeling methodology assuming a causal pathway for a genetic variant to influence each symptom via a single common latent variable. Twelve loci containing lead variants with a suggestive level of significance were identified. Two loci showed relatively strong signals and were associated with TRABD2B relative to the Wnt signaling pathway and SDK1 having neuronal adhesion and immune functions, respectively. By utilizing publicly available eQTL data, colocalization between GWAS and eQTL signals for four loci was detected, and a locus on 2p25.3 showed a strong colocalization (PPH4 > 0.9) on retinal MYT1L, known to play an important role in neuronal differentiation. This study suggested that the use of multivariate questionnaire data and multi-trait GWAS can lead to biologically reasonable findings and enhance our genetic understanding of complex relationships among symptoms related to CVS.

Entities:  

Year:  2021        PMID: 33941792     DOI: 10.1038/s41598-021-88827-y

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


  39 in total

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Authors:  Joel N Hirschhorn; Mark J Daly
Journal:  Nat Rev Genet       Date:  2005-02       Impact factor: 53.242

Review 2.  Genome-wide association study success in ophthalmology.

Authors:  David A Mackey; Alex W Hewitt
Journal:  Curr Opin Ophthalmol       Date:  2014-09       Impact factor: 3.761

Review 3.  Computer vision syndrome: a review.

Authors:  Clayton Blehm; Seema Vishnu; Ashbala Khattak; Shrabanee Mitra; Richard W Yee
Journal:  Surv Ophthalmol       Date:  2005 May-Jun       Impact factor: 6.048

4.  Is all asthenopia the same?

Authors:  James E Sheedy; John N Hayes; Jon Engle
Journal:  Optom Vis Sci       Date:  2003-11       Impact factor: 1.973

5.  Structural model analysis of multiple quantitative traits.

Authors:  Renhua Li; Shirng-Wern Tsaih; Keith Shockley; Ioannis M Stylianou; Jon Wergedal; Beverly Paigen; Gary A Churchill
Journal:  PLoS Genet       Date:  2006-06-07       Impact factor: 5.917

6.  Second-generation PLINK: rising to the challenge of larger and richer datasets.

Authors:  Christopher C Chang; Carson C Chow; Laurent Cam Tellier; Shashaank Vattikuti; Shaun M Purcell; James J Lee
Journal:  Gigascience       Date:  2015-02-25       Impact factor: 6.524

7.  A comparison of multivariate genome-wide association methods.

Authors:  Tessel E Galesloot; Kristel van Steen; Lambertus A L M Kiemeney; Luc L Janss; Sita H Vermeulen
Journal:  PLoS One       Date:  2014-04-24       Impact factor: 3.240

Review 8.  Statistical methods to detect pleiotropy in human complex traits.

Authors:  Sophie Hackinger; Eleftheria Zeggini
Journal:  Open Biol       Date:  2017-11       Impact factor: 6.411

9.  Structural equation modeling for hypertension and type 2 diabetes based on multiple SNPs and multiple phenotypes.

Authors:  Saebom Jeon; Ji-Yeon Shin; Jaeyong Yee; Taesung Park; Mira Park
Journal:  PLoS One       Date:  2019-09-12       Impact factor: 3.240

10.  Including Phenotypic Causal Networks in Genome-Wide Association Studies Using Mixed Effects Structural Equation Models.

Authors:  Mehdi Momen; Ahmad Ayatollahi Mehrgardi; Mahmoud Amiri Roudbar; Andreas Kranis; Renan Mercuri Pinto; Bruno D Valente; Gota Morota; Guilherme J M Rosa; Daniel Gianola
Journal:  Front Genet       Date:  2018-10-09       Impact factor: 4.599

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