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2. Secondary integrated analysis of multi-tissue transcriptomic responses to a combined lifestyle intervention in older adults from the GOTO nonrandomized trial

Author

Bogaards, F.A., Gehrmann, T., Beekman, M., Lakenberg, N., Suchiman, H.E.D., de Groot, C.P.G.M., Reinders, M.J.T., Slagboom, P.E., Bogaards, F.A., Gehrmann, T., Beekman, M., Lakenberg, N., Suchiman, H.E.D., de Groot, C.P.G.M., Reinders, M.J.T., and Slagboom, P.E.

Abstract

Molecular effects of lifestyle interventions are typically studied in a single tissue. Here, we perform a secondary analysis on the sex-specific effects of the Growing Old TOgether trial (GOTO, trial registration number GOT NL3301 (https://onderzoekmetmensen.nl/nl/trial/27183), NL-OMON27183, primary outcomes have been previously reported in ref. 1), a moderate 13-week combined lifestyle intervention on the transcriptomes of postprandial blood, subcutaneous adipose tissue (SAT) and muscle tissue in healthy older adults, the overlap in effect between tissues and their relation to whole-body parameters of metabolic health. The GOTO intervention has virtually no effect on the postprandial blood transcriptome, while the SAT and muscle transcriptomes respond significantly. In SAT, pathways involved in HDL remodeling, O2/CO2 exchange and signaling are overrepresented, while in muscle, collagen and extracellular matrix pathways are significantly overexpressed. Additionally, we find that the effects of the SAT transcriptome closest associates with gains in metabolic health. Lastly, in males, we identify a shared variation between the transcriptomes of the three tissues. We conclude that the GOTO intervention has a significant effect on metabolic and muscle fibre pathways in the SAT and muscle transcriptome, respectively. Aligning the response in the three tissues revealed a blood transcriptome component which may act as an integrated health marker for metabolic intervention effects across tissues.

Published
2024

3. Insights into the genetic architecture of osteoarthritis from stage 1 of the arcOGEN study

Author

Panoutsopoulou, K, Southam, L, Elliott, KS, Wrayner, N, Zhai, G, Beazley, C, Thorleifsson, G, Arden, NK, Carr, A, Chapman, K, Deloukas, P, Doherty, M, McCaskie, A, Ollier, WER, Ralston, SH, Spector, TD, Valdes, AM, Wallis, GA, Wilkinson, JM, Arden, E, Battley, K, Blackburn, H, Blanco, FJ, Bumpstead, S, Cupples, LA, Day-Williams, AG, Dixon, K, Doherty, SA, Esko, T, Evangelou, E, Felson, D, Gomez-Reino, JJ, Gonzalez, A, Gordon, A, Gwilliam, R, Halldorsson, BV, Hauksson, VB, Hofman, A, Hunt, SE, Ioannidis, JPA, Ingvarsson, T, Jonsdottir, I, Jonsson, H, Keen, R, Kerkhof, HJM, Kloppenburg, MG, Koller, N, Lakenberg, N, Lane, NE, Lee, AT, Metspalu, A, Meulenbelt, I, Nevitt, MC, O'Neill, F, Parimi, N, Potter, SC, Rego-Perez, I, Riancho, JA, Sherburn, K, Slagboom, PE, Stefansson, K, Styrkarsdottir, U, Sumillera, M, Swift, D, Thorsteinsdottir, U, Tsezou, A, Uitterlinden, AG, van Meurs, JBJ, Watkins, B, Wheeler, M, Mitchell, S, Zhu, Y, Zmuda, JM, Consortium, arcOGEN, Zeggini, E, and Loughlin, J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Immunology, Arthritis, Genetics, Prevention, Aging, Human Genome, Aetiology, 2.1 Biological and endogenous factors, Musculoskeletal, Case-Control Studies, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Multifactorial Inheritance, Osteoarthritis, Hip, Osteoarthritis, Knee, Polymorphism, Single Nucleotide, arcOGEN Consortium, Public Health and Health Services, Arthritis & Rheumatology, Clinical sciences
Abstract

ObjectivesThe genetic aetiology of osteoarthritis has not yet been elucidated. To enable a well-powered genome-wide association study (GWAS) for osteoarthritis, the authors have formed the arcOGEN Consortium, a UK-wide collaborative effort aiming to scan genome-wide over 7500 osteoarthritis cases in a two-stage genome-wide association scan. Here the authors report the findings of the stage 1 interim analysis.MethodsThe authors have performed a genome-wide association scan for knee and hip osteoarthritis in 3177 cases and 4894 population-based controls from the UK. Replication of promising signals was carried out in silico in five further scans (44,449 individuals), and de novo in 14 534 independent samples, all of European descent.ResultsNone of the association signals the authors identified reach genome-wide levels of statistical significance, therefore stressing the need for corroboration in sample sets of a larger size. Application of analytical approaches to examine the allelic architecture of disease to the stage 1 genome-wide association scan data suggests that osteoarthritis is a highly polygenic disease with multiple risk variants conferring small effects.ConclusionsIdentifying loci conferring susceptibility to osteoarthritis will require large-scale sample sizes and well-defined phenotypes to minimise heterogeneity.

Published
2011

5. 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

6. 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

8. 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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10. 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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11. 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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15. 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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16. 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

17. Unraveling the role of WWP2 in osteoarthritis pathophysiology

Author

Tuerlings, M., primary, Houtman, E., additional, van Hoolwerff, M., additional, Coutinho de Almeida, R., additional, Ruiz, A. Rodriguez, additional, Lakenberg, N., additional, Suchiman, H.E., additional, Timmermans, R.G., additional, Ramos, Y.F., additional, and Meulenbelt, I., additional

Published
2019
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20. Annotating Transcriptional Effects of Genetic Variants in Disease-Relevant Tissue: Transcriptome-Wide Allelic Imbalance in Osteoarthritic Cartilage

Author

Hollander, W. den, Pulyakhina, I., Boer, C., Bomer, N., Breggen, R. van der, Arindrarto, W., Almeida, R., Lakenberg, N., Sentner, T., Laros, J.F., Hoen, P.A.C. 't, Slagboom, E.P., Nelissen, R., Meurs, J. van, Ramos, Y.F.M., Meulenbelt, I., Hollander, W. den, Pulyakhina, I., Boer, C., Bomer, N., Breggen, R. van der, Arindrarto, W., Almeida, R., Lakenberg, N., Sentner, T., Laros, J.F., Hoen, P.A.C. 't, Slagboom, E.P., Nelissen, R., Meurs, J. van, Ramos, Y.F.M., and Meulenbelt, I.

Abstract

Contains fulltext : 202683.pdf (publisher's version ) (Open Access), OBJECTIVE: Multiple single-nucleotide polymorphisms (SNPs) conferring susceptibility to osteoarthritis (OA) mark imbalanced expression of positional genes in articular cartilage, reflected by unequally expressed alleles among heterozygotes (allelic imbalance [AI]). We undertook this study to explore the articular cartilage transcriptome from OA patients for AI events to identify putative disease-driving genetic variation. METHODS: AI was assessed in 42 preserved and 5 lesioned OA cartilage samples (from the Research Arthritis and Articular Cartilage study) for which RNA sequencing data were available. The count fraction of the alternative alleles among the alternative and reference alleles together (phi) was determined for heterozygous individuals. A meta-analysis was performed to generate a meta-phi and P value for each SNP with a false discovery rate (FDR) correction for multiple comparisons. To further validate AI events, we explored them as a function of multiple additional OA features. RESULTS: We observed a total of 2,070 SNPs that consistently marked AI of 1,031 unique genes in articular cartilage. Of these genes, 49 were found to be significantly differentially expressed (fold change <0.5 or >2, FDR <0.05) between preserved and paired lesioned cartilage, and 18 had previously been reported to confer susceptibility to OA and/or related phenotypes. Moreover, we identified notable highly significant AI SNPs in the CRLF1, WWP2, and RPS3 genes that were related to multiple OA features. CONCLUSION: We present a framework and resulting data set for researchers in the OA research field to probe for disease-relevant genetic variation that affects gene expression in pivotal disease-affected tissue. This likely includes putative novel compelling OA risk genes such as CRLF1, WWP2, and RPS3.

Published
2019

21. Annotating Transcriptional Effects of Genetic Variants in Disease-Relevant Tissue: Transcriptome-Wide Allelic Imbalance in Osteoarthritic Cartilage

Author

den Hollander, W, Pulyakhina, I, de Boer, C, Bomer, N, van der Breggen, R, Arindrarto, W, de Almeida, RC, Lakenberg, N, Sentner, T, Laros, JFJ, 't Hoen, PAC, Slagboom, PE (Eline), Nelissen, R, van Meurs, Joyce, Ramos, YFM, Meulenbelt, I, den Hollander, W, Pulyakhina, I, de Boer, C, Bomer, N, van der Breggen, R, Arindrarto, W, de Almeida, RC, Lakenberg, N, Sentner, T, Laros, JFJ, 't Hoen, PAC, Slagboom, PE (Eline), Nelissen, R, van Meurs, Joyce, Ramos, YFM, and Meulenbelt, I

Published
2019

22. 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
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23. 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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25. Osteoarthritis subtypes show distinguished transcriptomic landscapes

Author

Ramos, Y.F., primary, Sentner, T., additional, Coutinho de Almeida, R., additional, Hollander, W. den, additional, Meessen, J., additional, Houtman, E., additional, Heutink, K., additional, Lakenberg, N., additional, Slagboom, P., additional, Nelissen, R.G., additional, and Meulenbelt, I., additional

Published
2018
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30. 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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31. 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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32. 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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34. Genome scan for cardiovascular risk factors in three populations

Author

Heijmans, B.T., Beekman, M., Lakenberg, N., Suchiman, H.E.D., Vogler, G.P., DeFaire, U., Whitfield, J.B., de Knijff, P., Kluft, C., van Ommen, G.J.B., Frants, R.R., Pedersen, N.L., Martin, N.G., Boomsma, D.I., and Slagboom, P.E.

Subjects
Cardiovascular diseases -- Genetic aspects, Genetic disorders -- Research, Biological sciences
Published
2001

36. Genome-wide association study identifies a single major locus contributing to survival into old age; the APOE locus revisited

Author

Deelen J., Beekman M., Uh H. -W., Helmer Q., Kuningas M., Christiansen L., Kremer D., van der Breggen R., Suchiman H. E. D., Lakenberg N., van den Akker E. B., Passtoors W. M., Tiemeier H., van Heemst D., de Craen A. J., Rivadeneira F., de Geus E. J., Perola M., van der Ouderaa F. J., Gunn D. A., Boomsma D. I., Uitterlinden A. G., Christensen K., van Duijn C. M., Heijmans B. T., Houwing-Duistermaat J. J., Westendorp R. G. J., Slagboom P. E., Epidemiology, Child and Adolescent Psychiatry / Psychology, Internal Medicine, Biological Psychology, EMGO+ - Mental Health, Deelen J., Beekman M., Uh H.-W., Helmer Q., Kuningas M., Christiansen L., Kremer D., van der Breggen R., Suchiman H.E.D., Lakenberg N., van den Akker E.B., Passtoors W.M., Tiemeier H., van Heemst D., de Craen A.J., Rivadeneira F., de Geus E.J., Perola M., van der Ouderaa F.J., Gunn D.A., Boomsma D.I., Uitterlinden A.G., Christensen K., van Duijn C.M., Heijmans B.T., Houwing-Duistermaat J.J., Westendorp R.G.J., and Slagboom P.E.

Subjects
Adult, Male, Netherlands Twin Register (NTR), Longevity, Polymorphism, Single Nucleotide, Linkage Disequilibrium, Cohort Studies, Apolipoproteins E, Genetic, SDG 3 - Good Health and Well-being, Alzheimer Disease, Humans, Genetic Predisposition to Disease, genetics, Longitudinal Studies, human, Aged, apolipoprotein E, Aged, 80 and over, genome-wide association study, Genome, Human, Forkhead Box Protein O3, aging, Forkhead Transcription Factors, Original Articles, aging apolipoprotein E genetics genome-wide association study human longevity apolipoprotein-e genotype growth-factor-i human longevity leiden longevity familial longevity alzheimers-disease nonagenarian siblings exceptional longevity depressive disorder artery-disease, Middle Aged, humanities, Genetic Loci, Case-Control Studies, Female, Proto-Oncogene Proteins c-akt
Abstract

By studying the loci that contribute to human longevity, we aim to identify mechanisms that contribute to healthy aging. To identify such loci, we performed a genome-wide association study (GWAS) comparing 403 unrelated nonagenarians from long-living families included in the Leiden Longevity Study (LLS) and 1670 younger population controls. The strongest candidate SNPs from this GWAS have been analyzed in a meta-analysis of nonagenarian cases from the Rotterdam Study, Leiden 85-plus study, and Danish 1905 cohort. Only one of the 62 prioritized SNPs from the GWAS analysis (P

Published
2011
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37. To the editor: Uncompromised 10-year survival of oldest old carrying somatic mutations in DNMT3A and TET2

Author

Akker, E.B. (Erik) van den, Pitts, S.J. (Steven J.), Deelen, J. (Joris), Moed, H. (Heleen), Potluri, S. (Shobha), Rooij, J.G.J. (Jeroen) van, Suchiman, H.E.D. (Eka), Lakenberg, N. (Nico), De Dijcker, W.J. (Wesley J.), Uitterlinden, A.G. (André), Kraaij, R. (Robert), Hofman, A. (Albert), De Craen, A.J.M. (Anton J. M.), Houwing-Duistermaat, J.J. (Jeanine), Van Ommen, G.-J.B. (Gert-Jan B.), Cox, D.R. (David), Meurs, J.B.J. (Joyce) van, Beekman, M. (Marian), Reinders, M.J. (Marcel), Slagboom, P.E. (Eline), Akker, E.B. (Erik) van den, Pitts, S.J. (Steven J.), Deelen, J. (Joris), Moed, H. (Heleen), Potluri, S. (Shobha), Rooij, J.G.J. (Jeroen) van, Suchiman, H.E.D. (Eka), Lakenberg, N. (Nico), De Dijcker, W.J. (Wesley J.), Uitterlinden, A.G. (André), Kraaij, R. (Robert), Hofman, A. (Albert), De Craen, A.J.M. (Anton J. M.), Houwing-Duistermaat, J.J. (Jeanine), Van Ommen, G.-J.B. (Gert-Jan B.), Cox, D.R. (David), Meurs, J.B.J. (Joyce) van, Beekman, M. (Marian), Reinders, M.J. (Marcel), and Slagboom, P.E. (Eline)

Published
2016
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40. Genome-wide association study identifies a single major locus contributing to survival into old age; the APOE locus revisited

Author

Deelen, J., Beekman, M., Uh, H.W., Helmer, Q., Kuningas, M., Christiansen, L., Kremer, D., Breggen, R. van der, Suchiman, H.E.D., Lakenberg, N., Akker, E.B. van den, Passtoors, W.M., Tiemeier, H., Heemst, D. van, Craen, A.J. de, Rivadeneira, F., Geus, E.J. de, Perola, M., Ouderaa, F.J. van der, Gunn, D.A., Boomsma, D.I., Uitterlinden, A.G., Christensen, K., Duijn, C.M. van, Heijmans, B.T., Houwing-Duistermaat, J.J., Westendorp, R.G.J., and Slagboom, P.E.

Subjects
aging apolipoprotein E genetics genome-wide association study human longevity apolipoprotein-e genotype growth-factor-i human longevity leiden longevity familial longevity alzheimers-disease nonagenarian siblings exceptional longevity depressive disorder artery-disease
Abstract

By studying the loci that contribute to human longevity, we aim to identify mechanisms that contribute to healthy aging. To identify such loci, we performed a genome-wide association study (GWAS) comparing 403 unrelated nonagenarians from long-living families included in the Leiden Longevity Study (LLS) and 1670 younger population controls. The strongest candidate SNPs from this GWAS have been analyzed in a meta-analysis of nonagenarian cases from the Rotterdam Study, Leiden 85-plus study, and Danish 1905 cohort. Only one of the 62 prioritized SNPs from the GWAS analysis (P < 1 x 10(-4)) showed genome-wide significance with survival into old age in the meta-analysis of 4149 nonagenarian cases and 7582 younger controls [OR = 0.71 (95% CI 0.65-0.77), P = 3.39 x 10(-17)]. This SNP, rs2075650, is located in TOMM40 at chromosome 19q13.32 close to the apolipoprotein E (APOE) gene. Although there was only moderate linkage disequilibrium between rs2075650 and the ApoE epsilon 4 defining SNP rs429358, we could not find an APOE-independent effect of rs2075650 on longevity, either in cross-sectional or in longitudinal analyses. As expected, rs429358 associated with metabolic phenotypes in the offspring of the nonagenarian cases from the LLS and their partners. In addition, we observed a novel association between this locus and serum levels of IGF-1 in women (P = 0.005). In conclusion, the major locus determining familial longevity up to high age as detected by GWAS was marked by rs2075650, which tags the deleterious effects of the ApoE epsilon 4 allele. No other major longevity locus was found.

Published
2011

41. Underlying molecular mechanisms of DIO2 susceptibility in symptomatic osteoarthritis

Author

Bomer, N. (author), Den Hollander, W.T.H.F. (author), Ramos, Y.F.M. (author), Bos, S.D. (author), Van der Breggen, R. (author), Lakenberg, N. (author), Pepers, B.A. (author), Van Eeden, A.E. (author), Darvishan, A. (author), Tobi, E.W. (author), Duijnisveld, B.J. (author), Van den Akker, E.B. (author), Heijmans, B.T. (author), Van Roon-Mom, W.M.C. (author), Verbeek, F.J. (author), Osch, G.J.V.M. (author), Nelissen, R.G.H.H. (author), Slagboom, P.E. (author), Meulenbelt, I. (author), Bomer, N. (author), Den Hollander, W.T.H.F. (author), Ramos, Y.F.M. (author), Bos, S.D. (author), Van der Breggen, R. (author), Lakenberg, N. (author), Pepers, B.A. (author), Van Eeden, A.E. (author), Darvishan, A. (author), Tobi, E.W. (author), Duijnisveld, B.J. (author), Van den Akker, E.B. (author), Heijmans, B.T. (author), Van Roon-Mom, W.M.C. (author), Verbeek, F.J. (author), Osch, G.J.V.M. (author), Nelissen, R.G.H.H. (author), Slagboom, P.E. (author), and Meulenbelt, I. (author)

Abstract

Objectives: To investigate how the genetic susceptibility gene DIO2 confers risk to osteoarthritis (OA) onset in humans and to explore whether counteracting the deleterious effect could contribute to novel therapeutic approaches. Methods: Epigenetically regulated expression of DIO2 was explored by assessing methylation of positional CpG-dinucleotides and the respective DIO2 expression in OA-affected and macroscopically preserved articular cartilage from end-stage OA patients. In a human in vitro chondrogenesis model, we measured the effects when thyroid signalling during culturing was either enhanced (excess T3 or lentiviral induced DIO2 overexpression) or decreased (iopanoic acid). Results: OA-related changes in methylation at a specific CpG dinucleotide upstream of DIO2 caused significant upregulation of its expression (?=4.96; p=0.0016). This effect was enhanced and appeared driven specifically by DIO2 rs225014 risk allele carriers (?=5.58, p=0.0006). During in vitro chondrogenesis, DIO2 overexpression resulted in a significant reduced capacity of chondrocytes to deposit extracellular matrix (ECM) components, concurrent with significant induction of ECM degrading enzymes (ADAMTS5, MMP13) and markers of mineralisation (ALPL, COL1A1). Given their concurrent and significant upregulation of expression, this process is likely mediated via HIF-2?/RUNX2 signalling. In contrast, we showed that inhibiting deiodinases during in vitro chondrogenesis contributed to prolonged cartilage homeostasis as reflected by significant increased deposition of ECM components and attenuated upregulation of matrix degrading enzymes. Conclusions: Our findings show how genetic variation at DIO2 could confer risk to OA and raised the possibility that counteracting thyroid signalling may be a novel therapeutic approach., Intelligent Systems, Electrical Engineering, Mathematics and Computer Science

Published
2015

42. Underlying molecular mechanisms of DIO2 susceptibility in symptomatic osteoarthritis

Author

Bomer, N, den Hollander, W, Ramos, YFM, Bos, SD (Steffan Daniel), van der Breggen, R, Lakenberg, N, Pepers, BA, van Eeden, AE, Darvishan, A, Tobi, EW, Duijnisveld, BJ, van den Akker, EB, Heijmans, BT, van Roon-Mom, WMC, Verbeek, FJ, van Osch, Gerjo, Nelissen, RGHH, Slagboom, PE (Eline), Meulenbelt, I, Bomer, N, den Hollander, W, Ramos, YFM, Bos, SD (Steffan Daniel), van der Breggen, R, Lakenberg, N, Pepers, BA, van Eeden, AE, Darvishan, A, Tobi, EW, Duijnisveld, BJ, van den Akker, EB, Heijmans, BT, van Roon-Mom, WMC, Verbeek, FJ, van Osch, Gerjo, Nelissen, RGHH, Slagboom, PE (Eline), and Meulenbelt, I

Abstract

Objectives To investigate how the genetic susceptibility gene DIO2 confers risk to osteoarthritis (OA) onset in humans and to explore whether counteracting the deleterious effect could contribute to novel therapeutic approaches. Methods Epigenetically regulated expression of DIO2 was explored by assessing methylation of positional CpG-dinucleotides and the respective DIO2 expression in OA-affected and macroscopically preserved articular cartilage from end-stage OA patients. In a human in vitro chondrogenesis model, we measured the effects when thyroid signalling during culturing was either enhanced (excess T3 or lentiviral induced DIO2 overexpression) or decreased (iopanoic acid). Results OA-related changes in methylation at a specific CpG dinucleotide upstream of DIO2 caused significant upregulation of its expression (beta=4.96; p=0.0016). This effect was enhanced and appeared driven specifically by DIO2 rs225014 risk allele carriers (beta=5.58, p=0.0006). During in vitro chondrogenesis, DIO2 overexpression resulted in a significant reduced capacity of chondrocytes to deposit extracellular matrix (ECM) components, concurrent with significant induction of ECM degrading enzymes (ADAMTS5, MMP13) and markers of mineralisation (ALPL, COL1A1). Given their concurrent and significant upregulation of expression, this process is likely mediated via HIF-2 alpha/RUNX2 signalling. In contrast, we showed that inhibiting deiodinases during in vitro chondrogenesis contributed to prolonged cartilage homeostasis as reflected by significant increased deposition of ECM components and attenuated upregulation of matrix degrading enzymes. Conclusions Our findings show how genetic variation at DIO2 could confer risk to OA and raised the possibility that counteracting thyroid signalling may be a novel therapeutic approach.

Published
2015

43. THE arcOGEN CONSORTIUM: STAGE 1 OF A GENOME-WIDE ASSOCIATION SCAN FOR OSTEOARTHRITIS

Author

Panoutsopoulou, K., Southam, L., Rayner, W., Zhai, G., Beazley, C., Arden, N., Carr, A., Chapman, K., Deloukas, P., Doherty, M., McCaskie, A., Olher, W., Ralston, S., Spector, T., Valdes, A., Wallis, G., Wilkinson, M., Arden, E., Battley, K., Blackburn, H., Blanco, F., Bumpstead, S., Cupples, A., Day-Williams, A., Dixon, K., Dohertys, S., Elliott, K., Evangelou, E., Felson, D., Gomez-Reino, J., Gonzalez, A., Gordon, A., Gwilliam, R., Hofman, A., Hunt, S., Ioannidis, J., Jonsdottir, I., Keen, R., Kerkhof, H., Kloppenburg, M., Koller, N., Lakenberg, N., Lane, N., Lee, A., Meulenbelt, I., Nevitt, M., O'Neill, F., Parimi, N., Potter, S., Rego-Perez, I., Riancho, J., Sherburn, K., Slagboom, E., Styrkarsdottir, U., Sumillera, M., Swift, D., Tsezou, A., Uitterlinden, A., Meurs, J. van, Watkins, B., Wheeler, M., Mitchelle, S., Zhu, Y., Zmuda, J., Zeggini, E., Loughlin, J., and ArcOGEN Consortium

Abstract

Osteoarthritis and Cartilage

Published
2010

44. Genome-wide association study (GWAS)-identified disease risk alleles do not compromise human longevity

Author

Beekman, M., Nederstigt, C., Suchiman, H.E.D., Kremer, D., Breggen, R. van der, Lakenberg, N., Alemayehu, W.G., Craen, A.J.M. de, Westendorp, R.G.J., Boomsma, D.I., Geus, E.J.C. de, Houwing-Duistermaat, J.J., Heijmans, B.T., and Slagboom, P.E.

Subjects
association aging SNP major depressive disorder common genetic-variants prostate-cancer risk breast-cancer leiden longevity logic regression mortality centenarians prediction siblings
Abstract

A set of currently known alleles increasing the risk for coronary artery disease, cancer, and type 2 diabetes as identified by genome-wide association studies was tested for compatibility with human longevity. Here, we show that nonagenarian siblings from long-lived families and singletons older than 85 y of age from the general population carry the same number of disease risk alleles as young controls. Longevity in this study population is not compromised by the cumulative effect of this set of risk alleles for common disease.

Published
2010

49. Discriminating between putative epigenetic osteoarthritis disease drivers and sheer markers

Author

Hollander, W. den, primary, Ramos, Y.F., additional, Bomer, N., additional, Elzinga, S., additional, Breggen, R. van der, additional, Lakenberg, N., additional, de Dijcker, W.J., additional, Suchiman, E.H., additional, Duijnisveld, B.J., additional, Böhringer, S., additional, Houwing-Duistermaat, J.J., additional, Slagboom, E.P., additional, Bos, S.D., additional, Nelissen, R.G., additional, and Meulenbelt, I., additional

Published
2015
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50. High microsatellite and SNP genotyping success rate established in a large number of genomic DNA samples extracted from mouth swabs and genotypes

Author

Min, J.L., Lakenberg, N., Bakker-Verweij, M., Suchiman, E., Boomsma, D.I., Slagboom, P.E., Meulenbelt, I., and Biological Psychology

Subjects
Netherlands Twin Register (NTR), stomatognathic system
Abstract

In this article, we present the genomic DNA yield and the microsatellite and single nucleotide polymorphism (SNP) genotyping success rates of genomic DNA extracted from a large number of mouth swab samples. In total, the median yield and quality was determined in 714 individuals and the success rates in 378,480 genotypings of 915 individuals. The median yield of genomic DNA per mouth swab was 4.1 μg (range 0.1-42.2 μg) and was not reduced when mouth swabs were stored for at least 21 months prior to extraction. A maximum of 20 mouth swabs is collected per participant. Mouth swab samples showed in, respectively, 89% for 390 microsatellites and 99% for 24 SNPs a genotyping success rate higher than 75%. A very low success rate of genotyping (0%-10%) was obtained for 3.2% of the 915 mouth swab samples using microsatellite markers. Only 0.005% of the mouth swab samples showed a genotyping success rate lower than 75% (range 58%-71 %) using SNPs. Our results show that mouth swabs can be easily collected, stored by our conditions for months prior to DNA extraction and result in high yield and high-quality DNA appropriate for genotyping with high success rate including whole genome searches using microsatellites or SNPs.

Published
2006
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