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      Retinal transcriptome and eQTL analyses identify genes associated with age-related macular degeneration

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          Abstract

          Genome-wide association studies (GWAS) have identified genetic variants at 34 loci contributing to age-related macular degeneration (AMD) 13 . We generated transcriptional profiles of postmortem retina from 453 controls and cases at distinct stages of AMD and integrated retinal transcriptomes, covering 13,662 protein-coding and 1,462 non-coding genes, with genotypes at over 9 million common single nucleotide polymorphisms (SNPs) for expression quantitative trait loci (eQTL) analysis of a tissue not included in Genotype-Tissue Expression (GTEx) and other large datasets 4, 5 . Cis-eQTL analysis identified 10,474 genes under genetic regulation, including 4,541 eQTLs detected only in the retina. Integrated analysis of AMD-GWAS with eQTLs ascertained likely target genes at six reported loci. Using transcriptome-wide association analysis (TWAS), we identified three additional genes, RLBP1, HIC1 and PARP12, after Bonferroni correction. Our studies expand the genetic landscape of AMD and establish the Eye Genotype Expression (EyeGEx) database as a resource for post-GWAS interpretation of multifactorial ocular traits.

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          Most cited references20

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Identification of novel transcripts in annotated genomes using RNA-Seq.

            We describe a new 'reference annotation based transcript assembly' problem for RNA-Seq data that involves assembling novel transcripts in the context of an existing annotation. This problem arises in the analysis of expression in model organisms, where it is desirable to leverage existing annotations for discovering novel transcripts. We present an algorithm for reference annotation-based transcript assembly and show how it can be used to rapidly investigate novel transcripts revealed by RNA-Seq in comparison with a reference annotation. The methods described in this article are implemented in the Cufflinks suite of software for RNA-Seq, freely available from http://bio.math.berkeley.edu/cufflinks. The software is released under the BOOST license. cole@broadinstitute.org; lpachter@math.berkeley.edu Supplementary data are available at Bioinformatics online.
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              The Post-GWAS Era: From Association to Function

              During the past 12 years, genome-wide association studies (GWASs) have uncovered thousands of genetic variants that influence risk for complex human traits and diseases. Yet functional studies aimed at delineating the causal genetic variants and biological mechanisms underlying the observed statistical associations with disease risk have lagged. In this review, we highlight key advances in the field of functional genomics that may facilitate the derivation of biological meaning post-GWAS. We highlight the evidence suggesting that causal variants underlying disease risk often function through regulatory effects on the expression of target genes and that these expression effects might be modest and cell-type specific. We moreover discuss specific studies as proof-of-principle examples for current statistical, bioinformatic, and empirical bench-based approaches to downstream elucidation of GWAS-identified disease risk loci.
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                Author and article information

                Journal
                9216904
                2419
                Nat Genet
                Nat. Genet.
                Nature genetics
                1061-4036
                1546-1718
                22 January 2019
                11 February 2019
                April 2019
                11 August 2019
                : 51
                : 4
                : 606-610
                Affiliations
                [1 ]Neurobiology-Neurodegeneration & Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
                [2 ]Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
                [3 ]Department of Ophthalmology and Visual Neurosciences, University of Minnesota, Minneapolis, MN, USA
                [4 ]Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
                [5 ]Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
                [6 ]Division of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
                [7 ]Departments of Biomedical Engineering and Computer Science, Johns Hopkins University, MD, USA
                Author notes
                [# ]Correspondence to Anand Swaroop ( swaroopa@ 123456nei.nih.gov ), Nilanjan Chatterjee ( nchatte2@ 123456jhu.edu ) and Deb Ferrington ( ferri013@ 123456umn.edu )
                [$]

                Primary Contact: swaroopa@ 123456nei.nih.gov

                Author Contributions

                Overall Conceptualization, R.R. and A.S.; Clinical and Tissue Resources, D.A.F., R.J.K., S.R.M., E.C.; Transcriptome Data, R.R., M.R.S., L.G., A.W., A.P.; Genotyping Data, L.G.F., G.R.A.; Bioinformatic Analysis, M.R.S., R.R., M.K., A.W.; eQTL Analysis, O.A.S., N.C., M.A., A.B.; Statistical Supervision, N.C.; Data Curation, M.R.S.; Writing – Original Draft, R.R., M.R.S., O.A.S., M.K, N.C., A.S.; Writing – Review & Editing, all authors; Funding, D.A.F., A.S.; Supervision and Project Administration, A.S.

                Author information
                http://orcid.org/0000-0002-0469-4631
                http://orcid.org/0000-0003-2120-0338
                http://orcid.org/0000-0002-5274-2765
                http://orcid.org/0000-0002-2110-1690
                http://orcid.org/0000-0002-9982-6261
                http://orcid.org/0000-003-0999-9802
                http://orcid.org/0000-0002-5287-627X
                http://orcid.org/0000-0003-2561-7464
                http://orcid.org/0000-0002-9060-008X
                http://orcid.org/0000-0002-1975-1141
                Article
                NIHMS1518564
                10.1038/s41588-019-0351-9
                6441365
                30742112
                66e7542e-a4eb-46cf-99ff-d7243286138a

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                Genetics
                Genetics

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