April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
Meta-Analysis of Glaucoma Genome-Wide Imputed Dataset
Author Affiliations & Notes
  • Jessica Cooke Bailey
    Epidemiology & Biostatistics, Case Western Reserve University, Cleveland, OH
  • Stephanie Loomis
    Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA
    Channing Division of Network Medicine, Brigham and Women, Boston, MA
  • Michael A Hauser
    Department of Ophthalmology, Duke University Medical Center, Durham, NC
    Department of Medicine, Duke University Medical Center, Durham, NC
  • Louis R Pasquale
    Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA
    Channing Division of Network Medicine, Brigham and Women, Boston, MA
  • Jae H Kang
    Channing Division of Network Medicine, Brigham and Women, Boston, MA
  • R Rand Allingham
    Department of Ophthalmology, Duke University Medical Center, Durham, NC
  • Robert N Weinreb
    Department of Ophthalmology, Hamilton Eye Center, University of California, San Diego, San Diego, CA
  • Janey L Wiggs
    Department of Ophthalmology, Harvard Medical School, Massachusetts Eye and Ear Infirmary, Boston, MA
  • Jonathan Haines
    Epidemiology & Biostatistics, Case Western Reserve University, Cleveland, OH
  • Footnotes
    Commercial Relationships Jessica Cooke Bailey, None; Stephanie Loomis, None; Michael Hauser, None; Louis Pasquale, None; Jae Kang, None; R Allingham, None; Robert Weinreb, None; Janey Wiggs, None; Jonathan Haines, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 4569. doi:
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      Jessica Cooke Bailey, Stephanie Loomis, Michael A Hauser, Louis R Pasquale, Jae H Kang, R Rand Allingham, Robert N Weinreb, Janey L Wiggs, Jonathan Haines, ; Meta-Analysis of Glaucoma Genome-Wide Imputed Dataset. Invest. Ophthalmol. Vis. Sci. 2014;55(13):4569.

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

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Abstract

Purpose: Glaucoma is a phenotypically and genetically complex neurodegenerative disease that is the second leading cause of blindness. Though genetic factors are known to contribute, the few identified loci fail to fully account for the heritability of glaucoma. To extend the proportion of the genome testable for association with glaucoma as well as the power to detect association at numerous loci, we evaluated imputed genome-wide data in the NEIGHBORHOOD Consortium dataset which consists primarily of individuals of European descent.

Methods: To maximize the number of testable markers, we imputed all samples to 1000Genomes (March 2012) using IMPUTE2 and performed logistic regression analysis separately in each sample using PLINK dosage and adjusting for covariates age, gender, and significant Eigenvectors (to remove spurious influences on data due to primarily ancestry differences) for each dataset. We then performed a meta-analysis using METAL, implementing the standard error schedule and genomic control options. Variants with an info-metric <0.7 and minor allele frequency <0.05 were removed.

Results: A total of 485101 variants were tested in first-past analyses of a subset of the NEIGBORHOOD dataset that includes 2616 glaucoma cases and 2634 controls of European descent. While successfully replicating signals in previously implicated glaucoma genes (e.g. CAV1/CAV2, CDKN2BAS), review of coding variants associated with glaucoma in this analysis highlighted SNPs in PKD1L1, EYA1, UBQLNL, ALG8, FBXO47, KIF18B, TMEM145, and ZNF614 (P<5x10-9). Variants not present on prior genotyping arrays of particular interest including a synonymous variant (rs10103397) in eyes absent homolog 1-encoding gene EYA1 on chr8q13.3 and a missense variant (rs11335840) in F-box protein 47-encoding gene FBXO47 on chr17q12; both were significantly associated with glaucoma risk (rs1013397: P=1.40x10-9; OR=1.15, 95% CI 1.12-1.17; rs11335840: P=8.67x10-11; OR=1.41, 95% CI 1.34-1.49). Follow-up analyses of additional datasets within the NEIGHBORHOOD will confirm these associations and the impact of these variants on glaucoma risk overall and in the high-tension and normal-tension subgroups.

Conclusions: Imputation of genome-wide array data extends the genomic coverage beyond what can be interrogated by a single or multiple arrays. Meta-analyzing multiple imputed datasets can successfully be implemented to identify genetic variants contributing to glaucoma disease risk.

Keywords: 539 genetics  
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