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1. Efficient AdaBoost Region Classification

Author

Moed, M., Smirnov, E. N., Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, and Perner, Petra, editor

Published
2009
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2. Genome-wide study of DNA methylation shows alterations in metabolic, inflammatory, and cholesterol pathways in ALS

Author

Hop, P.J., Zwamborn, R.A.J., Hannon, E., Shireby, G.L., Nabais, M.F., Walker, E.M., van Rheenen, W., van Vugt, J.J.F.A., Dekker, A.M., Westeneng, H-J, Tazelaar, G.H.P., van Eijk, K.R., Moisse, M., Baird, D., Al Khleifat, A., Iacoangeli, A., Ticozzi, N., Ratti, A., Cooper-Knock, J., Morrison, K.E., Shaw, P.J., Basak, A.N., Chiò, A., Calvo, A., Moglia, C., Canosa, A., Brunetti, M., Grassano, M., Gotkine, M., Lerner, Y., Zabari, M., Vourc’h, P., Corcia, P., Couratier, P., Mora Pardina, J.S., Salas, T., Dion, P., Ross, J.P., Henderson, R.D., Mathers, S., McCombe, P.A., Needham, M., Nicholson, G., Rowe, D.B., Pamphlett, R., Mather, K.A., Sachdev, P.S., Furlong, S., Garton, F.C., Henders, A.K., Lin, T., Ngo, S.T., Steyn, F.J., Wallace, L., Williams, K.L., Neto, M.M., Cauchi, R.J., Blair, I.P., Kiernan, M.C., Drory, V., Povedano, M., de Carvalho, M., Pinto, S., Weber, M., Rouleau, G.A., Silani, V., Landers, J.E., Shaw, C.E., Andersen, P.M., McRae, A.F., van Es, M.A., Pasterkamp, R.J., Wray, N.R., McLaughlin, R.L., Hardiman, O., Kenna, K.P., Tsai, E., Runz, H., Al-Chalabi, A., van den Berg, L.H., Van Damme, P., Mill, J., Veldink, J.H., Heijmans, B.T., t Hoen, P.A.C., van Meurs, J., Jansen, R., Franke, L., Boomsma, D.I., Pool, R., van Dongen, J., Hottenga, J.J., van Greevenbroek, M.M.J., Stehouwer, C.D.A., van der Kallen, C.J.H., Schalkwijk, C.G., Wijmenga, C., Zhernakova, S., Tigchelaar, E.F., Slagboom, P.E., Beekman, M., Deelen, J., Van Heemst, D., van Duijn, C.M., Hofman, B.A., Isaacs, A., Uitterlinden, A.G., van Meurs, J.B.C., Jhamai, P.M., Verbiest, M., Suchiman, H.E.D., Verkerk, M., van der Breggen, R., van Rooij, J., Lakenberg, N., Mei, H., van Iterson, M., van Galen, M., Bot, J., Zhernakova, D.V., van ‘t Hof, P., Deelen, P., Nooren, I., Moed, M., Vermaat, M., Luijk, R., Jan Bonder, M., van Dijk, F., Arindrarto, W., Kielbasa, S.M., Swertz, M.A., van Zwet, E.W., Hoen, P.A.C., Bensimon, G., Chio, A., Smith, G.D., Hop, P.J., Zwamborn, R.A.J., Hannon, E., Shireby, G.L., Nabais, M.F., Walker, E.M., van Rheenen, W., van Vugt, J.J.F.A., Dekker, A.M., Westeneng, H-J, Tazelaar, G.H.P., van Eijk, K.R., Moisse, M., Baird, D., Al Khleifat, A., Iacoangeli, A., Ticozzi, N., Ratti, A., Cooper-Knock, J., Morrison, K.E., Shaw, P.J., Basak, A.N., Chiò, A., Calvo, A., Moglia, C., Canosa, A., Brunetti, M., Grassano, M., Gotkine, M., Lerner, Y., Zabari, M., Vourc’h, P., Corcia, P., Couratier, P., Mora Pardina, J.S., Salas, T., Dion, P., Ross, J.P., Henderson, R.D., Mathers, S., McCombe, P.A., Needham, M., Nicholson, G., Rowe, D.B., Pamphlett, R., Mather, K.A., Sachdev, P.S., Furlong, S., Garton, F.C., Henders, A.K., Lin, T., Ngo, S.T., Steyn, F.J., Wallace, L., Williams, K.L., Neto, M.M., Cauchi, R.J., Blair, I.P., Kiernan, M.C., Drory, V., Povedano, M., de Carvalho, M., Pinto, S., Weber, M., Rouleau, G.A., Silani, V., Landers, J.E., Shaw, C.E., Andersen, P.M., McRae, A.F., van Es, M.A., Pasterkamp, R.J., Wray, N.R., McLaughlin, R.L., Hardiman, O., Kenna, K.P., Tsai, E., Runz, H., Al-Chalabi, A., van den Berg, L.H., Van Damme, P., Mill, J., Veldink, J.H., Heijmans, B.T., t Hoen, P.A.C., van Meurs, J., Jansen, R., Franke, L., Boomsma, D.I., Pool, R., van Dongen, J., Hottenga, J.J., van Greevenbroek, M.M.J., Stehouwer, C.D.A., van der Kallen, C.J.H., Schalkwijk, C.G., Wijmenga, C., Zhernakova, S., Tigchelaar, E.F., Slagboom, P.E., Beekman, M., Deelen, J., Van Heemst, D., van Duijn, C.M., Hofman, B.A., Isaacs, A., Uitterlinden, A.G., van Meurs, J.B.C., Jhamai, P.M., Verbiest, M., Suchiman, H.E.D., Verkerk, M., van der Breggen, R., van Rooij, J., Lakenberg, N., Mei, H., van Iterson, M., van Galen, M., Bot, J., Zhernakova, D.V., van ‘t Hof, P., Deelen, P., Nooren, I., Moed, M., Vermaat, M., Luijk, R., Jan Bonder, M., van Dijk, F., Arindrarto, W., Kielbasa, S.M., Swertz, M.A., van Zwet, E.W., Hoen, P.A.C., Bensimon, G., Chio, A., and Smith, G.D.

Abstract

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with an estimated heritability between 40 and 50%. DNA methylation patterns can serve as proxies of (past) exposures and disease progression, as well as providing a potential mechanism that mediates genetic or environmental risk. Here, we present a blood-based epigenome-wide association study meta-analysis in 9706 samples passing stringent quality control (6763 patients, 2943 controls). We identified a total of 45 differentially methylated positions (DMPs) annotated to 42 genes, which are enriched for pathways and traits related to metabolism, cholesterol biosynthesis, and immunity. We then tested 39 DNA methylation–based proxies of putative ALS risk factors and found that high-density lipoprotein cholesterol, body mass index, white blood cell proportions, and alcohol intake were independently associated with ALS. Integration of these results with our latest genome-wide association study showed that cholesterol biosynthesis was potentially causally related to ALS. Last, DNA methylation at several DMPs and blood cell proportion estimates derived from DNA methylation data were associated with survival rate in patients, suggesting that they might represent indicators of underlying disease processes potentially amenable to therapeutic interventions.

Published
2022

3. Genetics and Not Shared Environment Explains Familial Resemblance in Adult Metabolomics Data

Author

Pool, Rene, Hagenbeek, Fiona A., Hendriks, Anne M., van Dongen, Jenny, Willemsen, Gonneke, de Geus, Eco, van Dijk, Ko Willems, Verhoeven, Aswin, Suchiman, H. Eka, Beekman, Marian, Slagboom, P. Eline, Harms, Amy C., Hankemeier, Thomas, Boomsma, Dorret, I, Beekman, M., Suchiman, H. E. D., Amin, N., Beulens, J. W., van der Bom, J. A., Bomer, N., Demirkan, A., van Hilten, J. A., Meessen, J. M. T. A., Pool, R., Moed, M. H., Fu, J., Onderwater, G. L. J., Rutters, F., So-Osman, C., van der Flier, W. M., van der Heijden, A. A. W. A., van der Spek, A., Asselbergs, F. W., Boersma, E., Elders, P. M., Geleijnse, J. M., Ikram, M. A., Kloppenburg, M., Meulenbelt, I, Mooijaart, S. P., Nelissen, R. G. H. H., Netea, M. G., Penninx, B. W. J. H., Stehouwer, C. D. A., Teunissen, C. E., Terwindt, G. M., 't Hart, L. M., van den Maagdenberg, A. M. J. M., van der Harst, P., van der Horst, I. C. C., van der Kallen, C. J. H., van Greevenbroek, M. M. J., van Spil, W. E., Wijmenga, C., Zwinderman, A. H., Zhernikova, A., Jukema, J. W., Wolf, J. J. H. Barkey, Cats, D., Mei, H., Slofstra, M., Swertz, M., van den Akker, E. B., Deelen, J., Reinders, M. J. T., Boomsma, D., I, van Duijn, C. M., Slagboom, P. E., Interne Geneeskunde, MUMC+: MA Interne Geneeskunde (3), MUMC+: MA Med Staf Artsass Interne Geneeskunde (9), MUMC+: MA Endocrinologie (9), MUMC+: MA Maag Darm Lever (9), MUMC+: MA Hematologie (9), MUMC+: MA Medische Oncologie (9), MUMC+: MA Nefrologie (9), MUMC+: MA Reumatologie (9), RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Molecular Neuroscience and Ageing Research (MOLAR), Cardiovascular Centre (CVC), Critical care, Anesthesiology, Peri-operative and Emergency medicine (CAPE), Center for Liver, Digestive and Metabolic Diseases (CLDM), Medical Microbiology and Infection Prevention, Graduate School, Tytgat Institute for Liver and Intestinal Research, AGEM - Amsterdam Gastroenterology Endocrinology Metabolism, Obstetrics and Gynaecology, Cardiology, Public and occupational health, Gastroenterology and Hepatology, Paediatric Endocrinology, Biological Psychology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, APH - Mental Health, APH - Methodology, Epidemiology and Data Science, Obstetrics and gynaecology, ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, Anatomy and neurosciences, Anesthesiology, General practice, Internal medicine, Psychiatry, Amsterdam Neuroscience - Complex Trait Genetics, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, Laboratory Medicine, Amsterdam Neuroscience - Neurodegeneration, Amsterdam Neuroscience - Neuroinfection & -inflammation, Amsterdam Reproduction & Development (AR&D), AMS - Musculoskeletal Health, AMS - Tissue Function & Regeneration, Rheumatology, Gastroenterology and hepatology, APH - Aging & Later Life, and APH - Digital Health

Subjects
Male, Netherlands Twin Register (NTR), 0301 basic medicine, Shared environment, Metabolite, lnfectious Diseases and Global Health Radboud Institute for Molecular Life Sciences [Radboudumc 4], Genome-wide association study, heritability, METABOLITES, Keywords: Classical twin design, chemistry.chemical_compound, 0302 clinical medicine, metabolite classes, Twins, Dizygotic, EPIDEMIOLOGY, Classical twin design, Genetics (clinical), Genetics, HERITABILITY, shared environment, Obstetrics and Gynecology, Phenotype, Quartile, Metabolome, Female, Adult, Dizygotic twin, POWER, TWIN, Environment, Biology, enrichment analysis, 03 medical and health sciences, All institutes and research themes of the Radboud University Medical Center, Metabolomics, AGE, Diseases in Twins, Humans, Family, GENOME-WIDE ASSOCIATION, Classical twin design [Keywords], Twins, Monozygotic, PROFILES, Heritability, Diet, Metabolomics data, 030104 developmental biology, chemistry, Pediatrics, Perinatology and Child Health, Gene-Environment Interaction, 030217 neurology & neurosurgery
Abstract

Metabolites are small molecules involved in cellular metabolism where they act as reaction substrates or products. The term ‘metabolomics’ refers to the comprehensive study of these molecules. The concentrations of metabolites in biological tissues are under genetic control, but this is limited by environmental factors such as diet. In adult mono- and dizygotic twin pairs, we estimated the contribution of genetic and shared environmental influences on metabolite levels by structural equation modeling and tested whether the familial resemblance for metabolite levels is mainly explained by genetic or by environmental factors that are shared by family members. Metabolites were measured across three platforms: two based on proton nuclear magnetic resonance techniques and one employing mass spectrometry. These three platforms comprised 237 single metabolic traits of several chemical classes. For the three platforms, metabolites were assessed in 1407, 1037 and 1116 twin pairs, respectively. We carried out power calculations to establish what percentage of shared environmental variance could be detected given these sample sizes. Our study did not find evidence for a systematic contribution of shared environment, defined as the influence of growing up together in the same household, on metabolites assessed in adulthood. Significant heritability was observed for nearly all 237 metabolites; significant contribution of the shared environment was limited to 6 metabolites. The top quartile of the heritability distribution was populated by 5 of the 11 investigated chemical classes. In this quartile, metabolites of the class lipoprotein were significantly overrepresented, whereas metabolites of classes glycerophospholipids and glycerolipids were significantly underrepresented.

Published
2020

4. Occupational exposure to gases/fumes and mineral dust affect DNA methylation levels of genes regulating expression

Author

Plaat, D. van der, Vonk, J.M., Terzikhan, N., Jong, K. de, Vries, M. de, Bastide-van Gemert, S. la, Diemen, C.C. van, Lahousse, L., Brusselle, G., Nedeljkovic, I., Amin, N., Kromhout, H., Vermeulen, R.C.H., Postma, D.S., Duijn, C.M. van, Boezen, H.M., Heijmans, B.T., Hoen, P.A.C.T., Meurs, J. van, Isaacs, A., Jansen, R., Franke, L., Boomsma, D.I., Pool, R., Dongen, J. van, Hottenga, J.J., Greevenbroek, M.M.J. van, Stehouwer, C.D.A., Kallen, C.J.H. van der, Schalkwijk, C.G., Wijmenga, C., Zhernakova, S., Tigchelaar, E.E., Slagboom, P.E., Beekman, M., Deelen, J., Heemst, D. van, Veldink, J.H., Berg, L.H. van den, Hofman, B.A., Uitterlinden, A.G., Jhamai, P.M., Verbiest, M., Suchiman, H.E.D., Verkerk, M., Breggen, R. van der, Rooij, J. van, Lakenberg, N., Mei, H., Iterson, M. van, Galen, M. van, Bot, J., Zhernakova, D.V., Hof, P.V., Deelen, P., Nooren, I., Moed, M., Vermaat, M., Luijk, R., Bonder, M.J., Dijk, F. van, Arindrarto, W., Kielbasa, S.M., Swertz, M.A., Zwet, E.W. van, Hoen, P.B. 't, BIOS Consortium, Groningen Research Institute for Asthma and COPD (GRIAC), Life Course Epidemiology (LCE), Interne Geneeskunde, RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome, RS: CARIM - R3 - Vascular biology, MUMC+: MA Interne Geneeskunde (3), RS: CARIM - R3.01 - Vascular complications of diabetes and the metabolic syndrome, Epidemiology, Pulmonary Medicine, APH - Methodology, APH - Mental Health, Amsterdam Reproduction & Development, Biological Psychology, APH - Personalized Medicine, and APH - Health Behaviors & Chronic Diseases

Subjects
Male, GASES, Rotterdam Study, FEV1, 0302 clinical medicine, Medicine and Health Sciences, Leukocytes, 030212 general & internal medicine, Association Studies Article, Genetics (clinical), 11 Medical and Health Sciences, Aged, 80 and over, Genetics & Heredity, RISK, 0303 health sciences, biology, Dust, General Medicine, Methylation, Middle Aged, Blood, DNA methylation, Female, BIOS Consortium, Life Sciences & Biomedicine, Adult, Biochemistry & Molecular Biology, Adolescent, Mineral dust, Young Adult, 03 medical and health sciences, SDG 3 - Good Health and Well-being, Occupational Exposure, Genetics, GNAS complex locus, Humans, Epigenetics, Molecular Biology, Gene, Aged, 030304 developmental biology, DECLINE, Science & Technology, Sequence Analysis, RNA, Biology and Life Sciences, DNA Methylation, 06 Biological Sciences, respiratory tract diseases, Differentially methylated regions, Gene Expression Regulation, DISCOVERY, Immunology, biology.protein, Genome-Wide Association Study
Abstract

Many workers are daily exposed to occupational agents like gases/fumes, mineral dust or biological dust, which could induce adverse health effects. Epigenetic mechanisms, such as DNA methylation, have been suggested to play a role. We therefore aimed to identify differentially methylated regions (DMRs) upon occupational exposures in never-smokers and investigated if these DMRs associated with gene expression levels. To determine the effects of occupational exposures independent of smoking, 903 never-smokers of the LifeLines cohort study were included. We performed three genome-wide methylation analyses (Illumina 450 K), one per occupational exposure being gases/fumes, mineral dust and biological dust, using robust linear regression adjusted for appropriate confounders. DMRs were identified using comb-p in Python. Results were validated in the Rotterdam Study (233 never-smokers) and methylation-expression associations were assessed using Biobank-based Integrative Omics Study data (n = 2802). Of the total 21 significant DMRs, 14 DMRs were associated with gases/fumes and 7 with mineral dust. Three of these DMRs were associated with both exposures (RPLP1 and LINC02169 (2×)) and 11 DMRs were located within transcript start sites of gene expression regulating genes. We replicated two DMRs with gases/fumes (VTRNA2-1 and GNAS) and one with mineral dust (CCDC144NL). In addition, nine gases/fumes DMRs and six mineral dust DMRs significantly associated with gene expression levels. Our data suggest that occupational exposures may induce differential methylation of gene expression regulating genes and thereby may induce adverse health effects. Given the millions of workers that are exposed daily to occupational exposures, further studies on this epigenetic mechanism and health outcomes are warranted.

Published
2019

5. Skewed X-inactivation is common in the general female population

Author

Shvetsova, E, Sofronova, A, Monajemi, R, Gagalova, K, Draisma, HHM, White, SJ, Santen, GWE, Lopes, SMCDS, Heijmans, BT, Van Meurs, J, Jansen, R, Franke, L, Kielbasa, SM, Den Dunnen, JT, 't Hoen, PAC, Boomsma, DI, Pool, R, Van Dongen, J, Hottenga, JJ, Van Greevenbroek, MMJ, Da Stehouwer, C, Van der Kallen, CJH, Schalkwijk, CG, Wijmenga, C, Zhernakova, S, Tigchelaar, EF, Slagboom, PE, Beekman, M, Deelen, J, Van Heemst, D, Veldink, JH, Van den Berg, LH, Van Duijn, CM, Hofman, BA, Uitterlinden, AG, Jhamai, PM, Verbiest, M, Suchiman, HED, Verkerk, M, Van der Breggen, R, Van Rooij, J, Lakenberg, N, Mei, H, Bot, J, Zhernakova, DV, 't Hof, PV, Deelen, P, Nooren, I, Moed, M, Vermaat, M, Luijk, R, Bonder, MJ, Van Iterson, M, Van Dijk, F, Van Galen, M, Arindrarto, W, Swertz, MA, Van Zwet, EW, Isaacs, A, Francioli, LC, Menelaou, A, Pulit, SL, Palamara, PF, Elbers, CC, Neerincx, PB, Ye, K, Guryev, V, Kloosterman, WP, Abdellaoui, A, Van Leeuwen, EM, Van Oven, M, Li, M, Laros, JF, Karssen, LC, Kanterakis, A, Amin, N, Lameijer, EW, Kattenberg, M, Dijkstra, M, Byelas, H, Van Setten, J, Van Schaik, BD, Nijman, IJ, Renkens, I, Marschall, T, Schonhuth, A, Hehir-Kwa, JY, Handsaker, RE, Polak, P, Sohail, M, Vuzman, D, Hormozdiari, F, Van Enckevort, D, Koval, V, Moed, MH, Van der Velde, KJ, Rivadeneira, F, Estrada, K, Medina-Gomez, C, McCarroll, SA, De Craen, AJ, Suchiman, HE, Oostra, B, Willemsen, G, Platteel, M, Pitts, SJ, Potluri, S, Sundar, P, Cox, DR, Sunyaev, SR, Stoneking, M, De Knijff, P, Kayser, M, Li, Q, Li, Y, Du, Y, Chen, R, Cao, H, Li, N, Cao, S, Wang, J, Bovenberg, JA, Pe'er, I, Van Ommen, GJ, De Bakker, PI, Consortium, Bios, Consortium, Gonl, BIOS consortium, GoNL consortium, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Translational Immunology Groningen (TRIGR), Groningen Research Institute for Asthma and COPD (GRIAC), Stem Cell Aging Leukemia and Lymphoma (SALL), Epidemiology and Data Science, AII - Inflammatory diseases, APH - Methodology, Experimental Immunology, AGEM - Amsterdam Gastroenterology Endocrinology Metabolism, APH - Personalized Medicine, Biological Psychology, APH - Mental Health, APH - Health Behaviors & Chronic Diseases, RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome, Interne Geneeskunde, RS: CARIM - R3 - Vascular biology, MUMC+: MA Reumatologie (9), MUMC+: MA Nefrologie (9), MUMC+: MA Medische Oncologie (9), MUMC+: MA Hematologie (9), MUMC+: MA Maag Darm Lever (9), MUMC+: MA Endocrinologie (9), MUMC+: HVC Pieken Maastricht Studie (9), RS: CARIM - R3.01 - Vascular complications of diabetes and the metabolic syndrome, MUMC+: MA Interne Geneeskunde (3), RS: Carim - B01 Blood proteins & engineering, RS: FHML MaCSBio, RS: CARIM - R1 - Thrombosis and haemostasis, RS: CARIM - R1.01 - Blood proteins & engineering, Biochemie, Psychiatry, VU University medical center, Pediatric surgery, Amsterdam Reproduction & Development (AR&D), Internal Medicine, Epidemiology, Genetic Identification, and Clinical Genetics

Subjects
Netherlands Twin Register (NTR), Male, 0301 basic medicine, Receptors, Cytoplasmic and Nuclear/genetics, CHROMOSOME-INACTIVATION, BIOS consortium, Receptors, Cytoplasmic and Nuclear, Septins/genetics, Population genetics, GoNL consortium, Population/genetics, Negative selection, 0302 clinical medicine, X Chromosome Inactivation, Receptors, Non-U.S. Gov't, Genetics (clinical), Netherlands, Genetics & Heredity, Genetics, education.field_of_study, Membrane Glycoproteins, Dosage compensation, DMD LOCUS, Research Support, Non-U.S. Gov't, Receptors, Peptide/genetics, Intracellular Signaling Peptides and Proteins, Peptide/genetics, Single Nucleotide, CARRIERS, TRANSLOCATION, VARIABILITY, Female, Life Sciences & Biomedicine, EXPRESSION, Biochemistry & Molecular Biology, Receptors, Peptide, Population, ADRENOLEUKODYSTROPHY, Biology, Research Support, Polymorphism, Single Nucleotide, Article, X-inactivation, DUCHENNE MUSCULAR-DYSTROPHY, 03 medical and health sciences, All institutes and research themes of the Radboud University Medical Center, Journal Article, Humans, Polymorphism, Allele, education, Skewed X-inactivation, Gene, 0604 Genetics, Calcium-Binding Proteins/genetics, Science & Technology, CONSEQUENCES, Calcium-Binding Proteins, Membrane Glycoproteins/genetics, 030104 developmental biology, Cytoplasmic and Nuclear/genetics, PATTERNS, Intracellular Signaling Peptides and Proteins/genetics, Nanomedicine Radboud Institute for Molecular Life Sciences [Radboudumc 19], Septins, 030217 neurology & neurosurgery
Abstract

X-inactivation is a well-established dosage compensation mechanism ensuring that X-chromosomal genes are expressed at comparable levels in males and females. Skewed X-inactivation is often explained by negative selection of one of the alleles. We demonstrate that imbalanced expression of the paternal and maternal X-chromosomes is common in the general population and that the random nature of the X-inactivation mechanism can be sufficient to explain the imbalance. To this end, we analyzed blood-derived RNA and whole-genome sequencing data from 79 female children and their parents from the Genome of the Netherlands project. We calculated the median ratio of the paternal over total counts at all X-chromosomal heterozygous single-nucleotide variants with coverage ≥10. We identified two individuals where the same X-chromosome was inactivated in all cells. Imbalanced expression of the two X-chromosomes (ratios ≤0.35 or ≥0.65) was observed in nearly 50% of the population. The empirically observed skewing is explained by a theoretical model where X-inactivation takes place in an embryonic stage in which eight cells give rise to the hematopoietic compartment. Genes escaping X-inactivation are expressed from both alleles and therefore demonstrate less skewing than inactivated genes. Using this characteristic, we identified three novel escapee genes (SSR4, REPS2, and SEPT6), but did not find support for many previously reported escapee genes in blood. Our collective data suggest that skewed X-inactivation is common in the general population. This may contribute to manifestation of symptoms in carriers of recessive X-linked disorders. We recommend that X-inactivation results should not be used lightly in the interpretation of X-linked variants.

Published
2019

6. Author Correction: Heritability estimates for 361 blood metabolites across 40 genome-wide association studies (Nature Communications, (2020), 11, 1, (39), 10.1038/s41467-019-13770-6)

Author

Hagenbeek, Fiona A., Pool, René, van Dongen, Jenny, Draisma, H. M., Jan Hottenga, Jouke, Willemsen, Gonneke, Abdellaoui, Abdel, Fedko, Iryna O., den Braber, Anouk, Visser, Pieter Jelle, de Geus, Eco J.C.N., Willems van Dijk, Ko, Verhoeven, Aswin, Suchiman, H. Eka, Beekman, Marian, Slagboom, P. Eline, van Duijn, Cornelia M., Barkey Wolf, J. J.H., Cats, D., Amin, N., Beulens, J. W., van der Bom, J. A., Bomer, N., Demirkan, A., van Hilten, J. A., Meessen, J. M.T.A., Moed, M. H., Fu, J., Onderwater, G. L.J., Rutters, F., So-Osman, C., van der Flier, W. M., van der Heijden, A. A.W.A., van der Spek, A., Asselbergs, F. W., Boersma, E., Elders, P. M., Geleijnse, J. M., Ikram, M. A., Kloppenburg, M., Meulenbelt, I., Mooijaart, S. P., Nelissen, R. G.H.H., Netea, M. G., Penninx, B. W.J.H., Stehouwer, C. D.A., Teunissen, C. E., Terwindt, G. M., ‘t Hart, L. M., van den Maagdenberg, A. M.J.M., van der Harst, P., van der Horst, I. C.C., van der Kallen, C. J.H., van Greevenbroek, M. M.J., van Spil, W. E., Wijmenga, C., Zwinderman, A. H., Zhernikova, A., Jukema, J. W., Mei, H., Slofstra, M., Swertz, M., van den Akker, E. B., Deelen, J., Reinders, M. J.T., Harms, Amy C., Hankemeier, Thomas, Bartels, Meike, Nivard, Michel G., Boomsma, Dorret I., Neurology, APH - Health Behaviors & Chronic Diseases, ACS - Heart failure & arrhythmias, Epidemiology and Data Science, General practice, APH - Methodology, APH - Mental Health, Psychiatry, Clinical chemistry, APH - Aging & Later Life, APH - Personalized Medicine, APH - Digital Health, and ACS - Diabetes & metabolism

Abstract

The original version of the Supplementary Information associated with this Article included an incorrect Supplementary Data 1 file, in which additional delimiters were included in the first column for a number of rows, resulting in column shifts for some of these rows. The HTML has been updated to include a corrected version of Supplementary Data 1; the original incorrect version of Supplementary Data 1 can be found as Supplementary Information associated with this Correction. In addition, the original version of this Article contained an error in the author affiliations. An affiliation of Abdel Abdellaoui with Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands was inadvertently omitted. This has now been corrected in both the PDF and HTML versions of the Article.

Published
2020

7. A characterization of cis- and trans-heritability of RNA-Seq-based gene expression

Author

Ouwens, K.G., Jansen, R., Nivard, M.G., Dongen, J. van, Frieser, M.J., Hottenga, J.J., Arindrarto, W., Claringbould, A., Iterson, M. van, Mei, H.L., Franke, L., Heijmans, B.T., Hoen, P.A.C. 't, Meurs, J. van, Brooks, A.I., Penninx, B.W.J.H., Boomsma, D.I., Isaacs, A., Pool, R., Greevenbroek, M.M.J. van, Stehouwer, C.D.A., Kallen, C.J.H. van der, Schalkwijk, C.G., Wijmenga, C., Zhernakova, S., Tigchelaar, E.F., Slagboom, P.E., Beekman, M., Deelen, J., Heemst, D. van, Veldink, J.H., Berg, L.H. van den, Duijn, C.M. van, Hofman, B.A., Uitterlinden, A.G., Jhamai, P.M., Verbiest, M., Suchiman, H.E.D., Verkerk, M., Breggen, R. van der, Rooij, J. van, Lakenberg, N., Galen, M. van, Bot, J., Zhernakova, D.V., van't Hof, P., Deelen, P., Nooren, I., Moed, M., Vermaat, M., Luijk, R., Bonder, M.J., Dijk, F. van, Kielbasa, S.M., Swertz, M.A., Zwet, E.W. van, Hoen, P.B. 't, BIOS Consortium, Biological Psychology, APH - Mental Health, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases, APH - Methodology, Internal Medicine, Epidemiology, Interne Geneeskunde, RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome, MUMC+: Centrum voor Chronische Zieken (3), MUMC+: MA Med Staf Artsass Interne Geneeskunde (9), MUMC+: HVC Pieken Maastricht Studie (9), MUMC+: MA Interne Geneeskunde (3), Psychiatry, Amsterdam Neuroscience - Complex Trait Genetics, APH - Digital Health, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Department of Health and Life Sciences, Translational Immunology Groningen (TRIGR), and Stem Cell Aging Leukemia and Lymphoma (SALL)

Subjects
Adult, Male, Netherlands Twin Register (NTR), Adolescent, Genotype, Dizygotic twin, Quantitative Trait Loci, Monozygotic twin, Genome-wide association study, Single-nucleotide polymorphism, Biology, Quantitative trait locus, Polymorphism, Single Nucleotide, Article, 03 medical and health sciences, Quantitative Trait, Heritable, AGE, SDG 3 - Good Health and Well-being, Twins, Dizygotic, Genetics, Humans, RNA-Seq, Genetics (clinical), Aged, 0303 health sciences, 030305 genetics & heredity, Twins, Monozygotic, Middle Aged, Heritability, Twin study, Expression quantitative trait loci, Female, Gene-Environment Interaction, Nanomedicine Radboud Institute for Molecular Life Sciences [Radboudumc 19], Genome-Wide Association Study
Abstract

Insights into individual differences in gene expression and its heritability (h2) can help in understanding pathways from DNA to phenotype. We estimated the heritability of gene expression of 52,844 genes measured in whole blood in the largest twin RNA-Seq sample to date (1497 individuals including 459 monozygotic twin pairs and 150 dizygotic twin pairs) from classical twin modeling and identity-by-state-based approaches. We estimated for each gene h2total, composed of cis-heritability (h2cis, the variance explained by single nucleotide polymorphisms in the cis-window of the gene), and trans-heritability (h2res, the residual variance explained by all other genome-wide variants). Mean h2total was 0.26, which was significantly higher than heritability estimates earlier found in a microarray-based study using largely overlapping (>60%) RNA samples (mean h2 = 0.14, p = 6.15 × 10−258). Mean h2cis was 0.06 and strongly correlated with beta of the top cis expression quantitative loci (eQTL, ρ = 0.76, p −308) and with estimates from earlier RNA-Seq-based studies. Mean h2res was 0.20 and correlated with the beta of the corresponding trans-eQTL (ρ = 0.04, p −3) and was significantly higher for genes involved in cytokine-cytokine interactions (p = 4.22 × 10−15), many other immune system pathways, and genes identified in genome-wide association studies for various traits including behavioral disorders and cancer. This study provides a thorough characterization of cis- and trans-h2 estimates of gene expression, which is of value for interpretation of GWAS and gene expression studies.

Published
2020

9. Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

Author

Porcu, E. (Eleonora), Rueger, S. (Sina), Lepik, K. (Kaido), Agbessi, M. (Mawusse), Ahsan, H. (Habibul), Alves, I. (Isabel), Andiappan, A. (Anand), Arindrarto, W. (Wibowo), Awadalla, P. (Philip), Battle, A. (Alexis), Beutner, F. (Frank), Bonder, M. J. (Marc Jan), Boomsma, D. (Dorret), Christiansen, M. (Mark), Claringbould, A. (Annique), Deelen, P. (Patrick), Esko, T. (Tonu), Fave, M.-J. (Marie-Julie), Franke, L. (Lude), Frayling, T. (Timothy), Gharib, S. A. (Sina A.), Gibson, G. (Gregory), Heijmans, B. T. (Bastiaan T.), Hemani, G. (Gibran), Jansen, R. (Rick), Kahonen, M. (Mika), Kalnapenkis, A. (Anette), Kasela, S. (Silva), Kettunen, J. (Johannes), Kim, Y. (Yungil), Kirsten, H. (Holger), Kovacs, P. (Peter), Krohn, K. (Knut), Kronberg-Guzman, J. (Jaanika), Kukushkina, V. (Viktorija), Lee, B. (Bernett), Lehtimaki, T. (Terho), Loeffler, M. (Markus), Marigorta, U. M. (Urko M.), Mei, H. (Hailang), Milani, L. (Lili), Montgomery, G. W. (Grant W.), Mueler-Nurasyid, M. (Martina), Nauck, M. (Matthias), Nivard, M. (Michel), Penninx, B. (Brenda), Perola, M. (Markus), Pervjakova, N. (Natalia), Pierce, B. L. (Brandon L.), Powell, J. (Joseph), Prokisch, H. (Holger), Psaty, B. M. (Bruce M.), Raitakari, O. T. (Olli T.), Ripatti, S. (Samuli), Rotzschke, O. (Olaf), Saha, A. (Ashis), Scholz, M. (Markus), Schramm, K. (Katharina), Seppala, I. (Ilkka), Slagboom, E. P. (Eline P.), Stehouwer, C. D. (Coen D. A.), Stumvoll, M. (Michael), Sullivan, P. (Patrick), Teumer, A. (Alexander), Thiery, J. (Joachim), Tong, L. (Lin), Tonjes, A. (Anke), van Dongen, J. (Jenny), van Iterson, M. (Maarten), van Meurs, J. (Joyce), Veldink, J. H. (Jan H.), Verlouw, J. (Joost), Visscher, P. M. (Peter M.), Volker, U. (Uwe), Vosa, U. (Urmo), Westra, H.-J. (Harm-Jan), Wijmenga, C. (Cisca), Yaghootkar, H. (Hanieh), Yang, J. (Jian), Zeng, B. (Biao), Zhang, F. (Futao), Beekman, M. (Marian), Boomsma, D. I. (Dorret I.), Bot, J. (Jan), Deelen, J. (Joris), Hofman, B. A. (Bert A.), Hottenga, J. J. (Jouke J.), Isaacs, A. (Aaron), Jhamai, P. M. (P. Mila), Kielbasa, S. M. (Szymon M.), Lakenberg, N. (Nico), Luijk, R. (Rene), Mei, H. (Hailiang), Moed, M. (Matthijs), Nooren, I. (Irene), Pool, R. (Rene), Schalkwijk, C. G. (Casper G.), Slagboom, P. E. (P. Eline), Suchiman, H. E. (H. Eka D.), Swertz, M. A. (Morris A.), Tigchelaar, E. F. (Ettje F.), Uitterlinden, A. G. (Andre G.), van den Berg, L. H. (Leonard H.), van der Breggen, R. (Ruud), van der Kallen, C. J. (Carla J. H.), van Dijk, F. (Freerk), van Duijn, C. M. (Cornelia M.), van Galen, M. (Michiel), van Greevenbroek, M. M. (Marleen M. J.), van Heemst, D. (Diana), van Rooij, J. (Jeroen), Van't Hof, P. (Peter), van Zwet, E. W. (Erik. W.), Vermaat, M. (Martijn), Verbiest, M. (Michael), Verkerk, M. (Marijn), Zhernakova, D. V. (Dasha V.), Zhernakova, S. (Sasha), Santoni, F. A. (Federico A.), Reymond, A. (Alexandre), and Kutalik, Z. (Zoltan)

Abstract

Genome-wide association studies (GWAS) have identified thousands of variants associated with complex traits, but their biological interpretation often remains unclear. Most of these variants overlap with expression QTLs, indicating their potential involvement in regulation of gene expression. Here, we propose a transcriptome-wide summary statistics-based Mendelian Randomization approach (TWMR) that uses multiple SNPs as instruments and multiple gene expression traits as exposures, simultaneously. Applied to 43 human phenotypes, it uncovers 3,913 putatively causal gene–trait associations, 36% of which have no genome-wide significant SNP nearby in previous GWAS. Using independent association summary statistics, we find that the majority of these loci were missed by GWAS due to power issues. Noteworthy among these links is educational attainment-associated BSCL2, known to carry mutations leading to a Mendelian form of encephalopathy. We also find pleiotropic causal effects suggestive of mechanistic connections. TWMR better accounts for pleiotropy and has the potential to identify biological mechanisms underlying complex traits.

Published
2019

10. A SNP panel for identification of DNA and RNA specimens

Author

Yousefi, Soheil, Abbassi-Daloii, Tooba, Kraaijenbrink, Thirsa, Vermaat, Martijn, Mei, Hailiang, van't Hof, Peter, van Iterson, Maarten, Zhernakova, Daria V., Claringbould, Annique, Franke, Lude, 't Hart, Leen M., Slieker, Roderick C., van der Heijden, Amber, de Knijff, Peter, 't Hoen, Peter A. C., Jansen, R., van Meurs, J., Heijmans, B.T., Boomsma, D.I., van Dongen, J., Hottenga, Jouke-Jan, Slagboom, P.E., Suchiman, H. Eka D., van Zwet, Erik W., 't Hoen, P., Pool, R., van Greevenbroek, Marleen, Stehouwer, Coen, van der Kallen, Carla, Schalkwijk, Casper, Wijmenga, C., Zhernakova, A., Tigchelaar, E.F., Beekman, M, Deelen, J, van Heemst, D., Veldink, J H., van den Berg, L.H., van Duijn, C.M., Hofman, B. A., Uitterlinden, A. G., Jhamai, P. Mila, Verbiest, M., Verkerk, M., van der Breggen, Ruud, van Rooij, J., Lakenberg, N., Mei, H., Bot, J., Zhernakova, D. V., Van't Hof, P., Deelen, P., Nooren, I., Moed, M., Vermaat, M., Bonder, M.J., van Dijk, F., van Galen, M., Arindrarto, Wibowo, Kielbasa, Szymon M., Swertz, Morris A., Isaacs, A., Franke, L., Biological Psychology, APH - Mental Health, APH - Methodology, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Stem Cell Aging Leukemia and Lymphoma (SALL), Epidemiology and Data Science, APH - Aging & Later Life, General practice, Amsterdam Neuroscience - Complex Trait Genetics, Psychiatry, RS: CARIM - R3.01 - Vascular complications of diabetes and the metabolic syndrome, Interne Geneeskunde, MUMC+: HVC Pieken Maastricht Studie (9), and MUMC+: MA Interne Geneeskunde (3)

Subjects
0301 basic medicine, Netherlands Twin Register (NTR), BLOOD, INDIVIDUAL IDENTIFICATION, Individuality, Linkage Disequilibrium, 0302 clinical medicine, Gene Frequency, MARKERS, Genotype, Ethnicity, GeneralLiterature_REFERENCE(e.g.,dictionaries,encyclopedias,glossaries), Mix up samples, Genetics, education.field_of_study, CODIS CORE LOCI, High-Throughput Nucleotide Sequencing, 16. Peace & justice, Justice and Strong Institutions, DNA profiling, POPULATIONS, DNA microarray, MESSENGER-RNA, Biotechnology, Research Article, Biobanking, Patient Identification Systems, SDG 16 - Peace, lcsh:QH426-470, lcsh:Biotechnology, Population, UNITED-STATES, Single-nucleotide polymorphism, Biology, Polymorphism, Single Nucleotide, VALIDATION, 03 medical and health sciences, All institutes and research themes of the Radboud University Medical Center, lcsh:TP248.13-248.65, Journal Article, SNP, Humans, 030216 legal & forensic medicine, Genetic Testing, Genetic variation, education, Genotyping, Forensics, SDG 16 - Peace, Justice and Strong Institutions, DNA, DNA Fingerprinting, Minor allele frequency, FORENSIC IDENTIFICATION, lcsh:Genetics, 030104 developmental biology, Genetics, Population, RNA, MULTIPLEX, Sample tracking, Nanomedicine Radboud Institute for Molecular Life Sciences [Radboudumc 19]
Abstract

Background SNP panels that uniquely identify an individual are useful for genetic and forensic research. Previously recommended SNP panels are based on DNA profiles and mostly contain intragenic SNPs. With the increasing interest in RNA expression profiles, we aimed for establishing a SNP panel for both DNA and RNA-based genotyping. Results To determine a small set of SNPs with maximally discriminative power, genotype calls were obtained from DNA and blood-derived RNA sequencing data belonging to healthy, geographically dispersed, Dutch individuals. SNPs were selected based on different criteria like genotype call rate, minor allele frequency, Hardy–Weinberg equilibrium and linkage disequilibrium. A panel of 50 SNPs was sufficient to identify an individual uniquely: the probability of identity was 6.9 × 10− 20 when assuming no family relations and 1.2 × 10− 10 when accounting for the presence of full sibs. The ability of the SNP panel to uniquely identify individuals on DNA and RNA level was validated in an independent population dataset. The panel is applicable to individuals from European descent, with slightly lower power in non-Europeans. Whereas most of the genes containing the 50 SNPs are expressed in various tissues, our SNP panel needs optimization for other tissues than blood. Conclusions This first DNA/RNA SNP panel will be useful to identify sample mix-ups in biomedical research and for assigning DNA and RNA stains in crime scenes to unique individuals. Electronic supplementary material The online version of this article (10.1186/s12864-018-4482-7) contains supplementary material, which is available to authorized users.

Published
2018

11. DNA Methylation Analysis Identifies Loci for Blood Pressure Regulation

Author

Richard, MA, Huan, T, Ligthart, S, Gondalia, R, Jhun, MA, Brody, JA, Irvin, MR, Marioni, R, Shen, J, Tsai, PC, Montasser, ME, Jia, Y, Syme, C, Salfati, EL, Boerwinkle, E, Guan, W, Mosley, TH, Bressler, J, Morrison, AC, Liu, C, Mendelson, MM, Uitterlinden, AG, van Meurs, JB, Heijmans, BT, ’t Hoen, PAC, van Meurs, J, Isaacs, A, Jansen, R, Franke, L, Boomsma, DI, Pool, R, van Dongen, J, Hottenga, JJ, van Greevenbroek, MMJ, Stehouwer, CDA, van der Kallen, CJH, Schalkwijk, CG, Wijmenga, C, Zhernakova, A, Tigchelaar, EF, Slagboom, PE, Beekman, M, Deelen, J, van Heemst, D, Veldink, JH, van den Berg, LH, van Duijn, CM, Hofman, A, Jhamai, PM, Verbiest, M, Suchiman, HED, Verkerk, M, van der Breggen, R, van Rooij, J, Lakenberg, N, Mei, H, van Iterson, M, van Galen, M, Bot, J, van ’t Hof, P, Deelen, P, Nooren, I, Moed, M, Vermaat, M, Zhernakova, DV, Luijk, R, Bonder, MJ, van Dijk, F, Arindrarto, W, Kielbasa, SM, Swertz, MA, van Zwet, EW, Franco, OH, Zhang, G, Li, Y, Stewart, JD, Bis, JC, Psaty, BM, Chen, YDI, Kardia, SLR, Zhao, W, Turner, ST, Absher, D, Aslibekyan, S, and Starr, JM

Abstract

© 2017 American Society of Human Genetics Genome-wide association studies have identified hundreds of genetic variants associated with blood pressure (BP), but sequence variation accounts for a small fraction of the phenotypic variance. Epigenetic changes may alter the expression of genes involved in BP regulation and explain part of the missing heritability. We therefore conducted a two-stage meta-analysis of the cross-sectional associations of systolic and diastolic BP with blood-derived genome-wide DNA methylation measured on the Infinium HumanMethylation450 BeadChip in 17,010 individuals of European, African American, and Hispanic ancestry. Of 31 discovery-stage cytosine-phosphate-guanine (CpG) dinucleotides, 13 replicated after Bonferroni correction (discovery: N = 9,828, p < 1.0 × 10−7; replication: N = 7,182, p < 1.6 × 10−3). The replicated methylation sites are heritable (h2 > 30%) and independent of known BP genetic variants, explaining an additional 1.4% and 2.0% of the interindividual variation in systolic and diastolic BP, respectively. Bidirectional Mendelian randomization among up to 4,513 individuals of European ancestry from 4 cohorts suggested that methylation at cg08035323 (TAF1B-YWHAQ) influences BP, while BP influences methylation at cg00533891 (ZMIZ1), cg00574958 (CPT1A), and cg02711608 (SLC1A5). Gene expression analyses further identified six genes (TSPAN2, SLC7A11, UNC93B1, CPT1A, PTMS, and LPCAT3) with evidence of triangular associations between methylation, gene expression, and BP. Additional integrative Mendelian randomization analyses of gene expression and DNA methylation suggested that the expression of TSPAN2 is a putative mediator of association between DNA methylation at cg23999170 and BP. These findings suggest that heritable DNA methylation plays a role in regulating BP independently of previously known genetic variants.

Published
2017

12. Skewed X-inactivation is common in the general female population

Author

Shvetsova, E. (Ekaterina), Sofronova, A. (Alina), Monajemi, R. (Ramin), Gagalova, K. (Kristina), Draisma, G. (Gerrit), White, S.J. (Stefan), Santen, G.W.E. (Gijs), Chuva De Sousa Lopes, S.M. (Susana M.), Heijmans, B.T. (Bastiaan T.), van Meurs, J. (Joyce), Jansen, R. (Rick), Franke, L. (Lude), Kielbasa, S.M. (Szymon M.), Dunnen, J.T. (Johan) den, ‘t Hoen, P.A.C. (Peter A. C.), Heijmans, B.T. (Bastiaan T), Meurs, J.B.J. (Joyce) van, Boomsma, D.I. (Dorret), Pool, R. (Reńe), Dongen, J. (Jenny) van, Hottenga, J.J. (Jouke Jan), Greevenbroek, M.M. van, Stehouwer, C.D. (Coen Da), Kallen, C.J. van der, Schalkwijk, C.G. (Casper), Wijmenga, C. (Cisca), Zhernakova, S. (Sasha), Tigchelaar, E.F. (Ettje F.), Slagboom, P.E. (Eline), Beekman, M. (Marian), Deelen, J. (Joris), Heemst, D. (Diana) van, Veldink, J.H. (Jan), Berg, L.H. (Leonard) van den, Duijn, C.M. (Cornelia) van, Hofman, B.A. (Bert A), Uitterlinden, A.G. (André), Jhamai, P.M. (Mila), Verbiest, M.M.P.J. (Michael), Suchiman, H.E.D. (H Eka D), Verkerk, M. (Marijn), Breggen, R. (Ruud) van der, van Rooij, J. (Jeroen), Lakenberg, N. (Nico), Mei, S. (Shan), Bot, J. (Jan), Zhernakova, D.V. (Dasha V), van ’t Hof, P. (Peter), Deelen, P. (Patrick), Nooren, I. (Irene), Moed, H. (Heleen), Vermaat, M. (Martijn), Luijk, R. (René), Jan Bonder, M. (Marc), Iterson, M. (Maarten) van, van Dijk, F. (Freerk), Van Galen, M. (Michiel), Arindrarto, W. (Wibowo), Swertz, M.A. (Morris A), Zwet, E.W. (Erik) van, Isaacs, A.J. (Aaron), Francioli, L.C. (Laurent), Menelaou, A. (Androniki), Pulit, S.L. (Sara), Dijk, F. (Freerk) van, Palamara, P.F. (Pier Francesco), Elbers, C.C. (Clara), Neerincx, P.B.T. (Pieter B T), Ye, K. (K.), Guryev, V. (Victor), Kloosterman, W. (Wp), Abdellaoui, A. (Abdel), van Leeuwen, E. (Em), Oven, M. (Mannis) van, Li, M. (M.), Laros, J. (Jf), Karssen, L.C. (Lennart), Kanterakis, A. (Alexandros), Amin, N. (Najaf), Hottenga, J. (Jj), Lameijer, E. (Ew), Kattenberg, V.M. (Mathijs), Dijkstra, M. (Martijn), Byelas, H. (Heorhiy), Setten, J. (Jessica) van, van Schaik, B. (Bd), Bot, J.J. (Jan), Nijman, I. (Ij), Renkens, I. (Ivo), Marschall, T. (Tanja), Schönhuth, A. (A.), Hehir-Kwa, J. (Jayne), Handsaker, R.E. (Robert), Polak, P., Sohail, M. (Mashaal), Vuzman, D. (Dana), Hormozdiari, F. (Fereydoun), Enckevort, D. (David) van, Mei, H. (H.), Koval, V. (Vyacheslav), Moed, M. (Mh), van der Velde, K. (Kj), Rivadeneira Ramirez, F. (Fernando), Estrada Gil, K. (Karol), Medina-Gomez, M.C. (Carolina), McCarroll, S. (Sa), de Craen, A. (Aj), Suchiman, H. (He), Hofman, B. (Ba), Oostra, B.A. (Ben), Uitterlinden, A. (Ag), Willemsen, G.A.H.M. (Gonneke), Platteel, I. (Inge), Veldink, J. (Jh), van den Berg, L. (Lh), Pitts, S. (Sj), Potluri, S. (Shobha), Sundar, P. (Purnima), Cox, D. (Dr), Sunyaev, S. (Sr), den Dunnen, J. (Jt), Stoneking, M. (Mark), Knijff, P. (Peter) de, Kayser, M.H. (Manfred), Li, Q. (Q.), Li, Y. (Y.), Du, Y. (Y.), Chen, R. (R.), Cao, H. (H.), Li, N. (N.), Cao, S. (Sherry), Wang, J. (J.), Bovenberg, J.A. (Jasper), Peer, I. (Itsik), Slagboom, P. (Pe), van Duijn, C. (Cm), Boomsma, D. (Di), van Ommen, G. (Gj), de Bakker, P. (Pi), Swertz, M. (Ma), Wijmenga, C. (C.), Shvetsova, E. (Ekaterina), Sofronova, A. (Alina), Monajemi, R. (Ramin), Gagalova, K. (Kristina), Draisma, G. (Gerrit), White, S.J. (Stefan), Santen, G.W.E. (Gijs), Chuva De Sousa Lopes, S.M. (Susana M.), Heijmans, B.T. (Bastiaan T.), van Meurs, J. (Joyce), Jansen, R. (Rick), Franke, L. (Lude), Kielbasa, S.M. (Szymon M.), Dunnen, J.T. (Johan) den, ‘t Hoen, P.A.C. (Peter A. C.), Heijmans, B.T. (Bastiaan T), Meurs, J.B.J. (Joyce) van, Boomsma, D.I. (Dorret), Pool, R. (Reńe), Dongen, J. (Jenny) van, Hottenga, J.J. (Jouke Jan), Greevenbroek, M.M. van, Stehouwer, C.D. (Coen Da), Kallen, C.J. van der, Schalkwijk, C.G. (Casper), Wijmenga, C. (Cisca), Zhernakova, S. (Sasha), Tigchelaar, E.F. (Ettje F.), Slagboom, P.E. (Eline), Beekman, M. (Marian), Deelen, J. (Joris), Heemst, D. (Diana) van, Veldink, J.H. (Jan), Berg, L.H. (Leonard) van den, Duijn, C.M. (Cornelia) van, Hofman, B.A. (Bert A), Uitterlinden, A.G. (André), Jhamai, P.M. (Mila), Verbiest, M.M.P.J. (Michael), Suchiman, H.E.D. (H Eka D), Verkerk, M. (Marijn), Breggen, R. (Ruud) van der, van Rooij, J. (Jeroen), Lakenberg, N. (Nico), Mei, S. (Shan), Bot, J. (Jan), Zhernakova, D.V. (Dasha V), van ’t Hof, P. (Peter), Deelen, P. (Patrick), Nooren, I. (Irene), Moed, H. (Heleen), Vermaat, M. (Martijn), Luijk, R. (René), Jan Bonder, M. (Marc), Iterson, M. (Maarten) van, van Dijk, F. (Freerk), Van Galen, M. (Michiel), Arindrarto, W. (Wibowo), Swertz, M.A. (Morris A), Zwet, E.W. (Erik) van, Isaacs, A.J. (Aaron), Francioli, L.C. (Laurent), Menelaou, A. (Androniki), Pulit, S.L. (Sara), Dijk, F. (Freerk) van, Palamara, P.F. (Pier Francesco), Elbers, C.C. (Clara), Neerincx, P.B.T. (Pieter B T), Ye, K. (K.), Guryev, V. (Victor), Kloosterman, W. (Wp), Abdellaoui, A. (Abdel), van Leeuwen, E. (Em), Oven, M. (Mannis) van, Li, M. (M.), Laros, J. (Jf), Karssen, L.C. (Lennart), Kanterakis, A. (Alexandros), Amin, N. (Najaf), Hottenga, J. (Jj), Lameijer, E. (Ew), Kattenberg, V.M. (Mathijs), Dijkstra, M. (Martijn), Byelas, H. (Heorhiy), Setten, J. (Jessica) van, van Schaik, B. (Bd), Bot, J.J. (Jan), Nijman, I. (Ij), Renkens, I. (Ivo), Marschall, T. (Tanja), Schönhuth, A. (A.), Hehir-Kwa, J. (Jayne), Handsaker, R.E. (Robert), Polak, P., Sohail, M. (Mashaal), Vuzman, D. (Dana), Hormozdiari, F. (Fereydoun), Enckevort, D. (David) van, Mei, H. (H.), Koval, V. (Vyacheslav), Moed, M. (Mh), van der Velde, K. (Kj), Rivadeneira Ramirez, F. (Fernando), Estrada Gil, K. (Karol), Medina-Gomez, M.C. (Carolina), McCarroll, S. (Sa), de Craen, A. (Aj), Suchiman, H. (He), Hofman, B. (Ba), Oostra, B.A. (Ben), Uitterlinden, A. (Ag), Willemsen, G.A.H.M. (Gonneke), Platteel, I. (Inge), Veldink, J. (Jh), van den Berg, L. (Lh), Pitts, S. (Sj), Potluri, S. (Shobha), Sundar, P. (Purnima), Cox, D. (Dr), Sunyaev, S. (Sr), den Dunnen, J. (Jt), Stoneking, M. (Mark), Knijff, P. (Peter) de, Kayser, M.H. (Manfred), Li, Q. (Q.), Li, Y. (Y.), Du, Y. (Y.), Chen, R. (R.), Cao, H. (H.), Li, N. (N.), Cao, S. (Sherry), Wang, J. (J.), Bovenberg, J.A. (Jasper), Peer, I. (Itsik), Slagboom, P. (Pe), van Duijn, C. (Cm), Boomsma, D. (Di), van Ommen, G. (Gj), de Bakker, P. (Pi), Swertz, M. (Ma), and Wijmenga, C. (C.)

Abstract

X-inactivation is a well-established dosage compensation mechanism ensuring that X-chromosomal genes are expressed at comparable levels in males and females. Skewed X-inactivation is often explained by negative selection of one of the alleles. We demonstrate that imbalanced expression of the paternal and maternal X-chromosomes is common in the general population and that the random nature of the X-inactivation mechanism can be sufficient to explain the imbalance. To this end, we analyzed blood-derived RNA and whole-genome sequencing data from 79 female children and their parents from the Genome of the Netherlands project. We calculated the median ratio of the paternal over total counts at all X-chromosomal heterozygous single-nucleotide variants with coverage ≥10. We identified two individuals where the same X-chromosome was inactivated in all cells. Imbalanced expression of the two X-chromosomes (ratios ≤0.35 or ≥0.65) was observed in nearly 50% of the population. The empirically observed skewing is explained by a theoretical model where X-inactivation takes place in an embryonic stage in which eight cells give rise to the hematopoietic compartment. Genes escaping X-inactivation are expressed from both alleles and therefore demonstrate less skewing than inactivated genes. Using this characteristic, we identified three novel escapee genes (SSR4, REPS2, and SEPT6), but did not find support for many previously reported escapee genes in blood. Our collective data suggest that skewed X-inactivation is common in the general population. This may contribute to manifestation of symptoms in carriers of recessive X-linked disorders. We recommend that X-inactivation results should not be used lightly in the interpretation of X-linked variants.

Published
2018
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13. Genome-wide analyses identify a role for SLC17A4 and AADAT in thyroid hormone regulation

Author

Teumer, A, Chaker, Layal, Groeneweg, Stefan, Li, Yi, Di Munno, Celia, Barbieri, C, Schultheiss, UT, Traglia, M, Ahluwalia, TS, Akiyama, M, Appel, EVR, Arking, DE, Arnold, A, Astrup, A, Beekman, M, Beilby, JP, Bekaert, S, Boerwinkle, E, Brown, SJ, de Buyzere, M, Campbell, PJ, Ceresini, G, Cerqueira, C, Cucca, F, Deary, IJ, Deelen, J, Eckardt, KU, Ekici, AB, Eriksson, JG, Ferrrucci, L, Fiers, T, Fiorillo, E, Ford, I, Fox, CS, Fuchsberger, C, Galesloot, TE, Gieger, C, Gogele, M, De Grandi, A, Grarup, N, Greiser, KH, Haljas, K, Hansen, T, Harris, SE, van Heemst, D, Heijer, M, Hicks, AA, den Hollander, W, Homuth, G, Hui, JN, Ikram, Arfan, Ittermann, T, Jensen, RA, Jing, J, Jukema, JW, Kajalltie, E, Kamatani, Y, Kasbohm, E, Kaufman, JM, Kiemeney, LA, Kloppenburg, M, Kronenberg, F, Kubo, M, Lahti, J, Lapauw, B, Li, S, Liewald, DCM, Lim, EM, Linneberg, A, Marina, M, Mascalzoni, D, Matsuda, K, Medenwald, D, Meisinger, C, Meulenbelt, I, Meyer, T, zu Schwabedissen, HEM, Mikolajczyk, R, Moed, M, Netea-Maier, RT, Nolte, IM, Okadah, Y, Pala, M, Pattaro, C, Pedersen, O, Petersmann, A, Porcu, E, Postmus, I, Pramstaller, PP, Psaty, BM, Ramos, YFM, Rawal, R, Redmond, P, Richards, JB, Rietzschel, ER, Rivadeneira, Fernando, Roef, G, Rotter, JI, Sala, CF, Schlessinger, D, Selvin, E, Slagboom, PE (Eline), Soranzo, N, Sorensen, TIA, Spector, TD, Starr, JM, Stott, DJ, Taes, Y, Taliun, D, Tanaka, T, Thuesen, B, Tiller, D, Toniolo, D, Uitterlinden, André, Visser, Edward, Walsh, JP, Wilson, SG, Wolffenbuttel, BHR, Yang, Q, Zheng, HF, Cappola, A, Peeters, Robin, Naitza, S, Volzke, H, Sanna, S, Kottgen, A, Visser, Theo, Medici, Marco, Teumer, A, Chaker, Layal, Groeneweg, Stefan, Li, Yi, Di Munno, Celia, Barbieri, C, Schultheiss, UT, Traglia, M, Ahluwalia, TS, Akiyama, M, Appel, EVR, Arking, DE, Arnold, A, Astrup, A, Beekman, M, Beilby, JP, Bekaert, S, Boerwinkle, E, Brown, SJ, de Buyzere, M, Campbell, PJ, Ceresini, G, Cerqueira, C, Cucca, F, Deary, IJ, Deelen, J, Eckardt, KU, Ekici, AB, Eriksson, JG, Ferrrucci, L, Fiers, T, Fiorillo, E, Ford, I, Fox, CS, Fuchsberger, C, Galesloot, TE, Gieger, C, Gogele, M, De Grandi, A, Grarup, N, Greiser, KH, Haljas, K, Hansen, T, Harris, SE, van Heemst, D, Heijer, M, Hicks, AA, den Hollander, W, Homuth, G, Hui, JN, Ikram, Arfan, Ittermann, T, Jensen, RA, Jing, J, Jukema, JW, Kajalltie, E, Kamatani, Y, Kasbohm, E, Kaufman, JM, Kiemeney, LA, Kloppenburg, M, Kronenberg, F, Kubo, M, Lahti, J, Lapauw, B, Li, S, Liewald, DCM, Lim, EM, Linneberg, A, Marina, M, Mascalzoni, D, Matsuda, K, Medenwald, D, Meisinger, C, Meulenbelt, I, Meyer, T, zu Schwabedissen, HEM, Mikolajczyk, R, Moed, M, Netea-Maier, RT, Nolte, IM, Okadah, Y, Pala, M, Pattaro, C, Pedersen, O, Petersmann, A, Porcu, E, Postmus, I, Pramstaller, PP, Psaty, BM, Ramos, YFM, Rawal, R, Redmond, P, Richards, JB, Rietzschel, ER, Rivadeneira, Fernando, Roef, G, Rotter, JI, Sala, CF, Schlessinger, D, Selvin, E, Slagboom, PE (Eline), Soranzo, N, Sorensen, TIA, Spector, TD, Starr, JM, Stott, DJ, Taes, Y, Taliun, D, Tanaka, T, Thuesen, B, Tiller, D, Toniolo, D, Uitterlinden, André, Visser, Edward, Walsh, JP, Wilson, SG, Wolffenbuttel, BHR, Yang, Q, Zheng, HF, Cappola, A, Peeters, Robin, Naitza, S, Volzke, H, Sanna, S, Kottgen, A, Visser, Theo, and Medici, Marco

Published
2018

14. The complex genetics of gait speed: genome-wide meta-analysis approach (vol 9, pg 209, 2017)

Author

Ben-Avraham, D., Karasik, D., Verghese, J., Lunetta, K.L., Smith, J.A., Eicher, J.D., Vered, R., Deelen, J., Arnold, A.M., Buchman, A.S., Tanaka, T., Faul, J.D., Nethander, M., Fornage, M., Adams, H.H., Matteini, A.M., Callisaya, M.L., Smith, A.V., Yu, L., Jager, P.L. de, Evans, D.A., Gudnason, V., Hofman, A., Pattie, A., Corley, J., Launer, L.J., Knopman, D.S., Parimi, N., Turner, S.T., Bandinelli, S., Beekman, M., Gutman, D., Sharvit, L., Mooijaart, S.P., Liewald, D.C., Houwing-Duistermaat, J.J., Ohlsson, C., Moed, M., Verlinden, V.J., Mellstrom, D., Geest, J.N. van der, Karlsson, M., Hernandez, D., McWhirter, R., Liu, Y.M., Thomson, R., Tranah, G.J., Uitterlinden, A.G., Weir, D.R., Zhao, W., Starr, J.M., Johnson, A.D., Ikram, M.A., Bennett, D.A., Cummings, S.R., Deary, I.J., Harris, T.B., Kardia, S.L.R., Mosley, T.H., Srikanth, V.K., Windham, B.G., Newman, A.B., Walston, J.D., Davies, G., Evans, D.S., Slagboom, E.P., Ferrucci, L., Kiel, D.P., Murabito, J.M., and Atzmon, G.

Published
2017

15. Correction: The complex genetics of gait speed: Genome-wide metaanalysis approach [Aging, (Albany NY), 9, 1, (2017), (209-246)]doi 10.18632/aging.101151

Author

Ben-Avraham, D, Karasik, D, Verghese, J, Lunetta, KL, Smith, JA, Eicher, JD, Vered, R, Deelen, J, Arnold, AM, Buchman, AS, Tanaka, T, Faul, JD, Nethander, M, Fornage, M, Adams, HH, Matteini, AM, Callisaya, ML, Smith, AV, Yu, L, De Jager, PL, Evans, DA, Gudnason, V, Hofman, A, Pattie, A, Corley, J, Launer, LJ, Knopman, DS, Parimi, N, Turner, ST, Bandinelli, S, Beekman, M, Gutman, D, Sharvit, L, Mooijaart, SP, Liewald, DC, Houwing-Duistermaat, JJ, Ohlsson, C, Moed, M, Verlinden, VJ, Mellström, D, van der Geest, JN, Karlsson, M, Hernandez, D, McWhirter, Rebekah, Liu, Y, Thomson, R, Tranah, GJ, Uitterlinden, AG, Weir, DR, Zhao, W, Starr, JM, Johnson, AD, Arfan Ikram, M, Bennett, DA, Cummings, SR, Deary, IJ, Harris, TB, Kardia, SLR, Mosley, TH, Srikanth, VK, Windham, BG, Newman, AB, Walston, JD, Davies, G, Evans, DS, Slagboom, EP, Ferrucci, L, Kiel, DP, Murabito, JM, Atzmon, G, Ben-Avraham, D, Karasik, D, Verghese, J, Lunetta, KL, Smith, JA, Eicher, JD, Vered, R, Deelen, J, Arnold, AM, Buchman, AS, Tanaka, T, Faul, JD, Nethander, M, Fornage, M, Adams, HH, Matteini, AM, Callisaya, ML, Smith, AV, Yu, L, De Jager, PL, Evans, DA, Gudnason, V, Hofman, A, Pattie, A, Corley, J, Launer, LJ, Knopman, DS, Parimi, N, Turner, ST, Bandinelli, S, Beekman, M, Gutman, D, Sharvit, L, Mooijaart, SP, Liewald, DC, Houwing-Duistermaat, JJ, Ohlsson, C, Moed, M, Verlinden, VJ, Mellström, D, van der Geest, JN, Karlsson, M, Hernandez, D, McWhirter, Rebekah, Liu, Y, Thomson, R, Tranah, GJ, Uitterlinden, AG, Weir, DR, Zhao, W, Starr, JM, Johnson, AD, Arfan Ikram, M, Bennett, DA, Cummings, SR, Deary, IJ, Harris, TB, Kardia, SLR, Mosley, TH, Srikanth, VK, Windham, BG, Newman, AB, Walston, JD, Davies, G, Evans, DS, Slagboom, EP, Ferrucci, L, Kiel, DP, Murabito, JM, and Atzmon, G

Published
2017

16. The complex genetics of gait speed: Genome-wide meta-analysis approach

Author

Ben-Avraham, D, Karasik, D, Verghese, J, Lunetta, KL, Smith, JA, Eicher, JD, Vered, R, Deelen, J, Arnold, AM, Buchman, AS, Tanaka, T, Faul, JD, Nethander, M, Fornage, M, Adams, HH, Matteini, AM, Callisaya, ML, Smith, AV, Yu, L, De Jager, PL, Evans, DA, Gudnason, V, Hofman, A, Pattie, A, Corley, J, Launer, LJ, Knopman, DS, Parimi, N, Turner, ST, Bandinelli, S, Beekman, M, Gutman, D, Sharvit, L, Mooijaart, SP, Liewald, DC, Houwing-Duistermaat, JJ, Ohlsson, C, Moed, M, Verlinden, VJ, Mellström, D, van der Geest, JN, Karlsson, M, Hernandez, D, McWhirter, Rebekah, Liu, Y, Thomson, R, Tranah, GJ, Uitterlinden, AG, Weir, DR, Zhao, W, Starr, JM, Johnson, AD, Arfan Ikram, M, Bennett, DA, Cummings, SR, Deary, IJ, Harris, TB, Sharon, SL, Mosley, TH, Srikanth, VK, Windham, BG, Newman, AB, Walston, JD, Davies, G, Evans, DS, Slagboom, EP, Ferrucci, L, Kiel, DP, Murabito, JM, Atzmon, G, Ben-Avraham, D, Karasik, D, Verghese, J, Lunetta, KL, Smith, JA, Eicher, JD, Vered, R, Deelen, J, Arnold, AM, Buchman, AS, Tanaka, T, Faul, JD, Nethander, M, Fornage, M, Adams, HH, Matteini, AM, Callisaya, ML, Smith, AV, Yu, L, De Jager, PL, Evans, DA, Gudnason, V, Hofman, A, Pattie, A, Corley, J, Launer, LJ, Knopman, DS, Parimi, N, Turner, ST, Bandinelli, S, Beekman, M, Gutman, D, Sharvit, L, Mooijaart, SP, Liewald, DC, Houwing-Duistermaat, JJ, Ohlsson, C, Moed, M, Verlinden, VJ, Mellström, D, van der Geest, JN, Karlsson, M, Hernandez, D, McWhirter, Rebekah, Liu, Y, Thomson, R, Tranah, GJ, Uitterlinden, AG, Weir, DR, Zhao, W, Starr, JM, Johnson, AD, Arfan Ikram, M, Bennett, DA, Cummings, SR, Deary, IJ, Harris, TB, Sharon, SL, Mosley, TH, Srikanth, VK, Windham, BG, Newman, AB, Walston, JD, Davies, G, Evans, DS, Slagboom, EP, Ferrucci, L, Kiel, DP, Murabito, JM, and Atzmon, G

Published
2017

19. Minimally-Sized Balanced Decomposition Schemes for Multi-class Classification

Author

Smirnov, E.N., Moed, M., Nalbantov, G.I., Sprinkhuizen-Kuyper, I.G., Okun, O., Valentini, G., Re, M., Okun, O., Valentini, G., and Re, M.

Subjects
Computational complexity theory, business.industry, Code word, Studies in computational Intelligence, Pattern recognition, Cognitive artificial intelligence, Code redundancy, Multiclass classification, Support vector machine, Brain Networks and Neuronal Communication [DI-BCB_DCC_Theme 4], ComputingMethodologies_PATTERNRECOGNITION, Binary classification, Artificial intelligence, business, Mathematics, Coding (social sciences)
Abstract

Item does not contain fulltext Error-Correcting Output Coding (ECOC) is a well-known class of decomposition schemes for multi-class classification. It allows representing any multiclass classification problem as a set of binary classification problems. Due to code redundancy ECOC schemes can significantly improve generalization performance on multi-class classification problems. However, they can face a computational complexity problem when the number of classes is large. In this paper we address the computational-complexity problem of the decomposition schemes. We study a particular class of minimally-sized ECOC decomposition schemes, namely the class of minimally-sized balanced decomposition schemes (MBDSs) [14].We show thatMBDSs do not face a computational-complexity problem for large number of classes. However we also show that MBDSs cannot correct the classification errors of the binary classifiers in MBDS ensembles. Therefore we propose voting with MBDS ensembles (VMBDSs).We show that the generalization performance of the VMBDSs ensembles improves with the number of MBDS classifiers. However this number can become large and thus the VMBDSs ensembles can have a computational-complexity problem as well. Fortunately our experiments show that VMBDSs are comparable with ECOC ensembles and can outperform one-against-all ensembles using only a small number of MBDS ensembles.

Published
2011

20. Minimally-Sized Balanced Decomposition Schemes for Multi-class Classification

Author

Okun, O., Valentini, G., Re, M., Smirnov, E.N., Moed, M., Nalbantov, G.I., Sprinkhuizen-Kuyper, I.G., Okun, O., Valentini, G., Re, M., Smirnov, E.N., Moed, M., Nalbantov, G.I., and Sprinkhuizen-Kuyper, I.G.

Abstract

Item does not contain fulltext, Error-Correcting Output Coding (ECOC) is a well-known class of decomposition schemes for multi-class classification. It allows representing any multiclass classification problem as a set of binary classification problems. Due to code redundancy ECOC schemes can significantly improve generalization performance on multi-class classification problems. However, they can face a computational complexity problem when the number of classes is large. In this paper we address the computational-complexity problem of the decomposition schemes. We study a particular class of minimally-sized ECOC decomposition schemes, namely the class of minimally-sized balanced decomposition schemes (MBDSs) [14].We show thatMBDSs do not face a computational-complexity problem for large number of classes. However we also show that MBDSs cannot correct the classification errors of the binary classifiers in MBDS ensembles. Therefore we propose voting with MBDS ensembles (VMBDSs).We show that the generalization performance of the VMBDSs ensembles improves with the number of MBDS classifiers. However this number can become large and thus the VMBDSs ensembles can have a computational-complexity problem as well. Fortunately our experiments show that VMBDSs are comparable with ECOC ensembles and can outperform one-against-all ensembles using only a small number of MBDS ensembles.

Published
2011

21. Reuniting the body of Christ

Author

Moed, M. de, Frederiks, M. T. (Thesis Advisor), Moed, M. de, and Frederiks, M. T. (Thesis Advisor)

Abstract

How do the Protestant brothers in Taizé reconcile with the Catholic faith of their brothers? What is the influence of the Taizé community on the ecumenical movement?

Published
2011

25. Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders

Author

Symen Ligthart, Ahmad Vaez, Urmo Võsa, Maria G. Stathopoulou, Paul S. de Vries, Bram P. Prins, Peter J. Van der Most, Toshiko Tanaka, Elnaz Naderi, Lynda M. Rose, Ying Wu, Robert Karlsson, Maja Barbalic, Honghuang Lin, René Pool, Gu Zhu, Aurélien Macé, Carlo Sidore, Stella Trompet, Massimo Mangino, Maria Sabater-Lleal, John P. Kemp, Ali Abbasi, Tim Kacprowski, Niek Verweij, Albert V. Smith, Tao Huang, Carola Marzi, Mary F. Feitosa, Kurt K. Lohman, Marcus E. Kleber, Yuri Milaneschi, Christian Mueller, Mahmudul Huq, Efthymia Vlachopoulou, Leo-Pekka Lyytikäinen, Christopher Oldmeadow, Joris Deelen, Markus Perola, Jing Hua Zhao, Bjarke Feenstra, Marzyeh Amini, Jari Lahti, Katharina E. Schraut, Myriam Fornage, Bhoom Suktitipat, Wei-Min Chen, Xiaohui Li, Teresa Nutile, Giovanni Malerba, Jian’an Luan, Tom Bak, Nicholas Schork, Fabiola Del Greco M., Elisabeth Thiering, Anubha Mahajan, Riccardo E. Marioni, Evelin Mihailov, Joel Eriksson, Ayse Bilge Ozel, Weihua Zhang, Maria Nethander, Yu-Ching Cheng, Stella Aslibekyan, Wei Ang, Ilaria Gandin, Loïc Yengo, Laura Portas, Charles Kooperberg, Edith Hofer, Kumar B. Rajan, Claudia Schurmann, Wouter den Hollander, Tarunveer S. Ahluwalia, Jing Zhao, Harmen H.M. Draisma, Ian Ford, Nicholas Timpson, Alexander Teumer, Hongyan Huang, Simone Wahl, YongMei Liu, Jie Huang, Hae-Won Uh, Frank Geller, Peter K. Joshi, Lisa R. Yanek, Elisabetta Trabetti, Benjamin Lehne, Diego Vozzi, Marie Verbanck, Ginevra Biino, Yasaman Saba, Ingrid Meulenbelt, Jeff R. O’Connell, Markku Laakso, Franco Giulianini, Patrik K.E. Magnusson, Christie M. Ballantyne, Jouke Jan Hottenga, Grant W. Montgomery, Fernando Rivadineira, Rico Rueedi, Maristella Steri, Karl-Heinz Herzig, David J. Stott, Cristina Menni, Mattias Frånberg, Beate St. Pourcain, Stephan B. Felix, Tune H. Pers, Stephan J.L. Bakker, Peter Kraft, Annette Peters, Dhananjay Vaidya, Graciela Delgado, Johannes H. Smit, Vera Großmann, Juha Sinisalo, Ilkka Seppälä, Stephen R. Williams, Elizabeth G. Holliday, Matthijs Moed, Claudia Langenberg, Katri Räikkönen, Jingzhong Ding, Harry Campbell, Michele M. Sale, Yii-Der I. Chen, Alan L. James, Daniela Ruggiero, Nicole Soranzo, Catharina A. Hartman, Erin N. Smith, Gerald S. Berenson, Christian Fuchsberger, Dena Hernandez, Carla M.T. Tiesler, Vilmantas Giedraitis, David Liewald, Krista Fischer, Dan Mellström, Anders Larsson, Yunmei Wang, William R. Scott, Matthias Lorentzon, John Beilby, Kathleen A. Ryan, Craig E. Pennell, Dragana Vuckovic, Beverly Balkau, Maria Pina Concas, Reinhold Schmidt, Carlos F. Mendes de Leon, Erwin P. Bottinger, Margreet Kloppenburg, Lavinia Paternoster, Michael Boehnke, A.W. Musk, Gonneke Willemsen, David M. Evans, Pamela A.F. Madden, Mika Kähönen, Zoltán Kutalik, Magdalena Zoledziewska, Ville Karhunen, Stephen B. Kritchevsky, Naveed Sattar, Genevieve Lachance, Robert Clarke, Tamara B. Harris, Olli T. Raitakari, John R. Attia, Diana van Heemst, Eero Kajantie, Rossella Sorice, Giovanni Gambaro, Robert A. Scott, Andrew A. Hicks, Luigi Ferrucci, Marie Standl, Cecilia M. Lindgren, John M. Starr, Magnus Karlsson, Lars Lind, Jun Z. Li, John C. Chambers, Trevor A. Mori, Eco J.C.N. de Geus, Andrew C. Heath, Nicholas G. Martin, Juha Auvinen, Brendan M. Buckley, Anton J.M. de Craen, Melanie Waldenberger, Konstantin Strauch, Thomas Meitinger, Rodney J. Scott, Mark McEvoy, Marian Beekman, Cristina Bombieri, Paul M. Ridker, Karen L. Mohlke, Nancy L. Pedersen, Alanna C. Morrison, Dorret I. Boomsma, John B. Whitfield, David P. Strachan, Albert Hofman, Peter Vollenweider, Francesco Cucca, Marjo-Riitta Jarvelin, J. Wouter Jukema, Tim D. Spector, Anders Hamsten, Tanja Zeller, André G. Uitterlinden, Matthias Nauck, Vilmundur Gudnason, Lu Qi, Harald Grallert, Ingrid B. Borecki, Jerome I. Rotter, Winfried März, Philipp S. Wild, Marja-Liisa Lokki, Michael Boyle, Veikko Salomaa, Mads Melbye, Johan G. Eriksson, James F. Wilson, Brenda W.J.H. Penninx, Diane M. Becker, Bradford B. Worrall, Greg Gibson, Ronald M. Krauss, Marina Ciullo, Gianluigi Zaza, Nicholas J. Wareham, Albertine J. Oldehinkel, Lyle J. Palmer, Sarah S. Murray, Peter P. Pramstaller, Stefania Bandinelli, Joachim Heinrich, Erik Ingelsson, Ian J. Deary, Reedik Mägi, Liesbeth Vandenput, Pim van der Harst, Karl C. Desch, Jaspal S. Kooner, Claes Ohlsson, Caroline Hayward, Terho Lehtimäki, Alan R. Shuldiner, Donna K. Arnett, Lawrence J. Beilin, Antonietta Robino, Philippe Froguel, Mario Pirastu, Tine Jess, Wolfgang Koenig, Ruth J.F. Loos, Denis A. Evans, Helena Schmidt, George Davey Smith, P. Eline Slagboom, Gudny Eiriksdottir, Andrew P. Morris, Bruce M. Psaty, Russell P. Tracy, Ilja M. Nolte, Eric Boerwinkle, Sophie Visvikis-Siest, Alex P. Reiner, Myron Gross, Joshua C. Bis, Lude Franke, Oscar H. Franco, Emelia J. Benjamin, Daniel I. Chasman, Josée Dupuis, Harold Snieder, Abbas Dehghan, Behrooz Z. Alizadeh, H. Marike Boezen, Gerjan Navis, Marianne Rots, Morris Swertz, Bruce H.R. Wolffenbuttel, Cisca Wijmenga, Emelia Benjamin, Tarunveer Singh Ahluwalia, James Meigs, Russell Tracy, Josh Bis, Nathan Pankratz, Alex Rainer, James G. Wilson, Josee Dupuis, Bram Prins, Urmo Vaso, Maria Stathopoulou, Terho Lehtimaki, Yalda Jamshidi, Sophie Siest, Andre G. Uitterlinden, Mohammadreza Abdollahi, Renate Schnabel, Ursula M. Schick, Aldi Kraja, Yi-Hsiang Hsu, Daniel S. Tylee, Alyson Zwicker, Rudolf Uher, George Davey-Smith, Andrew Hicks, Cornelia M. van Duijn, Cavin Ward-Caviness, J. Rotter, Ken Rice, Leslie Lange, Eco de Geus, Kari Matti Makela, David Stacey, Johan Eriksson, Tim M. Frayling, Eline P. Slagboom, Erasmus University Medical Center [Rotterdam] (Erasmus MC), University Medical Center Groningen [Groningen] (UMCG), University of Isfahan, University of Tartu, Interactions Gène-Environnement en Physiopathologie Cardio-Vasculaire (IGE-PCV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), The University of Texas Health Science Center at Houston (UTHealth), National Institute on Aging [Bethesda, USA] (NIA), National Institutes of Health [Bethesda] (NIH), Brigham and Women's Hospital [Boston], University of North Carolina [Chapel Hill] (UNC), University of North Carolina System (UNC), Department of Medical Epidemiology and Biostatistics (MEB), Karolinska Institutet [Stockholm], University of Split, Boston University School of Medicine (BUSM), Boston University [Boston] (BU), Process & Energy Laboratory, Delft University of Technology (TU Delft), Grand Lyon : communauté urbaine de Lyon, Interuniversity Cardiology Institute Netherlands, Department of Twin Research and Genetic Epidemiology, King's College London, London, Huazhong University of Science and Technology [Wuhan] (HUST), Division of Statistical Genomics, Washington University School of Medicine, Department of Psychiatry, VU University Medical Center [Amsterdam], Institut fuer Theoretische Physik (Institut fuer Theoretische Physik), Universität Heidelberg [Heidelberg] = Heidelberg University, Molecular Epidemiology, MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke's Hospital, Mahidol University [Bangkok], Northwest A and F University, Laboratoire d'Optimisation des Systèmes Industriels (LOSI), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS), Institute of Genetics and Biophysics, CNR, Naples, Università degli studi di Verona = University of Verona (UNIVR), Department of Molecular Medicine [Scripps Research Institute], The Scripps Research Institute [La Jolla, San Diego], Department of Physics, Indian Institute of Technology Kanpur (IIT Kanpur), Deptartment of Medical Biochemistry and Microbiology, Uppsala University, Department of Electrical and Computer Engineering [Waterloo] (ECE), University of Waterloo [Waterloo], University of Maryland School of Medicine, University of Maryland System, Institut National de l'Environnement Industriel et des Risques (INERIS), Institute of Pop. Genetics, CNR, Sassari, Interfaculty Institute for Genetics and Functional Genomics, Universität Greifswald - University of Greifswald, IT University of Copenhagen (ITU), Robertson Centre for Biostatistics, University of Glasgow, Centre for Causal Analyses in Translational Epidemiology, University of Bristol [Bristol]-Medical Research Council, King‘s College London, Jinan University [Guangzhou], Institute of Oceanology [China], School Medicine, University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE)-Pennsylvania Commonwealth System of Higher Education (PCSHE), General Internal Medicine, Johns Hopkins School of Medicine, Johns Hopkins University School of Medicine [Baltimore], Shardna life science Pula Cagliari, Section Molecular Epidemiology, Leiden University Medical Center (LUMC), Department of Medicine, University of Eastern Finland-Kuopio University Hospital, Medstar Research Institute, Department of Cardiology, Ernst-Moritz-Arndt University, Center for Biological Sequence Analysis [Lyngby], Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Department of Internal Medicine, University of Groningen and University Medical Center Groningen, Department of Epidemiology, Harvard School of Public Health, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Metacohorts Consortium, INEOS Technologies (SWITZERLAND), MRC Epidemiology Unit, University of Cambridge [UK] (CAM)-Institute of Metabolic Science, University of Edinburgh, School of Population Health [Crawley, Western Australia], The University of Western Australia (UWA), Institute of Genetics and Biophysics, National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), The Scripps Translational Science Institute and Scripps Health, Tulane Center for Cardiovascular Health, Tulane University Health Sciences Center, Centre for Population Health Sciences, Genomic Research Laboratory, Service of Infectious Disease, Hôpitaux Universitaires de Genève (HUG), Infectious diseases division, Department of internal medicine, Washington University in Saint Louis (WUSTL), Luleå University of Technology (LUT), Recherche en épidémiologie et biostatistique, Université Paris-Sud - Paris 11 (UP11)-Institut National de la Santé et de la Recherche Médicale (INSERM), Austrian Institute of Technology [Vienna] (AIT), Icahn School of Medicine at Mount Sinai [New York] (MSSM), Department of Rheumatology and Clinical Epidemiology, Leiden University Medical Center (LUMC), Department of Rheumatology and Clinical Epidemiology [Leiden University Medical Center] (LUMC), Leiden University Medical Center (LUMC), Universiteit Leiden-Universiteit Leiden-Leiden University Medical Center (LUMC), Universiteit Leiden-Universiteit Leiden, Department of Biostatistics and Center for Statistical Genetics, University of Michigan [Ann Arbor], University of Michigan System-University of Michigan System, University of Virginia, Tampere University Hospital, Department of Medical Genetics, Université de Lausanne = University of Lausanne (UNIL), Department of Pathological Biochemistry, Royal Infirmary, Oxford University, University of Oxford, University of Newcastle [Callaghan, Australia] (UoN), Department of neurology, Institute of Metabolic Science, MRC, The Wellcome Trust Centre for Human Genetics [Oxford], Uppsala Universitet [Uppsala], QIMR Berghofer Medical Research Institute, Institute of Genetic Epidemiology [Neuherberg, Germany], Institute of Human Genetics, Helmholtz Zentrum München = German Research Center for Environmental Health, Schizophrenia Research Institute [Sydney], Department of Genetics, University of North Carolina System (UNC)-University of North Carolina System (UNC), Vrije Universiteit Brussel (VUB), Population Health Sciences and Education, St George's University of London, Centre Hospitalier Universitaire Vaudois [Lausanne] (CHUV), Institute of Health Sciences and Biocenter Oulu, University of Oulu, Medizinische Klinik und Poliklinik, Johannes Gutenberg - Universität Mainz = Johannes Gutenberg University (JGU), Institute of Clinical Chemistry and Laboratory Medicine, Icelandic Heart Association, Heart Preventive Clinic and Research Institute, Departments of Epidemiology and Nutrition, Institute of Epidemiology [Neuherberg] (EPI), Medical University Graz, Transplantation Laboratory [Helsinki], Haartman Institute [Helsinki], Faculty of Medecine [Helsinki], Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Faculty of Medecine [Helsinki], Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Department of Chronic Disease Prevention, National Institute for Health and Welfare [Helsinki], Dept. of Epidemiology Research, Statens Serum Institut [Copenhagen], CLinical Psychology, Genetics and Pathology, Geriatric Rehabilitation Unit, Azienda Sanitaria Firenze, Center For Narcolepsy, Stanford University, Centre for Bone and Arthritis Research, University of Gothenburg (GU)-Institute of Medicine, MRC Human Gentics Unit, Inst Genet and Mol Med, Western General Hospital, Edinburgh, University of Maryland School of Medicine [Baltimore, MD, USA], Génétique des maladies multifactorielles (GMM), Université de Lille, Droit et Santé-Centre National de la Recherche Scientifique (CNRS), Department of Physics [Stockholm], Stockholm University, University of Bristol [Bristol], Universiteit Leiden, Department of Epidemiology, University of Washington, University of Washington [Seattle], Department of Epidemiology [Rotterdam], University of Groningen [Groningen], Dutch Initiative on Crohn and Colitis (ICC), Icelandic Heart Association [Kopavogur, Iceland] (IHA), Department of Physiology and Biophysics [Jackson, MS, USA], University of Southern Mississippi (USM), Human Genetics Branch, National Institutes of Health [Bethesda] (NIH)-National Institute of Mental Health (NIMH), Faculty of Medicine and Life Sciences [Tampere], University of Tampere [Finland], German Center for Cardiovascular Research (DZHK), Berlin Institute of Health (BIH), MRC Centre for Neuropsychiatric Genetics and Genomics, Medical Research Council-Cardiff University, Department of Social Medicine, School of Medicine [Los Angeles], University of California [Los Angeles] (UCLA), University of California (UC)-University of California (UC), Department of Medicine [Aurora, CO, USA], University of Colorado [Denver], Institute for Molecular Medicine Finland [Helsinki] (FIMM), Helsinki Institute of Life Science (HiLIFE), Mathematical Institute [Oxford] (MI), Institute of Psychiatry, Psychology & Neuroscience, King's College London, LifeLines Cohort Study, CHARGE Inflammation Working Group, Ligthart, S., Vaez, A., Vosa, U., Stathopoulou, M. G., de Vries, P. S., Prins, B. P., Van der Most, P. J., Tanaka, T., Naderi, E., Rose, L. M., Wu, Y., Karlsson, R., Barbalic, M., Lin, H., Pool, R., Zhu, G., Mace, A., Sidore, C., Trompet, S., Mangino, M., Sabater-Lleal, M., Kemp, J. P., Abbasi, A., Kacprowski, T., Verweij, N., Smith, A. V., Huang, T., Marzi, C., Feitosa, M. F., Lohman, K. K., Kleber, M. E., Milaneschi, Y., Mueller, C., Huq, M., Vlachopoulou, E., Lyytikainen, L. -P., Oldmeadow, C., Deelen, J., Perola, M., Zhao, J. H., Feenstra, B., Alizadeh, B. Z., Boezen, H. M., Franke, L., van der Harst, P., Navis, G., Rots, M., Snieder, H., Swertz, M., Wolffenbuttel, B. H. R., Wijmenga, C., Amini, M., Benjamin, E., Chasman, D. I., Dehghan, A., Ahluwalia, T. S., Meigs, J., Tracy, R., Bis, J., Eiriksdottir, G., Pankratz, N., Gross, M., Rainer, A., Wilson, J. G., Psaty, B. M., Dupuis, J., Prins, B., Vaso, U., Stathopoulou, M., Lehtimaki, T., Koenig, W., Jamshidi, Y., Siest, S., Uitterlinden, A. G., Abdollahi, M., Schnabel, R., Schick, U. M., Nolte, I. M., Kraja, A., Hsu, Y. -H., Tylee, D. S., Zwicker, A., Uher, R., Davey-Smith, G., Morrison, A. C., Hicks, A., van Duijn, C. M., Ward-Caviness, C., Boerwinkle, E., Rotter, J., Rice, K., Lange, L., de Geus, E., Morris, A. P., Makela, K. M., Stacey, D., Eriksson, J., Frayling, T. M., Slagboom, E. P., Lahti, J., Schraut, K. E., Fornage, M., Suktitipat, B., Chen, W. -M., Li, X., Nutile, T., Malerba, G., Luan, J., Bak, T., Schork, N., Del Greco, M. F., Thiering, E., Mahajan, A., Marioni, R. E., Mihailov, E., Ozel, A. B., Zhang, W., Nethander, M., Cheng, Y. -C., Aslibekyan, S., Ang, W., Gandin, I., Yengo, L., Portas, L., Kooperberg, C., Hofer, E., Rajan, K. B., Schurmann, C., den Hollander, W., Zhao, J., Draisma, H. H. M., Ford, I., Timpson, N., Teumer, A., Huang, H., Wahl, S., Liu, Y., Huang, J., Uh, H. -W., Geller, F., Joshi, P. K., Yanek, L. R., Trabetti, E., Lehne, B., Vozzi, D., Verbanck, M., Biino, G., Saba, Y., Meulenbelt, I., O'Connell, J. R., Laakso, M., Giulianini, F., Magnusson, P. K. E., Ballantyne, C. M., Hottenga, J. J., Montgomery, G. W., Rivadineira, F., Rueedi, R., Steri, M., Herzig, K. -H., Stott, D. J., Menni, C., Franberg, M., S, t. Pourcain B., Felix, S. B., Pers, T. H., Bakker, S. J. L., Kraft, P., Peters, A., Vaidya, D., Delgado, G., Smit, J. H., Grossmann, V., Sinisalo, J., Seppala, I., Williams, S. R., Holliday, E. G., Moed, M., Langenberg, C., Raikkonen, K., Ding, J., Campbell, H., Sale, M. M., Chen, Y. -D. I., James, A. L., Ruggiero, D., Soranzo, N., Hartman, C. A., Smith, E. N., Berenson, G. S., Fuchsberger, C., Hernandez, D., Tiesler, C. M. T., Giedraitis, V., Liewald, D., Fischer, K., Mellstrom, D., Larsson, A., Wang, Y., Scott, W. R., Lorentzon, M., Beilby, J., Ryan, K. A., Pennell, C. E., Vuckovic, D., Balkau, B., Concas, M. P., Schmidt, R., Mendes de Leon, C. F., Bottinger, E. P., Kloppenburg, M., Paternoster, L., Boehnke, M., Musk, A. W., Willemsen, G., Evans, D. M., Madden, P. A. F., Kahonen, M., Kutalik, Z., Zoledziewska, M., Karhunen, V., Kritchevsky, S. B., Sattar, N., Lachance, G., Clarke, R., Harris, T. B., Raitakari, O. T., Attia, J. R., van Heemst, D., Kajantie, E., Sorice, R., Gambaro, G., Scott, R. A., Hicks, A. A., Ferrucci, L., Standl, M., Lindgren, C. M., Starr, J. M., Karlsson, M., Lind, L., Li, J. Z., Chambers, J. C., Mori, T. A., de Geus, E. J. C. N., Heath, A. C., Martin, N. G., Auvinen, J., Buckley, B. M., de Craen, A. J. M., Waldenberger, M., Strauch, K., Meitinger, T., Scott, R. J., Mcevoy, M., Beekman, M., Bombieri, C., Ridker, P. M., Mohlke, K. L., Pedersen, N. L., Boomsma, D. I., Whitfield, J. B., Strachan, D. P., Hofman, A., Vollenweider, P., Cucca, F., Jarvelin, M. -R., Jukema, J. W., Spector, T. D., Hamsten, A., Zeller, T., Nauck, M., Gudnason, V., Qi, L., Grallert, H., Borecki, I. B., Rotter, J. I., Marz, W., Wild, P. S., Lokki, M. -L., Boyle, M., Salomaa, V., Melbye, M., Eriksson, J. G., Wilson, J. F., Penninx, B. W. J. H., Becker, D. M., Worrall, B. B., Gibson, G., Krauss, R. M., Ciullo, M., Zaza, G., Wareham, N. J., Oldehinkel, A. J., Palmer, L. J., Murray, S. S., Pramstaller, P. P., Bandinelli, S., Heinrich, J., Ingelsson, E., Deary, I. J., Magi, R., Vandenput, L., Desch, K. C., Kooner, J. S., Ohlsson, C., Hayward, C., Shuldiner, A. R., Arnett, D. K., Beilin, L. J., Robino, A., Froguel, P., Pirastu, M., Jess, T., Loos, R. J. F., Evans, D. A., Schmidt, H., Slagboom, P. E., Tracy, R. P., Visvikis-Siest, S., Reiner, A. P., Bis, J. C., Franco, O. H., Benjamin, E. J., AGEM - Amsterdam Gastroenterology Endocrinology Metabolism, Graduate School, Epidemiology, Internal Medicine, Groningen Institute for Organ Transplantation (GIOT), Lifestyle Medicine (LM), Groningen Kidney Center (GKC), Interdisciplinary Centre Psychopathology and Emotion regulation (ICPE), Cardiovascular Centre (CVC), Life Course Epidemiology (LCE), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Stem Cell Aging Leukemia and Lymphoma (SALL), Real World Studies in PharmacoEpidemiology, -Genetics, -Economics and -Therapy (PEGET), VU University medical center, Psychiatry, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, APH - Mental Health, APH - Methodology, APH - Digital Health, Biological Psychology, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases, Universität Heidelberg [Heidelberg], University of Verona (UNIVR), Department of Molecular and Experimental Medicine, The Scripps Research Institute, The Scripps Research Institute, Université Grenoble Alpes - UFR Sciences de l'Homme et de la Société (UGA UFR SHS), Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), IT University of Copenhagen, Technical University of Denmark [Lyngby] (DTU), Consiglio Nazionale delle Ricerche (CNR), University of Virginia [Charlottesville], Université de Lausanne (UNIL), University of Oxford [Oxford], University of Newcastle [Australia] (UoN), Centre d'économie industrielle i3 (CERNA i3), Centre National de la Recherche Scientifique (CNRS)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Helmholtz-Zentrum München (HZM), Laboratoire Interuniversitaire des Systèmes Atmosphériques (LISA (UMR_7583)), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris Diderot - Paris 7 (UPD7)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS), Universitätsmedizin der Johannes-Gutenberg Universität Mainz, University of Helsinki-University of Helsinki-Faculty of Medecine [Helsinki], University of Helsinki-University of Helsinki, Cardiff University-Medical Research Council, University of California-University of California, and DE CARVALHO, Philippe

Subjects
0301 basic medicine, Male, Netherlands Twin Register (NTR), Bipolar Disorder, LD SCORE REGRESSION, [SDV]Life Sciences [q-bio], Genome-wide association study, [SDV.GEN] Life Sciences [q-bio]/Genetics, Body Mass Index, inflammatory disorder, 80 and over, WIDE ASSOCIATION, EPIDEMIOLOGY, ta318, International HapMap Project, Child, Genetics (clinical), 2. Zero hunger, Genetics, Genetics & Heredity, Aged, 80 and over, [SDV.MHEP] Life Sciences [q-bio]/Human health and pathology, C-reactive proteingenome-wide association studyinflammationMendelian randomizationinflammatory disordersDEPICTcoronary artery diseaseschizophreniasystem biology, system biology, DEPICT, Mendelian Randomization Analysis, 11 Medical And Health Sciences, Middle Aged, C-reactive protein, coronary artery disease, genome-wide association study, inflammation, inflammatory disorders, Mendelian randomization, schizophrenia, Adolescent, Adult, Aged, Biomarkers, C-Reactive Protein, Female, Genetic Loci, Genome-Wide Association Study, Humans, Inflammation, Liver, Metabolic Networks and Pathways, Schizophrenia, Young Adult, 3. Good health, [SDV] Life Sciences [q-bio], Medical genetics, Biomarker (medicine), Life Sciences & Biomedicine, Human, medicine.medical_specialty, CHARGE Inflammation Working Group, Biology, IMMUNITY, ta3111, Article, 03 medical and health sciences, SDG 3 - Good Health and Well-being, medicine, CORONARY-HEART-DISEASE, Mendelian Randomization Analysi, 1000 Genomes Project, METAANALYSIS, Genetic association, [SDV.GEN]Life Sciences [q-bio]/Genetics, Science & Technology, ta1184, Metabolic Networks and Pathway, Biomarker, INSTRUMENTS, 06 Biological Sciences, 030104 developmental biology, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, LifeLines Cohort Study
Abstract

International audience; C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5 × 10-8). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences.Copyright © 2018 American Society of Human Genetics. All rights reserved.

Published
2018

26. SARS-CoV-2 Infection-Blocking Immunity Post Natural Infection: The Role of Vitamin D.

Author

Abu Fanne R, Moed M, Kedem A, Lidawi G, Maraga E, Mohsen F, Roguin A, and Meisel SR

Abstract

Objective and Aim: The extent of the protection against SARS-CoV-2 conferred by natural infection is unclear. Vitamin D may have a role in the interplay between SARS-CoV-2 infection and the evolving acquired immunity against it. We tested the correlation between baseline 25(OH) D content and both the reinfection rate and the anti-spike protein antibody titer following COVID-19 infection. Methods A retrospective observational survey that included a large convalescent COVID-19 population of subjects insured by the Leumit HMO was recorded between 1 February 2020 and 30 January 2022. Inclusion criteria required at least one available 25(OH)D level prior to enlistment. The association between 25(OH)D levels, the rate of breakthrough infection, and the anti-spike protein antibody titer was evaluated. Results A total of 10,132 COVID-19 convalescent subjects were included, of whom 322 (3.3%) sustained reinfection within a one-year follow-up. In the first 8 months after recovery, the reinfected patients were characterized by a higher incidence of low 25(OH)D levels (<30 ng/mL, 92% vs. 84.8%, p < 0.05), while during the following three months, the incidence of low 25(OH)D levels was non-significantly higher among PCR-negative convalescent subjects compared to those reinfected (86% vs. 81.7, p = 0.15). By multivariate analysis, age > 44 years (OR-0.39, 95% CI: 0.173-0.87, p = 0.02) and anti-spike protein antibody titer > 50 AU/mL (0.49, 95% CI: 0.25-0.96, p = 0.04) were inversely related to reinfection. No consistent correlation with vitamin D levels was observed among the 3351 available anti-spike protein antibody titers of convalescent subjects. However, the median anti-spike protein antibody titers tended to increase over time in the vitamin D-deficient group. Conclusion Higher pre-infection 25(OH)D level correlated with protective COVID-19 immunity during the first 8 months following COVID-19 infection, which could not be explained by anti-spike protein antibody titers. This effect dissipated beyond this period, demonstrating a biphasic 25(OH)D association that warrants future studies.

Published
2023
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27. Correlation between Baseline 25(OH) Vitamin D Levels and Both Humoral Immunity and Breakthrough Infection Post-COVID-19 Vaccination.

Author

Abu Fanne R, Lidawi G, Maraga E, Moed M, Roguin A, and Meisel SR

Abstract

Objective: Vaccines against COVID-19 induce specific antibodies whose titer is perceived as a reliable correlate of protection. Vitamin D confers complex regulatory effects on the innate and adaptive immunity. In this study, we explored a plausible impact of baseline vitamin D content on achieved immunity following COVID-19 vaccination. Methods: A retrospective observational study comprising 73,254 naïve subjects insured by the Leumit Health Service HMO, who were vaccinated between 1 February 2020 and 30 January 2022, with one available vitamin D level prior to vaccination, was performed. The association between 25(OH) vitamin D levels, SARS-CoV-2 antibody titer, and post-vaccination PCR results were evaluated. Results: Of the study population, 5026 (6.9%) tested positive for COVID-19. The proportion of low 25(OH)D levels (<30 ng/mL) was significantly higher in the PCR-positive group (81.5% vs. 79%, p < 0.001). Multivariate analysis showed a higher incidence of breakthrough infection among non-smokers [1.37 (95% CI 1.22−1.54, p < 0.001)] and lower incidences among subjects with sufficient 25(OH)D levels (>30 ng/mL) [0.87 (95% CI 0.79−0.95, p—0.004)], hyperlipidemia [0.84 (95% CI 0.76−0.93, p < 0.001], depression [OR-0.87 (95% CI: 0.79−0.96, p < 0.005], socio-economic status >10 [0.67 (95% CI 0.61−0.73, p < 0.001)], and age >44 years. SARS-CoV-2 antibody titers were available in 3659 vaccinated individuals. The prevalence of antibody titers (<50 AU) among PCR-positive subjects was 42% compared to 28% among PCR-negative subjects (p < 0.001). Baseline 25(OH)D levels showed an inverse relation to total antibody titers. However, no association was found with an antibody titer <50 AU/mL fraction. Conclusion Baseline 25(OH)D levels correlated with the vaccination-associated protective COVID-19 immunity. Antibody titers <50 AU/mL were significantly linked to breakthrough infection but did not correlate with 25(OH)D levels., Competing Interests: The authors declare no potential conflicts of interest with respect to financial or non-financial competing interests in the authorship and/or publication of this article.

Published
2022
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28. Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders.

Author

Ligthart S, Vaez A, Võsa U, Stathopoulou MG, de Vries PS, Prins BP, Van der Most PJ, Tanaka T, Naderi E, Rose LM, Wu Y, Karlsson R, Barbalic M, Lin H, Pool R, Zhu G, Macé A, Sidore C, Trompet S, Mangino M, Sabater-Lleal M, Kemp JP, Abbasi A, Kacprowski T, Verweij N, Smith AV, Huang T, Marzi C, Feitosa MF, Lohman KK, Kleber ME, Milaneschi Y, Mueller C, Huq M, Vlachopoulou E, Lyytikäinen LP, Oldmeadow C, Deelen J, Perola M, Zhao JH, Feenstra B, Amini M, Lahti J, Schraut KE, Fornage M, Suktitipat B, Chen WM, Li X, Nutile T, Malerba G, Luan J, Bak T, Schork N, Del Greco M F, Thiering E, Mahajan A, Marioni RE, Mihailov E, Eriksson J, Ozel AB, Zhang W, Nethander M, Cheng YC, Aslibekyan S, Ang W, Gandin I, Yengo L, Portas L, Kooperberg C, Hofer E, Rajan KB, Schurmann C, den Hollander W, Ahluwalia TS, Zhao J, Draisma HHM, Ford I, Timpson N, Teumer A, Huang H, Wahl S, Liu Y, Huang J, Uh HW, Geller F, Joshi PK, Yanek LR, Trabetti E, Lehne B, Vozzi D, Verbanck M, Biino G, Saba Y, Meulenbelt I, O'Connell JR, Laakso M, Giulianini F, Magnusson PKE, Ballantyne CM, Hottenga JJ, Montgomery GW, Rivadineira F, Rueedi R, Steri M, Herzig KH, Stott DJ, Menni C, Frånberg M, St Pourcain B, Felix SB, Pers TH, Bakker SJL, Kraft P, Peters A, Vaidya D, Delgado G, Smit JH, Großmann V, Sinisalo J, Seppälä I, Williams SR, Holliday EG, Moed M, Langenberg C, Räikkönen K, Ding J, Campbell H, Sale MM, Chen YI, James AL, Ruggiero D, Soranzo N, Hartman CA, Smith EN, Berenson GS, Fuchsberger C, Hernandez D, Tiesler CMT, Giedraitis V, Liewald D, Fischer K, Mellström D, Larsson A, Wang Y, Scott WR, Lorentzon M, Beilby J, Ryan KA, Pennell CE, Vuckovic D, Balkau B, Concas MP, Schmidt R, Mendes de Leon CF, Bottinger EP, Kloppenburg M, Paternoster L, Boehnke M, Musk AW, Willemsen G, Evans DM, Madden PAF, Kähönen M, Kutalik Z, Zoledziewska M, Karhunen V, Kritchevsky SB, Sattar N, Lachance G, Clarke R, Harris TB, Raitakari OT, Attia JR, van Heemst D, Kajantie E, Sorice R, Gambaro G, Scott RA, Hicks AA, Ferrucci L, Standl M, Lindgren CM, Starr JM, Karlsson M, Lind L, Li JZ, Chambers JC, Mori TA, de Geus EJCN, Heath AC, Martin NG, Auvinen J, Buckley BM, de Craen AJM, Waldenberger M, Strauch K, Meitinger T, Scott RJ, McEvoy M, Beekman M, Bombieri C, Ridker PM, Mohlke KL, Pedersen NL, Morrison AC, Boomsma DI, Whitfield JB, Strachan DP, Hofman A, Vollenweider P, Cucca F, Jarvelin MR, Jukema JW, Spector TD, Hamsten A, Zeller T, Uitterlinden AG, Nauck M, Gudnason V, Qi L, Grallert H, Borecki IB, Rotter JI, März W, Wild PS, Lokki ML, Boyle M, Salomaa V, Melbye M, Eriksson JG, Wilson JF, Penninx BWJH, Becker DM, Worrall BB, Gibson G, Krauss RM, Ciullo M, Zaza G, Wareham NJ, Oldehinkel AJ, Palmer LJ, Murray SS, Pramstaller PP, Bandinelli S, Heinrich J, Ingelsson E, Deary IJ, Mägi R, Vandenput L, van der Harst P, Desch KC, Kooner JS, Ohlsson C, Hayward C, Lehtimäki T, Shuldiner AR, Arnett DK, Beilin LJ, Robino A, Froguel P, Pirastu M, Jess T, Koenig W, Loos RJF, Evans DA, Schmidt H, Smith GD, Slagboom PE, Eiriksdottir G, Morris AP, Psaty BM, Tracy RP, Nolte IM, Boerwinkle E, Visvikis-Siest S, Reiner AP, Gross M, Bis JC, Franke L, Franco OH, Benjamin EJ, Chasman DI, Dupuis J, Snieder H, Dehghan A, and Alizadeh BZ

Subjects
Adolescent, Adult, Aged, Aged, 80 and over, Biomarkers metabolism, Bipolar Disorder genetics, Bipolar Disorder metabolism, Body Mass Index, C-Reactive Protein genetics, Child, Female, Genome-Wide Association Study methods, Humans, Inflammation metabolism, Liver metabolism, Liver pathology, Male, Mendelian Randomization Analysis methods, Middle Aged, Schizophrenia genetics, Schizophrenia metabolism, Young Adult, Genetic Loci genetics, Inflammation genetics, Metabolic Networks and Pathways genetics
Abstract

C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5 × 10 -8 ). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences., (Copyright © 2018 American Society of Human Genetics. All rights reserved.)

Published
2018
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29. Genome-wide analyses identify a role for SLC17A4 and AADAT in thyroid hormone regulation.

Author

Teumer A, Chaker L, Groeneweg S, Li Y, Di Munno C, Barbieri C, Schultheiss UT, Traglia M, Ahluwalia TS, Akiyama M, Appel EVR, Arking DE, Arnold A, Astrup A, Beekman M, Beilby JP, Bekaert S, Boerwinkle E, Brown SJ, De Buyzere M, Campbell PJ, Ceresini G, Cerqueira C, Cucca F, Deary IJ, Deelen J, Eckardt KU, Ekici AB, Eriksson JG, Ferrrucci L, Fiers T, Fiorillo E, Ford I, Fox CS, Fuchsberger C, Galesloot TE, Gieger C, Gögele M, De Grandi A, Grarup N, Greiser KH, Haljas K, Hansen T, Harris SE, van Heemst D, den Heijer M, Hicks AA, den Hollander W, Homuth G, Hui J, Ikram MA, Ittermann T, Jensen RA, Jing J, Jukema JW, Kajantie E, Kamatani Y, Kasbohm E, Kaufman JM, Kiemeney LA, Kloppenburg M, Kronenberg F, Kubo M, Lahti J, Lapauw B, Li S, Liewald DCM, Lim EM, Linneberg A, Marina M, Mascalzoni D, Matsuda K, Medenwald D, Meisinger C, Meulenbelt I, De Meyer T, Meyer Zu Schwabedissen HE, Mikolajczyk R, Moed M, Netea-Maier RT, Nolte IM, Okada Y, Pala M, Pattaro C, Pedersen O, Petersmann A, Porcu E, Postmus I, Pramstaller PP, Psaty BM, Ramos YFM, Rawal R, Redmond P, Richards JB, Rietzschel ER, Rivadeneira F, Roef G, Rotter JI, Sala CF, Schlessinger D, Selvin E, Slagboom PE, Soranzo N, Sørensen TIA, Spector TD, Starr JM, Stott DJ, Taes Y, Taliun D, Tanaka T, Thuesen B, Tiller D, Toniolo D, Uitterlinden AG, Visser WE, Walsh JP, Wilson SG, Wolffenbuttel BHR, Yang Q, Zheng HF, Cappola A, Peeters RP, Naitza S, Völzke H, Sanna S, Köttgen A, Visser TJ, and Medici M

Subjects
2-Aminoadipate Transaminase genetics, Animals, Biological Transport, COS Cells, Chlorocebus aethiops, Genome-Wide Association Study, Humans, Hyperthyroidism genetics, Hyperthyroidism physiopathology, Hypothyroidism genetics, Hypothyroidism physiopathology, Polymorphism, Single Nucleotide, Risk Factors, Sodium-Phosphate Cotransporter Proteins, Type I genetics, Thyroid Gland metabolism, Thyroid Gland physiopathology, Thyroid Hormones metabolism, White People, 2-Aminoadipate Transaminase metabolism, Gene Expression Regulation genetics, Sodium-Phosphate Cotransporter Proteins, Type I metabolism, Thyroid Hormones genetics, Thyrotropin metabolism
Abstract

Thyroid dysfunction is an important public health problem, which affects 10% of the general population and increases the risk of cardiovascular morbidity and mortality. Many aspects of thyroid hormone regulation have only partly been elucidated, including its transport, metabolism, and genetic determinants. Here we report a large meta-analysis of genome-wide association studies for thyroid function and dysfunction, testing 8 million genetic variants in up to 72,167 individuals. One-hundred-and-nine independent genetic variants are associated with these traits. A genetic risk score, calculated to assess their combined effects on clinical end points, shows significant associations with increased risk of both overt (Graves' disease) and subclinical thyroid disease, as well as clinical complications. By functional follow-up on selected signals, we identify a novel thyroid hormone transporter (SLC17A4) and a metabolizing enzyme (AADAT). Together, these results provide new knowledge about thyroid hormone physiology and disease, opening new possibilities for therapeutic targets.

Published
2018
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30. Force and Pressure Control of Soft Robotic Actuators.

Author

Ingicco J, AlSaidi M, Abd M, Ades C, and Engeberg E

Abstract

Soft Robotic Actuators (SRAs) have piqued the interest of researchers in recent years. SRAs are generally constructed of soft elastomers and use air or water as a mean of actuation. Due to the inherent properties of these actuators, they are ideal for HumanRobot Interactions (HRI), exoskeletons for rehabilitation and for grasping delicate objects. Since SRA's are actuated using a fluid, being able to effectively control the rate of actuation, pressure and the force applied is necessary so that the actuator and the object being grasped does not get damaged. This paper aims to evaluate three types of controllers, an open-loop controller, pressure-feedback controller, and a force-feedback controller, all used to control an SRA. A custom test stand was built to hold the SRA and test it with all three controllers. The pressure-feedback controller was set to limit the pressure to 8.9 kPa and the force was limited to 0.147 N in the force-feedback controller. Since the open-loop controller had no feedback, the SRA was actuated at a specified frequency while force and pressure measurements were taken. The force-feedback and the pressure-feedback controllers accurately controlled the actuators and the open loop-controller was able to actuate the SRA reliably.

Published
2018

31. Correction: The complex genetics of gait speed: genome-wide meta-analysis approach.

Author

Ben-Avraham D, Karasik D, Verghese J, Lunetta KL, Smith JA, Eicher JD, Vered R, Deelen J, Arnold AM, Buchman AS, Tanaka T, Faul JD, Nethander M, Fornage M, Adams HH, Matteini AM, Callisaya ML, Smith AV, Yu L, De Jager PL, Evans DA, Gudnason V, Hofman A, Pattie A, Corley J, Launer LJ, Knopman DS, Parimi N, Turner ST, Bandinelli S, Beekman M, Gutman D, Sharvit L, Mooijaart SP, Liewald DC, Houwing-Duistermaat JJ, Ohlsson C, Moed M, Verlinden VJ, Mellström D, van der Geest JN, Karlsson M, Hernandez D, McWhirter R, Liu Y, Thomson R, Tranah GJ, Uitterlinden AG, Weir DR, Zhao W, Starr JM, Johnson AD, Ikram MA, Bennett DA, Cummings SR, Deary IJ, Harris TB, Kardia SLR, Mosley TH, Srikanth VK, Windham BG, Newman AB, Walston JD, Davies G, Evans DS, Slagboom EP, Ferrucci L, Kiel DP, Murabito JM, and Atzmon G

Published
2017
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32. The complex genetics of gait speed: genome-wide meta-analysis approach.

Author

Ben-Avraham D, Karasik D, Verghese J, Lunetta KL, Smith JA, Eicher JD, Vered R, Deelen J, Arnold AM, Buchman AS, Tanaka T, Faul JD, Nethander M, Fornage M, Adams HH, Matteini AM, Callisaya ML, Smith AV, Yu L, De Jager PL, Evans DA, Gudnason V, Hofman A, Pattie A, Corley J, Launer LJ, Knopman DS, Parimi N, Turner ST, Bandinelli S, Beekman M, Gutman D, Sharvit L, Mooijaart SP, Liewald DC, Houwing-Duistermaat JJ, Ohlsson C, Moed M, Verlinden VJ, Mellström D, van der Geest JN, Karlsson M, Hernandez D, McWhirter R, Liu Y, Thomson R, Tranah GJ, Uitterlinden AG, Weir DR, Zhao W, Starr JM, Johnson AD, Ikram MA, Bennett DA, Cummings SR, Deary IJ, Harris TB, Kardia SL, Mosley TH, Srikanth VK, Windham BG, Newman AB, Walston JD, Davies G, Evans DS, Slagboom EP, Ferrucci L, Kiel DP, Murabito JM, and Atzmon G

Subjects
Aged, Aged, 80 and over, Female, Genome-Wide Association Study, Humans, Male, Middle Aged, Phenotype, Quantitative Trait Loci, Receptor-Like Protein Tyrosine Phosphatases, Class 2 genetics, Serine Endopeptidases genetics, Aging genetics, Gait genetics, Polymorphism, Single Nucleotide, Walking Speed genetics
Abstract

Emerging evidence suggests that the basis for variation in late-life mobility is attributable, in part, to genetic factors, which may become increasingly important with age. Our objective was to systematically assess the contribution of genetic variation to gait speed in older individuals. We conducted a meta-analysis of gait speed GWASs in 31,478 older adults from 17 cohorts of the CHARGE consortium, and validated our results in 2,588 older adults from 4 independent studies. We followed our initial discoveries with network and eQTL analysis of candidate signals in tissues. The meta-analysis resulted in a list of 536 suggestive genome wide significant SNPs in or near 69 genes. Further interrogation with Pathway Analysis placed gait speed as a polygenic complex trait in five major networks. Subsequent eQTL analysis revealed several SNPs significantly associated with the expression of PRSS16, WDSUB1 and PTPRT, which in addition to the meta-analysis and pathway suggested that genetic effects on gait speed may occur through synaptic function and neuronal development pathways. No genome-wide significant signals for gait speed were identified from this moderately large sample of older adults, suggesting that more refined physical function phenotypes will be needed to identify the genetic basis of gait speed in aging.

Published
2017
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33. Identification of context-dependent expression quantitative trait loci in whole blood.

Author

Zhernakova DV, Deelen P, Vermaat M, van Iterson M, van Galen M, Arindrarto W, van 't Hof P, Mei H, van Dijk F, Westra HJ, Bonder MJ, van Rooij J, Verkerk M, Jhamai PM, Moed M, Kielbasa SM, Bot J, Nooren I, Pool R, van Dongen J, Hottenga JJ, Stehouwer CD, van der Kallen CJ, Schalkwijk CG, Zhernakova A, Li Y, Tigchelaar EF, de Klein N, Beekman M, Deelen J, van Heemst D, van den Berg LH, Hofman A, Uitterlinden AG, van Greevenbroek MM, Veldink JH, Boomsma DI, van Duijn CM, Wijmenga C, Slagboom PE, Swertz MA, Isaacs A, van Meurs JB, Jansen R, Heijmans BT, 't Hoen PA, and Franke L

Subjects
Cohort Studies, Female, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Humans, Male, Middle Aged, RNA, Messenger genetics, Blood Proteins genetics, Cell Lineage genetics, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide genetics, Quantitative Trait Loci genetics, RNA, Messenger blood, Regulatory Sequences, Nucleic Acid genetics
Abstract

Genetic risk factors often localize to noncoding regions of the genome with unknown effects on disease etiology. Expression quantitative trait loci (eQTLs) help to explain the regulatory mechanisms underlying these genetic associations. Knowledge of the context that determines the nature and strength of eQTLs may help identify cell types relevant to pathophysiology and the regulatory networks underlying disease. Here we generated peripheral blood RNA-seq data from 2,116 unrelated individuals and systematically identified context-dependent eQTLs using a hypothesis-free strategy that does not require previous knowledge of the identity of the modifiers. Of the 23,060 significant cis-regulated genes (false discovery rate (FDR) ≤ 0.05), 2,743 (12%) showed context-dependent eQTL effects. The majority of these effects were influenced by cell type composition. A set of 145 cis-eQTLs depended on type I interferon signaling. Others were modulated by specific transcription factors binding to the eQTL SNPs.

Published
2017
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34. Disease variants alter transcription factor levels and methylation of their binding sites.

Author

Bonder MJ, Luijk R, Zhernakova DV, Moed M, Deelen P, Vermaat M, van Iterson M, van Dijk F, van Galen M, Bot J, Slieker RC, Jhamai PM, Verbiest M, Suchiman HE, Verkerk M, van der Breggen R, van Rooij J, Lakenberg N, Arindrarto W, Kielbasa SM, Jonkers I, van 't Hof P, Nooren I, Beekman M, Deelen J, van Heemst D, Zhernakova A, Tigchelaar EF, Swertz MA, Hofman A, Uitterlinden AG, Pool R, van Dongen J, Hottenga JJ, Stehouwer CD, van der Kallen CJ, Schalkwijk CG, van den Berg LH, van Zwet EW, Mei H, Li Y, Lemire M, Hudson TJ, Slagboom PE, Wijmenga C, Veldink JH, van Greevenbroek MM, van Duijn CM, Boomsma DI, Isaacs A, Jansen R, van Meurs JB, 't Hoen PA, Franke L, and Heijmans BT

Subjects
Binding Sites, Cohort Studies, Female, Genome, Human, Genome-Wide Association Study, Humans, Male, Middle Aged, Phenotype, DNA Methylation, Disease genetics, Gene Expression Regulation, Polymorphism, Single Nucleotide genetics, Quantitative Trait Loci, Transcription Factors metabolism
Abstract

Most disease-associated genetic variants are noncoding, making it challenging to design experiments to understand their functional consequences. Identification of expression quantitative trait loci (eQTLs) has been a powerful approach to infer the downstream effects of disease-associated variants, but most of these variants remain unexplained. The analysis of DNA methylation, a key component of the epigenome, offers highly complementary data on the regulatory potential of genomic regions. Here we show that disease-associated variants have widespread effects on DNA methylation in trans that likely reflect differential occupancy of trans binding sites by cis-regulated transcription factors. Using multiple omics data sets from 3,841 Dutch individuals, we identified 1,907 established trait-associated SNPs that affect the methylation levels of 10,141 different CpG sites in trans (false discovery rate (FDR) < 0.05). These included SNPs that affect both the expression of a nearby transcription factor (such as NFKB1, CTCF and NKX2-3) and methylation of its respective binding site across the genome. Trans methylation QTLs effectively expose the downstream effects of disease-associated variants.

Published
2017
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35. Systematic discovery of complex insertions and deletions in human cancers.

Author

Ye K, Wang J, Jayasinghe R, Lameijer EW, McMichael JF, Ning J, McLellan MD, Xie M, Cao S, Yellapantula V, Huang KL, Scott A, Foltz S, Niu B, Johnson KJ, Moed M, Slagboom PE, Chen F, Wendl MC, and Ding L

Subjects
Cell Line, Tumor, Class Ia Phosphatidylinositol 3-Kinase, DNA Helicases genetics, DNA-Binding Proteins genetics, ErbB Receptors genetics, GATA3 Transcription Factor genetics, High-Throughput Nucleotide Sequencing, Humans, Neoplasm Proteins genetics, Nuclear Proteins genetics, PTEN Phosphohydrolase genetics, Phosphatidylinositol 3-Kinases genetics, Proto-Oncogene Proteins c-kit genetics, Proto-Oncogene Proteins c-met genetics, Transcription Factors genetics, Tumor Suppressor Protein p53 genetics, Von Hippel-Lindau Tumor Suppressor Protein genetics, X-linked Nuclear Protein, Data Mining methods, Genomics methods, INDEL Mutation genetics, Neoplasms genetics
Abstract

Complex insertions and deletions (indels) are formed by simultaneously deleting and inserting DNA fragments of different sizes at a common genomic location. Here we present a systematic analysis of somatic complex indels in the coding sequences of samples from over 8,000 cancer cases using Pindel-C. We discovered 285 complex indels in cancer-associated genes (such as PIK3R1, TP53, ARID1A, GATA3 and KMT2D) in approximately 3.5% of cases analyzed; nearly all instances of complex indels were overlooked (81.1%) or misannotated (17.6%) in previous reports of 2,199 samples. In-frame complex indels are enriched in PIK3R1 and EGFR, whereas frameshifts are prevalent in VHL, GATA3, TP53, ARID1A, PTEN and ATRX. Furthermore, complex indels display strong tissue specificity (such as VHL in kidney cancer samples and GATA3 in breast cancer samples). Finally, structural analyses support findings of previously missed, but potentially druggable, mutations in the EGFR, MET and KIT oncogenes. This study indicates the critical importance of improving complex indel discovery and interpretation in medical research., Competing Interests: The authors declare no competing financial interests.

Published
2016
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36. Angeli's salt induces neurotoxicity in dopaminergic neurons in vivo and in vitro.

Author

Väänänen AJ, Moed M, Tuominen RK, Helkamaa TH, Wiksten M, Liesi P, Chiueh CC, and Rauhala P

Subjects
Animals, Brain metabolism, Brain pathology, Cells, Cultured, Dose-Response Relationship, Drug, Hydroxyl Radical, Immunohistochemistry, In Vitro Techniques, Lipid Peroxidation, Male, Neurons metabolism, Neurons pathology, Nitrites pharmacology, Oxidants metabolism, Oxidation-Reduction, Oxygen metabolism, Rats, Rats, Wistar, Salicylic Acid metabolism, Substantia Nigra metabolism, Sulfhydryl Compounds metabolism, Time Factors, Tyrosine 3-Monooxygenase metabolism, Dopamine metabolism, Neurons drug effects, Nitrites toxicity
Abstract

In this study, we investigated the hypothesis that the pro-oxidative properties of Angeli's salt (AS), a nitroxyl anion (HNO/NO-) releasing compound, cause neurotoxicity in dopaminergic neurons. The pro-oxidative properties were demonstrated in vitro by measuring hydroxylation products of salicylate and peroxidation of lipids under various redox conditions. AS (0-1000 microM) released high amounts of hydroxylating species in a concentration dependent manner. AS also increased lipid peroxidation in brain homogenates at concentrations below 100 microM, while inhibiting it at 1000 microM concentration. The AS induced pro-oxidative effects were completely suppressed by copper (II), which converts nitroxyl anion to nitric oxide, as well as by a potent nitroxyl anion scavenger glutathione. Neurotoxicity towards dopaminergic neurons was tested in rat nigrostriatal dopaminergic system in vivo and by using primary mesencephalic dopaminergic neuronal cultures in vitro. Intranigral infusion of AS (0-400 nmol) caused neurotoxicity reflected as a dose dependent decrease of striatal dopamine seven days after treatment. The effect of the 100 nmol dose was more pronounced whenmeasured 50 days after the infusion. Neurotoxicity was also confirmed as a decrease of tyrosine hydroxylase positive neurons in the substantia nigra. Neither sulphononoate, a close structural analog of AS, nor sodiumnitrite caused changes in striatal dopamine, thus reflecting lack of neurotoxicity. In primary dopaminergic neuronal cultures AS reduced [3H] dopamine uptake with concentrations over 200 microM confirming neurotoxicity. In line with the quite low efficacy to increase lipid peroxidation in vitro, infusion of AS into substantia nigra did not cause increased formation of fluorescent products of lipid peroxidation. These results support the hypothesis that AS derived species oxidize critical thiol groups, rather than membrane lipids, potentially leading to protein oxidation/dysfunction and demonstrated neurotoxicity These findings may have pathophysiological relevance in case of excess formation of nitroxyl anion.

Published
2003
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