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      Transcriptome analysis and molecular signature of human retinal pigment epithelium

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          Abstract

          Retinal pigment epithelium (RPE) is a polarized cell layer critical for photoreceptor function and survival. The unique physiology and relationship to the photoreceptors make the RPE a critical determinant of human vision. Therefore, we performed a global expression profiling of native and cultured human fetal and adult RPE and determined a set of highly expressed ‘signature’ genes by comparing the observed RPE gene profiles to the Novartis expression database (SymAtlas: http://wombat.gnf.org/index.html) of 78 tissues. Using stringent selection criteria of at least 10-fold higher expression in three distinct preparations, we identified 154 RPE signature genes, which were validated by qRT-PCR analysis in RPE and in an independent set of 11 tissues. Several of the highly expressed signature genes encode proteins involved in visual cycle, melanogenesis and cell adhesion and Gene ontology analysis enabled the assignment of RPE signature genes to epithelial channels and transporters ( ClCN4, BEST1, SLCA20) or matrix remodeling ( TIMP3, COL8A2). Fifteen RPE signature genes were associated with known ophthalmic diseases, and 25 others were mapped to regions of disease loci. An evaluation of the RPE signature genes in a recently completed AMD genomewide association (GWA) data set revealed that TIMP3, GRAMD3, PITPNA and CHRNA3 signature genes may have potential roles in AMD pathogenesis and deserve further examination. We propose that RPE signature genes are excellent candidates for retinal diseases and for physiological investigations (e.g. dopachrome tautomerase in melanogenesis). The RPE signature gene set should allow the validation of RPE-like cells derived from human embryonic or induced pluripotent stem cells for cell-based therapies of degenerative retinal diseases.

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists

            All tools in the DAVID Bioinformatics Resources aim to provide functional interpretation of large lists of genes derived from genomic studies. The newly updated DAVID Bioinformatics Resources consists of the DAVID Knowledgebase and five integrated, web-based functional annotation tool suites: the DAVID Gene Functional Classification Tool, the DAVID Functional Annotation Tool, the DAVID Gene ID Conversion Tool, the DAVID Gene Name Viewer and the DAVID NIAID Pathogen Genome Browser. The expanded DAVID Knowledgebase now integrates almost all major and well-known public bioinformatics resources centralized by the DAVID Gene Concept, a single-linkage method to agglomerate tens of millions of diverse gene/protein identifiers and annotation terms from a variety of public bioinformatics databases. For any uploaded gene list, the DAVID Resources now provides not only the typical gene-term enrichment analysis, but also new tools and functions that allow users to condense large gene lists into gene functional groups, convert between gene/protein identifiers, visualize many-genes-to-many-terms relationships, cluster redundant and heterogeneous terms into groups, search for interesting and related genes or terms, dynamically view genes from their lists on bio-pathways and more. With DAVID (http://david.niaid.nih.gov), investigators gain more power to interpret the biological mechanisms associated with large gene lists.
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              Genotype imputation.

              Genotype imputation is now an essential tool in the analysis of genome-wide association scans. This technique allows geneticists to accurately evaluate the evidence for association at genetic markers that are not directly genotyped. Genotype imputation is particularly useful for combining results across studies that rely on different genotyping platforms but also increases the power of individual scans. Here, we review the history and theoretical underpinnings of the technique. To illustrate performance of the approach, we summarize results from several gene mapping studies. Finally, we preview the role of genotype imputation in an era when whole genome resequencing is becoming increasingly common.
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                Author and article information

                Journal
                Hum Mol Genet
                hmg
                hmg
                Human Molecular Genetics
                Oxford University Press
                0964-6906
                1460-2083
                15 June 2010
                1 April 2010
                1 April 2010
                : 19
                : 12
                : 2468-2486
                Affiliations
                [1 ]Ophthalmic Genetics & Visual Function Branch,
                [2 ]Section of Epithelial and Retinal Physiology and Disease,
                [3 ]Neurobiology-Neurodegeneration & Repair Laboratory and
                [4 ]simpleNational Eye Institute/NIH , Bethesda, MD, USA,
                [5 ]Mathematical and Statistical Computing Laboratory, Center for Information Technology/NIH, Bethesda, MD, USA,
                [6 ]Biostatistics, School of Public Health, simpleUniversity of Michigan , Ann Arbor, MI, USA,
                [7 ]Institute for Molecular Biology, simpleUniversity of Oregon , Eugene, OR, USA and
                [8 ]Ophthalmology, simpleUniversity of Pennsylvania , Philadelphia, PA, USA
                Author notes
                [* ]To whom correspondence should be addressed at: NIH, NEI, 31 Center Drive MSC 2510, Bethesda, MD 20892-2510, USA. Tel: +1 3014516763; Fax: +1 3014515421; Email: millers@ 123456nei.nih.gov
                Article
                ddq129
                10.1093/hmg/ddq129
                2876890
                20360305
                403175f3-418f-42ae-bae1-064cc91d40d3
                Published by Oxford University Press 2010

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.5), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 14 December 2009
                : 3 March 2010
                : 30 March 2010
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                Genetics
                Genetics

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