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      Ultra-rare complement factor 8 coding variants in families with age-related macular degeneration

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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.

          Summary

          Genome-wide association studies have uncovered 52 independent common and rare variants across 34 genetic loci, which influence susceptibility to age related macular degeneration (AMD). Of the 5 AMD-associated complement genes, complement factor H (CFH) and CFI exhibit a significant rare variant burden implicating a major contribution of the complement pathway to disease pathology. However, the efforts for developing AMD therapy have been challenging as of yet. Here, we report the identification of ultra-rare variants in complement factors 8A and 8B, two components of the terminal complement membrane attack complex (MAC), by whole exome sequencing of a cohort of AMD families. The identified C8 variants impact local interactions among proteins of C8 triplex in vitro, indicating their effect on MAC stability. Our results suggest that MAC, and not the early steps of the complement pathway, might be a more effective target for designing treatments for AMD.

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          Highlights

          • •

            Ultra-rare C8A and C8B coding variants in age-related macular degeneration families

          • •

            C8 variants may also contribute to genetic risk in non-familial AMD cases

          • •

            C8 variants affect subunit interactions and may impact MAC stability

          Abstract

          Genetics; Molecular biology; Molecular Genetics; Molecular Structure; Molecular interaction

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          Most cited references44

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Anthony M. Bolger, Marc Lohse, Bjoern Usadel (2014)
          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
          Article 0 comments Cited 15946 times – based on 0 reviews     Review now
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            Fast and accurate short read alignment with Burrows–Wheeler transform

            Heng Li, Richard Durbin (2009)
            Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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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 5358 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Michael L. Klein
                Anand Swaroop
                Journal
                Journal ID (nlm-ta): iScience
                Journal ID (iso-abbrev): iScience
                Title: iScience
                Publisher: Elsevier
                ISSN (Electronic): 2589-0042
                Publication date PMC-release: 03 April 2023
                Publication date Collection: 21 April 2023
                Publication date (Electronic): 03 April 2023
                Volume: 26
                Issue: 4
                Electronic Location Identifier: 106417
                Affiliations
                [1 ]Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
                [2 ]Casey Eye Institute, Department of Ophthalmology, Oregon Health & Science University, Portland, OR, USA
                [3 ]Department of Molecular Microbiology & Immunology, Oregon Health & Science University, Portland, OR, USA
                [4 ]Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA
                [5 ]Division of Epidemiology and Clinical Applications, Clinical Trials Branch, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
                [6 ]Ophthalmic Genetics and Visual Function Branch, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
                [7 ]Biological Imaging Core, National Eye Institute, National Institutes of Health, Bethesda, MD, USA
                [8 ]23andMe, Inc, Sunnyvale, CA, USA
                Author notes
                [∗ ]Corresponding author kleinm@ 123456ohsu.edu
                [∗∗ ]Corresponding author swaroopa@ 123456nei.nih.gov
                [9]

                Lead contact

                Article
                Publisher Item ID: S2589-0042(23)00494-7 Publisher ID: 106417
                DOI: 10.1016/j.isci.2023.106417
                PMC ID: 10156737
                PubMed ID: 37153444
                SO-VID: d22d8738-83c4-4d41-bd84-69513b4ee854
                License:

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 5 December 2022
                Date revision received : 20 February 2023
                Date accepted : 11 March 2023
                Categories
                Subject: Article

                Keywords: genetics,molecular biology,molecular genetics,molecular structure,molecular interaction
                Data availability:
                Keywords: genetics, molecular biology, molecular genetics, molecular structure, molecular interaction

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