June 2023
Volume 64, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2023
Phecode-enabled GWASs identify known and novel loci for eye traits and disorders (EYEWas)
Author Affiliations & Notes
  • Sudha K Iyengar
    Department of Population & Quantitative Health Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States
    Research Service, VA Northeast Ohio Healthcare System, Cleveland, Ohio, United States
  • Bryan R Gorman
    VA Cooperative Studies Program, Veterans Affairs Boston Healthcare System, Boston, Massachusetts, United States
    Booz Allen Hamilton Inc, McLean, Virginia, United States
  • Cari L Nealon
    Eye Clinic, VA Northeast Ohio Healthcare System, Cleveland, Ohio, United States
  • Alexis A Rodriguez
    Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois, United States
  • Ravi K Madduri
    Data Science and Learning Division, Argonne National Laboratory, Lemont, Illinois, United States
  • Saiju Pyarajan
    VA Cooperative Studies Program, Veterans Affairs Boston Healthcare System, Boston, Massachusetts, United States
  • John Michael Gaziano
    Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), Veterans Affairs Boston Healthcare System, Boston, Massachusetts, United States
    Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
  • Pirro G Hysi
    Department of Twin Research and Genetic Epidemiology, King's College London, London, London, United Kingdom
  • Neal S Peachey
    Research Service, VA Northeast Ohio Healthcare System, Cleveland, Ohio, United States
    Cole Eye Institute, Cleveland Clinic Foundation, Cleveland, Ohio, United States
  • Footnotes
    Commercial Relationships   Sudha Iyengar None; Bryan Gorman None; Cari Nealon None; Alexis Rodriguez None; Ravi Madduri None; Saiju Pyarajan None; John Gaziano None; Pirro Hysi None; Neal Peachey None
  • Footnotes
    Support  VA Office of Research Development (I01 BX004557), NIH Core Grants (P30 EY025585, P30 EY011373)
Investigative Ophthalmology & Visual Science June 2023, Vol.64, 3065. doi:
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      Sudha K Iyengar, Bryan R Gorman, Cari L Nealon, Alexis A Rodriguez, Ravi K Madduri, Saiju Pyarajan, John Michael Gaziano, Pirro G Hysi, Neal S Peachey; Phecode-enabled GWASs identify known and novel loci for eye traits and disorders (EYEWas). Invest. Ophthalmol. Vis. Sci. 2023;64(8):3065.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To create contrasting pools of cases and controls from EHR, two basic methods exist for phenotyping. The first includes crafting specific algorithms based on disease features, demographics, laboratory values, and medications with or without natural language processing. This process requires validation with clinical records, but can achieve considerable precision. A second high-throughput method uses Phecodes which encompass inclusion and exclusion filters from the International Classification of Disease (ICD-9 and ICD-10) code atlas. While not as precise, examination of Phecodes together with genetics can also lead to discovery of novel loci. We used Phecode-enabled phenotypes to conduct a series of genome-wide association studies (GWAS) in the Million Veteran Program (MVP).

Methods : Veterans Affairs (VA) is the largest integrated health care system in the US, serving > 11 million Veterans yearly. Nested within the VA, the MVP is learning how genes, lifestyle, and military exposures affect health and illness in multi-ethnic veterans (N>900,000; ~650,000 with genetic data). The MVP has performed PheCode-based GWAS using mixed-models via SAIGE; adjustments for age, sex and ancestry-based principal components were included. Cases were individuals with at least two mentions of each PheCode (version 1.2), and controls were individuals without these Phecodes. We extracted data on the eye-related GWAS (EYEWas), and examined results for known and novel loci.

Results : Individuals of European American (EA), African American (AA) and Hispanic American (HA) ancestry yielded 86, 76 and 53 overlapping EYEWas scans, respectively. Among EA, 74 of 86 scans showed at least one genome-wide significant (GWS) locus; sample sizes and GWS loci were reduced for AA and HA. Considerable differences existed in top loci between ethnic groups illustrating that genetic architecture varies across populations. For example, blindness and low vision (Phecode 367.9) is powered by AMD among EA (N=19,521 cases vs. 414,088 controls) as genes CFH, ARMS2, C2 and C3 show GWS signals; AA and HA show lower sample sizes and dissimilar profiles. Some genes (e.g. EFEMP1) showed unexpected pleiotropy across unrelated codes. Joint analysis with the UK Biobank is ongoing.

Conclusions : EYEWas should yield new insights into ocular biology, clarify use of PheCodes, and provide genetic connections between apparently unrelated conditions.

This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.

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