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      New loci and coding variants confer risk for age-related macular degeneration in East Asians

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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 major cause of blindness, but presents differently in Europeans and Asians. Here, we perform a genome-wide and exome-wide association study on 2,119 patients with exudative AMD and 5,691 controls, with independent replication in 4,226 patients and 10,289 controls, all of East Asian descent, as part of The Genetics of AMD in Asians (GAMA) Consortium. We find a strong association between CETP Asp442Gly (rs2303790), an East Asian-specific mutation, and increased risk of AMD (odds ratio (OR)=1.70, P=5.60 × 10 −22). The AMD risk allele (442Gly), known to protect from coronary heart disease, increases HDL cholesterol levels by 0.17 mmol l −1 ( P=5.82 × 10 −21) in East Asians ( n=7,102). We also identify three novel AMD loci: C6orf223 Ala231Ala (OR=0.78, P=6.19 × 10 −18), SLC44A4 Asp47Val (OR=1.27, P=1.08 × 10 −11) and FGD6 Gln257Arg (OR=0.87, P=2.85 × 10 −8). Our findings suggest that some of the genetic loci conferring AMD susceptibility in East Asians are shared with Europeans, yet AMD in East Asians may also have a distinct genetic signature.

          Abstract

          Age-related macular degeneration (AMD) is a major cause of blindness worldwide. Here, the authors carry out a two-stage genome-wide association study for AMD and identify three new AMD risk loci, highlighting the shared and distinct genetic basis of the disease in East Asians and Europeans.

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          Most cited references 56

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

          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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            An Integrated Encyclopedia of DNA Elements in the Human Genome

            Summary The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure, and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall the project provides new insights into the organization and regulation of our genes and genome, and an expansive resource of functional annotations for biomedical research.
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              A method and server for predicting damaging missense mutations

              To the Editor: Applications of rapidly advancing sequencing technologies exacerbate the need to interpret individual sequence variants. Sequencing of phenotyped clinical subjects will soon become a method of choice in studies of the genetic causes of Mendelian and complex diseases. New exon capture techniques will direct sequencing efforts towards the most informative and easily interpretable protein-coding fraction of the genome. Thus, the demand for computational predictions of the impact of protein sequence variants will continue to grow. Here we present a new method and the corresponding software tool, PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/), which is different from the early tool PolyPhen1 in the set of predictive features, alignment pipeline, and the method of classification (Fig. 1a). PolyPhen-2 uses eight sequence-based and three structure-based predictive features (Supplementary Table 1) which were selected automatically by an iterative greedy algorithm (Supplementary Methods). Majority of these features involve comparison of a property of the wild-type (ancestral, normal) allele and the corresponding property of the mutant (derived, disease-causing) allele, which together define an amino acid replacement. Most informative features characterize how well the two human alleles fit into the pattern of amino acid replacements within the multiple sequence alignment of homologous proteins, how distant the protein harboring the first deviation from the human wild-type allele is from the human protein, and whether the mutant allele originated at a hypermutable site2. The alignment pipeline selects the set of homologous sequences for the analysis using a clustering algorithm and then constructs and refines their multiple alignment (Supplementary Fig. 1). The functional significance of an allele replacement is predicted from its individual features (Supplementary Figs. 2–4) by Naïve Bayes classifier (Supplementary Methods). We used two pairs of datasets to train and test PolyPhen-2. We compiled the first pair, HumDiv, from all 3,155 damaging alleles with known effects on the molecular function causing human Mendelian diseases, present in the UniProt database, together with 6,321 differences between human proteins and their closely related mammalian homologs, assumed to be non-damaging (Supplementary Methods). The second pair, HumVar3, consists of all the 13,032 human disease-causing mutations from UniProt, together with 8,946 human nsSNPs without annotated involvement in disease, which were treated as non-damaging. We found that PolyPhen-2 performance, as presented by its receiver operating characteristic curves, was consistently superior compared to PolyPhen (Fig. 1b) and it also compared favorably with the three other popular prediction tools4–6 (Fig. 1c). For a false positive rate of 20%, PolyPhen-2 achieves the rate of true positive predictions of 92% and 73% on HumDiv and HumVar, respectively (Supplementary Table 2). One reason for a lower accuracy of predictions on HumVar is that nsSNPs assumed to be non-damaging in HumVar contain a sizable fraction of mildly deleterious alleles. In contrast, most of amino acid replacements assumed non-damaging in HumDiv must be close to selective neutrality. Because alleles that are even mildly but unconditionally deleterious cannot be fixed in the evolving lineage, no method based on comparative sequence analysis is ideal for discriminating between drastically and mildly deleterious mutations, which are assigned to the opposite categories in HumVar. Another reason is that HumDiv uses an extra criterion to avoid possible erroneous annotations of damaging mutations. For a mutation, PolyPhen-2 calculates Naïve Bayes posterior probability that this mutation is damaging and reports estimates of false positive (the chance that the mutation is classified as damaging when it is in fact non-damaging) and true positive (the chance that the mutation is classified as damaging when it is indeed damaging) rates. A mutation is also appraised qualitatively, as benign, possibly damaging, or probably damaging (Supplementary Methods). The user can choose between HumDiv- and HumVar-trained PolyPhen-2. Diagnostics of Mendelian diseases requires distinguishing mutations with drastic effects from all the remaining human variation, including abundant mildly deleterious alleles. Thus, HumVar-trained PolyPhen-2 should be used for this task. In contrast, HumDiv-trained PolyPhen-2 should be used for evaluating rare alleles at loci potentially involved in complex phenotypes, dense mapping of regions identified by genome-wide association studies, and analysis of natural selection from sequence data, where even mildly deleterious alleles must be treated as damaging. Supplementary Material 1
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                Author and article information

                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Pub. Group
                2041-1723
                28 January 2015
                : 6
                Affiliations
                [1 ]Singapore Eye Research Institute , Singapore 169856, Singapore
                [2 ]Duke-NUS Graduate Medical School, National University of Singapore , Singapore 169857, Singapore
                [3 ]Department of Ophthalmology, National University of Singapore and National University Health System , Singapore 119228, Singapore
                [4 ]Singapore National Eye Center , Singapore 168751, Singapore
                [5 ]Department of Ophthalmology and Visual Sciences, Kyoto University Graduate School of Medicine , Kyoto 6068507, Japan
                [6 ]Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong , Hong Kong, China
                [7 ]Department of Ophthalmology, Seoul Metropolitan Government Seoul National University Boramae Medical Center , Seoul 156-707, Korea
                [8 ]Sichuan Provincial Key Laboratory for Human Disease Gene Study, Hospital of the University of Electronic Science and Technology of China and Sichuan Provincial People's Hospital , Chengdu 610072, China
                [9 ]School of Medicine, University of Electronic Science and Technology of China , Chengdu 610072, China
                [10 ]Key Laboratory of Vision Loss and Restoration, Ministry of Education of China , Beijing 100044, China
                [11 ]Beijing Key Laboratory of Diagnosis and Therapy of Retinal and Choroid Diseases , Beijing 100871, China
                [12 ]Department of Ophthalmology, People’s Hospital, Peking University , Beijing 100871, China
                [13 ]Center for Genomic Medicine/Inserm U.852, Kyoto University Graduate School of Medicine , Kyoto 6068507, Japan
                [14 ]Princess Alexandra Eye Pavilion , Edinburgh EH3 9HA, UK
                [15 ]National Healthcare Group Eye Institute, Tan Tock Seng Hospital , Singapore 308433, Singapore
                [16 ]Division of Human Genetics, Genome Institute of Singapore , Singapore 138672, Singapore
                [17 ]Eye and Retinal Surgeons, Camden Medical Centre , Singapore 248649, Singapore
                [18 ]Saw Swee Hock School of Public Health, National University of Singapore and National University Health System , Singapore 117549, Singapore
                [19 ]Department of Ophthalmology, Saitama Medical University , Iruma 3500495, Japan
                [20 ]Department of Ophthalmology, Faculty of Medicine, University of Yamanashi , Yamanashi 4093898, Japan
                [21 ]Department of Ophthalmology, Tokyo Women’s Medical University Hospital , Tokyo 1628666, Japan
                [22 ]Department of Surgery, Division of Ophthalmology, Kobe University Graduate School of Medicine , Kobe 6500017, Japan
                [23 ]Shantou University/Chinese University of Hong Kong Joint Shantou International Eye Center , Shantou 515041, China
                [24 ]Zhongshan Ophthalmic Center, Sun Yat-Sen University , Guangzhou 510060, China
                [25 ]Aier School of Ophthalmology, Central South University , Changsha 410000, China
                [26 ]Department of Ophthalmology, Xin Hua Hospital affiliated to Shanghai Jiao Tong University, School of Medicine , Shanghai 200025, China
                [27 ]Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Institute of Ophthalmology , Beijing 100730, China
                [28 ]Cancer Control and Population Sciences, University of Pittsburgh Cancer Institute , Pittsburgh, Pennsylvania 15260, USA
                [29 ]Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh , Pittsburgh, Pennsylvania 15260, USA
                [30 ]Hebrew University, School of Public Health , Jerusalem 91120, Israel
                [31 ]Department of Ophthalmology, Seoul National University Bundang Hospital , Gyeonggi 463-707, Korea
                [32 ]Department of Ophthalmology, University of Sydney and Westmead Millennium Institute , Sydney 2145, Australia
                [33 ]Eye and ENT Hospital of Fudan University , Shanghai 200433, China
                [34 ]BGI-Shenzhen , Shenzhen 518083, China
                [35 ]Ozaki Eye Hospital , Miyazaki 8830066, Japan
                [36 ]Mizoguchi Eye Hospital , Nagasaki 8570016, Japan
                [37 ]Department of Ophthalmology, Kobe City General Hospital , Kobe 6500046, Japan
                [38 ]Department of Ophthalmology, Seoul National University Hospital, Seoul National University College of Medicine , Seoul 110-744, Korea
                [39 ]Department of Ophthalmology, School of Medicine, Kyungpook National University , Daegu 700-721, Korea
                [40 ]Department of Ophthalmology, Yeungnam University College of Medicine , Daegu 705-802, Korea
                [41 ]Department of Ophthalmology, College of Medicine, Kosin University , Pusan 606-701, Korea
                [42 ]Department of Ophthalmology, Pusan Paik Hospital, Inje University College of Medicine , Pusan 614-735, Korea
                [43 ]Department of Ophthalmology, Pusan National University Hospital , Pusan 602-739, Korea
                [44 ]Medical Research Institute, Pusan National University , Pusan 602-739, Korea
                [45 ]Department of Pediatrics, National University Health System and National University of Singapore , Singapore 119228, Singapore
                [46 ]Department of Gastroenterology, Asan Medical Center and University of Ulsan College of Medicine , Seoul 138-736, Korea
                [47 ]Department of Biochemistry and Molecular Biology, University of Ulsan College of Medicine , Seoul 138-736, Korea
                [48 ]State Key Laboratory of Oncology in Southern China , Guangzhou 510060, China
                [49 ]Department of Experimental Research, Sun Yat-Sen University Cancer Center , Guangzhou 510080, China
                [50 ]Peking Union Medical College, Chinese Academy of Medical Science , Beijing 100730, China
                [51 ]Department of Medicine, National University Health System and National University of Singapore , Singapore 119228, Singapore
                Author notes
                [*]

                These authors contributed equally to this work

                [†]

                These authors jointly supervised this work

                Article
                ncomms7063
                10.1038/ncomms7063
                4317498
                25629512
                Copyright © 2015, Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

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