Author
Johan Rung
Other affiliations: Uppsala University, McGill University, European Bioinformatics Institute
Bio: Johan Rung is an academic researcher from Science for Life Laboratory. The author has contributed to research in topics: Genome-wide association study & Gene. The author has an hindex of 21, co-authored 32 publications receiving 5444 citations. Previous affiliations of Johan Rung include Uppsala University & McGill University.
Papers
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TL;DR: Four loci containing variants that confer type 2 diabetes risk are identified and constitute proof of principle for the genome-wide approach to the elucidation of complex genetic traits.
Abstract: Type 2 diabetes mellitus results from the interaction of environmental factors with a combination of genetic variants, most of which were hitherto unknown. A systematic search for these variants wa ...
2,945 citations
McGill University1, Centre national de la recherche scientifique2, University of Copenhagen3, European Bioinformatics Institute4, French Institute of Health and Medical Research5, Steno Diabetes Center6, Imperial College London7, University of Poitiers8, University of Oulu9, Finnish Institute of Occupational Health10, Aarhus University11, University of Paris12, Malmö University13, University of Southern Denmark14
TL;DR: The C allele of rs2943641 was associated with insulin resistance and hyperinsulinemia in 14,358 French, Danish and Finnish participants from population-based cohorts; this allele was also associated with reduced basal levels of IRS1 protein and decreased insulin induction of IRS 1-associated phosphatidylinositol-3-OH kinase activity in human skeletal muscle biopsies.
Abstract: Genome-wide association studies have identified common variants that only partially explain the genetic risk for type 2 diabetes (T2D). Using genome-wide association data from 1,376 French individuals, we identified 16,360 SNPs nominally associated with T2D and studied these SNPs in an independent sample of 4,977 French individuals. We then selected the 28 best hits for replication in 7,698 Danish subjects and identified 4 SNPs showing strong association with T2D, one of which (rs2943641, P = 9.3 x 10(-12), OR = 1.19) was located adjacent to the insulin receptor substrate 1 gene (IRS1). Unlike previously reported T2D risk loci, which predominantly associate with impaired beta cell function, the C allele of rs2943641 was associated with insulin resistance and hyperinsulinemia in 14,358 French, Danish and Finnish participants from population-based cohorts; this allele was also associated with reduced basal levels of IRS1 protein and decreased insulin induction of IRS1-associated phosphatidylinositol-3-OH kinase activity in human skeletal muscle biopsies.
443 citations
TL;DR: This survey focuses on classifying mappers through a wide number of characteristics to allow practitioners to compare the mappers more easily and find those that are most suitable for their specific problem.
Abstract: Motivation: A ubiquitous and fundamental step in high-throughput sequencing analysis is the alignment (mapping) of the generated reads to a reference sequence. To accomplish this task, numerous software tools have been proposed. Determining the mappers that are most suitable for a specific application is not trivial.
Results: This survey focuses on classifying mappers through a wide number of characteristics. The goal is to allow practitioners to compare the mappers more easily and find those that are most suitable for their specific problem.
Availability: A regularly updated compendium of mappers can be found at http://wwwdev.ebi.ac.uk/fg/hts_mappers/.
Contact: [email protected]
Supplementary information:Supplementary data are available at Bioinformatics online.
305 citations
University of Helsinki1, National Institutes of Health2, Wellcome Trust Centre for Human Genetics3, University of Tartu4, University of Ferrara5, University Medical Center Groningen6, Amgen7, Karolinska Institutet8, Uppsala University9, VU University Amsterdam10, Erasmus University Rotterdam11, Lund University12, Leiden University Medical Center13, National Institute for Health Research14, University of Lübeck15, Medical Research Council16, Technische Universität München17, University of Tampere18, Steno Diabetes Center19, Ludwig Maximilian University of Munich20, Harvard University21, Massachusetts Institute of Technology22, European Bioinformatics Institute23, University of Leicester24, Turku University Hospital25, Uppsala University Hospital26, Erasmus University Medical Center27, University College London28, University of Turku29, University of Oxford30, University of Iceland31, Minerva Foundation Institute for Medical Research32, University of Liverpool33, Imperial College London34, Wellcome Trust Sanger Institute35
TL;DR: Using a genome-wide screen of 9.6 million genetic variants achieved through 1000 Genomes Project imputation in 62,166 samples, association to lipid traits in 93 loci is identified, including 79 previously identified loci with new lead SNPs and 10 new loci, including 15 locu with a low-frequency lead SNP and 10 loco with a missense lead SNP.
Abstract: Using a genome-wide screen of 9.6 million genetic variants achieved through 1000 Genomes Project imputation in 62,166 samples, we identify association to lipid traits in 93 loci, including 79 previously identified loci with new lead SNPs and 10 new loci, 15 loci with a low-frequency lead SNP and 10 loci with a missense lead SNP, and 2 loci with an accumulation of rare variants. In six loci, SNPs with established function in lipid genetics (CELSR2, GCKR, LIPC and APOE) or candidate missense mutations with predicted damaging function (CD300LG and TM6SF2) explained the locus associations. The low-frequency variants increased the proportion of variance explained, particularly for low-density lipoprotein cholesterol and total cholesterol. Altogether, our results highlight the impact of low-frequency variants in complex traits and show that imputation offers a cost-effective alternative to resequencing.
279 citations
TL;DR: It is speculated that G6PC2 regulates FPG by modulating the set point for glucose-stimulated insulin secretion in pancreatic β cells, which is associated with type 2 diabetes risk.
Abstract: Several studies have shown that healthy individuals with fasting plasma glucose (FPG) levels at the high end of the normal range have an increased risk of mortality. To identify genetic determinants that contribute to interindividual variation in FPG, we tested 392,935 single-nucleotide polymorphisms (SNPs) in 654 normoglycemic participants for association with FPG, and we replicated the most strongly associated SNP (rs560887, P = 4 x 10(-7)) in 9353 participants. SNP rs560887 maps to intron 3 of the G6PC2 gene, which encodes glucose-6-phosphatase catalytic subunit-related protein (also known as IGRP), a protein selectively expressed in pancreatic islets. This SNP was associated with FPG (linear regression coefficient beta = -0.06 millimoles per liter per A allele, combined P = 4 x 10(-23)) and with pancreatic beta cell function (Homa-B model, combined P = 3 x 10(-13)) in three populations; however, it was not associated with type 2 diabetes risk. We speculate that G6PC2 regulates FPG by modulating the set point for glucose-stimulated insulin secretion in pancreatic beta cells.
244 citations
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TL;DR: FeatureCounts as discussed by the authors is a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments, which implements highly efficient chromosome hashing and feature blocking techniques.
Abstract: MOTIVATION: Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. RESULTS: We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. AVAILABILITY AND IMPLEMENTATION: featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
14,103 citations
TL;DR: In this paper, a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level.
Abstract: Resolving the molecular details of proteome variation in the different tissues and organs of the human body will greatly increase our knowledge of human biology and disease. Here, we present a map of the human tissue proteome based on an integrated omics approach that involves quantitative transcriptomics at the tissue and organ level, combined with tissue microarray-based immunohistochemistry, to achieve spatial localization of proteins down to the single-cell level. Our tissue-based analysis detected more than 90% of the putative protein-coding genes. We used this approach to explore the human secretome, the membrane proteome, the druggable proteome, the cancer proteome, and the metabolic functions in 32 different tissues and organs. All the data are integrated in an interactive Web-based database that allows exploration of individual proteins, as well as navigation of global expression patterns, in all major tissues and organs in the human body.
9,745 citations
TL;DR: This study has demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in theBritish population is generally modest.
Abstract: There is increasing evidence that genome-wide association ( GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study ( using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined similar to 2,000 individuals for each of 7 major diseases and a shared set of similar to 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 X 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals ( including 58 loci with single-point P values between 10(-5) and 5 X 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.
9,244 citations
TL;DR: March 5, 2019 e1 WRITING GROUP MEMBERS Emelia J. Virani, MD, PhD, FAHA, Chair Elect On behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee.
Abstract: March 5, 2019 e1 WRITING GROUP MEMBERS Emelia J. Benjamin, MD, ScM, FAHA, Chair Paul Muntner, PhD, MHS, FAHA, Vice Chair Alvaro Alonso, MD, PhD, FAHA Marcio S. Bittencourt, MD, PhD, MPH Clifton W. Callaway, MD, FAHA April P. Carson, PhD, MSPH, FAHA Alanna M. Chamberlain, PhD Alexander R. Chang, MD, MS Susan Cheng, MD, MMSc, MPH, FAHA Sandeep R. Das, MD, MPH, MBA, FAHA Francesca N. Delling, MD, MPH Luc Djousse, MD, ScD, MPH Mitchell S.V. Elkind, MD, MS, FAHA Jane F. Ferguson, PhD, FAHA Myriam Fornage, PhD, FAHA Lori Chaffin Jordan, MD, PhD, FAHA Sadiya S. Khan, MD, MSc Brett M. Kissela, MD, MS Kristen L. Knutson, PhD Tak W. Kwan, MD, FAHA Daniel T. Lackland, DrPH, FAHA Tené T. Lewis, PhD Judith H. Lichtman, PhD, MPH, FAHA Chris T. Longenecker, MD Matthew Shane Loop, PhD Pamela L. Lutsey, PhD, MPH, FAHA Seth S. Martin, MD, MHS, FAHA Kunihiro Matsushita, MD, PhD, FAHA Andrew E. Moran, MD, MPH, FAHA Michael E. Mussolino, PhD, FAHA Martin O’Flaherty, MD, MSc, PhD Ambarish Pandey, MD, MSCS Amanda M. Perak, MD, MS Wayne D. Rosamond, PhD, MS, FAHA Gregory A. Roth, MD, MPH, FAHA Uchechukwu K.A. Sampson, MD, MBA, MPH, FAHA Gary M. Satou, MD, FAHA Emily B. Schroeder, MD, PhD, FAHA Svati H. Shah, MD, MHS, FAHA Nicole L. Spartano, PhD Andrew Stokes, PhD David L. Tirschwell, MD, MS, MSc, FAHA Connie W. Tsao, MD, MPH, Vice Chair Elect Mintu P. Turakhia, MD, MAS, FAHA Lisa B. VanWagner, MD, MSc, FAST John T. Wilkins, MD, MS, FAHA Sally S. Wong, PhD, RD, CDN, FAHA Salim S. Virani, MD, PhD, FAHA, Chair Elect On behalf of the American Heart Association Council on Epidemiology and Prevention Statistics Committee and Stroke Statistics Subcommittee
5,739 citations
TL;DR: The Statistical Update represents the most up-to-date statistics related to heart disease, stroke, and the cardiovascular risk factors listed in the AHA's My Life Check - Life’s Simple 7, which include core health behaviors and health factors that contribute to cardiovascular health.
Abstract: Each chapter listed in the Table of Contents (see next page) is a hyperlink to that chapter. The reader clicks the chapter name to access that chapter.
Each chapter listed here is a hyperlink. Click on the chapter name to be taken to that chapter.
Each year, the American Heart Association (AHA), in conjunction with the Centers for Disease Control and Prevention, the National Institutes of Health, and other government agencies, brings together in a single document the most up-to-date statistics related to heart disease, stroke, and the cardiovascular risk factors listed in the AHA’s My Life Check - Life’s Simple 7 (Figure1), which include core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure [BP], and glucose control) that contribute to cardiovascular health. The Statistical Update represents …
5,102 citations