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      Common variants near ABCA1, AFAP1 and GMDS confer risk of primary open-angle glaucoma

      research-article
      Author(s): 1 , 2 , 3 , 2 , 2 , 4 , 2 , 1 , 5 , 6 , 7 , 8 , 8 , 9 , 6 , 7 , 10 , 11 , 12 , 13 , 14 , 12 , 15 , 16 , 16 , 17 , 2 , 2 , 2 , 2 , 18 , 4 , 19 , 17 , Wellcome Trust Case Control Consortium 2 , NEIGHBORHOOD consortium, 2 , 17 , 1 , 1 , 1 , 20 , 1 , 5 , 8 , 3 , 21 , 17 , 1 , 2 , 22
      Publication date (Electronic):
      Journal: Nature genetics

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          Abstract

          Primary open-angle glaucoma (POAG) is a major cause of irreversible blindness worldwide. We performed a genome-wide association study in an Australian discovery cohort comprising 1,155 advanced POAG cases and 1,992 controls. Association of the top SNPs from the discovery stage was investigated in two Australian replication cohorts (total 932 cases, 6,862 controls) and two US replication cohorts (total 2,616 cases, 2,634 controls). Meta-analysis of all cohorts revealed three novel loci associated with development of POAG. These loci are located upstream of ABCA1 (rs2472493 [G] OR=1.31, P= 2.1 × 10 −19), within AFAP1 (rs4619890 [G] OR=1.20, P= 7.0 × 10 −10 ) and within GMDS (rs11969985 [G] OR=1.31, and P= 7.7 × 10 −10). Using RT-PCR and immunolabelling, we also showed that these genes are expressed within human retina, optic nerve and trabecular meshwork and that ABCA1 and AFAP1 are also expressed in retinal ganglion cells.

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          Most cited references52

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Shaun Purcell, Benjamin M. Neale, Kathe Todd-Brown … (2007)
          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            Principal components analysis corrects for stratification in genome-wide association studies.

            Alkes L. Price, Nick Patterson, Robert Plenge … (2006)
            Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.
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              Is Open Access

              METAL: fast and efficient meta-analysis of genomewide association scans

              Cristen Willer, Yun Li, Gonçalo R Abecasis (2010)
              Summary: METAL provides a computationally efficient tool for meta-analysis of genome-wide association scans, which is a commonly used approach for improving power complex traits gene mapping studies. METAL provides a rich scripting interface and implements efficient memory management to allow analyses of very large data sets and to support a variety of input file formats. Availability and implementation: METAL, including source code, documentation, examples, and executables, is available at http://www.sph.umich.edu/csg/abecasis/metal/ Contact: goncalo@umich.edu
              Article 0 comments Cited 1185 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 9216904
                Journal ID (pubmed-jr-id): 2419
                Journal ID (nlm-ta): Nat Genet
                Journal ID (iso-abbrev): Nat. Genet.
                Title: Nature genetics
                ISSN (Print): 1061-4036
                ISSN (Electronic): 1546-1718
                Publication date Nihms-submitted: 27 August 2014
                Publication date (Electronic): 31 August 2014
                Publication date (Print): October 2014
                Publication date PMC-release: 01 April 2015
                Volume: 46
                Issue: 10
                Pages: 1120-1125
                Affiliations
                [1 ]QIMR Berghofer Medical Research Institute, Brisbane, QLD 4029, Australia.
                [2 ]Department of Ophthalmology, Flinders University, Adelaide, SA 5042, Australia.
                [3 ]Menzies Research Institute Tasmania, University of Tasmania, Hobart, TAS, 7000, Australia.
                [4 ]Centre for Eye Research Australia (CERA), University of Melbourne, Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia.
                [5 ]University of Queensland Diamantina Institute, Brisbane, QLD 4102, Australia.
                [6 ]Center for Human Genetics Research, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
                [7 ]Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA.
                [8 ]Department of Ophthalmology, Harvard Medical School and Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA.
                [9 ]Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA.
                [10 ]Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina, USA.
                [11 ]Department of Medicine, Duke University Medical Center, Durham, North Carolina, USA.
                [12 ]NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.
                [13 ]MRC Social Genetic and Developmental Psychiatry Research Centre, Institute of Psychiatry, King’s College, De Crespigny Park, London, UK.
                [14 ]Department of Ophthalmology, School of Medicine, Aristotle University of Thessaloniki, AHEPA Hospital, Thessaloniki, Greece.
                [15 ]Ophthalmology and Vision Science, Macquarie University, Sydney, New South Wales, Australia
                [16 ]South Australian Institute of Ophthalmology, University of Adelaide, Adelaide, South Australia, Australia
                [17 ]Centre for Vision Research, Westmead Millennium Institute, University of Sydney, Westmead, NSW 2145, Australia.
                [18 ]Department of Anatomical Pathology, Flinders University, Flinders Medical Centre, South Australia
                [19 ]Department of Ophthalmology, University of Sydney, Sydney Eye Hospital, Sydney, Australia
                [20 ]School of Medicine, University of Queensland, Herston Campus, Brisbane, QLD, Australia.
                [21 ]Centre for Ophthalmology and Visual Science, Lions Eye Institute, University of Western Australia, Perth, Australia.
                [22 ]South Australian Health and Medical Research Institute, Adelaide, South Australia.
                Author notes
                Corresponding author Correspondence should be addressed to Jamie Craig ( jamie.craig@ 123456flinders.edu.au ) or Puya Gharahkhani ( Puya.Gharahkhani@ 123456qimrberghofer.edu.au )
                [*]

                These authors jointly directed this work.

                AUTHOR CONTRIBUTIONS K.P.B., S.MacGregor, and J.E.C. were involved in designing the study. A.W.H., K.C., L.R.P., M.A.H., A.C.V., P.MG., F.T., P.J.F., J.J.W., G.W.M., N.G.M., G.RS., D.C.W., M.A.B., J.L.W., D.A.M., P.M. and J.E.C. were involved in participant recruitment, sample collection or genotyping. Analysis was performed by P.G., R.F., K.P.B., S.S., M.H.L., J.N.C.B., S.J.L., L.R.P., J.L.H., J.L.W., and S.MacGregor. Designing and conducting the laboratory experiments were performed by K.P.B., S.S., S.M., and R.F. Clinician assessments were performed by S.L.G., R.J.C., M.C., A.J.W., T.Z., E.S., J.L., J.T.F., S.K., J.B.R., I.G., P.R.H., R.M., D.A.M. and J.E.C. The initial draft was written by P.G., K.P.B., S.S., and S.MacGregor.

                Article
                Manuscript ID: EMS59932
                DOI: 10.1038/ng.3079
                PMC ID: 4177327
                PubMed ID: 25173105
                SO-VID: bfd3bbdd-d35d-4ace-8863-cd9b6329685d
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                ScienceOpen disciplines: Genetics

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