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      Red blood cell distribution width: Genetic evidence for aging pathways in 116,666 volunteers

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          Abstract

          Introduction

          Variability in red blood cell volumes (distribution width, RDW) increases with age and is strongly predictive of mortality, incident coronary heart disease and cancer. We investigated inherited genetic variation associated with RDW in 116,666 UK Biobank human volunteers.

          Results

          A large proportion RDW is explained by genetic variants (29%), especially in the older group (60+ year olds, 33.8%, <50 year olds, 28.4%). RDW was associated with 194 independent genetic signals; 71 are known for conditions including autoimmune disease, certain cancers, BMI, Alzheimer’s disease, longevity, age at menopause, bone density, myositis, Parkinson’s disease, and age-related macular degeneration. Exclusion of anemic participants did not affect the overall findings. Pathways analysis showed enrichment for telomere maintenance, ribosomal RNA, and apoptosis. The majority of RDW-associated signals were intronic (119 of 194), including SNP rs6602909 located in an intron of oncogene GAS6, an eQTL in whole blood.

          Conclusions

          Although increased RDW is predictive of cardiovascular outcomes, this was not explained by known CVD or related lipid genetic risks, and a RDW genetic score was not predictive of incident disease. The predictive value of RDW for a range of negative health outcomes may in part be due to variants influencing fundamental pathways of aging.

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          Most cited references16

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          Splicing in disease: disruption of the splicing code and the decoding machinery.

          Human genes contain a dense array of diverse cis-acting elements that make up a code required for the expression of correctly spliced mRNAs. Alternative splicing generates a highly dynamic human proteome through networks of coordinated splicing events. Cis- and trans-acting mutations that disrupt the splicing code or the machinery required for splicing and its regulation have roles in various diseases, and recent studies have provided new insights into the mechanisms by which these effects occur. An unexpectedly large fraction of exonic mutations exhibit a primary pathogenic effect on splicing. Furthermore, normal genetic variation significantly contributes to disease severity and susceptibility by affecting splicing efficiency.
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            GeneCards: integrating information about genes, proteins and diseases.

            M Rebhan (1997)
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              Fast Principal Component Analysis of Large-Scale Genome-Wide Data

              Principal component analysis (PCA) is routinely used to analyze genome-wide single-nucleotide polymorphism (SNP) data, for detecting population structure and potential outliers. However, the size of SNP datasets has increased immensely in recent years and PCA of large datasets has become a time consuming task. We have developed flashpca, a highly efficient PCA implementation based on randomized algorithms, which delivers identical accuracy in extracting the top principal components compared with existing tools, in substantially less time. We demonstrate the utility of flashpca on both HapMap3 and on a large Immunochip dataset. For the latter, flashpca performed PCA of 15,000 individuals up to 125 times faster than existing tools, with identical results, and PCA of 150,000 individuals using flashpca completed in 4 hours. The increasing size of SNP datasets will make tools such as flashpca essential as traditional approaches will not adequately scale. This approach will also help to scale other applications that leverage PCA or eigen-decomposition to substantially larger datasets.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: MethodologyRole: Writing – original draft
                Role: Data curationRole: Formal analysisRole: MethodologyRole: Software
                Role: Data curationRole: Formal analysisRole: MethodologyRole: Writing – original draft
                Role: Data curationRole: MethodologyRole: Writing – original draft
                Role: Data curationRole: Formal analysis
                Role: Data curationRole: Formal analysis
                Role: Data curationRole: Formal analysis
                Role: Data curationRole: Formal analysis
                Role: Data curationRole: Formal analysisRole: MethodologyRole: Writing – original draft
                Role: ConceptualizationRole: Funding acquisitionRole: Writing – original draft
                Role: ConceptualizationRole: MethodologyRole: Writing – original draft
                Role: ConceptualizationRole: Methodology
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Writing – original draft
                Role: ConceptualizationRole: InvestigationRole: Writing – original draft
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                28 September 2017
                2017
                : 12
                : 9
                : e0185083
                Affiliations
                [1 ] Epidemiology and Public Health Group, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, EX2 5DW, United Kingdom
                [2 ] Department of Genetics and Genome Sciences, Institute for Systems Genomics, University of Connecticut Health Center, Farmington, Connecticut, United States of America
                [3 ] Genetics of Complex Traits Group, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, EX2 5DW, United Kingdom
                [4 ] Department of Community Medicine and Health Care, Connecticut Institute for Clinical and Translational Science, Institute for Systems Genomics, University of Connecticut Health Center, Farmington, Connecticut, United States of America
                [5 ] Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, RILD Level 3, Royal Devon & Exeter Hospital, Exeter, United Kingdom
                [6 ] Center on Aging, University of Connecticut, Farmington, CT, United States of America
                [7 ] National Institute on Aging, Baltimore, MD, United States
                Johns Hopkins University, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-3332-8454
                http://orcid.org/0000-0003-4919-9068
                http://orcid.org/0000-0003-0750-8248
                http://orcid.org/0000-0003-0153-922X
                http://orcid.org/0000-0002-9256-6065
                http://orcid.org/0000-0003-4966-9170
                http://orcid.org/0000-0002-2351-2522
                http://orcid.org/0000-0001-7791-8061
                http://orcid.org/0000-0001-8387-7040
                http://orcid.org/0000-0001-8362-2603
                Article
                PONE-D-17-19474
                10.1371/journal.pone.0185083
                5619771
                28957414
                1eaf4a39-b65e-4c41-9f5e-67f19f191fc1
                © 2017 Pilling et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 May 2017
                : 6 September 2017
                Page count
                Figures: 2, Tables: 3, Pages: 14
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MR/M023095/1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: MR/M005070/1
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 104150/Z/14/Z
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: WT097835MF
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: WT097835MF
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: WT097835MF
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000781, European Research Council;
                Award ID: 323195:GLUCOSEGENES-FP7-IDEAS-ERC
                Award Recipient :
                This work was supported by an award to DM, TF, AM and LH by the UK Medical Research Council (grant number MR/M023095/1). SEJ is funded by the Medical Research Council (grant: MR/M005070/1). JT is funded by a Diabetes Research and Wellness Foundation Fellowship. RB is funded by the Wellcome Trust and Royal Society grant: 104150/Z/14/Z. MAT, MNW and AM are supported by the Wellcome Trust Institutional Strategic Support Award (WT097835MF). ARW, HY, and TF are supported by the European Research Council grant: 323195:GLUCOSEGENES-FP7-IDEAS-ERC. LF is supported by the Intramural Research Program of the National Institute on Aging, U.S. National Institutes of Health. Input from MD, CLK and GK was supported by the University of Connecticut Health Center. This research has been conducted using the UK Biobank Resource under Application Number 14631. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Human Genetics
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Genetics of Disease
                Medicine and Health Sciences
                Vascular Medicine
                Coronary Heart Disease
                Medicine and Health Sciences
                Cardiology
                Coronary Heart Disease
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Genetic Polymorphism
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                Genetics
                Population Genetics
                Genetic Polymorphism
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                Population Biology
                Population Genetics
                Genetic Polymorphism
                Biology and Life Sciences
                Genetics
                Heredity
                Genetic Mapping
                Variant Genotypes
                Medicine and Health Sciences
                Hematology
                Anemia
                Biology and Life Sciences
                Biochemistry
                Lipids
                Medicine and Health Sciences
                Oncology
                Cancer Risk Factors
                Aging and Cancer
                Custom metadata
                The data is available from figshare at https://doi.org/10.6084/m9.figshare.5395504.v1.

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