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2. 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

3. 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

4. Additional file 2 of Whole genome sequencing and the application of a SNP panel reveal primary evolutionary lineages and genomic variation in the lion (Panthera leo)

Author

Bertola, L. D., Vermaat, M., Lesilau, F., Chege, M., Tumenta, P. N., Sogbohossou, E. A., Schaap, O. D., Bauer, H., Patterson, B. D., White, P. A., de Iongh, H. H., Laros, J. F. J., and Vrieling, K.

Abstract

Additional file 2: Supplemental Information S2. Validation of phylogenetic inferences and populations structure from whole genome sequencing and SNP panel data (including Supplemental Figures S5-S10).

Published
2022
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5. Additional file 1 of Whole genome sequencing and the application of a SNP panel reveal primary evolutionary lineages and genomic variation in the lion (Panthera leo)

Author

Bertola, L. D., Vermaat, M., Lesilau, F., Chege, M., Tumenta, P. N., Sogbohossou, E. A., Schaap, O. D., Bauer, H., Patterson, B. D., White, P. A., de Iongh, H. H., Laros, J. F. J., and Vrieling, K.

Abstract

Additional file 1: Supplemental Information S1. Details on laboratory protocols, sequencing, mapping, SNP calling and quality control (including Supplemental Figures S1-S4).

Published
2022
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6. 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

7. 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

9. 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

10. [Diagnostic algorithm for COVID-19 at the ER]

Author

Dofferhoff, A.S., Swinkels, A., Sprong, T., Berk, Y., Spanbroek, M., Nabuurs-Franssen, M.H., Vermaat, M., Kerkhof, B. van de, Willekens, M.H.C., and Voss, A.

Subjects
lnfectious Diseases and Global Health Radboud Institute for Health Sciences [Radboudumc 4]
Abstract

Item does not contain fulltext OBJECTIVE: Evaluation of a diagnostic algorithm for estimating the risk of COVID-19 in patients who are referred to an emergency department for being suspected of having the disease. DESIGN: Retrospective study. METHOD: Patients with fever with no apparent cause and patients with recently developed respiratory symptoms, whether or not in combination with fever, were routinely given a PCR test, blood tests (lymphocyte count and LDH levels) and a chest CT scan. The CT scan was assessed according to the CO-RADS classification. Based on the findings, the patients were divided into 3 cohorts (proven COVID-19, strong suspicion of COVID-19, and low suspicion of COVID-19) and the appropriate isolation measures were taken. RESULTS: In the period from 8 to 31 March 2020, the algorithm was applied to 312 patients. COVID-19 was proven for 69 (22%) patients. COVID-19 was strongly suspected for 151 (48%) patients and suspicion was low for the remaining 92 (29%) patients. The percentage of patients with positive PCR results and the percentage of patients with abnormal laboratory test results increased as the CO-RADS score increased. Among patients with a CO-RADS score of 4 or 5, this percentage increased further when they also had lymphopenia or elevated LDH levels. We have adjusted the flowchart based on our findings. CONCLUSION: In case of patients who have been referred to an emergency department for suspected COVID-19, a good COVID-19 risk assessment can be made on the basis of clinical signs, laboratory abnormalities and low-dose CT scans. Even before the results of the PCR test are known and even if the results are negative, patients can be classified as 'proven COVID-19 patients' using the algorithm.

Published
2020

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. Integrated clinical and omics approach to rare diseases: novel genes and oligogenic inheritance in holoprosencephaly

Author

Kim, A., Savary, C., Dubourg, C., Carre, W., Mouden, C., Hamdi-Roze, H., Guyodo, H., Douce, J. le, Pasquier, L., Flori, E., Gonzales, M., Beneteau, C., Boute, O., Attie-Bitach, T., Roume, J., Goujon, L., Akloul, L., Odent, S., Watrin, E., Dupe, V., Tayrac, M. de, David, V., Genin, E., Campion, D., Dartigues, J.F.C.O., Deleuze, J.F., Lambert, J.C., Redon, R., Ludwig, T., Grenier-Boley, B., Letort, S., Lindenbaum, P., Meyer, V., Quenez, O., Dina, C., Bellenguez, C., Charbonnier-Le Clezio, C., Giemza, J., Chatel, S., Ferec, C., Marec, H. le, Letenneur, L., Nicolas, G., Rouault, K., Bacq, D., Boland, A., Lechner, D., Wijmenga, C., Swertz, M.A., Slagboom, P.E., Ommen, G.J.B. van, Duijn, C.M. van, Boomsma, D.I., Bakker, P.I.W. de, Bovenberg, J.A., Craen, A.J.M. de, Beekman, M., Hofman, A., Willemsen, G., Wolffenbuttel, B., Platteel, M., Y.P. du, Chen, R.Y., Cao, H.Z., Cao, R., Sun, Y.S., Cao, J.S., Dijk, F. van, Neerincx, P.B.T., Deelen, P., Dijkstra, M., Byelas, G., Kanterakis, A., Bot, J., Ye, K., Lameijer, E.W., Vermaat, M., Laros, J.F.J., Dunnen, J.T. den, Knijff, P. de, Karssen, L.C., Leeuwen, E.M. van, Amin, N., Koval, V., Rivadeneira, F., Estrada, K., Hehirkwa, J.Y., Ligt, J. de, Abdellaoui, A., Hottenga, J.J., Kattenberg, V.M., Enckevort, D. van, Mei, H., Santcroos, M., Schaik, B.D.C. van, Handsaker, R.E., McCarroll, S.A., Eichler, E.E., Ko, A., Sudmant, P., Francioli, L.C., Kloosterman, W.P., Nijman, I.J., Guryev, V., FREX Consortium, GoNL Consortium, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Lifestyle Medicine (LM), Nanomedicine & Drug Targeting, Groningen Research Institute for Asthma and COPD (GRIAC), Center for Liver, Digestive and Metabolic Diseases (CLDM), Institut de Génétique et Développement de Rennes (IGDR), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES), CHU Pontchaillou [Rennes], Service de génétique et embryologie médicales [CHU Trousseau], CHU Trousseau [APHP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Service de génétique médicale - Unité de génétique clinique [Nantes], Université de Nantes (UN)-Centre hospitalier universitaire de Nantes (CHU Nantes), This work was supported by Fondation Maladie Rares (grant PMO1201204), Agence Nationale de la Recherche (grant ANR-12-BSV1-0007-01) and the Agence de la Biomedecine (AMP2016). This work was supported by La Fondation Maladie Rares and the Agence de la Biomedecine. The authors acknowledge the Centre de Ressources Biologiques (CRB)-Santé (http://www.crbsante-rennes.com) of Rennes for managing patient samples. This Work was supported by France Génomique National infrastructure, funded as part of 'Investissement d'avenir' program managed by Agence Nationale pour la Recherche (contrat ANR-10-INBS-09) https://www.france-genomique.org/spip/spip.php?article158. This study makes use of data generated by the Genome of the Netherlands Project. Funding for the project was provided by the Netherlands Organization for Scientific Research under award number 184 021 007, dated July 9, 2009 and made available as a Rainbow Project of the Biobanking and Biomolecular Research Infrastructure Netherlands (BBMRI-NL). Samples where contributed by LifeLines (http://lifelines.nl/lifelines-research/general), The Leiden Longevity Study (http://www.healthy-ageing.nl, ANR-10-INBS-0009,France-Génomique,Organisation et montée en puissance d'une Infrastructure Nationale de Génomique(2010), APH - Methodology, APH - Mental Health, Biological Psychology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, Université de Rennes (UR)-Centre National de la Recherche Scientifique (CNRS)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)

Subjects
0301 basic medicine, Exome/genetics, Male, Multifactorial Inheritance, MOUSE, PHENOTYPE, GUIDELINES, PATHWAY, 0302 clinical medicine, Holoprosencephaly, Locus heterogeneity, SEQUENCE VARIANTS, oligogenic inheritance, Sonic hedgehog, Exome, Exome sequencing, Genetics, 0303 health sciences, Comparative Genomic Hybridization, Oligogenic Inheritance, Phenotype, 3. Good health, Pedigree, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, FAT1, musculoskeletal diseases, EXPRESSION, congenital, hereditary, and neonatal diseases and abnormalities, Holoprosencephaly/genetics, Clinical Neurology, Biology, MICE LACKING, 03 medical and health sciences, sonic hedgehog, Rare Diseases, Rare Diseases/genetics, primary cilia, DEFICIENT, medicine, Humans, Gene, Multifactorial Inheritance/genetics, 030304 developmental biology, [SDV.GEN]Life Sciences [q-bio]/Genetics, IDENTIFICATION, Genetic heterogeneity, MUTATIONS, medicine.disease, 030104 developmental biology, holoprosencephaly, Case-Control Studies, Forebrain, Mutation, biology.protein, Neurology (clinical), 030217 neurology & neurosurgery, exome
Abstract

Kim et al. identify novel genes and disease pathways in the forebrain developmental disorder holoprosencephaly, and show that many cases involve oligogenic inheritance. The findings underline the roles of Sonic Hedgehog and primary cilia in forebrain development, and show that integrating clinical phenotyping into genetic studies can uncover relevant mutations.Holoprosencephaly is a pathology of forebrain development characterized by high phenotypic heterogeneity. The disease presents with various clinical manifestations at the cerebral or facial levels. Several genes have been implicated in holoprosencephaly but its genetic basis remains unclear: different transmission patterns have been described including autosomal dominant, recessive and digenic inheritance. Conventional molecular testing approaches result in a very low diagnostic yield and most cases remain unsolved. In our study, we address the possibility that genetically unsolved cases of holoprosencephaly present an oligogenic origin and result from combined inherited mutations in several genes. Twenty-six unrelated families, for whom no genetic cause of holoprosencephaly could be identified in clinical settings [whole exome sequencing and comparative genomic hybridization (CGH)-array analyses], were reanalysed under the hypothesis of oligogenic inheritance. Standard variant analysis was improved with a gene prioritization strategy based on clinical ontologies and gene co-expression networks. Clinical phenotyping and exploration of cross-species similarities were further performed on a family-by-family basis. Statistical validation was performed on 248 ancestrally similar control trios provided by the Genome of the Netherlands project and on 574 ancestrally matched controls provided by the French Exome Project. Variants of clinical interest were identified in 180 genes significantly associated with key pathways of forebrain development including sonic hedgehog (SHH) and primary cilia. Oligogenic events were observed in 10 families and involved both known and novel holoprosencephaly genes including recurrently mutated FAT1, NDST1, COL2A1 and SCUBE2. The incidence of oligogenic combinations was significantly higher in holoprosencephaly patients compared to two control populations (P

Published
2019

18. WGS-based telomere length analysis in Dutch family trios implicates stronger maternal inheritance and a role for RRM1 gene

Author

Nersisyan, L. (Lilit), Nikoghosyan, M. (Maria), Arakelyan, A. (Arsen), Francioli, L.C. (Laurent), Menelaou, A. (Androniki), Pulit, S.L. (Sara), Elbers, C.C. (Clara), Kloosterman, W.P. (Wigard), Setten, J. (Jessica) van, Nijman, I.J. (Isaac ), Renkens, I. (Ivo), de Bakker, P.I.W. (Paul I. W.), Dijk, F. (Freerk) van, Neerincx, P.B.T. (Pieter B T), Deelen, P. (Patrick), Kanterakis, A. (Alexandros), Dijkstra, M. (Martijn), Byelas, H. (Heorhiy), van der Velde, K.J. (K. Joeri), Platteel, I. (Inge), Swertz, M.A. (Morris A.), Wijmenga, C. (Cisca), Palamara, P.F. (Pier Francesco), Peer, I. (Itsik), Ye, K. (Kai), Lameijer, E.-W. (Eric-Wubbo), Moed, H. (Heleen), Beekman, M. (Marian), Craen, A.J. (Anton) de, Suchiman, H.E.D. (H. Eka D.), Slagboom, P.E. (Eline), Guryev, V. (Victor), Abdellaoui, A. (Abdel), Hottenga, J.J. (Jouke Jan), Kattenberg, V.M. (Mathijs), Willemsen, G.A.H.M. (Gonneke), Boomsma, D.I. (Dorret), van Leeuwen, E.M. (Elisabeth M.), Karssen, L.C. (Lennart), Amin, N. (Najaf), Rivadeneira, F. (Fernando), Isaacs, A.J. (Aaron), Hofman, A. (Albert), Uitterlinden, A.G. (André), Duijn, C.M. (Cornelia) van, Oven, M. (Mannis) van, Kayser, M.H. (Manfred), Vermaat, M. (Martijn), Laros, J.F.J. (Jeroen F.), Dunnen, J.T. (Johan) den, Enckevort, D. (David) van, Mei, S. (Shan), Li, M. (Mingkun), Stoneking, M. (Mark), Schaik, B.D.C. (Barbera) van, Bot, J.J. (Jan), Marschall, T. (Tanja), Schönhuth, A. (Alexander), Hehir-Kwa, J. (Jayne), Handsaker, R.E. (Robert), Polak, P., Sohail, M. (Mashaal), Vuzman, D. (Dana), Estrada Gil, K. (Karol), McCarroll, S.A. (Steve), Sunyaev, S.R. (Shamil), Hormozdiari, F. (Fereydoun), Koval, V. (Vyacheslav), Medina-Gomez, M.C. (Carolina), Oostra, B.A. (Ben), Veldink, J.H. (Jan), Berg, L.H. (Leonard) van den, Pitts, S.J. (Steven J.), Potluri, S. (Shobha), Sundar, P. (Purnima), Cox, D.R. (David), Knijff, P. (Peter) de, Li, Q. (Qibin), Li, Y. (Yingrui), Du, Y. (Yuanping), Chen, R. (Ruoyan), Cao, H. (Hongzhi), Wang, J. (Jun), Li, N. (Ning), Cao, S. (Sujie), Bovenberg, J.A. (Jasper), Ommen, G.J. (Gert) van, Nersisyan, L. (Lilit), Nikoghosyan, M. (Maria), Arakelyan, A. (Arsen), Francioli, L.C. (Laurent), Menelaou, A. (Androniki), Pulit, S.L. (Sara), Elbers, C.C. (Clara), Kloosterman, W.P. (Wigard), Setten, J. (Jessica) van, Nijman, I.J. (Isaac ), Renkens, I. (Ivo), de Bakker, P.I.W. (Paul I. W.), Dijk, F. (Freerk) van, Neerincx, P.B.T. (Pieter B T), Deelen, P. (Patrick), Kanterakis, A. (Alexandros), Dijkstra, M. (Martijn), Byelas, H. (Heorhiy), van der Velde, K.J. (K. Joeri), Platteel, I. (Inge), Swertz, M.A. (Morris A.), Wijmenga, C. (Cisca), Palamara, P.F. (Pier Francesco), Peer, I. (Itsik), Ye, K. (Kai), Lameijer, E.-W. (Eric-Wubbo), Moed, H. (Heleen), Beekman, M. (Marian), Craen, A.J. (Anton) de, Suchiman, H.E.D. (H. Eka D.), Slagboom, P.E. (Eline), Guryev, V. (Victor), Abdellaoui, A. (Abdel), Hottenga, J.J. (Jouke Jan), Kattenberg, V.M. (Mathijs), Willemsen, G.A.H.M. (Gonneke), Boomsma, D.I. (Dorret), van Leeuwen, E.M. (Elisabeth M.), Karssen, L.C. (Lennart), Amin, N. (Najaf), Rivadeneira, F. (Fernando), Isaacs, A.J. (Aaron), Hofman, A. (Albert), Uitterlinden, A.G. (André), Duijn, C.M. (Cornelia) van, Oven, M. (Mannis) van, Kayser, M.H. (Manfred), Vermaat, M. (Martijn), Laros, J.F.J. (Jeroen F.), Dunnen, J.T. (Johan) den, Enckevort, D. (David) van, Mei, S. (Shan), Li, M. (Mingkun), Stoneking, M. (Mark), Schaik, B.D.C. (Barbera) van, Bot, J.J. (Jan), Marschall, T. (Tanja), Schönhuth, A. (Alexander), Hehir-Kwa, J. (Jayne), Handsaker, R.E. (Robert), Polak, P., Sohail, M. (Mashaal), Vuzman, D. (Dana), Estrada Gil, K. (Karol), McCarroll, S.A. (Steve), Sunyaev, S.R. (Shamil), Hormozdiari, F. (Fereydoun), Koval, V. (Vyacheslav), Medina-Gomez, M.C. (Carolina), Oostra, B.A. (Ben), Veldink, J.H. (Jan), Berg, L.H. (Leonard) van den, Pitts, S.J. (Steven J.), Potluri, S. (Shobha), Sundar, P. (Purnima), Cox, D.R. (David), Knijff, P. (Peter) de, Li, Q. (Qibin), Li, Y. (Yingrui), Du, Y. (Yuanping), Chen, R. (Ruoyan), Cao, H. (Hongzhi), Wang, J. (Jun), Li, N. (Ning), Cao, S. (Sujie), Bovenberg, J.A. (Jasper), and Ommen, G.J. (Gert) van

Abstract

Telomere length (TL) regulation is an important factor in ageing, reproduction and cancer development. Genetic, hereditary and environmental factors regulating TL are currently widely investigated, however, their relative contribution to TL variability is still understudied. We have used whole genome sequencing data of 250 family trios from the Genome of the Netherlands project to perform computational measurement of TL and a series of regression and genome-wide association analyses to reveal TL inheritance patterns and associated genetic factors. Our results confirm that TL is a largely heritable trait, primarily with mother’s, and, to a lesser extent, with father’s TL having the strongest influence on the offspring. In this cohort, mother’s, but not father’s age at conception was positively linked to offspring TL. Age-related TL attrition of 40 bp/year had relatively small influence on TL variability. Finally, we have identified TL-associated variations in ribonuclease reductase catalytic subunit M1 (RRM1 gene), which is known to regulate telomere maintenance in yeast. We also highlight the importance of multivariate approach and the limitations of existing tools for the analysis of TL as a polygenic heritable quantitative trait.

Published
2019
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19. 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

20. Whole genome sequencing and the application of a SNP panel reveal primary evolutionary lineages and genomic variation in the lion (Panthera leo)

Author

Bertola, L.D., primary, Vermaat, M., additional, Lesilau, F., additional, Chege, M., additional, Tumenta, P.N., additional, Sogbohossou, E.A., additional, Schaap, O.D., additional, Bauer, H., additional, Patterson, B.D., additional, White, P.A., additional, de Iongh, H.H., additional, Laros, J.F.J., additional, and Vrieling, K., additional

Published
2019
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21. 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

22. Full-length mRNA sequencing uncovers a widespread coupling between transcription initiation and mRNA processing

Author

Anvar, S.Y., Allard, G., Tseng, E., Sheynkman, G.M., Klerk, E. de, Vermaat, M., Yin, R.H., Johansson, H.E., Ariyurek, Y., Dunnen, J.T. den, Turner, S.W., Hoen, P.A.C. 't, Anvar, S.Y., Allard, G., Tseng, E., Sheynkman, G.M., Klerk, E. de, Vermaat, M., Yin, R.H., Johansson, H.E., Ariyurek, Y., Dunnen, J.T. den, Turner, S.W., and Hoen, P.A.C. 't

Abstract

Contains fulltext : 190630.pdf (publisher's version ) (Open Access), BACKGROUND: The multifaceted control of gene expression requires tight coordination of regulatory mechanisms at transcriptional and post-transcriptional level. Here, we studied the interdependence of transcription initiation, splicing and polyadenylation events on single mRNA molecules by full-length mRNA sequencing. RESULTS: In MCF-7 breast cancer cells, we find 2700 genes with interdependent alternative transcription initiation, splicing and polyadenylation events, both in proximal and distant parts of mRNA molecules, including examples of coupling between transcription start sites and polyadenylation sites. The analysis of three human primary tissues (brain, heart and liver) reveals similar patterns of interdependency between transcription initiation and mRNA processing events. We predict thousands of novel open reading frames from full-length mRNA sequences and obtained evidence for their translation by shotgun proteomics. The mapping database rescues 358 previously unassigned peptides and improves the assignment of others. By recognizing sample-specific amino-acid changes and novel splicing patterns, full-length mRNA sequencing improves proteogenomics analysis of MCF-7 cells. CONCLUSIONS: Our findings demonstrate that our understanding of transcriptome complexity is far from complete and provides a basis to reveal largely unresolved mechanisms that coordinate transcription initiation and mRNA processing.

Published
2018

23. 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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24. 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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25. 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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26. Genome-wide patterns and properties of de novo mutations in humans

Author

Francioli, L.C., Polak, P.P., Koren, A., Menelaou, A., Chun, S., Renkens, I., van Duijn, C.M., Swertz, M.A., Wijmenga, C., van Ommen, G.J., Slagboom, P.E., Boomsma, D.I., Ye, K., Guryev, V., Arndt, P.F., Kloosterman, W.P., Bakker, P.I.W., Sunyaev, S.R., Dijk, F., Neerincx, P.B.T., Pulit, S.L., Deelen, P., Elbers, C.C., Palamara, P.F., Pe'er, I., Abdellaoui, A., van Oven, M., Vermaat, M., Li, M., Laros, J.F.J., Stoneking, M., de Knijff, P., Kayser, M., Veldink, J.H., Van den Berg, L.H., Byelas, H., den Dunnen, J.T., Dijkstra, M., Amin, N., van der Velde, K.J., Hottenga, J.J., van Setten, J., van Leeuwen, E.M., Kanterakis, A., Kattenberg, V.M., Karssen, L.C., van Schaik, B.D.C., Bot, J., Nijman, I.J., van Enckevort, D., Mei, H., Koval, V., Estrada, K., Medina-Gomez, C., Lameijer, E.W., Moed, M.H., Hehir-Kwa, J.Y., Handsaker, R.E., McCarroll, S.A., Vuzman, D., Sohail, M., Hormozdiari, F., Marschall, T., Schönhuth, A., Beekman, M., de Craen, A.J., Suchiman, H.E.D., Hofman, A., Oostra, B., Isaacs, A., Rivadeneira, F., Uitterlinden, A.G., Willemsen, G., Platteel, M., Pitts, S.J., Potluri, S., Sundar, P., Cox, D.R., Li, Q., Li, Y., Du, Y., Chen, R., Cao, H., Li, N., Cao, S., Wang, J., Bovenberg, J.A., Brandsma, M., Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Stem Cell Aging Leukemia and Lymphoma (SALL), Groningen Research Institute for Asthma and COPD (GRIAC), Biological Psychology, Culture, Organization and Management, Neuroscience Campus Amsterdam - Neurobiology of Mental Health, Epidemiology, and Pharmacy

Subjects
Male, Netherlands Twin Register (NTR), Mutation rate, Population genetics, Twin Study, DISEASE, Nucleotide diversity, 0302 clinical medicine, Mutation Rate, ELEMENTS, Non-U.S. Gov't, POPULATION, Genetics, 0303 health sciences, education.field_of_study, Research Support, Non-U.S. Gov't, SUBSTITUTION, Mutation (genetic algorithm), Female, Pan troglodytes, Population, DNA-SEQUENCING DATA, Mutagenesis (molecular biology technique), Biology, Research Support, Article, Paternal Age, N.I.H, Evolution, Molecular, 03 medical and health sciences, Germline mutation, SDG 3 - Good Health and Well-being, Research Support, N.I.H., Extramural, Journal Article, Animals, Humans, education, Germ-Line Mutation, 030304 developmental biology, Models, Genetic, Genome, Human, Extramural, FRAMEWORK, POLYMORPHISM, RECOMBINATION RATES, RESOLUTION, RADIATION, Human genome, 030217 neurology & neurosurgery
Abstract

Mutations create variation in the population, fuel evolution and cause genetic diseases. Current knowledge about de novo mutations is incomplete and mostly indirect(1-10). Here we analyze 11,020 de novo mutations from the whole genomes of 250 families. We show that de novo mutations in the offspring of older fathers are not only more numerous(11-13) but also occur more frequently in early-replicating, genic regions. Functional regions exhibit higher mutation rates due to CpG dinucleotides and show signatures of transcriptioncoupled repair, whereas mutation clusters with a unique signature point to a new mutational mechanism. Mutation and recombination rates independently associate with nucleotide diversity, and regional variation in human-chimpanzee divergence is only partly explained by heterogeneity in mutation rate. Finally, we provide a genome-wide mutation rate map for medical and population genetics applications. Our results provide new insights and refine long-standing hypotheses about human mutagenesis.

Published
2015

27. Genome of the Netherlands population-specific imputations identify an ABCA6 variant associated with cholesterol levels

Author

Van Leeuwen, EM, Karssen, LC, Deelen, J, Isaacs, A, Medina-Gomez, C, Mbarek, H, Kanterakis, A, Trompet, S, Postmus, I, Verweij, N, Van Enckevort, DJ, Huffman, JE, White, CC, Feitosa, MF, Bartz, TM, Manichaikul, A, Joshi, PK, Peloso, GM, Deelen, P, Van Dijk, F, Willemsen, G, De Geus, EJ, Milaneschi, Y, Penninx, BWJH, Francioli, LC, Menelaou, A, Pulit, SL, Rivadeneira, F, Hofman, A, Oostra, BA, Franco, OH, Leach, IM, Beekman, M, De Craen, AJM, Uh, HW, Trochet, H, Hocking, LJ, Porteous, DJ, Sattar, N, Packard, CJ, Buckley, BM, Brody, JA, Bis, JC, Rotter, JI, Mychaleckyj, JC, Campbell, H, Duan, Q, Lange, LA, Wilson, JF, Hayward, C, Polasek, O, Vitart, V, Rudan, I, Wright, AF, Rich, SS, Psaty, BM, Borecki, IB, Kearney, PM, Stott, DJ, Cupples, LA, Jukema, JW, Van Der Harst, P, Sijbrands, EJ, Hottenga, JJ, Uitterlinden, AG, Swertz, MA, Van Ommen, GJB, De Bakker, PIW, Eline Slagboom, P, Boomsma, DI, Wijmenga, C, Van Duijn, CM, Neerincx, PBT, Elbers, CC, Palamara, PF, Peer, I, Abdellaoui, A, Kloosterman, WP, Van Oven, M, Vermaat, M, Li, M, Laros, JFJ, Stoneking, M, De Knijff, P, Kayser, M, Veldink, JH, Van Den Berg, LH, Byelas, H, Den Dunnen, JT, Dijkstra, M, Amin, N, Van Der Velde, KJ, and Van Setten, J

Abstract

© 2015 Macmillan Publishers Limited. All rights reserved. Variants associated with blood lipid levels may be population-specific. To identify low-frequency variants associated with this phenotype, population-specific reference panels may be used. Here we impute nine large Dutch biobanks (∼35,000 samples) with the population-specific reference panel created by the Genome of the Netherlands Project and perform association testing with blood lipid levels. We report the discovery of five novel associations at four loci (P value

Published
2015

28. Transmission of human mtDNA heteroplasmy in the genome of the Netherlands families: Support for a variable-size bottleneck

Author

Li, M. (Mingkun), Rothwell, R. (Rebecca), Vermaat, M. (Martijn), Wachsmuth, M. (Manja), Schröder, R. (Roland), Laros, J.F.J. (Jeroen F.), Oven, M. (Mannis) van, Bakker, P.I.W. (Paul) de, Bovenberg, J.A. (Jasper), Duijn, C.M. (Cornelia) van, Van Ommen, G.-J.B. (Gert-Jan B.), Slagboom, P.E. (Eline), Swertz, M. (Morris), Wijmenga, C. (Cisca), Kayser, M.H. (Manfred), Boomsma, D.I. (Dorret), Zöllner, S. (Sebastian), Knijff, P. (Peter) de, Stoneking, M. (Mark), Li, M. (Mingkun), Rothwell, R. (Rebecca), Vermaat, M. (Martijn), Wachsmuth, M. (Manja), Schröder, R. (Roland), Laros, J.F.J. (Jeroen F.), Oven, M. (Mannis) van, Bakker, P.I.W. (Paul) de, Bovenberg, J.A. (Jasper), Duijn, C.M. (Cornelia) van, Van Ommen, G.-J.B. (Gert-Jan B.), Slagboom, P.E. (Eline), Swertz, M. (Morris), Wijmenga, C. (Cisca), Kayser, M.H. (Manfred), Boomsma, D.I. (Dorret), Zöllner, S. (Sebastian), Knijff, P. (Peter) de, and Stoneking, M. (Mark)

Abstract

Although previous studies have documented a bottleneck in the transmission of mtDNA genomes from mothers to offspring, several aspects remain unclear, including the size and nature of the bottleneck. Here, we analyze the dynamics of mtDNA heteroplasmy transmission in the Genomes of the Netherlands (GoNL) data, which consists of complete mtDNA genome sequences from 228 trios, eight dizygotic (DZ) twin quartets, and 10 monozygotic (MZ) twin quartets. Using a minor allele frequency (MAF) threshold of 2%, we identified 189 heteroplasmies in the trio mothers, of which 59% were transmitted to offspring, and 159 heteroplasmies in the trio offspring, of which 70% were inherited from the mothers. MZ twin pairs exhibited greater similarity in MAF at heteroplasmic sites than DZ twin pairs, suggesting that the heteroplasmy MAF in the oocyte is the major determinant of the heteroplasmy MAF in the offspring. We used a likelihood method to estimate the effective number of mtDNA genomes transmitted to offspring under different bottleneck models; a variable bottleneck size model provided the best fit to the data, with an estimated mean of nine individual mtDNA genomes transmitted. We also found evidence for negative selection during transmission against novel heteroplasmies (in which the minor allele has never been observed in polymorphism data). These novel heteroplasmies are enhanced for tRNA and rRNA genes, and mutations associated with mtDNA diseases frequently occur in these genes. Our results thus suggest that the female germ line is able to recognize and select against deleterious heteroplasmies.

Published
2016
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29. A high-quality human reference panel reveals the complexity and distribution of genomic structural variants

Author

Hehir-Kwa, J.Y. (Jayne), Marschall, T. (Tobias), Kloosterman, W.P. (Wigard), Francioli, L.C. (Laurent), Baaijens, J.A. (Jasmijn), Dijkstra, L.J. (Louis), Abdellaoui, A. (Abdel), Koval, V. (Vyacheslav), Thung, (), D.T. (Djie Tjwan), Wardenaar, R. (René), Renkens, I. (Ivo), Coe, B.P. (Bradley), Deelen, P. (Patrick), Ligt, J. (Joep) de, Lameijer, E.-W. (Eric-Wubbo), Dijk, F. (Freerk) van, Hormozdiari, F. (Fereydoun), Uitterlinden, A.G. (André), Duijn, C.M. (Cornelia) van, Eichler, E.E. (Evan), Bakker, P.I.W. (Paul) de, Swertz, M.A. (Morris), Wijmenga, C. (Cisca), Ommen, G.-J.B. (Gert-Jan) van, Slagboom, P.E. (Eline), Boomsma, D.I. (Dorret), Schönhuth, A. (Alexander), Ye, K. (Kai), Guryev, V. (Victor), Bovenberg, J.A. (Jasper), Craen, A.J.M. (Anton) de, Beekman, M. (Marian), Hofman, A. (Albert), Willemsen, G. (Gonneke), Wolffenbuttel, B. (Bruce), Platteel, M. (Mathieu), Du, Y. (Yuanping), Chen, R. (Ruoyan), Cao, H. (Hongzhi), Cao, R. (Rui), Sun, Y. (Yushen), Cao, J.S. (Jeremy Sujie), Neerincx, P.B.T. (Pieter), Dijkstra, M. (Martijn), Byelas, G. (George), Kanterakis, A. (Alexandros), Bot, J. (Jan), Vermaat, M. (Martijn), Laros, J.F.J. (Jeroen), Dunnen, J.T. (Johan) den, Knijff, P. (Peter) de, Karssen, L.C. (Lennart), van Leeuwen, E.M. (Elisa), Amin, N. (Najaf), Rivadeneira, F. (Fernando), Estrada, K. (Karol), Hottenga, J.-J. (Jouke-Jan), Kattenberg, V.M. (Mathijs), Enckevort, D. (David) van, Mei, H. (Hailiang), Santcroos, M. (Mark), Schaik, B.D.C. (Barbera) van, Handsaker, R.E. (Robert), McCarroll, S.A. (Steven), Ko, A. (Arthur), Sudmant, P. (Peter), Nijman, I.J. (Isaac), Hehir-Kwa, J.Y. (Jayne), Marschall, T. (Tobias), Kloosterman, W.P. (Wigard), Francioli, L.C. (Laurent), Baaijens, J.A. (Jasmijn), Dijkstra, L.J. (Louis), Abdellaoui, A. (Abdel), Koval, V. (Vyacheslav), Thung, (), D.T. (Djie Tjwan), Wardenaar, R. (René), Renkens, I. (Ivo), Coe, B.P. (Bradley), Deelen, P. (Patrick), Ligt, J. (Joep) de, Lameijer, E.-W. (Eric-Wubbo), Dijk, F. (Freerk) van, Hormozdiari, F. (Fereydoun), Uitterlinden, A.G. (André), Duijn, C.M. (Cornelia) van, Eichler, E.E. (Evan), Bakker, P.I.W. (Paul) de, Swertz, M.A. (Morris), Wijmenga, C. (Cisca), Ommen, G.-J.B. (Gert-Jan) van, Slagboom, P.E. (Eline), Boomsma, D.I. (Dorret), Schönhuth, A. (Alexander), Ye, K. (Kai), Guryev, V. (Victor), Bovenberg, J.A. (Jasper), Craen, A.J.M. (Anton) de, Beekman, M. (Marian), Hofman, A. (Albert), Willemsen, G. (Gonneke), Wolffenbuttel, B. (Bruce), Platteel, M. (Mathieu), Du, Y. (Yuanping), Chen, R. (Ruoyan), Cao, H. (Hongzhi), Cao, R. (Rui), Sun, Y. (Yushen), Cao, J.S. (Jeremy Sujie), Neerincx, P.B.T. (Pieter), Dijkstra, M. (Martijn), Byelas, G. (George), Kanterakis, A. (Alexandros), Bot, J. (Jan), Vermaat, M. (Martijn), Laros, J.F.J. (Jeroen), Dunnen, J.T. (Johan) den, Knijff, P. (Peter) de, Karssen, L.C. (Lennart), van Leeuwen, E.M. (Elisa), Amin, N. (Najaf), Rivadeneira, F. (Fernando), Estrada, K. (Karol), Hottenga, J.-J. (Jouke-Jan), Kattenberg, V.M. (Mathijs), Enckevort, D. (David) van, Mei, H. (Hailiang), Santcroos, M. (Mark), Schaik, B.D.C. (Barbera) van, Handsaker, R.E. (Robert), McCarroll, S.A. (Steven), Ko, A. (Arthur), Sudmant, P. (Peter), and Nijman, I.J. (Isaac)

Abstract

Structural variation (SV) represents a major source of differences between individual human genomes and has been linked to disease phenotypes. However, the majority of studies provide neither a global view of the full spectrum of these variants nor integrate them into reference panels of genetic variation. Here, we analyse whole genome sequencing data of 769 individuals from 250 Dutch families, and provide a haplotype-resolved map of 1.9 million genome variants across 9 different variant classes, including novel forms of complex indels, and retrotransposition-mediated insertions of mobile elements and processed RNAs. A large proportion are previously under reported variants sized between 21 and 100 bp. We detect 4 megabases of novel sequence, encoding 11 new transcripts. Finally, we show 191 known, trait-associated SNPs to be in strong linkage disequilibrium with SVs and demonstrate that our panel facilitates accurate imputation of SVs in unrelated individuals.

Published
2016
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30. Next-generation sequencing-based genome diagnostics across clinical genetics centers: implementation choices and their effects

Author

Vrijenhoek, T., Kraaijeveld, K., Elferink, M., Ligt, J. de, Kranendonk, E., Santen, G., Nijman, IJ, Butler, D., Claes, G., Costessi, A., Dorlijn, W., Eyndhoven, W. van, Halley, D.J., Hout, M.C. van den, Hove, S. van, Johansson, L.F., Jongbloed, J.D., Kamps, R., Kockx, C.E., Koning, B. de, Kriek, M., Deprez, R.L., Lunstroo, H., Mannens, M., Mook, O.R., Nelen, M.R., Ploem, C., Rijnen, M., Saris, J.J., Sinke, R., Sistermans, E., Slegtenhorst, M. van, Sleutels, F., Stoep, N. van der, Tienhoven, M. van, Vermaat, M., Vogel, M., Waisfisz, Q., Weiss, J.M., Wijngaard, A. van den, Workum, W. van, IJntema, H., Zwaag, B. van der, van, I.W.F., Dunnen, J.T. den, Veltman, J.A., Hennekam, R., Cuppen, E., Vrijenhoek, T., Kraaijeveld, K., Elferink, M., Ligt, J. de, Kranendonk, E., Santen, G., Nijman, IJ, Butler, D., Claes, G., Costessi, A., Dorlijn, W., Eyndhoven, W. van, Halley, D.J., Hout, M.C. van den, Hove, S. van, Johansson, L.F., Jongbloed, J.D., Kamps, R., Kockx, C.E., Koning, B. de, Kriek, M., Deprez, R.L., Lunstroo, H., Mannens, M., Mook, O.R., Nelen, M.R., Ploem, C., Rijnen, M., Saris, J.J., Sinke, R., Sistermans, E., Slegtenhorst, M. van, Sleutels, F., Stoep, N. van der, Tienhoven, M. van, Vermaat, M., Vogel, M., Waisfisz, Q., Weiss, J.M., Wijngaard, A. van den, Workum, W. van, IJntema, H., Zwaag, B. van der, van, I.W.F., Dunnen, J.T. den, Veltman, J.A., Hennekam, R., and Cuppen, E.

Abstract

Item does not contain fulltext

Published
2015

31. Next-generation sequencing-based genome diagnostics across clinical genetics centers: Implementation choices and their effects

Author

Vrijenhoek, T. (T.), Kraaijeveld, K. (Ken), Elferink, M.G. (Martin), Ligt, J. (Joep) de, Kranendonk, E. (Elcke), Santen, G.W.E. (Gijs), Nijman, I.J. (Isaac ), Butler, D. (Derek), Claes, G. (Godelieve), Costessi, A. (Adalberto), Dorlijn, W. (Wim), Van Eyndhoven, W. (Winfried), Halley, D.J.J. (Dicky), Van Den Hout, M.C.G.N. (Mirjam C.G.N.), Van Hove, S. (Steven), Johansson, L.F. (Lennart F.), Jongbloed, J.D.H. (Jan), Kamps, R. (Rick), Kockx, C. (Christel), De Koning, B. (Bart), Kriek, N. (Nadia), Lekanne Dit Deprez, R.H., Lunstroo, H. (Hans), Mannens, M.M.A.M. (Marcel), Mook, O. (Olaf), Nelen, M.R. (Marcel), Ploem, C. (Corrette), Rijnen, M. (Marco), Saris, J.J. (Jasper), Sinke, R.J. (Richard J), Sistermans, E. (Erik), Slegtenhorst, M.A. (Marjon) van, Sleutels, F. (Frank), Stoep, N. (Nienke) van der, Tienhoven, M. (Marianne) van, Vermaat, M. (Martijn), Vogel, M.J. (Maartje), Waisfisz, Q. (Quinten), Weiss, J.M. (Janneke), Wijngaard, A. (Arthur) van den, Workum, W. (W) van, Ijntema, H. (Helger), Zwaag, B. (Bert) van der, IJcken, W.F.J. (Wilfred) van, Dunnen, J.T. (Johan) den, Veltman, J.A. (Joris), Hennekam, R.C.M. (Raoul), Cuppen, E. (Edwin), Vrijenhoek, T. (T.), Kraaijeveld, K. (Ken), Elferink, M.G. (Martin), Ligt, J. (Joep) de, Kranendonk, E. (Elcke), Santen, G.W.E. (Gijs), Nijman, I.J. (Isaac ), Butler, D. (Derek), Claes, G. (Godelieve), Costessi, A. (Adalberto), Dorlijn, W. (Wim), Van Eyndhoven, W. (Winfried), Halley, D.J.J. (Dicky), Van Den Hout, M.C.G.N. (Mirjam C.G.N.), Van Hove, S. (Steven), Johansson, L.F. (Lennart F.), Jongbloed, J.D.H. (Jan), Kamps, R. (Rick), Kockx, C. (Christel), De Koning, B. (Bart), Kriek, N. (Nadia), Lekanne Dit Deprez, R.H., Lunstroo, H. (Hans), Mannens, M.M.A.M. (Marcel), Mook, O. (Olaf), Nelen, M.R. (Marcel), Ploem, C. (Corrette), Rijnen, M. (Marco), Saris, J.J. (Jasper), Sinke, R.J. (Richard J), Sistermans, E. (Erik), Slegtenhorst, M.A. (Marjon) van, Sleutels, F. (Frank), Stoep, N. (Nienke) van der, Tienhoven, M. (Marianne) van, Vermaat, M. (Martijn), Vogel, M.J. (Maartje), Waisfisz, Q. (Quinten), Weiss, J.M. (Janneke), Wijngaard, A. (Arthur) van den, Workum, W. (W) van, Ijntema, H. (Helger), Zwaag, B. (Bert) van der, IJcken, W.F.J. (Wilfred) van, Dunnen, J.T. (Johan) den, Veltman, J.A. (Joris), Hennekam, R.C.M. (Raoul), and Cuppen, E. (Edwin)

Abstract

Implementation of next-generation DNA sequencing (NGS) technology into routine diagnostic genome care requires strategic choices. Instead of theoretical discussions on the consequences of such choices, we compared NGS-based diagnostic practices in eight clinical genetic centers in the Netherlands, based on genetic testing of nine pre-selected patients with cardiomyopathy. We highlight critical implementation choices, including the specific contributions of laboratory and medical specialists, bioinformaticians and researchers to diagnostic genome care, and how these affect interpretation and reporting of variants. Reported pathogenic mutations were consistent for all but one patient. Of the two centers that were inconsistent in their diagnosis, one reported to have found 'no causal variant', thereby underdiagnosing this patient. The other provided an alternative diagnosis, identifying another variant as causal than the other centers. Ethical and legal analysis showed that informed consent procedures in all centers were generally adequate for diagnostic NGS applications that target a limited set of genes, but not for exome- and genome-based diagnosis. We propose changes to further improve and align these procedures, taking into account the blurring boundary between diagnostics and research, and specific counseling options for exome- and genome-based diagnostics. We conclude that alternative diagnoses may infer a certain level of 'greediness' to come to a positive diagnosis in interpreting sequencing results. Moreover, there is an increasing interdependence of clinic, diagnostics and research departments for comprehensive diagnostic genome care. Therefore, we invite clinical geneticists, physicians, researchers, bioinformatics experts and patients to reconsider their role and position in future diagnostic genome care.

Published
2015
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32. Next-generation sequencing-based genome diagnostics across clinical genetics centers: implementation choices and their effects

Author

Vrijenhoek, T, Kraaijeveld, K, Elferink, M, de Ligt, J, Kranendonk, E, Santen, G, Nijman, IJ, Butler, D, Claes, G, Costessi, A, Dorlijn, W, van Eyndhoven, W, Halley, Dicky, Van den Hout - van Vroonhoven, Mirjam, van Hove, S, Johansson, LF, Jongbloed, JDH, Kamps, R, Kockx, Christel, de Koning, B, Kriek, M, Deprez, RLD, Lunstroo, H, Mannens, M, Mook, OR, Nelen, M, Ploem, C, Rijnen, M, Saris, Jasper, Sinke, R, Sistermans, E, van Slegtenhorst, Marjon, Sleutels, Frank, van der Stoep, N, Tienhoven, Marianne, Vermaat, M, Vogel, M, Waisfisz, Q, Weiss, JM, van den Wijngaard, A, van Workum, W, Ijntema, H, Van der Zwaag, B, van Ijcken, Wilfred, den Dunnen, J, Veltman, JA, Hennekam, R, Cuppen, E, Vrijenhoek, T, Kraaijeveld, K, Elferink, M, de Ligt, J, Kranendonk, E, Santen, G, Nijman, IJ, Butler, D, Claes, G, Costessi, A, Dorlijn, W, van Eyndhoven, W, Halley, Dicky, Van den Hout - van Vroonhoven, Mirjam, van Hove, S, Johansson, LF, Jongbloed, JDH, Kamps, R, Kockx, Christel, de Koning, B, Kriek, M, Deprez, RLD, Lunstroo, H, Mannens, M, Mook, OR, Nelen, M, Ploem, C, Rijnen, M, Saris, Jasper, Sinke, R, Sistermans, E, van Slegtenhorst, Marjon, Sleutels, Frank, van der Stoep, N, Tienhoven, Marianne, Vermaat, M, Vogel, M, Waisfisz, Q, Weiss, JM, van den Wijngaard, A, van Workum, W, Ijntema, H, Van der Zwaag, B, van Ijcken, Wilfred, den Dunnen, J, Veltman, JA, Hennekam, R, and Cuppen, E

Abstract

Implementation of next-generation DNA sequencing (NGS) technology into routine diagnostic genome care requires strategic choices. Instead of theoretical discussions on the consequences of such choices, we compared NGS-based diagnostic practices in eight clinical genetic centers in the Netherlands, based on genetic testing of nine pre-selected patients with cardiomyopathy. We highlight critical implementation choices, including the specific contributions of laboratory and medical specialists, bioinformaticians and researchers to diagnostic genome care, and how these affect interpretation and reporting of variants. Reported pathogenic mutations were consistent for all but one patient. Of the two centers that were inconsistent in their diagnosis, one reported to have found 'no causal variant', thereby underdiagnosing this patient. The other provided an alternative diagnosis, identifying another variant as causal than the other centers. Ethical and legal analysis showed that informed consent procedures in all centers were generally adequate for diagnostic NGS applications that target a limited set of genes, but not for exome-and genome-based diagnosis. We propose changes to further improve and align these procedures, taking into account the blurring boundary between diagnostics and research, and specific counseling options for exome- and genome-based diagnostics. We conclude that alternative diagnoses may infer a certain level of 'greediness' to come to a positive diagnosis in interpreting sequencing results. Moreover, there is an increasing interdependence of clinic, diagnostics and research departments for comprehensive diagnostic genome care. Therefore, we invite clinical geneticists, physicians, researchers, bioinformatics experts and patients to reconsider their role and position in future diagnostic genome care.

Published
2015

35. Whole-genome sequence variation, population structure and demographic history of the Dutch population

Author

Francioli, L.C., Menelaou, A., Pulit, S.L., Dijk, F. van, Palamara, P.F., Elbers, C.C., Neerincx, P.B., Ye, K., Guryev, V., Kloosterman, W.P., Deelen, P., Abdellaoui, A., Leeuwen, E.M. van, Oven, M. van, Vermaat, M., Li, M., Laros, J.F., Karssen, L.C., Kanterakis, A., Amin, N., Hottenga, J.J., Lameijer, E.W., Kattenberg, M., Dijkstra, M., Byelas, H., Setten, J. van, Schaik, B.D. van, Bot, J., Nijman, I.J., Renkens, I., Marschall, T., Schönhuth, A., Hehir-Kwa, J.Y., Handsaker, R.E., Polak, P., Sohail, M., Vuzman, D., Hormozdiari, F., Enckevort, D. van, Mei, H., Koval, V., Moed, M.H., Velde, K.J. van der, Rivadeneira, F., Estrada, K., Medina-Gomez, C., Isaacs, A., McCarroll, S.A., Beekman, M., Craen, A.J. de, Suchiman, H.E., Hofman, A., Oostra, B., Uitterlinden, A.G., Willemsen, G., Platteel, M., Veldink, J.H., Berg, L.H. van den, Pitts, S.J., Potluri, S., Sundar, P., Cox, D.R., Sunyaev, S.R., Dunnen, J.T. den, Stoneking, M., Knijff, P. de, Kayser, M., Li, Q., Li, Y., Du, Y., Chen, R., Cao, H., Li, N., Cao, S., Wang, J, Bovenberg, J.A., Pe'er, I., Slagboom, P.E., Duijn, C.M. van, Boomsma, D.I., Ommen, G.J. van, Bakker, P.I. de, Swertz, M.A., Wijmenga, C., Francioli, L.C., Menelaou, A., Pulit, S.L., Dijk, F. van, Palamara, P.F., Elbers, C.C., Neerincx, P.B., Ye, K., Guryev, V., Kloosterman, W.P., Deelen, P., Abdellaoui, A., Leeuwen, E.M. van, Oven, M. van, Vermaat, M., Li, M., Laros, J.F., Karssen, L.C., Kanterakis, A., Amin, N., Hottenga, J.J., Lameijer, E.W., Kattenberg, M., Dijkstra, M., Byelas, H., Setten, J. van, Schaik, B.D. van, Bot, J., Nijman, I.J., Renkens, I., Marschall, T., Schönhuth, A., Hehir-Kwa, J.Y., Handsaker, R.E., Polak, P., Sohail, M., Vuzman, D., Hormozdiari, F., Enckevort, D. van, Mei, H., Koval, V., Moed, M.H., Velde, K.J. van der, Rivadeneira, F., Estrada, K., Medina-Gomez, C., Isaacs, A., McCarroll, S.A., Beekman, M., Craen, A.J. de, Suchiman, H.E., Hofman, A., Oostra, B., Uitterlinden, A.G., Willemsen, G., Platteel, M., Veldink, J.H., Berg, L.H. van den, Pitts, S.J., Potluri, S., Sundar, P., Cox, D.R., Sunyaev, S.R., Dunnen, J.T. den, Stoneking, M., Knijff, P. de, Kayser, M., Li, Q., Li, Y., Du, Y., Chen, R., Cao, H., Li, N., Cao, S., Wang, J, Bovenberg, J.A., Pe'er, I., Slagboom, P.E., Duijn, C.M. van, Boomsma, D.I., Ommen, G.J. van, Bakker, P.I. de, Swertz, M.A., and Wijmenga, C.

Abstract

Contains fulltext : 137213.pdf (publisher's version ) (Closed access), Whole-genome sequencing enables complete characterization of genetic variation, but geographic clustering of rare alleles demands many diverse populations be studied. Here we describe the Genome of the Netherlands (GoNL) Project, in which we sequenced the whole genomes of 250 Dutch parent-offspring families and constructed a haplotype map of 20.4 million single-nucleotide variants and 1.2 million insertions and deletions. The intermediate coverage ( approximately 13x) and trio design enabled extensive characterization of structural variation, including midsize events (30-500 bp) previously poorly catalogued and de novo mutations. We demonstrate that the quality of the haplotypes boosts imputation accuracy in independent samples, especially for lower frequency alleles. Population genetic analyses demonstrate fine-scale structure across the country and support multiple ancient migrations, consistent with historical changes in sea level and flooding. The GoNL Project illustrates how single-population whole-genome sequencing can provide detailed characterization of genetic variation and may guide the design of future population studies.

Published
2014

36. The Genome of the Netherlands: design, and project goals

Author

Boomsma, DI, Wijmenga, C, Slagboom, EP, Swertz, MA, Karssen, Lennart, Abdellaoui, A, Ye, K, Guryev, V, Vermaat, M, Dijk, Femke, Francioli, LC, Hottenga, JJ (Jouke Jan), Laros, JFJ, Li, QB, Li, YR, Cao, HZ, Chen, RY, Du, YP, Li, N (Nan), Cao, SJ, van Setten, J, Menelaou, A, Pulit, SL, Hehir-Kwa, JY, Beekman, M, Elbers, CC, Byelas, H, de Craen, AJM, Deelen, P, Dijkstra, M, Dunnen, JT, de Knijff, P, Houwing-Duistermaat, J, Koval, Slavik, Estrada Gil, Karol, Hofman, Bert, Kanterakis, A, van Enckevort, D, Mai, HL, Kattenberg, M, Leeuwen, Elisa, Neerincx, PBT, Oostra, Ben, Rivadeneira, Fernando, Suchiman, EHD, Uitterlinden, André, Willemsen, G, Wolffenbuttel, BH, Wang, Johnny, de Bakker, PIW, van Ommen, GJ, Duijn, Cornelia, Boomsma, DI, Wijmenga, C, Slagboom, EP, Swertz, MA, Karssen, Lennart, Abdellaoui, A, Ye, K, Guryev, V, Vermaat, M, Dijk, Femke, Francioli, LC, Hottenga, JJ (Jouke Jan), Laros, JFJ, Li, QB, Li, YR, Cao, HZ, Chen, RY, Du, YP, Li, N (Nan), Cao, SJ, van Setten, J, Menelaou, A, Pulit, SL, Hehir-Kwa, JY, Beekman, M, Elbers, CC, Byelas, H, de Craen, AJM, Deelen, P, Dijkstra, M, Dunnen, JT, de Knijff, P, Houwing-Duistermaat, J, Koval, Slavik, Estrada Gil, Karol, Hofman, Bert, Kanterakis, A, van Enckevort, D, Mai, HL, Kattenberg, M, Leeuwen, Elisa, Neerincx, PBT, Oostra, Ben, Rivadeneira, Fernando, Suchiman, EHD, Uitterlinden, André, Willemsen, G, Wolffenbuttel, BH, Wang, Johnny, de Bakker, PIW, van Ommen, GJ, and Duijn, Cornelia

Abstract

Within the Netherlands a national network of biobanks has been established (Biobanking and Biomolecular Research Infrastructure-Netherlands (BBMRI-NL)) as a national node of the European BBMRI. One of the aims of BBMRI-NL is to enrich biobanks with different types of molecular and phenotype data. Here, we describe the Genome of the Netherlands (GoNL), one of the projects within BBMRI-NL. GoNL is a whole-genome-sequencing project in a representative sample consisting of 250 trio-families from all provinces in the Netherlands, which aims to characterize DNA sequence variation in the Dutch population. The parent-offspring trios include adult individuals ranging in age from 19 to 87 years (mean = 53 years; SD = 16 years) from birth cohorts 1910-1994. Sequencing was done on blood-derived DNA from uncultured cells and accomplished coverage was 14-15x. The family-based design represents a unique resource to assess the frequency of regional variants, accurately reconstruct haplotypes by family-based phasing, characterize short indels and complex structural variants, and establish the rate of de novo mutational events. GoNL will also serve as a reference panel for imputation in the available genome-wide association studies in Dutch and other cohorts to refine association signals and uncover population-specific variants. GoNL will create a catalog of human genetic variation in this sample that is uniquely characterized with respect to micro-geographic location and a wide range of phenotypes. The resource will be made available to the research and medical community to guide the interpretation of sequencing projects. The present paper summarizes the global characteristics of the project.

Published
2014

40. Ultrapuur water stuwt oliewinning

Author

Vermaat, M. and Vermaat, M.

Abstract

Op een kale vlakte bij Emmen verrijst een fabriek die ultrapuur water gaat maken uit afvalwater. In zeven zuiveringsstappen wordt het effluent van de rioolwaterzuivering opgewerkt tot extreem zuiver water, en dat bijna geheel zonder chemicaliën. Vanaf 2010 gaat de NAM het ultrapuurwater gebruiken bij de oliewinning in Schoonebeek. WaterForum nam alvast een kijkje.

Published
2009

46. Rapportage Consortium Zware opvoedproblematiek en multiprobleemgezinnen – Fase 1

Author

Klaassen-Vermaat, M., Els Evenboer, Veerman, J. W., Scholte, R. H. J., Zoon, M., Jana Knot-Dickscheit, Yperen, T., Danielle Jansen, Sijmen Reijneveld, Ontwikkelings- en Gedragsstoornissen in Onderwijs en Zorg: Assessment en Interventie, Sociologisch Instituut (Gronings Centrum voor Sociaal-Wetenschappelijk Onderzoek), and Public Health Research

48. Mutalyzer 2: next generation HGVS nomenclature checker.

Author

Lefter M, Vis JK, Vermaat M, den Dunnen JT, Taschner PEM, and Laros JFJ

Subjects
Humans, Genome, Human, Genetic Variation, Software
Abstract

Motivation: Unambiguous variant descriptions are of utmost importance in clinical genetic diagnostics, scientific literature and genetic databases. The Human Genome Variation Society (HGVS) publishes a comprehensive set of guidelines on how variants should be correctly and unambiguously described. We present the implementation of the Mutalyzer 2 tool suite, designed to automatically apply the HGVS guidelines so users do not have to deal with the HGVS intricacies explicitly to check and correct their variant descriptions., Results: Mutalyzer is profusely used by the community, having processed over 133 million descriptions since its launch. Over a five year period, Mutalyzer reported a correct input in ∼50% of cases. In 41% of the cases either a syntactic or semantic error was identified and for ∼7% of cases, Mutalyzer was able to automatically correct the description., Availability and Implementation: Mutalyzer is an Open Source project under the GNU Affero General Public License. The source code is available on GitHub (https://github.com/mutalyzer/mutalyzer) and a running instance is available at: https://mutalyzer.nl., (© The Author(s) 2021. Published by Oxford University Press.)

Published
2021
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49. Chest CT in the Emergency Department for Diagnosis of COVID-19 Pneumonia: Dutch Experience.

Author

Schalekamp S, Bleeker-Rovers CP, Beenen LFM, Quarles van Ufford HME, Gietema HA, Stöger JL, Harris V, Reijers MHE, Rahamat-Langendoen J, Korevaar DA, Smits LP, Korteweg C, van Rees Vellinga TFD, Vermaat M, Stassen PM, Scheper H, Wijnakker R, Borm FJ, Dofferhoff ASM, and Prokop M

Subjects
Aged, Female, Humans, Male, Middle Aged, Netherlands, Retrospective Studies, SARS-CoV-2, Sensitivity and Specificity, COVID-19 diagnostic imaging, Emergency Service, Hospital, Lung diagnostic imaging, Tomography, X-Ray Computed methods
Abstract

Background Clinicians need to rapidly and reliably diagnose coronavirus disease 2019 (COVID-19) for proper risk stratification, isolation strategies, and treatment decisions. Purpose To assess the real-life performance of radiologist emergency department chest CT interpretation for diagnosing COVID-19 during the acute phase of the pandemic, using the COVID-19 Reporting and Data System (CO-RADS). Materials and Methods This retrospective multicenter study included consecutive patients who presented to emergency departments in six medical centers between March and April 2020 with moderate to severe upper respiratory symptoms suspicious for COVID-19. As part of clinical practice, chest CT scans were obtained for primary work-up and scored using the five-point CO-RADS scheme for suspicion of COVID-19. CT was compared with severe acute respiratory syndrome coronavirus 2 reverse-transcription polymerase chain reaction (RT-PCR) assay and a clinical reference standard established by a multidisciplinary group of clinicians based on RT-PCR, COVID-19 contact history, oxygen therapy, timing of RT-PCR testing, and likely alternative diagnosis. Performance of CT was estimated using area under the receiver operating characteristic curve (AUC) analysis and diagnostic odds ratios against both reference standards. Subgroup analysis was performed on the basis of symptom duration grouped presentations of less than 48 hours, 48 hours through 7 days, and more than 7 days. Results A total of 1070 patients (median age, 66 years; interquartile range, 54-75 years; 626 men) were included, of whom 536 (50%) had a positive RT-PCR result and 137 (13%) of whom were considered to have a possible or probable COVID-19 diagnosis based on the clinical reference standard. Chest CT yielded an AUC of 0.87 (95% CI: 0.84, 0.89) compared with RT-PCR and 0.87 (95% CI: 0.85, 0.89) compared with the clinical reference standard. A CO-RADS score of 4 or greater yielded an odds ratio of 25.9 (95% CI: 18.7, 35.9) for a COVID-19 diagnosis with RT-PCR and an odds ratio of 30.6 (95% CI: 21.1, 44.4) with the clinical reference standard. For symptom duration of less than 48 hours, the AUC fell to 0.71 (95% CI: 0.62, 0.80; P < .001). Conclusion Chest CT analysis using the coronavirus disease 2019 (COVID-19) Reporting and Data System enables rapid and reliable diagnosis of COVID-19, particularly when symptom duration is greater than 48 hours. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Elicker in this issue.

Published
2021
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50. Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence.

Author

Lessmann N, Sánchez CI, Beenen L, Boulogne LH, Brink M, Calli E, Charbonnier JP, Dofferhoff T, van Everdingen WM, Gerke PK, Geurts B, Gietema HA, Groeneveld M, van Harten L, Hendrix N, Hendrix W, Huisman HJ, Išgum I, Jacobs C, Kluge R, Kok M, Krdzalic J, Lassen-Schmidt B, van Leeuwen K, Meakin J, Overkamp M, van Rees Vellinga T, van Rikxoort EM, Samperna R, Schaefer-Prokop C, Schalekamp S, Scholten ET, Sital C, Stöger JL, Teuwen J, Venkadesh KV, de Vente C, Vermaat M, Xie W, de Wilde B, Prokop M, and van Ginneken B

Subjects
Aged, Data Systems, Female, Humans, Male, Middle Aged, Research Design, Retrospective Studies, Artificial Intelligence, COVID-19 diagnostic imaging, Severity of Illness Index, Thorax diagnostic imaging, Tomography, X-Ray Computed
Abstract

Background The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed. Purpose To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems. Materials and Methods The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted κ values, and classification accuracy. Results A total of 105 patients (mean age, 62 years ± 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years ± 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted κ values of 0.60 ± 0.01 for CO-RADS scores and 0.54 ± 0.01 for CT severity scores. Conclusion With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. © RSNA, 2020 Supplemental material is available for this article.

Published
2021
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