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    Identifying Genetic Pleiotropy through a Literature-wide Association Study (LitWAS) and a Phenotype Association Study (PheWAS) in the Age-related Eye Disease Study 2 (AREDS2)

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    Author
    Simmons, Michael
    Affiliation
    The University of Arizona College of Medicine - Phoenix
    Issue Date
    2017-05-26
    Keywords
    Text Mining
    Genetic Association Studies
    GWAS
    PheWAS
    Age-related Macular Degeneration
    AMD
    AREDS2
    ARMS2
    Phenome
    HtrA1 Protein
    MeSH Subjects
    Eye Diseases
    Macular Degeneration
    Data Mining
    Genetic Pleiotropy
    Phenotype
    Polymorphism, Genetic
    Randomized Controlled Trials as Topic
    Genetic Testing
    Retinal Hemorrhage
    
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    Publisher
    The University of Arizona.
    Description
    A Thesis submitted to The University of Arizona College of Medicine - Phoenix in partial fulfillment of the requirements for the Degree of Doctor of Medicine.
    URI
    http://hdl.handle.net/10150/623630
    Abstract
    Genetic association studies simplify genotype‐phenotype relationship investigation by considering only the presence of a given polymorphism and the presence or absence of a given downstream phenotype. Although such associations do not indicate causation, collections of phenotypes sharing association with a single genetic polymorphism may provide valuable mechanistic insights. In this thesis we explore such genetic pleiotropy with Deep Phenotype Association Studies (DeePAS) using data from the Age‐Related Eye Study 2 (AREDS2). We also employ a novel text mining approach to extract pleiotropic associations from the published literature as a hypothesis generation mechanism. Is it possible to identify pleiotropic genetic associations across multiple published abstracts and validate these in data from AREDS2? Data from the AREDS2 trial includes 123 phenotypes including AMD features, other ocular conditions, cognitive function and cardiovascular, neurological, gastrointestinal and endocrine disease. A previously validated relationship extraction algorithm was used to isolate descriptions of genetic associations with these phenotypes in MEDLINE abstracts. Results were filtered to exclude negated findings and normalize variant mentions. Genotype data was available for 1826 AREDS2 participants. A DeePAS was performed by evaluating the association between selected SNPs and all available phenotypes. Associations that remained significant after Bonferroni‐correction were replicated in AREDS. LitWAS analysis identified 9372 SNPs with literature support for at least two distinct phenotypes, with an average of 3.1 phenotypes/SNP. PheWAS analyses revealed that two variants of the ARMS2‐HTRA1 locus at 10q26, rs10490924 and rs3750846, were significantly associated with sub‐retinal hemorrhage in AMD (rs3750846 OR 1.79 (1.41‐2.27), p=1.17*10‐7). This associated remained significant even in populations of participants with neovascular AMD. Furthermore, odds ratios for the development of sub‐retinal hemorrhage in the presence of the rs3750846 SNP were similar between incident and prevalent AREDS2 sub‐populations (OR: 1.94 vs 1.75). This association was also replicated in data from the AREDS trial. No literature‐defined pleiotropic associations tested remained significant after multiple‐testing correction. The rs3750846 variant of the ARMS2‐HTRA1 locus is associated with sub‐retinal hemorrhage. Automatic literature mining, when paired with clinical data, is a promising method for exploring genotype‐phenotype relationships.
    Type
    text; Electronic Thesis
    Language
    en_US
    Collections
    College of Medicine - Phoenix, Scholarly Projects

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