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1. Correction for both common and rare cell types in blood is important to identify genes that correlate with age

Author

Pellegrino Coppola, D, Claringbould, A, Stutvoet, M, Heijmans, B, ‘t Hoen, P, van Meurs, J, Isaacs, A, Jansen, R, Pool, R, van Dongen, J, Hottenga, J, van Greevenbroek, M, Stehouwer, C, van der Kallen, C, Schalkwijk, C, Wijmenga, C, Zhernakova, S, Tigchelaar, E, Beekman, M, Deelen, J, van Heemst, D, Veldink, J, van den Berg, L, van Duijn, C, Hofman, B, Uitterlinden, A, Jhamai, P, Verbiest, M, Suchiman, H, Verkerk, M, van der Breggen, R, van Rooij, J, Lakenberg, N, Mei, H, van Iterson, M, van Galen, M, Bot, J, Zhernakova, D, van ‘t Hof, P, Deelen, P, Nooren, I, Vermaat, M, Luijk, R, Bonder, M, van Dijk, F, Arindrarto, W, Kielbasa, S, Swertz, M, van Zwet, E, Boomsma, D, Ikram, M, Slagboom, P, Westra, H, Franke, L, Pellegrino Coppola D., Claringbould A., Stutvoet M., Heijmans B. T., ‘t Hoen P. A. C., van Meurs J., Isaacs A., Jansen R., 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., 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. 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., Vermaat M., Luijk R., Bonder M. J., van Dijk F., Arindrarto W., Kielbasa S. M., Swertz M. A., van Zwet E. W., ‘t Hoen P. B., Boomsma D. I., Ikram M. A., Slagboom P. E., Westra H. J., Franke L., Pellegrino Coppola, D, Claringbould, A, Stutvoet, M, Heijmans, B, ‘t Hoen, P, van Meurs, J, Isaacs, A, Jansen, R, Pool, R, van Dongen, J, Hottenga, J, van Greevenbroek, M, Stehouwer, C, van der Kallen, C, Schalkwijk, C, Wijmenga, C, Zhernakova, S, Tigchelaar, E, Beekman, M, Deelen, J, van Heemst, D, Veldink, J, van den Berg, L, van Duijn, C, Hofman, B, Uitterlinden, A, Jhamai, P, Verbiest, M, Suchiman, H, Verkerk, M, van der Breggen, R, van Rooij, J, Lakenberg, N, Mei, H, van Iterson, M, van Galen, M, Bot, J, Zhernakova, D, van ‘t Hof, P, Deelen, P, Nooren, I, Vermaat, M, Luijk, R, Bonder, M, van Dijk, F, Arindrarto, W, Kielbasa, S, Swertz, M, van Zwet, E, Boomsma, D, Ikram, M, Slagboom, P, Westra, H, Franke, L, Pellegrino Coppola D., Claringbould A., Stutvoet M., Heijmans B. T., ‘t Hoen P. A. C., van Meurs J., Isaacs A., Jansen R., 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., 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. 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., Vermaat M., Luijk R., Bonder M. J., van Dijk F., Arindrarto W., Kielbasa S. M., Swertz M. A., van Zwet E. W., ‘t Hoen P. B., Boomsma D. I., Ikram M. A., Slagboom P. E., Westra H. J., and Franke L.

Abstract

Background Aging is a multifactorial process that affects multiple tissues and is characterized by changes in homeostasis over time, leading to increased morbidity. Whole blood gene expression signatures have been associated with aging and have been used to gain information on its biological mechanisms, which are still not fully understood. However, blood is composed of many cell types whose proportions in blood vary with age. As a result, previously observed associations between gene expression levels and aging might be driven by cell type composition rather than intracellular aging mechanisms. To overcome this, previous aging studies already accounted for major cell types, but the possibility that the reported associations are false positives driven by less prevalent cell subtypes remains. Results Here, we compared the regression model from our previous work to an extended model that corrects for 33 additional white blood cell subtypes. Both models were applied to whole blood gene expression data from 3165 individuals belonging to the general population (age range of 18-81 years). We evaluated that the new model is a better fit for the data and it identified fewer genes associated with aging (625, compared to the 2808 of the initial model; P <= 2.5x10(-6)). Moreover, 511 genes (similar to 18% of the 2808 genes identified by the initial model) were found using both models, indicating that the other previously reported genes could be proxies for less abundant cell types. In particular, functional enrichment of the genes identified by the new model highlighted pathways and GO terms specifically associated with platelet activity. Conclusions We conclude that gene expression analyses in blood strongly benefit from correction for both common and rare blood cell types, and recommend using blood-cell count estimates as standard covariates when studying whole blood gene expression.

Published
2021

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. 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
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4. 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
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5. Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

Author

Porcu, E., Rueger, S., Lepik, K., Agbessi, M., Ahsan, H., Alves, I., Andiappan, A., Arindrarto, W., Awadalla, P., Battle, A., Beutner, F., Bonder, M.J., Boomsma, D., Christiansen, M., Claringbould, A., Deelen, P., Esko, T., Fave, M.J., Franke, L., Frayling, T., Gharib, S.A., Gibson, G., Heijmans, B.T., Hemani, G., Jansen, R., Kahonen, M., Kalnapenkis, A., Kasela, S., Kettunen, J., Kim, Y., Kirsten, H., Kovacs, P., Krohn, K., Kronberg-Guzman, J., Kukushkina, V., Lee, B., Lehtimaki, T., Loeffler, M., Marigorta, U.M., Mei, H.L., Milani, L., Montgomery, G.W., Muller-Nurasyid, M., Nauck, M., Nivard, M., Penninx, B., Perola, M., Pervjakova, N., Pierce, B.L., Powell, J., Prokisch, H., Psaty, B.M., Raitakari, O.T., Ripatti, S., Rotzschke, O., Saha, A., Scholz, M., Schramm, K., Seppala, I., Slagboom, E.P., Stehouwer, C.D.A., Stumvoll, M., Sullivan, P., Hoen, P.A.C. 't, Teumer, A., Thiery, J., Tong, L., Tonjes, A., Dongen, J. van, Iterson, M. van, Meurs, J. van, Veldink, J.H., Verlouw, J., Visscher, P.M., Volker, U., Vosa, U., Westra, H.J., Wijmenga, C., Yaghootkar, H., Yang, J., Zeng, B., Zhang, F.T., Beekman, M., Boomsma, D.I., Bot, J., Deelen, J., Hofman, B.A., Hottenga, J.J., Isaacs, A., Jhamai, P.M., Kielbasa, S.M., Lakenberg, N., Luijk, R., Mei, H., Moed, M., Nooren, I., Pool, R., Schalkwijk, C.G., Slagboom, P.E., Suchiman, H.E.D., Swertz, M.A., Tigchelaar, E.F., Uitterlinden, A.G., Berg, L.H. van den, Breggen, R. van der, Kallen, C.J.H. van der, Dijk, F. van, Duijn, C.M. van, Galen, M. van, Greevenbroek, M.M.J. van, Heemst, D. van, Rooij, J. van, Van't Hof, P., Zwet, E.W. van, Vermaat, M., Verbiest, M., Verkerk, M., Zhernakova, D.V., Zhernakova, S., Santoni, F.A., Reymond, A., Kutalik, Z., eQTLGen Consortium, BIOS Consortium, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Translational Immunology Groningen (TRIGR), Stem Cell Aging Leukemia and Lymphoma (SALL), APH - Methodology, APH - Mental Health, Biological Psychology, APH - Personalized Medicine, APH - Health Behaviors & Chronic Diseases, Psychiatry, Amsterdam Neuroscience - Complex Trait Genetics, Laboratory Medicine, Human genetics, VU University medical center, APH - Digital Health, eQTLGen Consortium, BIOS Consortium, Agbessi, M., Ahsan, H., Alves, I., Andiappan, A., Arindrarto, W., Awadalla, P., Battle, A., Beutner, F., Jan Bonder, M., Boomsma, D., Christiansen, M., Claringbould, A., Deelen, P., Esko, T., Favé, M.J., Franke, L., Frayling, T., Gharib, S.A., Gibson, G., Heijmans, B.T., Hemani, G., Jansen, R., Kähönen, M., Kalnapenkis, A., Kasela, S., Kettunen, J., Kim, Y., Kirsten, H., Kovacs, P., Krohn, K., Kronberg-Guzman, J., Kukushkina, V., Lee, B., Lehtimäki, T., Loeffler, M., Marigorta, U.M., Mei, H., Milani, L., Montgomery, G.W., Müller-Nurasyid, M., Nauck, M., Nivard, M., Penninx, B., Perola, M., Pervjakova, N., Pierce, B.L., Powell, J., Prokisch, H., Psaty, B.M., Raitakari, O.T., Ripatti, S., Rotzschke, O., Saha, A., Scholz, M., Schramm, K., Seppälä, I., Slagboom, E.P., Stehouwer, CDA, Stumvoll, M., Sullivan, P., 't Hoen, PAC, Teumer, A., Thiery, J., Tong, L., Tönjes, A., van Dongen, J., van Iterson, M., van Meurs, J., Veldink, J.H., Verlouw, J., Visscher, P.M., Völker, U., Võsa, U., Westra, H.J., Wijmenga, C., Yaghootkar, H., Yang, J., Zeng, B., Zhang, F., Beekman, M., Boomsma, D.I., Bot, J., Deelen, J., Hofman, B.A., Hottenga, J.J., Isaacs, A., Bonder, M.J., Jhamai, P.M., Kielbasa, S.M., Lakenberg, N., Luijk, R., Moed, M., Nooren, I., Pool, R., Schalkwijk, C.G., Slagboom, P.E., Suchiman, HED, Swertz, M.A., Tigchelaar, E.F., Uitterlinden, A.G., van den Berg, L.H., van der Breggen, R., van der Kallen, CJH, van Dijk, F., van Duijn, C.M., van Galen, M., van Greevenbroek, MMJ, van Heemst, D., van Rooij, J., Van't Hof, P., van Zwet, E.W., Vermaat, M., Verbiest, M., Verkerk, M., Zhernakova, D.V., Zhernakova, S., Epidemiology, University Management, Department of Public Health, Centre of Excellence in Complex Disease Genetics, Samuli Olli Ripatti / Principal Investigator, Biostatistics Helsinki, Institute for Molecular Medicine Finland, Complex Disease Genetics, MUMC+: HVC Pieken Maastricht Studie (9), MUMC+: MA Interne Geneeskunde (3), Interne Geneeskunde, RS: CARIM - R3.01 - Vascular complications of diabetes and the metabolic syndrome, RS: CARIM - R3 - Vascular biology, MUMC+: MA Endocrinologie (9), MUMC+: MA Maag Darm Lever (9), MUMC+: MA Hematologie (9), MUMC+: MA Medische Oncologie (9), RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome, MUMC+: MA Med Staf Artsass Interne Geneeskunde (9), MUMC+: MA Nefrologie (9), MUMC+: MA Reumatologie (9), RS: CARIM - R1 - Thrombosis and haemostasis, Biochemie, RS: Carim - B01 Blood proteins & engineering, and RS: FHML MaCSBio

Subjects
0301 basic medicine, Netherlands Twin Register (NTR), Statistical methods, General Physics and Astronomy, Genome-wide association study, 02 engineering and technology, VARIANTS, Quantitative trait, DISEASE, 0302 clinical medicine, Pleiotropy, GTP-Binding Protein gamma Subunits, lcsh:Science, MUTATION, 0303 health sciences, Brain Diseases, Multidisciplinary, 1184 Genetics, developmental biology, physiology, Mendelian Randomization Analysis, ASSOCIATION, 021001 nanoscience & nanotechnology, Phenotype, STATISTICS, ddc, FAMILY, OBESITY, symbols, 0210 nano-technology, EXPRESSION, Science, Quantitative Trait Loci, Single-nucleotide polymorphism, Computational biology, Biology, Quantitative trait locus, Polymorphism, Single Nucleotide, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, symbols.namesake, Mendelian randomization, Brain Diseases/genetics, Gene Expression Profiling, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Transcriptome, INSTRUMENTAL VARIABLES, SNP, 030304 developmental biology, Genetic association, General Chemistry, 030104 developmental biology, Expression quantitative trait loci, Mendelian inheritance, PLEIOTROPY, lcsh:Q, Gene expression, 030217 neurology & neurosurgery
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., Many genetic variants identified in genome-wide association studies are associated with gene expression. Here, Porcu et al. propose a transcriptome-wide summary statistics-based Mendelian randomization approach (TWMR) that, applied to 43 human traits, uncovers hundreds of previously unreported gene–trait associations.

Published
2019
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6. 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
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7. From correlation to causation: Data-driven exploration of transcriptional regulation using population genomics

Author

Luijk, R., Slagboom, P., Heijmans, B.T., Zwet, E.W.van, Boomsma, D.I., Cannegieter, S.C., Goeman, J.J., Gribnau, J.H., and Leiden University

Subjects
Epigenomics, Causality, Transcriptional regulation, Genomics
Abstract

In this thesis, we aimed to better understand how genetic variation affect the processes underlying health and disease, as trait-associated genetic variants are often located in non-coding regions. This hampers their interpretability, and has prompted the exploration of their effects on transcriptional regulation, a process that is crucial in the development of common and complex diseases. To do this, we have used a variety of omics data in a large collection of individuals from the general population. Using these data, we have investigated the local and distal effects of genetic variants on other molecular phenotypes, such as gene expression levels and DNA methylation levels of CpG sites, and the underlying mechanisms. This has resulted in a framework enabling the exploration of causal hypotheses about transcriptional regulation using genetics as a causal anchor. The approaches used in this thesis have yielded insight into transcriptional (dys)regulation and several underlying mechanisms. This will be helpful in better understanding how transcriptional regulation contributes to complex phenotypes related to health and disease, such as common diseases.

Published
2019

10. Genome-wide identification of genes regulating DNA methylation using genetic anchors for causal inference

Author

Hop, P.J. (Paul J.), Luijk, R. (René), Daxinger, L. (Lucia), Iterson, M. (Maarten) van, Dekkers, K.F. (Koen F.), Jansen, R. (Rick), Meurs, J.B.J. (Joyce) van, 't Hoen, P.A.C. (Peter A C), Ikram, M.A. (Arfan), van Greevenbroek, M.M.J. (Marleen M J), Boomsma, D.I. (Dorret), Slagboom, P.E. (Eline), Veldink, J.H. (Jan), Zwet, E.W. (Erik) van, Heijmans, B.T. (Bastiaan T.), Hop, P.J. (Paul J.), Luijk, R. (René), Daxinger, L. (Lucia), Iterson, M. (Maarten) van, Dekkers, K.F. (Koen F.), Jansen, R. (Rick), Meurs, J.B.J. (Joyce) van, 't Hoen, P.A.C. (Peter A C), Ikram, M.A. (Arfan), van Greevenbroek, M.M.J. (Marleen M J), Boomsma, D.I. (Dorret), Slagboom, P.E. (Eline), Veldink, J.H. (Jan), Zwet, E.W. (Erik) van, and Heijmans, B.T. (Bastiaan T.)

Abstract

BACKGROUND: DNA methylation is a key epigenetic modification in human development and disease, yet there is limited understanding of its highly coordinated regulation. Here, we identify 818 genes that affect DNA methylation patterns in blood using large-scale population genomics data. RESULTS: By employing genetic instruments as causal anchors, we establish directed associations between gene expression and distant DNA methylation levels, while ensuring specificity of the associations by correcting for linkage disequilibrium and pleiotropy among neighboring genes. The identified genes are enriched for transcription factors, of which many consistently increased or decreased DNA methylation levels at multiple CpG sites. In addition, we show that a substantial number of transcription factors affected DNA methylation at their experimentally determined binding sites. We also observe genes encoding proteins with heterogenous functions that have widespread effects on DNA methylation, e.g., NFKBIE, CDCA7(L), and NLRC5, and for several examples, we suggest plausible mechanisms underlying their effect on DNA methylation. CONCLUSION: We report hundreds of genes that affect DNA methylation and provide key insights in the principles underlying epigenetic regulation.

Published
2020
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11. Genome-wide identification of genes regulating DNA methylation using genetic anchors for causal inference

Author

Hop, PJ, Luijk, R, Daxinger, L, van Iterson, M, Dekkers, K F, Jansen, R, van Meurs, Joyce, 't Hoen, PAC, Ikram, Arfan, van Greevenbroek, MMJ, Boomsma, DI, Slagboom, PE, Veldink, JH, van Zwet, EW, Heijmans, BT, Hop, PJ, Luijk, R, Daxinger, L, van Iterson, M, Dekkers, K F, Jansen, R, van Meurs, Joyce, 't Hoen, PAC, Ikram, Arfan, van Greevenbroek, MMJ, Boomsma, DI, Slagboom, PE, Veldink, JH, van Zwet, EW, and Heijmans, BT

Published
2020

12. 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

13. Genome-wide identification of directed gene networks using large-scale population genomics data

Author

Luijk, R., Dekkers, K.F., Iterson, M. van, Arindrarto, W., Claringbould, A., Hop, P., Boomsma, D.I., Duijn, C.M. van, Greevenbroek, M.M.J. van, Veldink, J.H., Wijmenga, C., Franke, L., Hoend, P.A.C. 't, Jansen, R., Meurs, J. van, Mei, H.L., Slagboomi, P.E., Heijmans, B.T., Zwet, E.W. van, BIOS Consortium, Psychiatry, APH - Mental Health, Amsterdam Neuroscience - Complex Trait Genetics, Epidemiology, Internal Medicine, Interne Geneeskunde, RS: CARIM - R3.01 - Vascular complications of diabetes and the metabolic syndrome, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Stem Cell Aging Leukemia and Lymphoma (SALL), Biological Psychology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, and APH - Methodology

Subjects
0301 basic medicine, Netherlands Twin Register (NTR), Linkage disequilibrium, ENDOGENOUS RETROVIRUSES, Transcription, Genetic, ZAC1, Gene regulatory network, General Physics and Astronomy, Gene Expression, Genome, Linkage Disequilibrium, Population genomics, Cohort Studies, 0302 clinical medicine, Gene Regulatory Networks, RNA-SEQ, lcsh:Science, Regulator gene, education.field_of_study, 0303 health sciences, Multidisciplinary, TRANSCRIPTIONAL REGULATION, Zinc Fingers, ASSOCIATION, APOPTOSIS, Phenotype, Proto-Oncogene Proteins c-bcl-2, MENDELIAN RANDOMIZATION, EXPRESSION, Genotype, Science, Population, Genomics, Computational biology, Biology, HUMAN TISSUES, INSULIN-SECRETION, General Biochemistry, Genetics and Molecular Biology, Article, Minor Histocompatibility Antigens, 03 medical and health sciences, Mendelian randomization, Endopeptidases, Humans, HAIR, education, Gene, 030304 developmental biology, Sequence Analysis, RNA, Gene Expression Profiling, TRANS-EQTLS, Epistasis, Genetic, General Chemistry, Gene expression profiling, 030104 developmental biology, Genetics, Population, Gene Expression Regulation, CELL-DEATH, SAMPLE-SIZE, lcsh:Q, Metagenomics, Transcriptome, 030217 neurology & neurosurgery, Transcription Factors
Abstract

Identification of causal drivers behind regulatory gene networks is crucial in understanding gene function. Here, we develop a method for the large-scale inference of gene–gene interactions in observational population genomics data that are both directed (using local genetic instruments as causal anchors, akin to Mendelian Randomization) and specific (by controlling for linkage disequilibrium and pleiotropy). Analysis of genotype and whole-blood RNA-sequencing data from 3072 individuals identified 49 genes as drivers of downstream transcriptional changes (Wald P, Identification of causal drivers behind expression is essential for understanding gene function. Here authors develop a method for the large-scale inference of gene–gene interactions in observational population genomics data and characterize a network of trans-effects for 6600 genes.

Published
2018
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15. [Cost-effectiveness in Dutch mental health care: future because of ROM?]

Author

van Agthoven M, van der Kolk A, Knegtering H, Pa, Delespaul, Arends J, Pp, Jeurissen, Pf, Krabbe, Huijsman R, Luijk R, De Beurs E, Hakkaart-Van Roijen L, and Richard Bruggeman

Subjects
Mental Health Services, Patient Satisfaction, Cost-Benefit Analysis, Outcome Assessment, Health Care, Quality of Life, Humans, health care economics and organizations, Netherlands, Healthcare improvement science Radboud Institute for Health Sciences [Radboudumc 18]
Abstract

Item does not contain fulltext BACKGROUND: The document reporting Dutch mental health care negotiations for 2014 - 2017 calls for a cost decrease based on cost-effectiveness. Thanks to rom, the Dutch mental health care seems well prepared for cost-effectiveness research. AIM: Evaluate how valid cost-effectiveness research should be established in mental health care and the role of rom therein. METHOD: Evaluation of requirements of cost-effectiveness research, trends, and a translation to Dutch mental health care. RESULTS: Valid cost-effectiveness research in mental health care requires the application of a societal perspective, a long time-horizon and an adequate evaluation of quality of life of patients. Healthcare consumption, outcome of care and characterisation of the patient population should be measured systematically and continuously. Currently, rom-data are not suitable to serve as a basis for cost-effectiveness research, although a proper basis is present. Further development of rom could lead to a situation in which mental health care is purchased on the basis of cost-effectiveness. However, cost-effectiveness will only really be improved if quality of care is rewarded, rather than rewarding activities that are not always related to outcome of care. CONCLUSION: Cost-effectiveness research in mental health care should focus on societal costs and benefits, quality of life and a long time-horizon. If developed further, rom has the potential to be a basis for cost-effectiveness research in the future.

Published
2015

16. 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
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17. Autosomal genetic variation is associated with DNA methylation in regions variably escaping X-chromosome inactivation

Author

Luijk, R. (René), Wu, H. (Haoyu), Ward-Caviness, C.K. (Cavin K.), Hannon, E. (Eilis), Carnero-Montoro, E. (Elena), Min, J. (Josine), Mandaviya, P.R. (Pooja), Müller-Nurasyid, M. (Martina), Mei, H. (Hailiang), Maarel, S.M. (Silvre) van der, Beekman, M. (Marian), der Breggen, R. (Ruud van), Deelen, J. (Joris), Lakenberg, N. (Nico), Moed, H. (Heleen), Suchiman, H.E.D. (Eka), Arindrarto, W. (Wibowo), van’t Hof, P. (Peter), Jan Bonder, M. (Marc), Deelen, P. (Patrick), Tigchelaar, E.F. (Ettje F.), Zhernakova, A. (Alexandra), Zhernakova, D.V. (Dasha V.), Dongen, J. (Jenny) van, Hottenga, J.J. (Jouke Jan), Pool, R. (Reńe), Isaacs, A. (Aaron), Hofman, B.A. (Bert A.), Jhamai, M. (Mila), Kallen, C.J. van der, Schalkwijk, C.G. (Casper), Stehouwer, C.D. (Coen), Berg, L.H. (Leonard) van den, Van Galen, M. (Michiel), Vermaat, M. (Martijn), van Rooij, J. (Jeroen), Uitterlinden, A.G. (André), Verbiest, M.M.P.J. (Michael), Verkerk, M. (Marijn), Kielbasa, P.S.M. (P. Szymon M.), Bot, J.J. (Jan), Nooren, I. (Irene), Dijk, F. (Freerk) van, Swertz, M.A. (Morris A.), Heemst, D. (Diana) van, Relton, C.L. (Caroline), Mill, J. (Jonathan), Waldenberger, M. (Melanie), Bell, J.T. (Jordana T.), Jansen, R. (Rick), Franke, L. (Lude), ‘t Hoen, P.A.C. (Peter A. C.), Boomsma, D.I. (Dorret), Duijn, C.M. (Cornelia) van, Greevenbroek, M.M. van, Veldink, J.H. (Jan), Wijmenga, C. (Cisca), Meurs, J.B.J. (Joyce) van, Daxinger, L. (Lucia), Slagboom, P.E. (Eline), Zwet, E.W. (Erik) van, Heijmans, B.T. (Bastiaan T.), Luijk, R. (René), Wu, H. (Haoyu), Ward-Caviness, C.K. (Cavin K.), Hannon, E. (Eilis), Carnero-Montoro, E. (Elena), Min, J. (Josine), Mandaviya, P.R. (Pooja), Müller-Nurasyid, M. (Martina), Mei, H. (Hailiang), Maarel, S.M. (Silvre) van der, Beekman, M. (Marian), der Breggen, R. (Ruud van), Deelen, J. (Joris), Lakenberg, N. (Nico), Moed, H. (Heleen), Suchiman, H.E.D. (Eka), Arindrarto, W. (Wibowo), van’t Hof, P. (Peter), Jan Bonder, M. (Marc), Deelen, P. (Patrick), Tigchelaar, E.F. (Ettje F.), Zhernakova, A. (Alexandra), Zhernakova, D.V. (Dasha V.), Dongen, J. (Jenny) van, Hottenga, J.J. (Jouke Jan), Pool, R. (Reńe), Isaacs, A. (Aaron), Hofman, B.A. (Bert A.), Jhamai, M. (Mila), Kallen, C.J. van der, Schalkwijk, C.G. (Casper), Stehouwer, C.D. (Coen), Berg, L.H. (Leonard) van den, Van Galen, M. (Michiel), Vermaat, M. (Martijn), van Rooij, J. (Jeroen), Uitterlinden, A.G. (André), Verbiest, M.M.P.J. (Michael), Verkerk, M. (Marijn), Kielbasa, P.S.M. (P. Szymon M.), Bot, J.J. (Jan), Nooren, I. (Irene), Dijk, F. (Freerk) van, Swertz, M.A. (Morris A.), Heemst, D. (Diana) van, Relton, C.L. (Caroline), Mill, J. (Jonathan), Waldenberger, M. (Melanie), Bell, J.T. (Jordana T.), Jansen, R. (Rick), Franke, L. (Lude), ‘t Hoen, P.A.C. (Peter A. C.), Boomsma, D.I. (Dorret), Duijn, C.M. (Cornelia) van, Greevenbroek, M.M. van, Veldink, J.H. (Jan), Wijmenga, C. (Cisca), Meurs, J.B.J. (Joyce) van, Daxinger, L. (Lucia), Slagboom, P.E. (Eline), Zwet, E.W. (Erik) van, and Heijmans, B.T. (Bastiaan T.)

Abstract

X-chromosome inactivation (XCI), i.e., the inactivation of one of the female X chromosomes, restores equal expression of X-chromosomal genes between females and males. However, ~10% of genes show variable degrees of escape from XCI between females, although little is known about the causes of variable XCI. Using a discovery data-set of 1867 females and 1398 males and a replication sample of 3351 females, we show that genetic variation at three autosomal loci is associated with female-specific changes in X-chromosome methylation. Through cis-eQTL expression analysis, we map these loci to the genes SMCHD1/METTL4, TRIM6/HBG2, and ZSCAN9. Low-expression alleles of the loci are predominantly associated with mild hypomethylation of CpG islands near genes known to variably escape XCI, implicating the autosomal genes in variable XCI. Together, these results suggest a genetic basis for variable escape from XCI and highlight the potential of a population genomics approach to identify genes involved in XCI.

Published
2018
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18. 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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19. Genome-wide identification of directed gene networks using large-scale population genomics data

Author

Luijk, R. (René), Dekkers, K.F. (Koen F.), Iterson, M. (Maarten) van, Arindrarto, W. (Wibowo), Claringbould, A. (Annique), Hop, P. (Paul), Beekman, M. (Marian), Breggen, R. (Ruud) van der, Deelen, J. (Joris), Lakenberg, N. (Nico), Moed, H. (Heleen), Suchiman, H.E.D. (Eka), van ’t Hof, P. (Peter), Bonder, M.J. (Marc), Deelen, P. (Patrick), Tigchelaar, E.F. (Ettje F.), Zhernakova, A. (Alexandra), Zhernakova, D.V. (Dasha V.), Dongen, J. (Jenny) van, Hottenga, J.J. (Jouke Jan), Pool, R. (Reńe), Isaacs, A. (Aaron), Hofman, B.A. (Bert A.), Jhamai, M. (Mila), Kallen, C.J. van der, Schalkwijk, C.G. (Casper), Stehouwer, C.D. (Coen), Berg, L.H. (Leonard) van den, Van Galen, M. (Michiel), Vermaat, M. (Martijn), van Rooij, J. (Jeroen), Uitterlinden, A.G. (André), Verbiest, M.M.P.J. (Michael), Verkerk, M. (Marijn), Kielbasa, P.S.M. (P. Szymon M.), Bot, J.J. (Jan), Nooren, I. (Irene), Dijk, F. (Freerk) van, Swertz, M.A. (Morris A.), Heemst, D. (Diana) van, Boomsma, D.I. (Dorret I.), Duijn, C.M. (Cornelia) van, Greevenbroek, M.M. van, Veldink, J.H. (Jan), Wijmenga, C. (Cisca), Franke, L. (Lude), ’t Hoen, P.A.C. (Peter A. C.), Jansen, R. (Rick), Meurs, J.B.J. (Joyce) van, Mei, H. (Hailiang), Slagboom, P.E. (Eline), Heijmans, B.T. (Bastiaan T.), Zwet, E.W. (Erik) van, Luijk, R. (René), Dekkers, K.F. (Koen F.), Iterson, M. (Maarten) van, Arindrarto, W. (Wibowo), Claringbould, A. (Annique), Hop, P. (Paul), Beekman, M. (Marian), Breggen, R. (Ruud) van der, Deelen, J. (Joris), Lakenberg, N. (Nico), Moed, H. (Heleen), Suchiman, H.E.D. (Eka), van ’t Hof, P. (Peter), Bonder, M.J. (Marc), Deelen, P. (Patrick), Tigchelaar, E.F. (Ettje F.), Zhernakova, A. (Alexandra), Zhernakova, D.V. (Dasha V.), Dongen, J. (Jenny) van, Hottenga, J.J. (Jouke Jan), Pool, R. (Reńe), Isaacs, A. (Aaron), Hofman, B.A. (Bert A.), Jhamai, M. (Mila), Kallen, C.J. van der, Schalkwijk, C.G. (Casper), Stehouwer, C.D. (Coen), Berg, L.H. (Leonard) van den, Van Galen, M. (Michiel), Vermaat, M. (Martijn), van Rooij, J. (Jeroen), Uitterlinden, A.G. (André), Verbiest, M.M.P.J. (Michael), Verkerk, M. (Marijn), Kielbasa, P.S.M. (P. Szymon M.), Bot, J.J. (Jan), Nooren, I. (Irene), Dijk, F. (Freerk) van, Swertz, M.A. (Morris A.), Heemst, D. (Diana) van, Boomsma, D.I. (Dorret I.), Duijn, C.M. (Cornelia) van, Greevenbroek, M.M. van, Veldink, J.H. (Jan), Wijmenga, C. (Cisca), Franke, L. (Lude), ’t Hoen, P.A.C. (Peter A. C.), Jansen, R. (Rick), Meurs, J.B.J. (Joyce) van, Mei, H. (Hailiang), Slagboom, P.E. (Eline), Heijmans, B.T. (Bastiaan T.), and Zwet, E.W. (Erik) van

Abstract

Identification of causal drivers behind regulatory gene networks is crucial in understanding gene function. Here, we develop a method for the large-scale inference of gene–gene interactions in observational population genomics data that are both directed (using local genetic instruments as causal anchors, akin to Mendelian Randomization) and specific (by controlling for linkage disequilibrium and pleiotropy). Analysis of genotype and whole-blood RNA-sequencing data from 3072 individuals identified 49 genes as drivers of downstream transcriptional changes (Wald P < 7 × 10−10), among which transcription factors were overrepresented (Fisher’s P = 3.3 × 10−7). Our analysis suggests new gene functions and targets, including for SENP7 (zinc-finger genes involved in retroviral repression) and BCL2A1 (target genes possibly involved in auditory dysfunction). Our work highlights the utility of population genomics data in deriving directed gene expression networks. A resource of trans-effects for all 6600 genes with a genetic instrument can be explored individually using a web-based browser.

Published
2018
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20. Genome-wide identification of directed gene networks using large-scale population genomics data

Author

Luijk, R, Dekkers, K F, van Iterson, M, Arindrarto, W, Claringbould, A, Hop, P, Boomsma, DI, Duijn, Cornelia, van Greevenbroek, MMJ, Veldink, JH, Wijmenga, C, Franke, L, t Hoend, PAC, Jansen, R, van Meurs, Joyce, Mei, H L, Slagboom, PE (Eline), Heijmans, BT, van Zwet, EW, Luijk, R, Dekkers, K F, van Iterson, M, Arindrarto, W, Claringbould, A, Hop, P, Boomsma, DI, Duijn, Cornelia, van Greevenbroek, MMJ, Veldink, JH, Wijmenga, C, Franke, L, t Hoend, PAC, Jansen, R, van Meurs, Joyce, Mei, H L, Slagboom, PE (Eline), Heijmans, BT, and van Zwet, EW

Published
2018

21. Autosomal genetic variation is associated with DNA methylation in regions variably escaping X-chromosome inactivation

Author

Luijk, R, Wu, HY, Ward-Caviness, CK, Hannon, E, Carnero Montoro, Elena, Min, JL, Mandaviya, Pooja, Muller-Nurasyid, M, Mei, H, van der Maarel, SM, Relton, C, van Mill, J, Waldenberger, M, Bell, JT, Jansen, R, Zhernakova, A, Franke, L, 't Hoen, PAC, Boomsma, DI, Duijn, Cornelia, van Greevenbroek, MMJ, Veldink, JH, Wijmenga, C, van Meurs, Joyce, Daxinger, L, Slagboom, PE (Eline), van Zwet, EW, Heijmans, BT, Luijk, R, Wu, HY, Ward-Caviness, CK, Hannon, E, Carnero Montoro, Elena, Min, JL, Mandaviya, Pooja, Muller-Nurasyid, M, Mei, H, van der Maarel, SM, Relton, C, van Mill, J, Waldenberger, M, Bell, JT, Jansen, R, Zhernakova, A, Franke, L, 't Hoen, PAC, Boomsma, DI, Duijn, Cornelia, van Greevenbroek, MMJ, Veldink, JH, Wijmenga, C, van Meurs, Joyce, Daxinger, L, Slagboom, PE (Eline), van Zwet, EW, and Heijmans, BT

Published
2018

24. Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms

Author

Slieker, R.C., Iterson, M. van, Luijk, R., Beekman, M., Zhernakova, D.V., Moed, M.H., Mei, H.L., Galen, M. van, Deelen, P., Bonder, M.J., Zhernakova, A., Uitterlinden, A.G., Tigchelaar, E.F., Stehouwer, C.D.A., Schalkwijk, C.G., Kallen, C.J.H. van der, Hofman, A., Heemst, D. van, Geus, E.J. de, Dongen, J. van, Deelen, J., Berg, L.H. van den, Meurs, J. van, Jansen, R., Hoen, P.A.C. 't, Franke, L., Wijmenga, C., Veldink, J.H., Swertz, M.A., Greevenbroek, M.M.J. van, Duijn, C.M. van, Boomsma, D.I., Slagboom, P.E., Heijmans, B.T., BIOS Consortium, Epidemiology and Data Science, Laboratory Medicine, Psychiatry, Amsterdam Neuroscience - Complex Trait Genetics, Internal Medicine, Epidemiology, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Translational Immunology Groningen (TRIGR), Stem Cell Aging Leukemia and Lymphoma (SALL), MUMC+: HVC Pieken Maastricht Studie (9), RS: CARIM - R3.01 - Vascular complications of diabetes and the metabolic syndrome, Interne Geneeskunde, MUMC+: MA Interne Geneeskunde (3), and Biological Psychology

Subjects
0301 basic medicine, Netherlands Twin Register (NTR), DNA damage, DNA repair, HYPOMETHYLATION, Biology, DNA-METHYLATION CHANGES, 03 medical and health sciences, Epigenetics of physical exercise, SDG 3 - Good Health and Well-being, Journal Article, SDG 14 - Life Below Water, Variability, Gene, GENE-EXPRESSION, Genetics, DAMAGE, DNA methylation, Research, WIDE, Methylation, REGIONS, CANCER, 3. Good health, 450k, GENOME, Ageing, 030104 developmental biology, CpG site, UPDATE, STEM-CELLS
Abstract

Background Epigenetic change is a hallmark of ageing but its link to ageing mechanisms in humans remains poorly understood. While DNA methylation at many CpG sites closely tracks chronological age, DNA methylation changes relevant to biological age are expected to gradually dissociate from chronological age, mirroring the increased heterogeneity in health status at older ages. Results Here, we report on the large-scale identification of 6366 age-related variably methylated positions (aVMPs) identified in 3295 whole blood DNA methylation profiles, 2044 of which have a matching RNA-seq gene expression profile. aVMPs are enriched at polycomb repressed regions and, accordingly, methylation at those positions is associated with the expression of genes encoding components of polycomb repressive complex 2 (PRC2) in trans. Further analysis revealed trans-associations for 1816 aVMPs with an additional 854 genes. These trans-associated aVMPs are characterized by either an age-related gain of methylation at CpG islands marked by PRC2 or a loss of methylation at enhancers. This distinct pattern extends to other tissues and multiple cancer types. Finally, genes associated with aVMPs in trans whose expression is variably upregulated with age (733 genes) play a key role in DNA repair and apoptosis, whereas downregulated aVMP-associated genes (121 genes) are mapped to defined pathways in cellular metabolism. Conclusions Our results link age-related changes in DNA methylation to fundamental mechanisms that are thought to drive human ageing. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1053-6) contains supplementary material, which is available to authorized users.

Published
2016
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25. Doelmatigheid in de GGZ: toekomst dankzij ROM?

Author

Van Agthoven, M., Van der Kolk, A., Knegtering, H., Delespaul, P.A.E.G., Arends, J., Jeurissen, P.P.T., Krabbe, P.F.M., Huijsman, R., Luijk, R., Beurs, E. de, Hakkaart-Van Roijen, L., Bruggeman, R., Public Health, Health Services Management & Organisation (HSMO), and Health Technology Assessment (HTA)

Published
2015

27. Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms

Author

Slieker, R. (Roderick), Iterson, M. (Maarten) van, Luijk, R. (René), Beekman, M. (Marian), Zhernakova, A. (Alexandra), Moed, H. (Heleen), Mei, S. (Shan), Van Galen, M. (Michiel), Deelen, P. (Patrick), Bonder, M.J. (Marc), Uitterlinden, A.G. (André), Tigchelaar, E.F. (Ettje F.), Stehouwer, C.D.A. (Coen D.A.), Schalkwijk, C.G. (Casper), Kallen, C.J. van der, Hofman, A. (Albert), Heemst, D. (Diana) van, Geus, E.J.C. (Eco) de, Dongen, J. (Jenny) van, Deelen, J. (Joris), Berg, L.H. (Leonard) van den, Meurs, J.B.J. (Joyce) van, Jansen, R., Hoen, P.A.C. (Peter) 't, Franke, L. (Lude), Wijmenga, C. (Cisca), Veldink, J.H. (Jan), Swertz, M. (Morris), Greevenbroek, M.M. van, Duijn, C.M. (Cornelia) van, Boomsma, D.I. (Dorret), Slagboom, P.E. (Eline), Heijmans, B.T. (Bastiaan), Slieker, R. (Roderick), Iterson, M. (Maarten) van, Luijk, R. (René), Beekman, M. (Marian), Zhernakova, A. (Alexandra), Moed, H. (Heleen), Mei, S. (Shan), Van Galen, M. (Michiel), Deelen, P. (Patrick), Bonder, M.J. (Marc), Uitterlinden, A.G. (André), Tigchelaar, E.F. (Ettje F.), Stehouwer, C.D.A. (Coen D.A.), Schalkwijk, C.G. (Casper), Kallen, C.J. van der, Hofman, A. (Albert), Heemst, D. (Diana) van, Geus, E.J.C. (Eco) de, Dongen, J. (Jenny) van, Deelen, J. (Joris), Berg, L.H. (Leonard) van den, Meurs, J.B.J. (Joyce) van, Jansen, R., Hoen, P.A.C. (Peter) 't, Franke, L. (Lude), Wijmenga, C. (Cisca), Veldink, J.H. (Jan), Swertz, M. (Morris), Greevenbroek, M.M. van, Duijn, C.M. (Cornelia) van, Boomsma, D.I. (Dorret), Slagboom, P.E. (Eline), and Heijmans, B.T. (Bastiaan)

Abstract

Background: Epigenetic change is a hallmark of ageing but its link to ageing mechanisms in humans remains poorly understood. While DNA methylation at many CpG sites closely tracks chronological age, DNA methylation changes relevant to biological age are expected to gradually dissociate from chronological age, mirroring the increased heterogeneity in health status at older ages. Results: Here, we report on the large-scale identification of 6366 age-related variably methylated positions (aVMPs) identified in 3295 whole blood DNA methylation profiles, 2044 of which have a matching RNA-seq gene expression profile. aVMPs are enriched at polycomb repressed regions and, accordingly, methylation at those positions is associated with the expression of genes encoding components of polycomb repressive complex 2 (PRC2) in trans. Further analysis revealed trans-associations for 1816 aVMPs with an additional 854 genes. These trans-associated aVMPs are characterized by either an age-related g

Published
2016
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28. Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms

Author

Slieker, R C, van Iterson, M, Luijk, R, Beekman, M, Zhernakova, DV, Moed, MH, Mei, H L, van Galen, M, Deelen, P, Bonder, MJ, Zhernakova, A, Uitterlinden, André, Tigchelaar, E F, Stehouwer, CDA, Schalkwijk, CG, van der Kallen, CJH, Hofman, Bert, van Heemst, D, de Geus, EJ, Dongen, J, Deelen, J, van den Berg, LH, van Meurs, Joyce, Jansen, R, 't Hoen, PAC, Franke, L, Wijmenga, C, Veldink, JH, Swertz, MA, van Greevenbroek, MMJ, Duijn, Cornelia, Boomsma, DI, Slagboom, PE (Eline), Heijmans, BT, Slieker, R C, van Iterson, M, Luijk, R, Beekman, M, Zhernakova, DV, Moed, MH, Mei, H L, van Galen, M, Deelen, P, Bonder, MJ, Zhernakova, A, Uitterlinden, André, Tigchelaar, E F, Stehouwer, CDA, Schalkwijk, CG, van der Kallen, CJH, Hofman, Bert, van Heemst, D, de Geus, EJ, Dongen, J, Deelen, J, van den Berg, LH, van Meurs, Joyce, Jansen, R, 't Hoen, PAC, Franke, L, Wijmenga, C, Veldink, JH, Swertz, MA, van Greevenbroek, MMJ, Duijn, Cornelia, Boomsma, DI, Slagboom, PE (Eline), and Heijmans, BT

Published
2016

29. [Cost-effectiveness in Dutch mental health care: future because of ROM?]

Author

Agthoven, M. van, Kolk, A. van der, Knegtering, H., Delespaul, P.A., Arends, J., Jeurissen, P.P.T., Krabbe, P.F.M., Huijsman, R., Luijk, R., Beurs, E. de, Hakkaart-van Roijen, L., Bruggeman, R., Agthoven, M. van, Kolk, A. van der, Knegtering, H., Delespaul, P.A., Arends, J., Jeurissen, P.P.T., Krabbe, P.F.M., Huijsman, R., Luijk, R., Beurs, E. de, Hakkaart-van Roijen, L., and Bruggeman, R.

Abstract

Item does not contain fulltext, BACKGROUND: The document reporting Dutch mental health care negotiations for 2014 - 2017 calls for a cost decrease based on cost-effectiveness. Thanks to rom, the Dutch mental health care seems well prepared for cost-effectiveness research. AIM: Evaluate how valid cost-effectiveness research should be established in mental health care and the role of rom therein. METHOD: Evaluation of requirements of cost-effectiveness research, trends, and a translation to Dutch mental health care. RESULTS: Valid cost-effectiveness research in mental health care requires the application of a societal perspective, a long time-horizon and an adequate evaluation of quality of life of patients. Healthcare consumption, outcome of care and characterisation of the patient population should be measured systematically and continuously. Currently, rom-data are not suitable to serve as a basis for cost-effectiveness research, although a proper basis is present. Further development of rom could lead to a situation in which mental health care is purchased on the basis of cost-effectiveness. However, cost-effectiveness will only really be improved if quality of care is rewarded, rather than rewarding activities that are not always related to outcome of care. CONCLUSION: Cost-effectiveness research in mental health care should focus on societal costs and benefits, quality of life and a long time-horizon. If developed further, rom has the potential to be a basis for cost-effectiveness research in the future.

Published
2015

30. An alternative approach to multiple testing for methylation QTL mapping reduces the proportion of falsely identified CpGs

Author

Luijk, R., Goeman, J.J., Slagboom, E.P., Heijmans, B.T., Zwet, E.W. van, Luijk, R., Goeman, J.J., Slagboom, E.P., Heijmans, B.T., and Zwet, E.W. van

Abstract

Item does not contain fulltext, INTRODUCTION: An increasing number of studies investigates the influence of local genetic variation on DNA methylation levels, so-called in cis methylation quantitative trait loci (meQTLs). A common multiple testing approach in genome-wide cis meQTL studies limits the false discovery rate (FDR) among all CpG-SNP pairs to 0.05 and reports on CpGs from the significant CpG-SNP pairs. However, a statistical test for each CpG is not performed, potentially increasing the proportion of CpGs falsely reported on. Here, we presented an alternative approach that properly control for multiple testing at the CpG level. RESULTS: We performed cis meQTL mapping for varying window sizes using publicly available single-nucleotide polymorphism (SNP) and 450 kb data, extracting the CpGs from the significant CpG-SNP pairs ([Formula: see text]). Using a new bait-and-switch simulation approach, we show that up to 50% of the CpGs found in the simulated data may be false-positive results. We present an alternative two-step multiple testing approach using the Simes and Benjamini-Hochberg procedures that does control the FDR among the CpGs, as confirmed by the bait-and-switch simulation. This approach indicates the use of window sizes in cis meQTL mapping studies that are significantly smaller than commonly adopted. DISCUSSION: Our approach to cis meQTL mapping properly controls the FDR at the CpG level, is computationally fast and can also be applied to cis eQTL studies. AVAILABILITY AND IMPLEMENTATION: An examplary R script for performing the Simes procedure is available as supplementary material. CONTACT: e.w.van_zwet@lumc.nl or b.t.heijmans@lumc.nl SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Published
2015

31. Prestatie-indicatoren voor de consument

Author

Broerse, A., Groenewoud, A.S., Luijk, R., Berg, M., Erasmus School of Health Policy & Management, and TNO Kwaliteit van Leven

Subjects
Jeugd en Gezondheid, Health
Published
2005

33. Genome wide DNA methylation profiling of osteoarthritic articular cartilage

Author

den Hollander, W., primary, Ramos, Y.F., additional, Bos, S.D., additional, Bomer, N., additional, van der Breggen, R., additional, Lakenberg, N., additional, Sliecker, R., additional, Luijk, R., additional, Tobi, E.W., additional, Duijnisveld, B.J., additional, van den Akker, E.B., additional, Heijmans, B.T., additional, Slagboom, P.E., additional, Nelissen, R.G., additional, and Meulenbelt, I., additional

Published
2014
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36. A linear algebra exercise

Author

Beukers, F., Luijk, R. van, and Vidunas, R.

Subjects
Wiskunde en Informatica
Abstract

Anyone who has ever composed a written test on linear algebra knows that it often taken considerable effort to make the 'numbers come out right'. In fact, this problem may even be harder than the linear algebra problem itself. A particular subject where this problem arises, is eigenvalues of symmetric matrices. Usually we consider only matrices with integral coefficients. We know that 4 x 4-matrices tend to become too laborious. Since 2 x 2-matrices are not very exciting we have to confine ourselves to 3 x 3-matrices. We know that there are 3 real eigenvalues in this case. We cannot let the poor students solve irreducible cubic equations, so we might see to it that the eigenvalues are integers, which the students can recognize by inspection. Moreover, computing the eigenvalue equation of an arbitrary symmetric 3 x 3-matrix may be a little too stressful....

Published
2002

39. Multiplex ligation‐dependent probe amplification to detect subtelomeric rearrangements in routine diagnostics

Author

Rooms, L, primary, Reyniers, E, additional, Wuyts, W, additional, Storm, K, additional, Van Luijk, R, additional, Scheers, S, additional, Wauters, J, additional, Van Den Ende, J, additional, Biervliet, M, additional, Eyskens, F, additional, Van Goethem, G, additional, Laridon, A, additional, Ceulemans, B, additional, Courtens, W, additional, and Kooy, RF, additional

Published
2005
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40. Formation of polybrominated dibenzofurans during extrusion of high-impact polystyrene/decabromodiphenyl ether/antimony(III) oxide

Author

Luijk, R., Govers, H.A.J., Nelissen, L.N.I.H., Luijk, R., Govers, H.A.J., and Nelissen, L.N.I.H.

Abstract

Polybrominated dibenzofurans (PBDFs) are formed during extrusion of high-impact polystyrene (I) contg. the flame retardant system decabromodiphenyl ether(II)/Sb2O3. The process of formation of PBDFs was examd. at an extrusion temp. of 275 °. To get a better understanding of processes taking place in the polymer melt during extrusion, the thermal stability of I chains was also examd. The flame retardant system II/Sb2O3 showed a stabilizing activity toward thermal degrdn. of polystyrene chains. Temp. development during extrusion processing of I/II/Sb2O3, simulated in a computer model, supported a mechanism of formation of PBDFs which was initiated by thermal degrdn. of I. I degrdn. products, i.e., (poly)styrene radicals, were stabilized by radical scavenging properties of the flame retardant II. Debromination of II was combined with a ring-closure reaction forming PBDFs. PBDFs were formed during extrusion of polybrominated di-Ph ethers in addn. polymers such as I. A tentative quantification of the yield of PBDFs in I/II/Sb2O3 by reversed-phase HPLC showed an increase from 1.5 to 9 ppm for heptabromodibenzofuran and from 4.5 to 45 ppm for octabromodibenzofuran after four extrusion cycles

Published
1992

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