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Lin HJ, Huang YT, Liao WL, Huang YC, Chang YW, Weng AL, Tsai FJ. Developing a Polygenic Risk Score with Age and Sex to Identify High-Risk Myopia in Taiwan. Biomedicines 2024; 12:1619. [PMID: 39062192 PMCID: PMC11274619 DOI: 10.3390/biomedicines12071619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 07/12/2024] [Accepted: 07/15/2024] [Indexed: 07/28/2024] Open
Abstract
Myopia is the leading cause of impaired vision, and its prevalence is increasing among Asian populations. This study aimed to develop a polygenic risk score (PRS) followed by replication to predict myopia in the Taiwanese population. In total, 23,688 participants with cycloplegic autorefraction-measured mean spherical equivalent (SE), genetic, and demographic data were included. The myopia PRS was generated based on genome-wide association study (GWAS) outcomes in a Taiwanese population and previously published GWAS reports. The results demonstrated that the inclusion of age and sex in the PRS had an area under the curve (AUC) of 0.80, 0.78, and 0.73 (p < 0.001) for participants aged >18 years with high (SE < -6.0 diopters (D); n = 1089), moderate (-6.0 D < SE ≤ -3.0 D; n = 3929), and mild myopia (-3.0 D < SE ≤ -1.0 D; n = 2241), respectively. Participants in the top PRS quartile had a 1.30-fold greater risk of high myopia (95% confidence interval = 1.09-1.55, p = 0.003) compared with that in the remaining participants. Further, a higher PRS significantly increased the risk of high myopia (SE ≤ -2.0 D) in children ≤6 years of age (p = 0.027). In conclusion, including the PRS, age, and sex improved the prediction of high myopia risk in the Taiwanese population.
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Affiliation(s)
- Hui-Ju Lin
- Department of Ophthalmology, China Medical University Hospital, Taichung 404327, Taiwan; (H.-J.L.); (Y.-T.H.)
- School of Chinese Medicine, China Medical University, Taichung 404328, Taiwan;
| | - Yu-Te Huang
- Department of Ophthalmology, China Medical University Hospital, Taichung 404327, Taiwan; (H.-J.L.); (Y.-T.H.)
| | - Wen-Ling Liao
- Center for Personalized Medicine, China Medical University Hospital, Taichung 404327, Taiwan;
- Graduate Institute of Integrated Medicine, China Medical University, Taichung 404328, Taiwan
| | - Yu-Chuen Huang
- School of Chinese Medicine, China Medical University, Taichung 404328, Taiwan;
- Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan;
| | - Ya-Wen Chang
- Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung 404327, Taiwan;
| | - Angel L. Weng
- American School in Taichung, Taichung 406051, Taiwan;
| | - Fuu-Jen Tsai
- School of Chinese Medicine, China Medical University, Taichung 404328, Taiwan;
- Department of Medical Genetics, China Medical University Hospital, Taichung 404327, Taiwan
- Children’s Hospital of China Medical University, Taichung 404327, Taiwan
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2
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Flitcroft I, Ainsworth J, Chia A, Cotter S, Harb E, Jin ZB, Klaver CCW, Moore AT, Nischal KK, Ohno-Matsui K, Paysse EA, Repka MX, Smirnova IY, Snead M, Verhoeven VJM, Verkicharla PK. IMI-Management and Investigation of High Myopia in Infants and Young Children. Invest Ophthalmol Vis Sci 2023; 64:3. [PMID: 37126360 PMCID: PMC10153576 DOI: 10.1167/iovs.64.6.3] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023] Open
Abstract
Purpose The purpose of this study was to evaluate the epidemiology, etiology, clinical assessment, investigation, management, and visual consequences of high myopia (≤-6 diopters [D]) in infants and young children. Findings High myopia is rare in pre-school children with a prevalence less than 1%. The etiology of myopia in such children is different than in older children, with a high rate of secondary myopia associated with prematurity or genetic causes. The priority following the diagnosis of high myopia in childhood is to determine whether there is an associated medical diagnosis that may be of greater overall importance to the health of the child through a clinical evaluation that targets the commonest features associated with syndromic forms of myopia. Biometric evaluation (including axial length and corneal curvature) is important to distinguishing axial myopia from refractive myopia associated with abnormal development of the anterior segment. Additional investigation includes ocular imaging, electrophysiological tests, genetic testing, and involvement of pediatricians and clinical geneticists is often warranted. Following investigation, optical correction is essential, but this may be more challenging and complex than in older children. Application of myopia control interventions in this group of children requires a case-by-case approach due to the lack of evidence of efficacy and clinical heterogeneity of high myopia in young children. Conclusions High myopia in infants and young children is a rare condition with a different pattern of etiology to that seen in older children. The clinical management of such children, in terms of investigation, optical correction, and use of myopia control treatments, is a complex and often multidisciplinary process.
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Affiliation(s)
- Ian Flitcroft
- Children's Health Ireland (CHI) at Temple Street, Dublin, Ireland
- Centre for Eye Research Ireland, Technological University of Dublin, Dublin, Ireland
| | - John Ainsworth
- Birmingham Children's Hospital, Steelhouse Lane Birmingham, United Kingdom
| | | | - Susan Cotter
- Southern California College of Optometry, Marshall B Ketchum University, Fullerton, California, United States
| | - Elise Harb
- Wertheim School Optometry and Vision Science, Berkeley, California, United States
- University of California - San Francisco, School of Medicine, San Francisco, California, United States
| | - Zi-Bing Jin
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Caroline C W Klaver
- Department of Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The Netherlands
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Anthony T Moore
- University of California - San Francisco, School of Medicine, San Francisco, California, United States
| | - Ken K Nischal
- UPMC Children's Hospital of Pittsburgh, Pittsburgh, Pennsylvania, United States
| | | | - Evelyn A Paysse
- Baylor College of Medicine and Texas Children's Hospital, Houston, Texas, United States
| | - Michael X Repka
- Wilmer Eye Institute, The John Hopkins University School of Medicine, Baltimore, Maryland, United States
| | | | - Martin Snead
- Department of Vitreoretinal Research, John van Geest Centre for Brain Repair, University of Cambridge, United Kingdom
| | - Virginie J M Verhoeven
- Department of Ophthalmology, Erasmus Medical Center, Rotterdam, The Netherlands
- Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands
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3
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Clark R, Lee SSY, Du R, Wang Y, Kneepkens SCM, Charng J, Huang Y, Hunter ML, Jiang C, Tideman JWL, Melles RB, Klaver CCW, Mackey DA, Williams C, Choquet H, Ohno-Matsui K, Guggenheim JA. A new polygenic score for refractive error improves detection of children at risk of high myopia but not the prediction of those at risk of myopic macular degeneration. EBioMedicine 2023; 91:104551. [PMID: 37055258 PMCID: PMC10203044 DOI: 10.1016/j.ebiom.2023.104551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 03/17/2023] [Accepted: 03/17/2023] [Indexed: 04/15/2023] Open
Abstract
BACKGROUND High myopia (HM), defined as a spherical equivalent refractive error (SER) ≤ -6.00 diopters (D), is a leading cause of sight impairment, through myopic macular degeneration (MMD). We aimed to derive an improved polygenic score (PGS) for predicting children at risk of HM and to test if a PGS is predictive of MMD after accounting for SER. METHODS The PGS was derived from genome-wide association studies in participants of UK Biobank, CREAM Consortium, and Genetic Epidemiology Research on Adult Health and Aging. MMD severity was quantified by a deep learning algorithm. Prediction of HM was quantified as the area under the receiver operating curve (AUROC). Prediction of severe MMD was assessed by logistic regression. FINDINGS In independent samples of European, African, South Asian and East Asian ancestry, the PGS explained 19% (95% confidence interval 17-21%), 2% (1-3%), 8% (7-10%) and 6% (3-9%) of the variation in SER, respectively. The AUROC for HM in these samples was 0.78 (0.75-0.81), 0.58 (0.53-0.64), 0.71 (0.69-0.74) and 0.67 (0.62-0.72), respectively. The PGS was not associated with the risk of MMD after accounting for SER: OR = 1.07 (0.92-1.24). INTERPRETATION Performance of the PGS approached the level required for clinical utility in Europeans but not in other ancestries. A PGS for refractive error was not predictive of MMD risk once SER was accounted for. FUNDING Supported by the Welsh Government and Fight for Sight (24WG201).
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Affiliation(s)
- Rosie Clark
- School of Optometry & Vision Sciences, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK
| | - Samantha Sze-Yee Lee
- University of Western Australia, Centre for Ophthalmology and Visual Science (incorporating the Lions Eye Institute), Perth, Western Australia, Australia
| | - Ran Du
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 1138510, Japan; Department of Ophthalmology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, 100045, China
| | - Yining Wang
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 1138510, Japan
| | - Sander C M Kneepkens
- Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands; Generation R Study Group, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Jason Charng
- University of Western Australia, Centre for Ophthalmology and Visual Science (incorporating the Lions Eye Institute), Perth, Western Australia, Australia; Department of Optometry, School of Allied Health, University of Western Australia, Perth, Australia
| | - Yu Huang
- Department of Ophthalmology, Guangdong Eye Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Michael L Hunter
- Busselton Health Study Centre, Busselton Population Medical Research Institute, Busselton, Western Australia; School of Population and Global Health, University of Western Australia, Perth, Western Australia
| | - Chen Jiang
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - J Willem L Tideman
- Department of Ophthalmology, Martini Hospital, Groningen, the Netherlands; Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands
| | - Ronald B Melles
- Department of Ophthalmology Kaiser Permanente Northern California, Redwood City, CA, USA
| | - Caroline C W Klaver
- Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, the Netherlands; Department of Epidemiology, Erasmus University Medical Center, Rotterdam, the Netherlands; Generation R Study Group, Erasmus University Medical Center, Rotterdam, the Netherlands; Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland; Department of Ophthalmology, Radboud University Medical Center, Nijmegen, the Netherlands
| | - David A Mackey
- University of Western Australia, Centre for Ophthalmology and Visual Science (incorporating the Lions Eye Institute), Perth, Western Australia, Australia; Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, University of Melbourne, East Melbourne, Victoria, Australia; School of Medicine, Menzies Research Institute Tasmania, University of Tasmania, Hobart, Tasmania, Australia
| | - Cathy Williams
- Centre for Academic Child Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, BS81NU, UK
| | - Hélène Choquet
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Kyoko Ohno-Matsui
- Department of Ophthalmology and Visual Science, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 1138510, Japan
| | - Jeremy A Guggenheim
- School of Optometry & Vision Sciences, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, UK.
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Li Y, Yip MYT, Ting DSW, Ang M. Artificial intelligence and digital solutions for myopia. Taiwan J Ophthalmol 2023; 13:142-150. [PMID: 37484621 PMCID: PMC10361438 DOI: 10.4103/tjo.tjo-d-23-00032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2023] [Accepted: 03/16/2023] [Indexed: 07/25/2023] Open
Abstract
Myopia as an uncorrected visual impairment is recognized as a global public health issue with an increasing burden on health-care systems. Moreover, high myopia increases one's risk of developing pathologic myopia, which can lead to irreversible visual impairment. Thus, increased resources are needed for the early identification of complications, timely intervention to prevent myopia progression, and treatment of complications. Emerging artificial intelligence (AI) and digital technologies may have the potential to tackle these unmet needs through automated detection for screening and risk stratification, individualized prediction, and prognostication of myopia progression. AI applications in myopia for children and adults have been developed for the detection, diagnosis, and prediction of progression. Novel AI technologies, including multimodal AI, explainable AI, federated learning, automated machine learning, and blockchain, may further improve prediction performance, safety, accessibility, and also circumvent concerns of explainability. Digital technology advancements include digital therapeutics, self-monitoring devices, virtual reality or augmented reality technology, and wearable devices - which provide possible avenues for monitoring myopia progression and control. However, there are challenges in the implementation of these technologies, which include requirements for specific infrastructure and resources, demonstrating clinically acceptable performance and safety of data management. Nonetheless, this remains an evolving field with the potential to address the growing global burden of myopia.
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Affiliation(s)
- Yong Li
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology and Visual Sciences, Duke-NUS Medical School, National University of Singapore, Singapore
| | - Michelle Y. T. Yip
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Daniel S. W. Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology and Visual Sciences, Duke-NUS Medical School, National University of Singapore, Singapore
| | - Marcus Ang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology and Visual Sciences, Duke-NUS Medical School, National University of Singapore, Singapore
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5
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Foo LL, Lim GYS, Lanca C, Wong CW, Hoang QV, Zhang XJ, Yam JC, Schmetterer L, Chia A, Wong TY, Ting DSW, Saw SM, Ang M. Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children. NPJ Digit Med 2023; 6:10. [PMID: 36702878 PMCID: PMC9879938 DOI: 10.1038/s41746-023-00752-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 01/10/2023] [Indexed: 01/27/2023] Open
Abstract
Our study aims to identify children at risk of developing high myopia for timely assessment and intervention, preventing myopia progression and complications in adulthood through the development of a deep learning system (DLS). Using a school-based cohort in Singapore comprising of 998 children (aged 6-12 years old), we train and perform primary validation of the DLS using 7456 baseline fundus images of 1878 eyes; with external validation using an independent test dataset of 821 baseline fundus images of 189 eyes together with clinical data (age, gender, race, parental myopia, and baseline spherical equivalent (SE)). We derive three distinct algorithms - image, clinical and mix (image + clinical) models to predict high myopia development (SE ≤ -6.00 diopter) during teenage years (5 years later, age 11-17). Model performance is evaluated using area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93-0.95; Test dataset 0.91-0.93), clinical models (Primary dataset AUC 0.90-0.97; Test dataset 0.93-0.94) and mixed (image + clinical) models (Primary dataset AUC 0.97; Test dataset 0.97-0.98) achieve clinically acceptable performance. The addition of 1 year SE progression variable has minimal impact on the DLS performance (clinical model AUC 0.98 versus 0.97 in primary dataset, 0.97 versus 0.94 in test dataset; mixed model AUC 0.99 versus 0.97 in primary dataset, 0.95 versus 0.98 in test dataset). Thus, our DLS allows prediction of the development of high myopia by teenage years amongst school-going children. This has potential utility as a clinical-decision support tool to identify "at-risk" children for early intervention.
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Affiliation(s)
- Li Lian Foo
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Gilbert Yong San Lim
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore
| | - Carla Lanca
- grid.418858.80000 0000 9084 0599Escola Superior de Tecnologia da Saúde de Lisboa (ESTeSL), Instituto Politécnico de Lisboa, Lisboa, Portugal ,grid.10772.330000000121511713Comprehensive Health Research Center (CHRC), Escola Nacional de Saúde Pública, Universidade Nova de Lisboa, Lisboa, Portugal
| | - Chee Wai Wong
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore ,grid.415572.00000 0004 0620 9577Asia Pacific Eye Centre, Gleneagles Hospital, Singapore, Singapore
| | - Quan V. Hoang
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore ,grid.21729.3f0000000419368729Dept. of Ophthalmology, Columbia University, Columbia, SC USA
| | - Xiu Juan Zhang
- grid.10784.3a0000 0004 1937 0482Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Jason C. Yam
- grid.10784.3a0000 0004 1937 0482Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China ,grid.490089.c0000 0004 1803 8779Hong Kong Eye Hospital, Hong Kong, China ,grid.415197.f0000 0004 1764 7206Department of Ophthalmology and Visual Sciences, Prince of Wales Hospital, Hong Kong, China ,grid.10784.3a0000 0004 1937 0482Hong Kong Hub of Paediatric Excellence, The Chinese University of Hong Kong, Hong Kong, China ,Department of Ophthalmology, Hong Kong Children’s Hospital, Hong Kong, China
| | - Leopold Schmetterer
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Audrey Chia
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Tien Yin Wong
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Daniel S. W. Ting
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Seang-Mei Saw
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Marcus Ang
- grid.272555.20000 0001 0706 4670Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore ,grid.4280.e0000 0001 2180 6431Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
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Xiao J, Liu M, Huang Q, Sun Z, Ning L, Duan J, Zhu S, Huang J, Lin H, Yang H. Analysis and modeling of myopia-related factors based on questionnaire survey. Comput Biol Med 2022; 150:106162. [PMID: 36252365 DOI: 10.1016/j.compbiomed.2022.106162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 09/12/2022] [Accepted: 10/01/2022] [Indexed: 11/03/2022]
Abstract
With the rapid development of science and technology, the trend of low age myopia is becoming increasingly significant. The latest national survey done by the Chinese government found that more than 80% of Chinese teenagers suffer from myopia. Adolescent myopia is closely related to living environment, heredity, and living habits. Quantifying the relationship between myopia and living environment, heredity, and living habits is conductive to the prevention and intervention of adolescent myopia. In this study, we investigated the relationships between four main factors (environment, habits, parental vision, and demographic) and myopia status by analyzing the questionnaire data. Data were collected from Chengdu, China in 2021, including 2808 myopia samples and 5693 non-myopia samples, with a total of 22 features. Then, these 22 features were inputted into three machine learning algorithms to discriminate the two classes of samples. Results show that the computational model could produce an AUC of 0.768. To pick out the most important features which play important roles in classification, we used incremental feature selection strategy to screen the 22 features. As a result, we found that the 4 most influential features with XGBoost could achieve a competitive AUC of 0.764. To further investigate the risk and protective factors affecting adolescent myopia, we used OR values derived from MLE-LR to analyze the relationship between 22 features and adolescent myopia. Results showed that the age variable was the most significant risk factor for myopia, followed by the myopia status of parents. The most protective factor for eyesight is the measure taken by the children, followed by the distance between books and eyes when reading. These discoveries can guide the prevention and control of myopia in children and adolescents.
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Affiliation(s)
- Jianqiang Xiao
- Eye School, Chengdu University of Traditional Chinese Medicine, Ineye Hospital of Chengdu University of TCM, China
| | - Mujiexin Liu
- Eye School, Chengdu University of Traditional Chinese Medicine, Ineye Hospital of Chengdu University of TCM, China
| | - Qinlai Huang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Zijie Sun
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Lin Ning
- School of Healthcare Technology, Chengdu Neusoft University, Chengdu, 611844, China
| | - Junguo Duan
- Eye School, Chengdu University of Traditional Chinese Medicine, Ineye Hospital of Chengdu University of TCM, China
| | - Siquan Zhu
- Eye School, Chengdu University of Traditional Chinese Medicine, Ineye Hospital of Chengdu University of TCM, China
| | - Jian Huang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China
| | - Hao Lin
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China.
| | - Hui Yang
- School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, 610054, China; School of Computer Science, Chengdu University of Information Technology, Chengdu, 611844, China.
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7
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Balagué-Dobón L, Cáceres A, González JR. Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure. Brief Bioinform 2022; 23:bbac043. [PMID: 35211719 PMCID: PMC8921734 DOI: 10.1093/bib/bbac043] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/25/2022] [Accepted: 01/28/2022] [Indexed: 12/12/2022] Open
Abstract
Single nucleotide polymorphisms (SNPs) are the most abundant type of genomic variation and the most accessible to genotype in large cohorts. However, they individually explain a small proportion of phenotypic differences between individuals. Ancestry, collective SNP effects, structural variants, somatic mutations or even differences in historic recombination can potentially explain a high percentage of genomic divergence. These genetic differences can be infrequent or laborious to characterize; however, many of them leave distinctive marks on the SNPs across the genome allowing their study in large population samples. Consequently, several methods have been developed over the last decade to detect and analyze different genomic structures using SNP arrays, to complement genome-wide association studies and determine the contribution of these structures to explain the phenotypic differences between individuals. We present an up-to-date collection of available bioinformatics tools that can be used to extract relevant genomic information from SNP array data including population structure and ancestry; polygenic risk scores; identity-by-descent fragments; linkage disequilibrium; heritability and structural variants such as inversions, copy number variants, genetic mosaicisms and recombination histories. From a systematic review of recently published applications of the methods, we describe the main characteristics of R packages, command-line tools and desktop applications, both free and commercial, to help make the most of a large amount of publicly available SNP data.
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8
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Myopia Genetics and Heredity. CHILDREN 2022; 9:children9030382. [PMID: 35327754 PMCID: PMC8947159 DOI: 10.3390/children9030382] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 02/19/2022] [Accepted: 03/03/2022] [Indexed: 11/18/2022]
Abstract
Myopia is the most common eye condition leading to visual impairment and is greatly influenced by genetics. Over the last two decades, more than 400 associated gene loci have been mapped for myopia and refractive errors via family linkage analyses, candidate gene studies, genome-wide association studies (GWAS), and next-generation sequencing (NGS). Lifestyle factors, such as excessive near work and short outdoor time, are the primary external factors affecting myopia onset and progression. Notably, besides becoming a global health issue, myopia is more prevalent and severe among East Asians than among Caucasians, especially individuals of Chinese, Japanese, and Korean ancestry. Myopia, especially high myopia, can be serious in consequences. The etiology of high myopia is complex. Prediction for progression of myopia to high myopia can help with prevention and early interventions. Prediction models are thus warranted for risk stratification. There have been vigorous investigations on molecular genetics and lifestyle factors to establish polygenic risk estimations for myopia. However, genes causing myopia have to be identified in order to shed light on pathogenesis and pathway mechanisms. This report aims to examine current evidence regarding (1) the genetic architecture of myopia; (2) currently associated myopia loci identified from the OMIM database, genetic association studies, and NGS studies; (3) gene-environment interactions; and (4) the prediction of myopia via polygenic risk scores (PRSs). The report also discusses various perspectives on myopia genetics and heredity.
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9
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Kassam I, Foo LL, Lanca C, Xu L, Hoang QV, Cheng CY, Hysi P, Saw SM. The potential of current polygenic risk scores to predict high myopia and myopic macular degeneration in multi-ethnic Singapore adults. Ophthalmology 2022; 129:890-902. [DOI: 10.1016/j.ophtha.2022.03.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 02/26/2022] [Accepted: 03/23/2022] [Indexed: 10/18/2022] Open
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10
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Pérez-Flores I, Macías-Murelaga B, Barrio-Barrio J. A multicenter Spanish study of atropine 0.01% in childhood myopia progression. Sci Rep 2021; 11:21748. [PMID: 34741059 PMCID: PMC8571279 DOI: 10.1038/s41598-021-00923-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 10/14/2021] [Indexed: 12/24/2022] Open
Abstract
To evaluate the efficacy and safety of atropine 0.01% eye drops for myopia control in a multicentric pediatric Spanish cohort. An interventional, prospective, multicenter study was designed. Children aged between 6 and 14 years, with myopia between - 2.00 D to - 6.00 D, astigmatism < 1.50 D and documented previous annual progression greater than - 0.5 D (cycloplegic spherical equivalent, SE) were included. Once nightly atropine 0.01% eye drops in each eye were prescribed to all participants for 12 months. Age, gender, ethnicity and iris color were registered. All patients underwent the same follow-up protocol in every center: baseline visit, telephone consultation 2 weeks later and office controls at 4, 8 and 12 months. At each visit, best-corrected visual acuity, and cycloplegic autorefraction were assessed. Axial length (AL), anterior chamber depth and pupil diameter were measured on an IOL Master (Carl Zeiss Meditec, Inc, Dublin, CA). Adverse effects were registered in a specific questionnaire. Mean changes in cycloplegic SE and AL in the 12 months follow-up were analyzed. SE progression during treatment was compared with the SE progression in the year before enrollment for each patient. Correlation between SE and AL, and annual progression distribution were evaluated. Progression risk factors were analyzed by multivariate logistic regression analyses. Of the 105 recruited children, 92 completed the treatment. Mean SE and AL changes were - 0.44 ± 0.41 D and 0.27 ± 0.20 mm respectively. Mean SE progression was lower than the year before treatment (- 0.44 ± 0.41 D versus - 1.01 ± 0.38 D; p < 0.0001). An inverse correlation between SE progression and AL progression (r: - 0.42; p < 0.0001) was found. Fifty-seven patients (62%) had a SE progression less than - 0.50 D. No risk factors associated with progression could be identified in multivariate analyses. Mean pupil diameter increment at 12-months visit was 0.74 ± 1.76 mm. The adverse effects were mild and infrequent, and decreased over the time. Atropine 0.01% is effective and safe for myopia progression control in a multicentric Spanish children cohort. We believe this efficacy might be extensible to the myopic pediatric population from Western countries with similar social and demographic features. More studies about myopia progression risk factors among atropine treated patients are needed.
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Affiliation(s)
| | | | - Jesús Barrio-Barrio
- Department of Ophthalmology, Navarra University Clinic Hospital, Navarra Institute for Health Research, IdiSNA, Pio XII, 36. Pamplona, 31008, Navarra, ES, Spain.
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