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      Seven New Loci Associated with Age-Related Macular Degeneration

      research-article
      Author(s): The AMD Gene Consortium
      Publication date (Electronic):
      Journal: Nature genetics

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Age-related macular degeneration (AMD) is a common cause of blindness in older individuals. To accelerate understanding of AMD biology and help design new therapies, we executed a collaborative genomewide association study, examining >17,100 advanced AMD cases and >60,000 controls of European and Asian ancestry. We identified 19 genomic loci associated with AMD with p<5×10 −8 and enriched for genes involved in regulation of complement activity, lipid metabolism, extracellular matrix remodeling and angiogenesis. Our results include 7 loci reaching p<5×10 −8 for the first time, near the genes COL8A1/FILIP1L, IER3/DDR1, SLC16A8, TGFBR1, RAD51B, ADAMTS9/ MIR548A2, and B3GALTL. A genetic risk score combining SNPs from all loci displayed similar good ability to distinguish cases and controls in all samples examined. Our findings provide new directions for biological, genetic and therapeutic studies of AMD.

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          Most cited references66

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          A. Subramanian, P. Tamayo, V. K. Mootha … (2005)
          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
          Article 0 comments Cited 12454 times – based on 0 reviews     Review now
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            Measuring inconsistency in meta-analyses.

            Julian P. T. Higgins, Simon Thompson, Jonathan Deeks … (2003)
            Article 0 comments Cited 9594 times – based on 0 reviews     Review now
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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.
              Article 0 comments Cited 5193 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: 21 May 2013
                Publication date (Electronic): 03 March 2013
                Publication date (Print): April 2013
                Publication date PMC-release: 01 October 2013
                Volume: 45
                Issue: 4
                Pages: 433-439e2
                Author notes
                Correspondence To: Gonçalo R. Abecasis, University of Michigan School of Public Health, Ann Arbor, MI, USA, goncalo@ 123456umich.edu . Iris Heid, University of Regensburg, Regensburg, Germany, iris.heid@ 123456klinik.uni-regensburg.de . Lindsay A. Farrer, Boston Universitym, Boston, MA, USA, farrer@ 123456bu.edu . Jonathan L. Haines, Vanderbilt University, Nashville, TN, USA, jonathan@ 123456chgr.mc.vanderbilt.edu
                Article
                Manuscript ID: NIHMS474886
                DOI: 10.1038/ng.2578
                PMC ID: 3739472
                PubMed ID: 23455636
                SO-VID: bf7996ae-6f19-46fe-896a-3c1882e9623c
                License:

                Users may view, print, copy, download and text and data- mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

                History
                Funding
                Funded by: National Human Genome Research Institute : NHGRI
                Award ID: R01 HG007022 || HG
                Funded by: National Eye Institute : NEI
                Award ID: R01 EY022005 || EY
                Categories
                Subject: Article

                ScienceOpen disciplines: Genetics
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                ScienceOpen disciplines: Genetics

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