Genetic causal inference between amblyopia and perinatal factors

被引:0
|
作者
Ju-Yeun Lee
Sangjun Lee
Sue K. Park
机构
[1] Seoul National University College of Medicine,Department of Preventive Medicine
[2] Seoul National University College of Medicine,Integrated Major in Innovative Medical Science
[3] Hanyang University College of Medicine,Department of Ophthalmology, Myongji Hospital
[4] Seoul National University,Cancer Research Institute
[5] Seoul National University Graduated School,Department of Biomedicine Science
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Amblyopia is a common visual disorder that causes significant vision problems globally. Most non-ocular risk factors for amblyopia are closely related to the intrauterine environment, and are strongly influenced by parent-origin effects. Parent-origin perinatal factors may have a direct causal inference on amblyopia development; therefore, we investigated the causal association between perinatal factors and amblyopia risk using a one-sample Mendelian Randomization (MR) with data from the UK Biobank Cohort Data (UKBB). Four distinct MR methods were employed to analyze the association between three perinatal factors (birth weight [BW], maternal smoking, and breastfeeding) and amblyopia risk, based on the summary statistics of genome-wide association studies in the European population. The inverse variance weighting method showed an inverse causal association between BW and amblyopia risk (odds ratio, 0.48 [95% CI, 0.29–0.80]; p = 0.004). Maternal smoking and breastfeeding were not causally associated with amblyopia risk. Our findings provided a possible evidence of a significant genetic causal association between low BW and increased amblyopia risk. This evidence may highlight the potential of BW as a predictive factor for visual maldevelopment and the need for careful management of amblyopia risk in patients with low BW.
引用
收藏
相关论文
共 50 条
  • [31] Vector Causal Inference between Two Groups of Variables
    Wahl, Jonas
    Ninad, Urmi
    Runge, Jakob
    THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 10, 2023, : 12305 - 12312
  • [32] Causal Inference
    Kuang, Kun
    Li, Lian
    Geng, Zhi
    Xu, Lei
    Zhang, Kun
    Liao, Beishui
    Huang, Huaxin
    Ding, Peng
    Miao, Wang
    Jiang, Zhichao
    ENGINEERING, 2020, 6 (03) : 253 - 263
  • [33] CAUSAL INFERENCE
    ROTHMAN, KJ
    LANES, S
    ROBINS, J
    EPIDEMIOLOGY, 1993, 4 (06) : 555 - 556
  • [34] Misunderstandings between experimentalists and observationalists about causal inference
    Imai, Kosuke
    King, Gary
    Stuart, Elizabeth A.
    JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY, 2008, 171 : 481 - 502
  • [35] Association between bilirubin and cardiovascular disease risk factors: using Mendelian randomization to assess causal inference
    Patrick F McArdle
    Brian W Whitcomb
    Keith Tanner
    Braxton D Mitchell
    Alan R Shuldiner
    Afshin Parsa
    BMC Cardiovascular Disorders, 12
  • [36] Association between bilirubin and cardiovascular disease risk factors: using Mendelian randomization to assess causal inference
    McArdle, Patrick F.
    Whitcomb, Brian W.
    Tanner, Keith
    Mitchell, Braxton D.
    Shuldiner, Alan R.
    Parsa, Afshin
    BMC CARDIOVASCULAR DISORDERS, 2012, 12
  • [37] Causal inference for psychologists who think that causal inference is not for them
    Rohrer, Julia M.
    SOCIAL AND PERSONALITY PSYCHOLOGY COMPASS, 2024, 18 (03)
  • [38] Association Between Premorbid Body Mass Index and Amyotrophic Lateral Sclerosis: Causal Inference Through Genetic Approaches
    Zeng, Ping
    Yu, Xinghao
    Xu, Haibo
    FRONTIERS IN NEUROLOGY, 2019, 10
  • [39] Shared genetic risk factors and causal association between psoriasis and coronary artery disease
    Matthew T. Patrick
    Qinmengge Li
    Rachael Wasikowski
    Nehal Mehta
    Johann E. Gudjonsson
    James T. Elder
    Xiang Zhou
    Lam C. Tsoi
    Nature Communications, 13
  • [40] Shared genetic risk factors and causal association between psoriasis and coronary artery disease
    Patrick, Matthew T.
    Li, Qinmengge
    Wasikowski, Rachael
    Mehta, Nehal
    Gudjonsson, Johann E.
    Elder, James T.
    Zhou, Xiang
    Tsoi, Lam C.
    NATURE COMMUNICATIONS, 2022, 13 (01)