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632 results on '"Slagboom, P.E."'

2. NMR metabolomics-guided DNA methylation mortality predictors

Author

Geleijnse, J.M., Boersma, E., van Spil, W.E., van Greevenbroek, M.M.J., Stehouwer, C.D.A., van der Kallen, C.J.H., Arts, I.C.W., Rutters, F., Beulens, J.W.J., Muilwijk, M., Elders, P.J.M., 't Hart, L.M., Ghanbari, M., Ikram, M.A., Netea, M.G., Kloppenburg, M., Ramos, Y.F.M., Bomer, N., Meulenbelt, I., Stronks, K., Snijder, M.B., Zwinderman, A.H., Heijmans, B.T., Lumey, L.H., Wijmenga, C., Fu, J., Zhernakova, A., Deelen, J., Mooijaart, S.P., Beekman, M., Slagboom, P.E., Onderwater, G.L.J., van den Maagdenberg, A.M.J.M., Terwindt, G.M., Thesing, C., Bot, M., Penninx, B.W.J.H., Trompet, S., Jukema, J.W., Sattar, N., van der Horst, I.C.C., van der Harst, P., So-Osman, C., van Hilten, J.A., Nelissen, R.G.H.H., Höfer, I.E., Asselbergs, F.W., Scheltens, P., Teunissen, C.E., van der Flier, W.M., van Dongen, J., Pool, R., Willemsen, A.H.M., Boomsma, D.I., Suchiman, H.E.D., Barkey Wolf, J.J.H., Cats, D., Mei, H., Slofstra, M., Swertz, M., Reinders, M.J.T., van den Akker, E.B., Bizzarri, Daniele, Reinders, Marcel J.T., Kuiper, Lieke, Beekman, Marian, Deelen, Joris, van Meurs, Joyce B.J., van Dongen, Jenny, Pool, René, Boomsma, Dorret I., Ghanbari, Mohsen, Franke, Lude, Slagboom, Pieternella E., and van den Akker, Erik B.

Published
2024
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4. Secondary integrated analysis of multi-tissue transcriptomic responses to a combined lifestyle intervention in older adults from the GOTO nonrandomized trial

Author

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

Abstract

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

Published
2024

5. Tissue‐specific methylomic responses to a lifestyle intervention in older adults associate with metabolic and physiological health improvements

Author

Sinke, Lucy, Beekman, Marian, Raz, Yotam, Gehrmann, Thies, Moustakas, Ioannis, Boulinguiez, Alexis, Lakenberg, Nico, Suchiman, Eka, Bogaards, Fatih A., Bizzarri, Daniele, van den Akker, Erik B., Waldenberger, Melanie, Butler‐Browne, Gillian, Trollet, Capucine, de Groot, C.P.G.M., Heijmans, Bastiaan T., Slagboom, P.E., Sinke, Lucy, Beekman, Marian, Raz, Yotam, Gehrmann, Thies, Moustakas, Ioannis, Boulinguiez, Alexis, Lakenberg, Nico, Suchiman, Eka, Bogaards, Fatih A., Bizzarri, Daniele, van den Akker, Erik B., Waldenberger, Melanie, Butler‐Browne, Gillian, Trollet, Capucine, de Groot, C.P.G.M., Heijmans, Bastiaan T., and Slagboom, P.E.

Abstract

Across the lifespan, diet and physical activity profiles substantially influence immunometabolic health. DNA methylation, as a tissue-specific marker sensitive to behavioral change, may mediate these effects through modulation of transcription factor binding and subsequent gene expression. Despite this, few human studies have profiled DNA methylation and gene expression simultaneously in multiple tissues or examined how molecular levels react and interact in response to lifestyle changes. The Growing Old Together (GOTO) study is a 13-week lifestyle intervention in older adults, which imparted health benefits to participants. Here, we characterize the DNA methylation response to this intervention at over 750 thousand CpGs in muscle, adipose, and blood. Differentially methylated sites are enriched for active chromatin states, located close to relevant transcription factor binding sites, and associated with changing expression of insulin sensitivity genes and health parameters. In addition, measures of biological age are consistently reduced, with decreases in grimAge associated with observed health improvements. Taken together, our results identify responsive molecular markers and demonstrate their potential to measure progression and finetune treatment of age-related risks and diseases.

Published
2024

10. Functional genomics analysis identifies T and NK cell activation as a driver of epigenetic clock progression

Author

Jonkman, T.H., Dekkers, K.F., Slieker, R.C., Grant, C.D., Ikram, M.A., Greevenbroek, M.M.J. van, Franke, L., Veldink, J.H., Boomsma, D.I., Slagboom, P.E., Consortium, B.I.O.S., Heijmans, B.T., Epidemiology, Epidemiology and Data Science, APH - Aging & Later Life, APH - Health Behaviors & Chronic Diseases, Biological Psychology, APH - Mental Health, APH - Methodology, Stem Cell Aging Leukemia and Lymphoma (SALL), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Interne Geneeskunde, and RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome

Subjects
Epigenomics, QH301-705.5, Research, MEMORY, QH426-470, DNA Methylation, DECONVOLUTION, Epigenesis, Genetic, Killer Cells, Natural, AGE, Genetics, HEART, Biology (General), CD4(+), PACKAGE
Abstract

BackgroundEpigenetic clocks use DNA methylation (DNAm) levels of specific sets of CpG dinucleotides to accurately predict individual chronological age. A popular application of these clocks is to explore whether the deviation of predicted age from chronological age is associated with disease phenotypes, where this deviation is interpreted as a potential biomarker of biological age. This wide application, however, contrasts with the limited insight in the processes that may drive the running of epigenetic clocks.ResultsWe perform a functional genomics analysis on four epigenetic clocks, including Hannum’s blood predictor and Horvath’s multi-tissue predictor, using blood DNA methylome and transcriptome data from 3132 individuals. The four clocks result in similar predictions of individual chronological age, and their constituting CpGs are correlated in DNAm level and are enriched for similar histone modifications and chromatin states. Interestingly, DNAm levels of CpGs from the clocks are commonly associated with gene expressionin trans. The gene sets involved are highly overlapping and enriched for T cell processes. Further analysis of the transcriptome and methylome of sorted blood cell types identifies differences in DNAm between naive and activated T and NK cells as a probable contributor to the clocks. Indeed, within the same donor, the four epigenetic clocks predict naive cells to be up to 40 years younger than activated cells.ConclusionsThe ability of epigenetic clocks to predict chronological age involves their ability to detect changes in proportions of naive and activated immune blood cells, an established feature of immuno-senescence. This finding may contribute to the interpretation of associations between clock-derived measures and age-related health outcomes.

Published
2022
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11. The association between continuous ambulatory heart rate, heart rate variability, and 24-h rhythms of heart rate with familial longevity and aging

Author

Wiersema, J.M., Kamphuis, A.E.P., Rohling, J.H.T., Kervezee, L., Akintola, A.A., Jansen, S.W., Slagboom, P.E., Heemst, D. van, and Spoel, E. van der

Subjects
longevity, continuous ambulatory measurements, aging, heart rate, heart rate variability
Abstract

Aging is associated with changes in heart rate (HR), heart rate variability (HRV), and 24-h rhythms in HR. Longevity has been linked to lower resting HR, while a higher resting HR and a decreased HRV were linked to cardiovascular events and increased mortality risk. HR and HRV are often investigated during a short electrocardiogram (ECG) measurement at a hospital. In this study, we aim to investigate the relationship between HR parameters with familial longevity and chronological age derived from continuous ambulatory ECG measurements collected over a period of 24 to 90 hours. We included 73 middle-aged participants (mean (SD) age: 67.0 (6.16) years), comprising 37 offspring of long-lived families, 36 of their partners, and 35 young participants (22.8 (3.96) years). We found no association with familial longevity, but middle-aged participants had lower 24-h HR (average and maximum HR, not minimum HR), lower amplitudes, and earlier trough and peak times than young participants. Associations in HR with chronological age could be caused by the aging process or by differences in environmental factors. Interestingly, middle-aged participants had a less optimal HRV during long-term recordings in both the sleep and awake periods, which might indicate that their heart is less adaptable than that of young participants. This could be a first indication of deteriorated cardiovascular health in middle-aged individuals.

Published
2022

12. The association of blood biomarkers with outcomes in older patients with solid tumors: a systematic review

Author

van Holstein, Y., primary, van den Berkmortel, P.J.E., additional, Trompet, S., additional, van Heemst, D., additional, Van den Bos, F., additional, Roemeling-van Rhijn, M., additional, De Glas, N.A., additional, Beekman, M., additional, Slagboom, P.E., additional, Portielje, J.E.A., additional, Mooijaart, S.P., additional, and Van Munster, B., additional

Published
2022
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13. Association of metabolomics mortality score with geriatric assessment and mortality in older patients with solid tumors

Author

van Holstein, Y., primary, Mooijaart, S.P., additional, Van Oevelen, M., additional, Van Deudekom, F.J., additional, Vojinovic, D., additional, Van den Bos, F., additional, Labots, G., additional, De Glas, N.A., additional, Beekman, M., additional, Van Munster, B., additional, Slagboom, P.E., additional, Portielje, J.E.A., additional, and Trompet, S., additional

Published
2022
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16. Pooled analysis of epigenome-wide association studies of food consumption in KORA, TwinsUK and LLS

Author

Hellbach, F., Sinke, L., Costeira, R., Baumeister, S.E., Beekman, M., Louca, P., Leeming, E.R., Mompeo, O., Berry, S., Wilson, R., Wawro, N., Freuer, D., Hauner, H., Peters, A., Winkelmann, J., Koenig, W., Meisinger, C., Waldenberger, M., Heijmans, B.T., Slagboom, P.E., Bell, J.T., and Linseisen, J.

Subjects
Nutrition and Dietetics, Medicine (miscellaneous), Humans, Diet, Ewas, Food Group, High-fat Foods, ddc:610, Food group, High-fat foods, EWAS
Abstract

Purpose Examining epigenetic patterns is a crucial step in identifying molecular changes of disease pathophysiology, with DNA methylation as the most accessible epigenetic measure. Diet is suggested to affect metabolism and health via epigenetic modifications. Thus, our aim was to explore the association between food consumption and DNA methylation. Methods Epigenome-wide association studies were conducted in three cohorts: KORA FF4, TwinsUK, and Leiden Longevity Study, and 37 dietary exposures were evaluated. Food group definition was harmonized across the three cohorts. DNA methylation was measured using Infinium MethylationEPIC BeadChip in KORA and Infinium HumanMethylation450 BeadChip in the Leiden study and the TwinsUK study. Overall, data from 2293 middle-aged men and women were included. A fixed-effects meta-analysis pooled study-specific estimates. The significance threshold was set at 0.05 for false-discovery rate-adjusted p values per food group. Results We identified significant associations between the methylation level of CpG sites and the consumption of onions and garlic (2), nuts and seeds (18), milk (1), cream (11), plant oils (4), butter (13), and alcoholic beverages (27). The signals targeted genes of metabolic health relevance, for example, GLI1, RPTOR, and DIO1, among others. Conclusion This EWAS is unique with its focus on food groups that are part of a Western diet. Significant findings were mostly related to food groups with a high-fat content.

Published
2022
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17. Common Genetic Variation and Age of Onset of Anorexia Nervosa

Author

Watson, H.J. Thornton, L.M. Yilmaz, Z. Baker, J.H. Coleman, J.R.I. Adan, R.A.H. Alfredsson, L. Andreassen, O.A. Ask, H. Berrettini, W.H. Boehnke, M. Boehm, I. Boni, C. Buehren, K. Bulant, J. Burghardt, R. Chang, X. Cichon, S. Cone, R.D. Courtet, P. Crow, S. Crowley, J.J. Danner, U.N. de Zwaan, M. Dedoussis, G. DeSocio, J.E. Dick, D.M. Dikeos, D. Dina, C. Djurovic, S. Dmitrzak-Weglarz, M. Docampo-Martinez, E. Duriez, P. Egberts, K. Ehrlich, S. Eriksson, J.G. Escaramís, G. Esko, T. Estivill, X. Farmer, A. Fernández-Aranda, F. Fichter, M.M. Föcker, M. Foretova, L. Forstner, A.J. Frei, O. Gallinger, S. Giegling, I. Giuranna, J. Gonidakis, F. Gorwood, P. Gratacòs, M. Guillaume, S. Guo, Y. Hakonarson, H. Hauser, J. Havdahl, A. Hebebrand, J. Helder, S.G. Herms, S. Herpertz-Dahlmann, B. Herzog, W. Hinney, A. Hübel, C. Hudson, J.I. Imgart, H. Jamain, S. Janout, V. Jiménez-Murcia, S. Jones, I.R. Julià, A. Kalsi, G. Kaminská, D. Kaprio, J. Karhunen, L. Kas, M.J.H. Keel, P.K. Kennedy, J.L. Keski-Rahkonen, A. Kiezebrink, K. Klareskog, L. Klump, K.L. Knudsen, G.P.S. La Via, M.C. Le Hellard, S. Leboyer, M. Li, D. Lilenfeld, L. Lin, B. Lissowska, J. Luykx, J. Magistretti, P. Maj, M. Marsal, S. Marshall, C.R. Mattingsdal, M. Meulenbelt, I. Micali, N. Mitchell, K.S. Monteleone, A.M. Monteleone, P. Myers, R. Navratilova, M. Ntalla, I. O'Toole, J.K. Ophoff, R.A. Padyukov, L. Pantel, J. Papežová, H. Pinto, D. Raevuori, A. Ramoz, N. Reichborn-Kjennerud, T. Ricca, V. Ripatti, S. Ripke, S. Ritschel, F. Roberts, M. Rotondo, A. Rujescu, D. Rybakowski, F. Scherag, A. Scherer, S.W. Schmidt, U. Scott, L.J. Seitz, J. Silén, Y. Šlachtová, L. Slagboom, P.E. Slof-Op ‘t Landt, M.C.T. Slopien, A. Sorbi, S. Świątkowska, B. Tortorella, A. Tozzi, F. Treasure, J. Tsitsika, A. Tyszkiewicz-Nwafor, M. Tziouvas, K. van Elburg, A.A. van Furth, E.F. Walton, E. Widen, E. Zerwas, S. Zipfel, S. Bergen, A.W. Boden, J.M. Brandt, H. Crawford, S. Halmi, K.A. Horwood, L.J. Johnson, C. Kaplan, A.S. Kaye, W.H. Mitc and Watson, H.J. Thornton, L.M. Yilmaz, Z. Baker, J.H. Coleman, J.R.I. Adan, R.A.H. Alfredsson, L. Andreassen, O.A. Ask, H. Berrettini, W.H. Boehnke, M. Boehm, I. Boni, C. Buehren, K. Bulant, J. Burghardt, R. Chang, X. Cichon, S. Cone, R.D. Courtet, P. Crow, S. Crowley, J.J. Danner, U.N. de Zwaan, M. Dedoussis, G. DeSocio, J.E. Dick, D.M. Dikeos, D. Dina, C. Djurovic, S. Dmitrzak-Weglarz, M. Docampo-Martinez, E. Duriez, P. Egberts, K. Ehrlich, S. Eriksson, J.G. Escaramís, G. Esko, T. Estivill, X. Farmer, A. Fernández-Aranda, F. Fichter, M.M. Föcker, M. Foretova, L. Forstner, A.J. Frei, O. Gallinger, S. Giegling, I. Giuranna, J. Gonidakis, F. Gorwood, P. Gratacòs, M. Guillaume, S. Guo, Y. Hakonarson, H. Hauser, J. Havdahl, A. Hebebrand, J. Helder, S.G. Herms, S. Herpertz-Dahlmann, B. Herzog, W. Hinney, A. Hübel, C. Hudson, J.I. Imgart, H. Jamain, S. Janout, V. Jiménez-Murcia, S. Jones, I.R. Julià, A. Kalsi, G. Kaminská, D. Kaprio, J. Karhunen, L. Kas, M.J.H. Keel, P.K. Kennedy, J.L. Keski-Rahkonen, A. Kiezebrink, K. Klareskog, L. Klump, K.L. Knudsen, G.P.S. La Via, M.C. Le Hellard, S. Leboyer, M. Li, D. Lilenfeld, L. Lin, B. Lissowska, J. Luykx, J. Magistretti, P. Maj, M. Marsal, S. Marshall, C.R. Mattingsdal, M. Meulenbelt, I. Micali, N. Mitchell, K.S. Monteleone, A.M. Monteleone, P. Myers, R. Navratilova, M. Ntalla, I. O'Toole, J.K. Ophoff, R.A. Padyukov, L. Pantel, J. Papežová, H. Pinto, D. Raevuori, A. Ramoz, N. Reichborn-Kjennerud, T. Ricca, V. Ripatti, S. Ripke, S. Ritschel, F. Roberts, M. Rotondo, A. Rujescu, D. Rybakowski, F. Scherag, A. Scherer, S.W. Schmidt, U. Scott, L.J. Seitz, J. Silén, Y. Šlachtová, L. Slagboom, P.E. Slof-Op ‘t Landt, M.C.T. Slopien, A. Sorbi, S. Świątkowska, B. Tortorella, A. Tozzi, F. Treasure, J. Tsitsika, A. Tyszkiewicz-Nwafor, M. Tziouvas, K. van Elburg, A.A. van Furth, E.F. Walton, E. Widen, E. Zerwas, S. Zipfel, S. Bergen, A.W. Boden, J.M. Brandt, H. Crawford, S. Halmi, K.A. Horwood, L.J. Johnson, C. Kaplan, A.S. Kaye, W.H. Mitc

Abstract

Background: Genetics and biology may influence the age of onset of anorexia nervosa (AN). The aims of this study were to determine whether common genetic variation contributes to age of onset of AN and to investigate the genetic associations between age of onset of AN and age at menarche. Methods: A secondary analysis of the Psychiatric Genomics Consortium genome-wide association study (GWAS) of AN was performed, which included 9335 cases and 31,981 screened controls, all from European ancestries. We conducted GWASs of age of onset, early-onset AN (<13 years), and typical-onset AN, and genetic correlation, genetic risk score, and Mendelian randomization analyses. Results: Two loci were genome-wide significant in the typical-onset AN GWAS. Heritability estimates (single nucleotide polymorphism–h2) were 0.01–0.04 for age of onset, 0.16–0.25 for early-onset AN, and 0.17–0.25 for typical-onset AN. Early- and typical-onset AN showed distinct genetic correlation patterns with putative risk factors for AN. Specifically, early-onset AN was significantly genetically correlated with younger age at menarche, and typical-onset AN was significantly negatively genetically correlated with anthropometric traits. Genetic risk scores for age of onset and early-onset AN estimated from independent GWASs significantly predicted age of onset. Mendelian randomization analysis suggested a causal link between younger age at menarche and early-onset AN. Conclusions: Our results provide evidence consistent with a common variant genetic basis for age of onset and implicate biological pathways regulating menarche and reproduction. © 2021 The Authors

Published
2022

18. Using multivariable Mendelian randomization to estimate the causal effect of bone mineral density on osteoarthritis risk, independently of body mass index

Author

Hartley, A. Sanderson, E. Granell, R. Paternoster, L. Zheng, J. Smith, G.D. Southam, L. Hatzikotoulas, K. Boer, C.G. Van Meurs, J. Zeggini, E. Gregson, C.L. Tobias, J.H. Stefánsdóttir, L. Zhang, Y. De Almeida, R.C. Wu, T.T. Teder-Laving, M. Skogholt, A.-H. Terao, C. Zengini, E. Alexiadis, G. Barysenka, A. Bjornsdottir, G. Gabrielsen, M.E. Gilly, A. Ingvarsson, T. Johnsen, M.B. Jonsson, H. Kloppenburg, M.G. Luetge, A. Mägi, R. Mangino, M. Nelissen, R.R.G.H.H. Shivakumar, M. Steinberg, J. Takuwa, H. Thomas, L. Tuerlings, M. Babis, G. Cheung, J.P.Y. Samartzis, D. Lietman, S.A. Slagboom, P.E. Stefansson, K. Uitterlinden, A.G. Winsvold, B. Zwart, J.-A. Sham, P.C. Thorleifsson, G. Gaunt, T.R. Morris, A.P. Valdes, A.M. Tsezou, A. Cheah, K.S.E. Ikegawa, S. Hveem, K. Esko, T. Wilkinson, J.M. Meulenbelt, I. Michael Lee, M.T. Styrkársdóttir, U. and Hartley, A. Sanderson, E. Granell, R. Paternoster, L. Zheng, J. Smith, G.D. Southam, L. Hatzikotoulas, K. Boer, C.G. Van Meurs, J. Zeggini, E. Gregson, C.L. Tobias, J.H. Stefánsdóttir, L. Zhang, Y. De Almeida, R.C. Wu, T.T. Teder-Laving, M. Skogholt, A.-H. Terao, C. Zengini, E. Alexiadis, G. Barysenka, A. Bjornsdottir, G. Gabrielsen, M.E. Gilly, A. Ingvarsson, T. Johnsen, M.B. Jonsson, H. Kloppenburg, M.G. Luetge, A. Mägi, R. Mangino, M. Nelissen, R.R.G.H.H. Shivakumar, M. Steinberg, J. Takuwa, H. Thomas, L. Tuerlings, M. Babis, G. Cheung, J.P.Y. Samartzis, D. Lietman, S.A. Slagboom, P.E. Stefansson, K. Uitterlinden, A.G. Winsvold, B. Zwart, J.-A. Sham, P.C. Thorleifsson, G. Gaunt, T.R. Morris, A.P. Valdes, A.M. Tsezou, A. Cheah, K.S.E. Ikegawa, S. Hveem, K. Esko, T. Wilkinson, J.M. Meulenbelt, I. Michael Lee, M.T. Styrkársdóttir, U.

Published
2022

19. Author Correction: Rare SLC13A1 variants associate with intervertebral disc disorder highlighting role of sulfate in disc pathology (Nature Communications, (2022), 13, 1, (634), 10.1038/s41467-022-28167-1)

Author

Bjornsdottir, G. Stefansdottir, L. Thorleifsson, G. Sulem, P. Norland, K. Ferkingstad, E. Oddsson, A. Zink, F. Lund, S.H. Nawaz, M.S. Bragi Walters, G. Skuladottir, A.T. Gudjonsson, S.A. Einarsson, G. Halldorsson, G.H. Bjarnadottir, V. Sveinbjornsson, G. Helgadottir, A. Styrkarsdottir, U. Gudmundsson, L.J. Pedersen, O.B. Hansen, T.F. Werge, T. Banasik, K. Troelsen, A. Skou, S.T. Thørner, L.W. Erikstrup, C. Nielsen, K.R. Mikkelsen, S. Andersen, S. Brunak, S. Burgdorf, K. Hjalgrim, H. Jemec, G. Jennum, P. Johansson, P.I. Nyegaard, M. Bruun, M.T. Dinh, K.M. Sørensen, E. Johansson, P.I. Gudbjartsson, D. Stefánsson, H. Þorsteinsdóttir, U. Larsen, M.A.H. Didriksen, M. Sækmose, S. Zeggini, E. Hatzikotoulas, K. Southam, L. Gilly, A. Barysenka, A. van Meurs, J.B.J. Boer, C.G. Uitterlinden, A.G. Styrkársdóttir, U. Stefánsdóttir, L. Jonsson, H. Ingvarsson, T. Esko, T. Mägi, R. Teder-Laving, M. Ikegawa, S. Terao, C. Takuwa, H. Meulenbelt, I. Coutinho de Almeida, R. Kloppenburg, M. Tuerlings, M. Slagboom, P.E. Nelissen, R.R.G.H.H. Valdes, A.M. Mangino, M. Tsezou, A. Zengini, E. Alexiadis, G. Babis, G.C. Cheah, K.S.E. Wu, T.T. Samartzis, D. Cheung, J.P.Y. Sham, P.C. Kraft, P. Kang, J.H. Hveem, K. Zwart, J.-A. Luetge, A. Skogholt, A.H. Johnsen, M.B. Thomas, L.F. Winsvold, B. Gabrielsen, M.E. Lee, M.T.M. Zhang, Y. Lietman, S.A. Shivakumar, M. Smith, G.D. Tobias, J.H. Hartley, A. Gaunt, T.R. Zheng, J. Wilkinson, J.M. Steinberg, J. Morris, A.P. Jonsdottir, I. Bjornsson, A. Olafsson, I.H. Ulfarsson, E. Blondal, J. Vikingsson, A. Brunak, S. Ostrowski, S.R. Ullum, H. Thorsteinsdottir, U. Stefansson, H. Gudbjartsson, D.F. Thorgeirsson, T.E. Stefansson, K. DBDS Genetic Consortium GO Consortium and Bjornsdottir, G. Stefansdottir, L. Thorleifsson, G. Sulem, P. Norland, K. Ferkingstad, E. Oddsson, A. Zink, F. Lund, S.H. Nawaz, M.S. Bragi Walters, G. Skuladottir, A.T. Gudjonsson, S.A. Einarsson, G. Halldorsson, G.H. Bjarnadottir, V. Sveinbjornsson, G. Helgadottir, A. Styrkarsdottir, U. Gudmundsson, L.J. Pedersen, O.B. Hansen, T.F. Werge, T. Banasik, K. Troelsen, A. Skou, S.T. Thørner, L.W. Erikstrup, C. Nielsen, K.R. Mikkelsen, S. Andersen, S. Brunak, S. Burgdorf, K. Hjalgrim, H. Jemec, G. Jennum, P. Johansson, P.I. Nyegaard, M. Bruun, M.T. Dinh, K.M. Sørensen, E. Johansson, P.I. Gudbjartsson, D. Stefánsson, H. Þorsteinsdóttir, U. Larsen, M.A.H. Didriksen, M. Sækmose, S. Zeggini, E. Hatzikotoulas, K. Southam, L. Gilly, A. Barysenka, A. van Meurs, J.B.J. Boer, C.G. Uitterlinden, A.G. Styrkársdóttir, U. Stefánsdóttir, L. Jonsson, H. Ingvarsson, T. Esko, T. Mägi, R. Teder-Laving, M. Ikegawa, S. Terao, C. Takuwa, H. Meulenbelt, I. Coutinho de Almeida, R. Kloppenburg, M. Tuerlings, M. Slagboom, P.E. Nelissen, R.R.G.H.H. Valdes, A.M. Mangino, M. Tsezou, A. Zengini, E. Alexiadis, G. Babis, G.C. Cheah, K.S.E. Wu, T.T. Samartzis, D. Cheung, J.P.Y. Sham, P.C. Kraft, P. Kang, J.H. Hveem, K. Zwart, J.-A. Luetge, A. Skogholt, A.H. Johnsen, M.B. Thomas, L.F. Winsvold, B. Gabrielsen, M.E. Lee, M.T.M. Zhang, Y. Lietman, S.A. Shivakumar, M. Smith, G.D. Tobias, J.H. Hartley, A. Gaunt, T.R. Zheng, J. Wilkinson, J.M. Steinberg, J. Morris, A.P. Jonsdottir, I. Bjornsson, A. Olafsson, I.H. Ulfarsson, E. Blondal, J. Vikingsson, A. Brunak, S. Ostrowski, S.R. Ullum, H. Thorsteinsdottir, U. Stefansson, H. Gudbjartsson, D.F. Thorgeirsson, T.E. Stefansson, K. DBDS Genetic Consortium GO Consortium

Abstract

The original version of this Article contained an error in Fig. 3, in which the blue and red trend lines on the left plot were incorrect. In addition, the text “Dorsalgia variants” in the table should have been italicized and underlined. The correct version of Fig. 3 is: (Figure presented.) which replaces the previous incorrect version: (Figure presented.). This has been corrected in both the PDF and HTML versions of the Article. © The Author(s) 2022.

Published
2022

20. Rare SLC13A1 variants associate with intervertebral disc disorder highlighting role of sulfate in disc pathology

Author

Bjornsdottir, G. Stefansdottir, L. Thorleifsson, G. Sulem, P. Norland, K. Ferkingstad, E. Oddsson, A. Zink, F. Lund, S.H. Nawaz, M.S. Bragi Walters, G. Skuladottir, A.T. Gudjonsson, S.A. Einarsson, G. Halldorsson, G.H. Bjarnadottir, V. Sveinbjornsson, G. Helgadottir, A. Styrkarsdottir, U. Gudmundsson, L.J. Pedersen, O.B. Hansen, T.F. Werge, T. Banasik, K. Troelsen, A. Skou, S.T. Thørner, L.W. Erikstrup, C. Nielsen, K.R. Mikkelsen, S. Andersen, S. Brunak, S. Burgdorf, K. Hjalgrim, H. Jemec, G. Jennum, P. Johansson, P.I. Nielsen, K.R. Nyegaard, M. Bruun, M.T. Pedersen, O.B. Dinh, K.M. Sørensen, E. Ostrowski, S. Johansson, P.I. Gudbjartsson, D. Stefánsson, H. Þorsteinsdóttir, U. Larsen, M.A.H. Didriksen, M. Sækmose, S. Zeggini, E. Hatzikotoulas, K. Southam, L. Gilly, A. Barysenka, A. van Meurs, J.B.J. Boer, C.G. Uitterlinden, A.G. Styrkársdóttir, U. Stefánsdóttir, L. Jonsson, H. Ingvarsson, T. Esko, T. Mägi, R. Teder-Laving, M. Ikegawa, S. Terao, C. Takuwa, H. Meulenbelt, I. Coutinho de Almeida, R. Kloppenburg, M. Tuerlings, M. Slagboom, P.E. Nelissen, R.R.G.H.H. Valdes, A.M. Mangino, M. Tsezou, A. Zengini, E. Alexiadis, G. Babis, G.C. Cheah, K.S.E. Wu, T.T. Samartzis, D. Cheung, J.P.Y. Sham, P.C. Kraft, P. Kang, J.H. Hveem, K. Zwart, J.-A. Luetge, A. Skogholt, A.H. Johnsen, M.B. Thomas, L.F. Winsvold, B. Gabrielsen, M.E. Lee, M.T.M. Zhang, Y. Lietman, S.A. Shivakumar, M. Smith, G.D. Tobias, J.H. Hartley, A. Gaunt, T.R. Zheng, J. Wilkinson, J.M. Steinberg, J. Morris, A.P. Jonsdottir, I. Bjornsson, A. Olafsson, I.H. Ulfarsson, E. Blondal, J. Vikingsson, A. Brunak, S. Ostrowski, S.R. Ullum, H. Thorsteinsdottir, U. Stefansson, H. Gudbjartsson, D.F. Thorgeirsson, T.E. Stefansson, K. DBDS Genetic Consortium GO Consortium and Bjornsdottir, G. Stefansdottir, L. Thorleifsson, G. Sulem, P. Norland, K. Ferkingstad, E. Oddsson, A. Zink, F. Lund, S.H. Nawaz, M.S. Bragi Walters, G. Skuladottir, A.T. Gudjonsson, S.A. Einarsson, G. Halldorsson, G.H. Bjarnadottir, V. Sveinbjornsson, G. Helgadottir, A. Styrkarsdottir, U. Gudmundsson, L.J. Pedersen, O.B. Hansen, T.F. Werge, T. Banasik, K. Troelsen, A. Skou, S.T. Thørner, L.W. Erikstrup, C. Nielsen, K.R. Mikkelsen, S. Andersen, S. Brunak, S. Burgdorf, K. Hjalgrim, H. Jemec, G. Jennum, P. Johansson, P.I. Nielsen, K.R. Nyegaard, M. Bruun, M.T. Pedersen, O.B. Dinh, K.M. Sørensen, E. Ostrowski, S. Johansson, P.I. Gudbjartsson, D. Stefánsson, H. Þorsteinsdóttir, U. Larsen, M.A.H. Didriksen, M. Sækmose, S. Zeggini, E. Hatzikotoulas, K. Southam, L. Gilly, A. Barysenka, A. van Meurs, J.B.J. Boer, C.G. Uitterlinden, A.G. Styrkársdóttir, U. Stefánsdóttir, L. Jonsson, H. Ingvarsson, T. Esko, T. Mägi, R. Teder-Laving, M. Ikegawa, S. Terao, C. Takuwa, H. Meulenbelt, I. Coutinho de Almeida, R. Kloppenburg, M. Tuerlings, M. Slagboom, P.E. Nelissen, R.R.G.H.H. Valdes, A.M. Mangino, M. Tsezou, A. Zengini, E. Alexiadis, G. Babis, G.C. Cheah, K.S.E. Wu, T.T. Samartzis, D. Cheung, J.P.Y. Sham, P.C. Kraft, P. Kang, J.H. Hveem, K. Zwart, J.-A. Luetge, A. Skogholt, A.H. Johnsen, M.B. Thomas, L.F. Winsvold, B. Gabrielsen, M.E. Lee, M.T.M. Zhang, Y. Lietman, S.A. Shivakumar, M. Smith, G.D. Tobias, J.H. Hartley, A. Gaunt, T.R. Zheng, J. Wilkinson, J.M. Steinberg, J. Morris, A.P. Jonsdottir, I. Bjornsson, A. Olafsson, I.H. Ulfarsson, E. Blondal, J. Vikingsson, A. Brunak, S. Ostrowski, S.R. Ullum, H. Thorsteinsdottir, U. Stefansson, H. Gudbjartsson, D.F. Thorgeirsson, T.E. Stefansson, K. DBDS Genetic Consortium GO Consortium

Abstract

Back pain is a common and debilitating disorder with largely unknown underlying biology. Here we report a genome-wide association study of back pain using diagnoses assigned in clinical practice; dorsalgia (119,100 cases, 909,847 controls) and intervertebral disc disorder (IDD) (58,854 cases, 922,958 controls). We identify 41 variants at 33 loci. The most significant association (ORIDD = 0.92, P = 1.6 × 10−39; ORdorsalgia = 0.92, P = 7.2 × 10−15) is with a 3’UTR variant (rs1871452-T) in CHST3, encoding a sulfotransferase enzyme expressed in intervertebral discs. The largest effects on IDD are conferred by rare (MAF = 0.07 − 0.32%) loss-of-function (LoF) variants in SLC13A1, encoding a sodium-sulfate co-transporter (LoF burden OR = 1.44, P = 3.1 × 10−11); variants that also associate with reduced serum sulfate. Genes implicated by this study are involved in cartilage and bone biology, as well as neurological and inflammatory processes. © 2022, The Author(s).

Published
2022

21. 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

22. Metabolomic predictors of phenotypic traits can replace and complement measured clinical variables in population-scale expression profiling studies

Author

Niehues, A., Bizzarri, Daniele, Reinders, Marcel J.T., Slagboom, P.E., Gool, A.J. van, Akker, Erik B. van den, Hoen, Peter A.C. 't, Niehues, A., Bizzarri, Daniele, Reinders, Marcel J.T., Slagboom, P.E., Gool, A.J. van, Akker, Erik B. van den, and Hoen, Peter A.C. 't

Abstract

Contains fulltext : 253174.pdf (Publisher’s version ) (Open Access)

Published
2022

23. PLIS: A metabolomic response monitor to a lifestyle intervention study in older adults

Author

Bogaards, Fatih A., Gehrmann, Thies, Beekman, Marian, Van Den Akker, Erik Ben, Van De Rest, Ondine, Hangelbroek, Roland W.J., Noordam, Raymond, Mooijaart, Simon P., De Groot, Lisette C.P.G.M., Reinders, Marcel J.T., Slagboom, P.E., Bogaards, Fatih A., Gehrmann, Thies, Beekman, Marian, Van Den Akker, Erik Ben, Van De Rest, Ondine, Hangelbroek, Roland W.J., Noordam, Raymond, Mooijaart, Simon P., De Groot, Lisette C.P.G.M., Reinders, Marcel J.T., and Slagboom, P.E.

Abstract

The response to lifestyle intervention studies is often heterogeneous, especially in older adults. Subtle responses that may represent a health gain for individuals are not always detected by classical health variables, stressing the need for novel biomarkers that detect intermediate changes in metabolic, inflammatory, and immunity-related health. Here, our aim was to develop and validate a molecular multivariate biomarker maximally sensitive to the individual effect of a lifestyle intervention; the Personalized Lifestyle Intervention Status (PLIS). We used 1H-NMR fasting blood metabolite measurements from before and after the 13-week combined physical and nutritional Growing Old TOgether (GOTO) lifestyle intervention study in combination with a fivefold cross-validation and a bootstrapping method to train a separate PLIS score for men and women. The PLIS scores consisted of 14 and four metabolites for females and males, respectively. Performance of the PLIS score in tracking health gain was illustrated by association of the sex-specific PLIS scores with several classical metabolic health markers, such as BMI, trunk fat%, fasting HDL cholesterol, and fasting insulin, the primary outcome of the GOTO study. We also showed that the baseline PLIS score indicated which participants respond positively to the intervention. Finally, we explored PLIS in an independent physical activity lifestyle intervention study, showing similar, albeit remarkably weaker, associations of PLIS with classical metabolic health markers. To conclude, we found that the sex-specific PLIS score was able to track the individual short-term metabolic health gain of the GOTO lifestyle intervention study. The methodology used to train the PLIS score potentially provides a useful instrument to track personal responses and predict the participant's health benefit in lifestyle interventions similar to the GOTO study.

Published
2022

24. Self-rated health in individuals with and without disease is associated with multiple biomarkers representing multiple biological domains

Author

Kananen, L., Enroth, L., Raitanen, J., Jylhava, J., Burkle, A., Moreno-Villanueva, M., Bernhardt, J., Toussaint, O., Grubeck-Loebenstein, B., Malavolta, M., Basso, A., Piacenza, F., Collino, S., Gonos, E.S., Sikora, E., Gradinaru, D., Jansen, E.H.J.M., Dolle, M.E.T., Salmon, M., Stuetz, W., Weber, D., Grune, T., Breusing, N., Simm, A., Capri, M., Franceschi, C., Slagboom, P.E., Talbot, D.C.S., Libert, C., Koskinen, S., Bruunsgaard, H., Hansen, A.M., Lund, R., Hurme, M., Jylha, M., Kananen L., Enroth L., Raitanen J., Jylhava J., Burkle A., Moreno-Villanueva M., Bernhardt J., Toussaint O., Grubeck-Loebenstein B., Malavolta M., Basso A., Piacenza F., Collino S., Gonos E.S., Sikora E., Gradinaru D., Jansen E.H.J.M., Dolle M.E.T., Salmon M., Stuetz W., Weber D., Grune T., Breusing N., Simm A., Capri M., Franceschi C., Slagboom P.E., Talbot D.C.S., Libert C., Koskinen S., Bruunsgaard H., Hansen A., Lund R., Hurme M., Jylha M., Tampere University, Health Sciences, and BioMediTech

Subjects
Quality of life, Adult, Male, Aging, Adolescent, Health Status, Science, education, Predictive markers, Article, Diagnostic Self Evaluation, Young Adult, ddc:570, Medicine and Health Sciences, Humans, Aged, Aged, 80 and over, Health care, Biology and Life Sciences, biomarkers, Middle Aged, 3141 Health care science, body regions, Somatosensory system, Medicine, Female, 3111 Biomedicine, Self Report
Abstract

Self-rated health (SRH) is one of the most frequently used indicators in health and social research. Its robust association with mortality in very different populations implies that it is a comprehensive measure of health status and may even reflect the condition of the human organism beyond clinical diagnoses. Yet the biological basis of SRH is poorly understood. We used data from three independent European population samples (N approx. 15,000) to investigate the associations of SRH with 150 biomolecules in blood or urine (biomarkers). Altogether 57 biomarkers representing different organ systems were associated with SRH. In almost half of the cases the association was independent of disease and physical functioning. Biomarkers weakened but did not remove the association between SRH and mortality. We propose three potential pathways through which biomarkers may be incorporated into an individual’s subjective health assessment, including (1) their role in clinical diseases; (2) their association with health-related lifestyles; and (3) their potential to stimulate physical sensations through interoceptive mechanisms. Our findings indicate that SRH has a solid biological basis and it is a valid but non-specific indicator of the biological condition of the human organism. publishedVersion

Published
2021
Full Text
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26. A recurrent neural network architecture to model physical activity energy expenditure in older people

Author

Paraschiakos, S., Sá, C.R. de, Okai, J., Slagboom, P.E., Beekman, M., and Knobbe, A.J.

Subjects
Accelerometer, Recurrent neural networks, Monitoring older adults, Computer Networks and Communications, Wearables, Physical activity energy expenditure, Indirect calorimetry, Computer Science Applications, Information Systems
Abstract

Through the quantification of physical activity energy expenditure (PAEE), health care monitoring has the potential to stimulate vital and healthy ageing, inducing behavioural changes in older people and linking these to personal health gains. To be able to measure PAEE in a health care perspective, methods from wearable accelerometers have been developed, however, mainly targeted towards younger people. Since elderly subjects differ in energy requirements and range of physical activities, the current models may not be suitable for estimating PAEE among the elderly. Furthermore, currently available methods seem to be either simple but non-generalizable or require elaborate (manual) feature construction steps. Because past activities influence present PAEE, we propose a modeling approach known for its ability to model sequential data, the recurrent neural network (RNN). To train the RNN for an elderly population, we used the growing old together validation (GOTOV) dataset with 34 healthy participants of 60 years and older (mean 65 years old), performing 16 different activities. We used accelerometers placed on wrist and ankle, and measurements of energy counts by means of indirect calorimetry. After optimization, we propose an architecture consisting of an RNN with 3 GRU layers and a feedforward network combining both accelerometer and participant-level data. Our efforts included switching mean to standard deviation for down-sampling the input data and combining temporal and static data (person-specific details such as age, weight, BMI). The resulting architecture produces accurate PAEE estimations while decreasing training input and time by a factor of 10. Subsequently, compared to the state-of-the-art, it is capable to integrate longer activity data which lead to more accurate estimations of low intensity activities EE. It can thus be employed to investigate associations of PAEE with vitality parameters of older people related to metabolic and cognitive health and mental well-being.

Published
2022

27. 1H-NMR metabolomics-based surrogates to impute common clinical risk factors and endpoints

Author

Bizzarri, D., Reinders, M.J.T., Beekman, M., Slagboom, P.E., BBMRI-NL, and Akker, E.B. van den

Subjects
Medicine (General), metabolic surrogates, posterior probability obtained applying the models, Magnetic Resonance Spectroscopy, Epidemiology, Proton Magnetic Resonance Spectroscopy, chol, cholesterol, General Biochemistry, Genetics and Molecular Biology, Article, R5-920, H-1-NMR metabolomics, Risk Factors, Humans, Metabolomics, wbc, white blood cells, Missing values, hsCRP, high-sensitivity C-Reactive Protein, MetaboWAS, metabolome-wide association studies, med, medication (e.g. lipid or blood pressure lowering medication, Association studies, Aged, eGFR, estimated Glomerular Filtration Rate, hgb, haemoglobin, General Medicine, 1H-NMR metabolomics, Regression models, H-NMR metabolomics, Surrogate clinical variables, Medicine, T2D, Type 2 Diabetes status, BMI, Body Mass Index
Abstract

Background: Missing or incomplete phenotypic information can severely deteriorate the statistical power in epidemiological studies. High-throughput quantification of small-molecules in bio-samples, i.e. ‘metabolomics’, is steadily gaining popularity, as it is highly informative for various phenotypical characteristics. Here we aim to leverage metabolomics to impute missing data in clinical variables routinely assessed in large epidemiological and clinical studies. Methods: To this end, we have employed ∼26,000 1H-NMR metabolomics samples from 28 Dutch cohorts collected within the BBMRI-NL consortium, to create 19 metabolomics-based predictors for clinical variables, including diabetes status (AUC5-Fold CV = 0·94) and lipid medication usage (AUC5-Fold CV = 0·90). Findings: Subsequent application in independent cohorts confirmed that our metabolomics-based predictors can indeed be used to impute a wide array of missing clinical variables from a single metabolomics data resource. In addition, application highlighted the potential use of our predictors to explore the effects of totally unobserved confounders in omics association studies. Finally, we show that our predictors can be used to explore risk factor profiles contributing to mortality in older participants. Interpretation: To conclude, we provide 1H-NMR metabolomics-based models to impute clinical variables routinely assessed in epidemiological studies and illustrate their merit in scenarios when phenotypic variables are partially incomplete or totally unobserved. Funding: BBMRI-NL, X-omics, VOILA, Medical Delta and the Dutch Research Council (NWO-VENI).

Published
2021

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

Author

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

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

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

Published
2019
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32. 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., Boomsma, D.I., Ikram, M.A., Slagboom, P.E., Westra, H.J., Franke, L., BIOS Consortium, Stem Cell Aging Leukemia and Lymphoma (SALL), Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Psychiatry, Amsterdam Neuroscience - Complex Trait Genetics, VU University medical center, Clinical chemistry, Human genetics, APH - Mental Health, Biological Psychology, and APH - Methodology

Subjects
Adult, Cell type, Aging, lcsh:QH426-470, Adolescent, lcsh:Biotechnology, Population, Biology, Blood cell, 03 medical and health sciences, Young Adult, 0302 clinical medicine, SDG 3 - Good Health and Well-being, lcsh:TP248.13-248.65, White blood cell, Gene expression, Genetics, medicine, 80 and over, Aging/genetics, Humans, Platelet activation, education, Gene, Cell counts correction, 030304 developmental biology, Whole blood, Aged, Aged, 80 and over, 0303 health sciences, education.field_of_study, Middle Aged, Platelet activity, lcsh:Genetics, medicine.anatomical_structure, DNA microarray, Transcriptome, 030217 neurology & neurosurgery, Research Article, Biotechnology
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 ≤ 2.5⨯10−6). Moreover, 511 genes (~ 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
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33. Mendelian randomization integrating GWAS and eQTL data reveals genetic determinants of complex and clinical traits

Author

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

Subjects
0301 basic medicine, Netherlands Twin Register (NTR), Statistical methods, General Physics and Astronomy, Genome-wide association study, 02 engineering and technology, VARIANTS, Quantitative trait, DISEASE, 0302 clinical medicine, Pleiotropy, GTP-Binding Protein gamma Subunits, lcsh:Science, MUTATION, 0303 health sciences, Brain Diseases, Multidisciplinary, 1184 Genetics, developmental biology, physiology, Mendelian Randomization Analysis, ASSOCIATION, 021001 nanoscience & nanotechnology, Phenotype, STATISTICS, ddc, FAMILY, OBESITY, symbols, 0210 nano-technology, EXPRESSION, Science, Quantitative Trait Loci, Single-nucleotide polymorphism, Computational biology, Biology, Quantitative trait locus, Polymorphism, Single Nucleotide, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, symbols.namesake, Mendelian randomization, Brain Diseases/genetics, Gene Expression Profiling, Genetic Predisposition to Disease, Genetic Variation, Genome-Wide Association Study, Humans, Transcriptome, INSTRUMENTAL VARIABLES, SNP, 030304 developmental biology, Genetic association, General Chemistry, 030104 developmental biology, Expression quantitative trait loci, Mendelian inheritance, PLEIOTROPY, lcsh:Q, Gene expression, 030217 neurology & neurosurgery
Abstract

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

Published
2019
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34. Shared genetic risk between eating disorder- and substance-use-related phenotypes: Evidence from genome-wide association studies

Author

Munn-Chernoff, M.A. Johnson, E.C. Chou, Y.-L. Coleman, J.R.I. Thornton, L.M. Walters, R.K. Yilmaz, Z. Baker, J.H. Hübel, C. Gordon, S. Medland, S.E. Watson, H.J. Gaspar, H.A. Bryois, J. Hinney, A. Leppä, V.M. Mattheisen, M. Ripke, S. Yao, S. Giusti-Rodríguez, P. Hanscombe, K.B. Adan, R.A.H. Alfredsson, L. Ando, T. Andreassen, O.A. Berrettini, W.H. Boehm, I. Boni, C. Boraska Perica, V. Buehren, K. Burghardt, R. Cassina, M. Cichon, S. Clementi, M. Cone, R.D. Courtet, P. Crow, S. Crowley, J.J. Danner, U.N. Davis, O.S.P. de Zwaan, M. Dedoussis, G. Degortes, D. DeSocio, J.E. Dick, D.M. Dikeos, D. Dina, C. Dmitrzak-Weglarz, M. Docampo, E. Duncan, L.E. Egberts, K. Ehrlich, S. Escaramís, G. Esko, T. Estivill, X. Farmer, A. Favaro, A. Fernández-Aranda, F. Fichter, M.M. Fischer, K. Föcker, M. Foretova, L. Forstner, A.J. Forzan, M. Franklin, C.S. Gallinger, S. Giegling, I. Giuranna, J. Gonidakis, F. Gorwood, P. Gratacos Mayora, M. Guillaume, S. Guo, Y. Hakonarson, H. Hatzikotoulas, K. Hauser, J. Hebebrand, J. Helder, S.G. Herms, S. Herpertz-Dahlmann, B. Herzog, W. Huckins, L.M. Hudson, J.I. Imgart, H. Inoko, H. Janout, V. Jiménez-Murcia, S. Julià, A. Kalsi, G. Kaminská, D. Karhunen, L. Karwautz, A. Kas, M.J.H. Kennedy, J.L. Keski-Rahkonen, A. Kiezebrink, K. Kim, Y.-R. Klump, K.L. Knudsen, G.P.S. La Via, M.C. Le Hellard, S. Levitan, R.D. Li, D. Lilenfeld, L. Lin, B.D. Lissowska, J. Luykx, J. Magistretti, P.J. Maj, M. Mannik, K. Marsal, S. Marshall, C.R. Mattingsdal, M. McDevitt, S. McGuffin, P. Metspalu, A. Meulenbelt, I. Micali, N. Mitchell, K. Monteleone, A.M. Monteleone, P. Nacmias, B. Navratilova, M. Ntalla, I. O'Toole, J.K. Ophoff, R.A. Padyukov, L. Palotie, A. Pantel, J. Papezova, H. Pinto, D. Rabionet, R. Raevuori, A. Ramoz, N. Reichborn-Kjennerud, T. Ricca, V. Ripatti, S. Ritschel, F. Roberts, M. Rotondo, A. Rujescu, D. Rybakowski, F. Santonastaso, P. Scherag, A. Scherer, S.W. Schmidt, U. Schork, N.J. Schosser, A. Seitz, J. Slachtova, L. Slagboom, P.E. Slof-Op't Landt, M.C.T. Slopien, A. Sorbi, S. Świątkowska, B. Szatkiewicz, J.P. Tachmazidou, I. Tenconi, E. Tortorella, A. Tozzi, F. Treasure, J. Tsitsika, A. Tyszkiewicz-Nwafor, M. Tziouvas, K. van Elburg, A.A. van Furth, E.F. Wagner, G. Walton, E. Widen, E. Zeggini, E. Zerwas, S. Zipfel, S. Bergen, A.W. Boden, J.M. Brandt, H. Crawford, S. Halmi, K.A. Horwood, L.J. Johnson, C. Kaplan, A.S. Kaye, W.H. Mitchell, J. Olsen, C.M. Pearson, J.F. Pedersen, N.L. Strober, M. Werge, T. Whiteman, D.C. Woodside, D.B. Grove, J. Henders, A.K. Larsen, J.T. Parker, R. Petersen, L.V. Jordan, J. Kennedy, M.A. Birgegård, A. Lichtenstein, P. Norring, C. Landén, M. Mortensen, P.B. Polimanti, R. McClintick, J.N. Adkins, A.E. Aliev, F. Bacanu, S.-A. Batzler, A. Bertelsen, S. Biernacka, J.M. Bigdeli, T.B. Chen, L.-S. Clarke, T.-K. Degenhardt, F. Docherty, A.R. Edwards, A.C. Foo, J.C. Fox, L. Frank, J. Hack, L.M. Hartmann, A.M. Hartz, S.M. Heilmann-Heimbach, S. Hodgkinson, C. Hoffmann, P. Hottenga, J.-J. Konte, B. Lahti, J. Lahti-Pulkkinen, M. Lai, D. Ligthart, L. Loukola, A. Maher, B.S. Mbarek, H. McIntosh, A.M. McQueen, M.B. Meyers, J.L. Milaneschi, Y. Palviainen, T. Peterson, R.E. Ryu, E. Saccone, N.L. Salvatore, J.E. Sanchez-Roige, S. Schwandt, M. Sherva, R. Streit, F. Strohmaier, J. Thomas, N. Wang, J.-C. Webb, B.T. Wedow, R. Wetherill, L. Wills, A.G. Zhou, H. Boardman, J.D. Chen, D. Choi, D.-S. Copeland, W.E. Culverhouse, R.C. Dahmen, N. Degenhardt, L. Domingue, B.W. Frye, M.A. Gäebel, W. Hayward, C. Ising, M. Keyes, M. Kiefer, F. Koller, G. Kramer, J. Kuperman, S. Lucae, S. Lynskey, M.T. Maier, W. Mann, K. Männistö, S. Müller-Myhsok, B. Murray, A.D. Nurnberger, J.I. Preuss, U. Räikkönen, K. Reynolds, M.D. Ridinger, M. Scherbaum, N. Schuckit, M.A. Soyka, M. Treutlein, J. Witt, S.H. Wodarz, N. Zill, P. Adkins, D.E. Boomsma, D.I. Bierut, L.J. Brown, S.A. Bucholz, K.K. Costello, E.J. de Wit, H. Diazgranados, N. Eriksson, J.G. Farrer, L.A. Foroud, T.M. Gillespie, N.A. Goate, A.M. Goldman, D. Grucza, R.A. Hancock, D.B. Harris, K.M. Hesselbrock, V. Hewitt, J.K. Hopfer, C.J. Iacono, W.G. Johnson, E.O. Karpyak, V.M. Kendler, K.S. Kranzler, H.R. Krauter, K. Lind, P.A. McGue, M. MacKillop, J. Madden, P.A.F. Maes, H.H. Magnusson, P.K.E. Nelson, E.C. Nöthen, M.M. Palmer, A.A. Penninx, B.W.J.H. Porjesz, B. Rice, J.P. Rietschel, M. Riley, B.P. Rose, R.J. Shen, P.-H. Silberg, J. Stallings, M.C. Tarter, R.E. Vanyukov, M.M. Vrieze, S. Wall, T.L. Whitfield, J.B. Zhao, H. Neale, B.M. Wade, T.D. Heath, A.C. Montgomery, G.W. Martin, N.G. Sullivan, P.F. Kaprio, J. Breen, G. Gelernter, J. Edenberg, H.J. Bulik, C.M. Agrawal, A.

Subjects
mental disorders
Abstract

Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [rg], twin-based = 0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder- and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (rg = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN (rg = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating (rg = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating (rgs = −0.19 to −0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for major depressive disorder loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships among these behaviors. © 2020 Society for the Study of Addiction

Published
2021

35. Investigating the relationships between unfavorable sleep and metabolomic traits: Evidence from multi-cohort multivariable regression and mendelian randomization analyses

Author

Bos, M.M., primary, Goulding, N.J., additional, Lee, M., additional, Hofman, A., additional, Bot, M., additional, Pool, R., additional, Vijfhuizen, L., additional, Zhang, X., additional, Li, C., additional, Mustafa, R., additional, Neville, M.J., additional, Li-Gao, R., additional, Trompet, S., additional, Beekman, M., additional, Biermasz, N.R., additional, Boomsma, D.I., additional, De Boer, I., additional, Christodoulides, C., additional, Dehghan, A., additional, Van Dijk, K. Willems, additional, Ford, I., additional, Ghanbari, M., additional, Heijmans, B.T., additional, Ikram, M.A., additional, Jukema, J.W., additional, Mook-Kanamori, D.O., additional, Karpe, F., additional, Luik, A.I., additional, Lumey, L., additional, Van Den Maagdenberg, A.M., additional, Mooijaart, S.P., additional, De Mutsert, R., additional, Penninx, B.W.J.H., additional, Rensen, P.C.N., additional, Richmond, R.C., additional, Rosendaal, F.R., additional, Sattar, N., additional, Schoevers, R., additional, Slagboom, P.E., additional, Terwindt, G.M., additional, Thesing, C.S., additional, Wade, K., additional, Wijsman, C.A., additional, Willemsen, G., additional, Zinderman, A., additional, Verwoert, G.C., additional, Noordam, R., additional, and Lawlor, D.A., additional

Published
2021
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36. Higher thyroid stimulating hormone leads to cardiovascular disease and an unfavorable lipid profile: EVidence from multi-cohort Mendelian randomization and metabolomic profiling

Author

Van Vliet, N.A., primary, Bos, M.M., additional, Thesing, C.S., additional, Chaker, L., additional, Pietzner, M., additional, Houtman, E., additional, Neville, M.J., additional, Li-Gao, R., additional, Trompet, S., additional, Mustafa, R., additional, Ahmadizar, F., additional, Beekman, M., additional, Bot, M., additional, Budde, K., additional, Christodoulides, C., additional, Dehghan, A., additional, Delles, C., additional, Elliott, P., additional, Evangelou, M., additional, Gao, H., additional, Ghanbari, M., additional, Van Herwaarden, A.E., additional, Ikram, M.A., additional, Jaeger, M., additional, Jukema, J.W., additional, Karaman, I., additional, Karpe, F., additional, Kloppenburg, M., additional, Meessen, J.M.T.A., additional, Meulenbelt, I., additional, Milaneschi, Y., additional, Mooijaart, S.P., additional, Mook-Kanamori, D.O., additional, Netea, M.G., additional, Netea-Maier, R.T., additional, Peeters, R.P., additional, Penninx, B.W.J.H., additional, Sattar, N., additional, Slagboom, P.E., additional, Suchiman, H.E.D., additional, Völzke, H., additional, Van Dijk, K. Willems, additional, and Noordam, R., additional

Published
2021
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38. Association of common genetic variants with brain microbleeds

Author

Knol, M.J., Lu, D.W., Traylor, M., Adams, H.H.H., Romero, J.R.J., Smith, A.V., Fornage, M., Hofer, E., Liu, J.F., Hostettler, I.C., Luciano, M., Trompet, S., Giese, A.K., Hilal, S., Akker, E.B. van den, Vojinovic, D., Li, S., Sigurdsson, S., Lee, S.J. van der, Jack, C.R., Wilson, D., Yilmaz, P., Satizabal, C.L., Liewald, D.C.M., Grond, J. van der, Chen, C., Saba, Y., Lugt, A. van der, Bastin, M.E., Windham, B.G., Cheng, C.Y., Pirpamer, L., Kantarci, K., Himali, J.J., Yang, Q., Morris, Z., Beiser, A.S., Tozer, D.J., Vernooij, M.W., Amin, N., Beekman, M., Koh, J.Y., Stott, D.J., Houlden, H., Schmidt, R., Gottesman, R.F., MacKinnon, A.D., DeCarli, C., Gudnason, V., Deary, I.J., Duijn, C.M. van, Slagboom, P.E., Wong, T.Y., Rost, N.S., Jukema, J.W., Mosley, T.H., Werring, D.J., Schmidt, H., Wardlaw, J.M., Ikram, M.A., Seshadri, S., Launer, L.J., Markus, H.S., Neurology, Amsterdam Neuroscience - Neurodegeneration, Epidemiology, Neurosciences, Clinical Genetics, and Radiology & Nuclear Medicine

Subjects
0301 basic medicine, medicine.medical_specialty, education.field_of_study, business.industry, Population, Genome-wide association study, Single-nucleotide polymorphism, Odds ratio, Gastroenterology, Hyperintensity, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, Internal medicine, Genetic predisposition, medicine, Neurology (clinical), Allele, business, education, 030217 neurology & neurosurgery, Genetic association
Abstract

ObjectiveTo identify common genetic variants associated with the presence of brain microbleeds (BMBs).MethodsWe performed genome-wide association studies in 11 population-based cohort studies and 3 case–control or case-only stroke cohorts. Genotypes were imputed to the Haplotype Reference Consortium or 1000 Genomes reference panel. BMBs were rated on susceptibility-weighted or T2*-weighted gradient echo MRI sequences, and further classified as lobar or mixed (including strictly deep and infratentorial, possibly with lobar BMB). In a subset, we assessed the effects of APOE ε2 and ε4 alleles on BMB counts. We also related previously identified cerebral small vessel disease variants to BMBs.ResultsBMBs were detected in 3,556 of the 25,862 participants, of which 2,179 were strictly lobar and 1,293 mixed. One locus in the APOE region reached genome-wide significance for its association with BMB (lead single nucleotide polymorphism rs769449; odds ratio [OR]any BMB [95% confidence interval (CI)] 1.33 [1.21–1.45]; p = 2.5 × 10−10). APOE ε4 alleles were associated with strictly lobar (OR [95% CI] 1.34 [1.19–1.50]; p = 1.0 × 10−6) but not with mixed BMB counts (OR [95% CI] 1.04 [0.86–1.25]; p = 0.68). APOE ε2 alleles did not show associations with BMB counts. Variants previously related to deep intracerebral hemorrhage and lacunar stroke, and a risk score of cerebral white matter hyperintensity variants, were associated with BMB.ConclusionsGenetic variants in the APOE region are associated with the presence of BMB, most likely due to the APOE ε4 allele count related to a higher number of strictly lobar BMBs. Genetic predisposition to small vessel disease confers risk of BMB, indicating genetic overlap with other cerebral small vessel disease markers.

Published
2020
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39. Shared genetic risk between eating disorder- and substance-use-related phenotypes: Evidence from genome-wide association studies

Author

Munn-Chernoff, M.A. Johnson, E.C. Chou, Y.-L. Coleman, J.R.I. Thornton, L.M. Walters, R.K. Yilmaz, Z. Baker, J.H. Hübel, C. Gordon, S. Medland, S.E. Watson, H.J. Gaspar, H.A. Bryois, J. Hinney, A. Leppä, V.M. Mattheisen, M. Ripke, S. Yao, S. Giusti-Rodríguez, P. Hanscombe, K.B. Adan, R.A.H. Alfredsson, L. Ando, T. Andreassen, O.A. Berrettini, W.H. Boehm, I. Boni, C. Boraska Perica, V. Buehren, K. Burghardt, R. Cassina, M. Cichon, S. Clementi, M. Cone, R.D. Courtet, P. Crow, S. Crowley, J.J. Danner, U.N. Davis, O.S.P. de Zwaan, M. Dedoussis, G. Degortes, D. DeSocio, J.E. Dick, D.M. Dikeos, D. Dina, C. Dmitrzak-Weglarz, M. Docampo, E. Duncan, L.E. Egberts, K. Ehrlich, S. Escaramís, G. Esko, T. Estivill, X. Farmer, A. Favaro, A. Fernández-Aranda, F. Fichter, M.M. Fischer, K. Föcker, M. Foretova, L. Forstner, A.J. Forzan, M. Franklin, C.S. Gallinger, S. Giegling, I. Giuranna, J. Gonidakis, F. Gorwood, P. Gratacos Mayora, M. Guillaume, S. Guo, Y. Hakonarson, H. Hatzikotoulas, K. Hauser, J. Hebebrand, J. Helder, S.G. Herms, S. Herpertz-Dahlmann, B. Herzog, W. Huckins, L.M. Hudson, J.I. Imgart, H. Inoko, H. Janout, V. Jiménez-Murcia, S. Julià, A. Kalsi, G. Kaminská, D. Karhunen, L. Karwautz, A. Kas, M.J.H. Kennedy, J.L. Keski-Rahkonen, A. Kiezebrink, K. Kim, Y.-R. Klump, K.L. Knudsen, G.P.S. La Via, M.C. Le Hellard, S. Levitan, R.D. Li, D. Lilenfeld, L. Lin, B.D. Lissowska, J. Luykx, J. Magistretti, P.J. Maj, M. Mannik, K. Marsal, S. Marshall, C.R. Mattingsdal, M. McDevitt, S. McGuffin, P. Metspalu, A. Meulenbelt, I. Micali, N. Mitchell, K. Monteleone, A.M. Monteleone, P. Nacmias, B. Navratilova, M. Ntalla, I. O'Toole, J.K. Ophoff, R.A. Padyukov, L. Palotie, A. Pantel, J. Papezova, H. Pinto, D. Rabionet, R. Raevuori, A. Ramoz, N. Reichborn-Kjennerud, T. Ricca, V. Ripatti, S. Ritschel, F. Roberts, M. Rotondo, A. Rujescu, D. Rybakowski, F. Santonastaso, P. Scherag, A. Scherer, S.W. Schmidt, U. Schork, N.J. Schosser, A. Seitz, J. Slachtova, L. Slagboom, P.E. Slof-Op&apos and Munn-Chernoff, M.A. Johnson, E.C. Chou, Y.-L. Coleman, J.R.I. Thornton, L.M. Walters, R.K. Yilmaz, Z. Baker, J.H. Hübel, C. Gordon, S. Medland, S.E. Watson, H.J. Gaspar, H.A. Bryois, J. Hinney, A. Leppä, V.M. Mattheisen, M. Ripke, S. Yao, S. Giusti-Rodríguez, P. Hanscombe, K.B. Adan, R.A.H. Alfredsson, L. Ando, T. Andreassen, O.A. Berrettini, W.H. Boehm, I. Boni, C. Boraska Perica, V. Buehren, K. Burghardt, R. Cassina, M. Cichon, S. Clementi, M. Cone, R.D. Courtet, P. Crow, S. Crowley, J.J. Danner, U.N. Davis, O.S.P. de Zwaan, M. Dedoussis, G. Degortes, D. DeSocio, J.E. Dick, D.M. Dikeos, D. Dina, C. Dmitrzak-Weglarz, M. Docampo, E. Duncan, L.E. Egberts, K. Ehrlich, S. Escaramís, G. Esko, T. Estivill, X. Farmer, A. Favaro, A. Fernández-Aranda, F. Fichter, M.M. Fischer, K. Föcker, M. Foretova, L. Forstner, A.J. Forzan, M. Franklin, C.S. Gallinger, S. Giegling, I. Giuranna, J. Gonidakis, F. Gorwood, P. Gratacos Mayora, M. Guillaume, S. Guo, Y. Hakonarson, H. Hatzikotoulas, K. Hauser, J. Hebebrand, J. Helder, S.G. Herms, S. Herpertz-Dahlmann, B. Herzog, W. Huckins, L.M. Hudson, J.I. Imgart, H. Inoko, H. Janout, V. Jiménez-Murcia, S. Julià, A. Kalsi, G. Kaminská, D. Karhunen, L. Karwautz, A. Kas, M.J.H. Kennedy, J.L. Keski-Rahkonen, A. Kiezebrink, K. Kim, Y.-R. Klump, K.L. Knudsen, G.P.S. La Via, M.C. Le Hellard, S. Levitan, R.D. Li, D. Lilenfeld, L. Lin, B.D. Lissowska, J. Luykx, J. Magistretti, P.J. Maj, M. Mannik, K. Marsal, S. Marshall, C.R. Mattingsdal, M. McDevitt, S. McGuffin, P. Metspalu, A. Meulenbelt, I. Micali, N. Mitchell, K. Monteleone, A.M. Monteleone, P. Nacmias, B. Navratilova, M. Ntalla, I. O'Toole, J.K. Ophoff, R.A. Padyukov, L. Palotie, A. Pantel, J. Papezova, H. Pinto, D. Rabionet, R. Raevuori, A. Ramoz, N. Reichborn-Kjennerud, T. Ricca, V. Ripatti, S. Ritschel, F. Roberts, M. Rotondo, A. Rujescu, D. Rybakowski, F. Santonastaso, P. Scherag, A. Scherer, S.W. Schmidt, U. Schork, N.J. Schosser, A. Seitz, J. Slachtova, L. Slagboom, P.E. Slof-Op&apos

Abstract

Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [rg], twin-based = 0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder- and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (rg = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN (rg = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating (rg = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating (rgs = −0.19 to −0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for major depressive disorder loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substanc

Published
2021

40. Families in comparison: An individual-level comparison of life-course and family reconstructions between population and vital event registers

Author

Van den Berg, Niels, Van Dijk, Ingrid, Mourits, R.J., Slagboom, P.E., Janssens, Angelique, Mandemakers, Kees, Van den Berg, Niels, Van Dijk, Ingrid, Mourits, R.J., Slagboom, P.E., Janssens, Angelique, and Mandemakers, Kees

Abstract

It remains unknown how different types of sources affect the reconstruction of life courses and families in large-scale databases increasingly common in demographic research. Here, we compare family and life-course reconstructions for 495 individuals simultaneously present in two well-known Dutch data sets: LINKS, based on the Zeeland province’s full-population vital event registration data (passive registration), and the Historical Sample of the Netherlands (HSN), based on a national sample of birth certificates, with follow-up of individuals in population registers (active registration). We compare indicators of fertility, marriage, mortality, and occupational status, and conclude that reconstructions in the HSN and LINKS reflect each other well: LINKS provides more complete information on siblings and parents, whereas the HSN provides more complete life-course information. We conclude that life-course and family reconstructions based on linked passive registration of individuals constitute a reliable alternative to reconstructions based on active registration, if case selection is carefully considered.

Published
2021

43. Investigating the relationships between unfavourable habitual sleep and metabolomic traits : evidence from multi-cohort multivariable regression and Mendelian randomization analyses

Author

Bos, Maxime M., Goulding, Neil J., Lee, Matthew A., Hofman, Amy, Bot, Mariska, Pool, René, Vijfhuizen, Lisanne S., Zhang, Xiang, Li, Chihua, Mustafa, Rima, Neville, Matt J., Li-Gao, Ruifang, Trompet, Stella, Beekman, Marian, Biermasz, Nienke R., Boomsma, Dorret I., de Boer, Irene, Christodoulides, Constantinos, Dehghan, Abbas, van Dijk, Ko Willems, Ford, Ian, Ghanbari, Mohsen, Heijmans, Bastiaan T., Ikram, M.A., Jukema, J.W., Mook-Kanamori, Dennis O., Karpe, Fredrik, Luik, Annemarie I., Lumey, L.H., van den Maagdenberg, Arn M.J.M., Mooijaart, Simon P., de Mutsert, Renée, Penninx, Brenda W.J.H., Rensen, Patrick C.N., Richmond, Rebecca C., Rosendaal, Frits R., Sattar, Naveed, Schoevers, Robert A., Slagboom, P.E., Terwindt, Gisela M., Thesing, Carisha S., Wade, Kaitlin H., Wijsman, Carolien A., Willemsen, Gonneke, Zwinderman, Aeilko H., van Heemst, Diana, Noordam, Raymond, Lawlor, Deborah A., Bos, Maxime M., Goulding, Neil J., Lee, Matthew A., Hofman, Amy, Bot, Mariska, Pool, René, Vijfhuizen, Lisanne S., Zhang, Xiang, Li, Chihua, Mustafa, Rima, Neville, Matt J., Li-Gao, Ruifang, Trompet, Stella, Beekman, Marian, Biermasz, Nienke R., Boomsma, Dorret I., de Boer, Irene, Christodoulides, Constantinos, Dehghan, Abbas, van Dijk, Ko Willems, Ford, Ian, Ghanbari, Mohsen, Heijmans, Bastiaan T., Ikram, M.A., Jukema, J.W., Mook-Kanamori, Dennis O., Karpe, Fredrik, Luik, Annemarie I., Lumey, L.H., van den Maagdenberg, Arn M.J.M., Mooijaart, Simon P., de Mutsert, Renée, Penninx, Brenda W.J.H., Rensen, Patrick C.N., Richmond, Rebecca C., Rosendaal, Frits R., Sattar, Naveed, Schoevers, Robert A., Slagboom, P.E., Terwindt, Gisela M., Thesing, Carisha S., Wade, Kaitlin H., Wijsman, Carolien A., Willemsen, Gonneke, Zwinderman, Aeilko H., van Heemst, Diana, Noordam, Raymond, and Lawlor, Deborah A.

Abstract

Background: Sleep traits are associated with cardiometabolic disease risk, with evidence from Mendelian randomization (MR) suggesting that insomnia symptoms and shorter sleep duration increase coronary artery disease risk. We combined adjusted multivariable regression (AMV) and MR analyses of phenotypes of unfavourable sleep on 113 metabolomic traits to investigate possible biochemical mechanisms linking sleep to cardiovascular disease. Methods: We used AMV (N = 17,368) combined with two-sample MR (N = 38,618) to examine effects of self-reported insomnia symptoms, total habitual sleep duration, and chronotype on 113 metabolomic traits. The AMV analyses were conducted on data from 10 cohorts of mostly Europeans, adjusted for age, sex, and body mass index. For the MR analyses, we used summary results from published European-ancestry genome-wide association studies of self-reported sleep traits and of nuclear magnetic resonance (NMR) serum metabolites. We used the inverse-variance weighted (IVW) method and complemented this with sensitivity analyses to assess MR assumptions. Results: We found consistent evidence from AMV and MR analyses for associations of usual vs. sometimes/rare/never insomnia symptoms with lower citrate (− 0.08 standard deviation (SD)[95% confidence interval (CI) − 0.12, − 0.03] in AMV and − 0.03SD [− 0.07, − 0.003] in MR), higher glycoprotein acetyls (0.08SD [95% CI 0.03, 0.12] in AMV and 0.06SD [0.03, 0.10) in MR]), lower total very large HDL particles (− 0.04SD [− 0.08, 0.00] in AMV and − 0.05SD [− 0.09, − 0.02] in MR), and lower phospholipids in very large HDL particles (− 0.04SD [− 0.08, 0.002] in AMV and − 0.05SD [− 0.08, − 0.02] in MR). Longer total sleep duration associated with higher creatinine concentrations using both methods (0.02SD per 1 h [0.01, 0.03] in AMV and 0.15SD [0.02, 0.29] in MR) and with isoleucine in MR analyses (0.22SD [0.08, 0.35]). No consistent evidence was observed for effects of chronotype on metabolomic measures. Con

Published
2021

46. Circulating levels of metabolic biomarkers of site-specific and sex-specific arterial calcification in the multi-cohort BBMRI setting

Author

Bos, M.M, primary, Van Vliet, N.A, additional, Beekman, M, additional, Slagboom, P.E, additional, Vernooij, M, additional, Van Der Grond, J, additional, Van Der Lugt, A, additional, Ahmadizar, F, additional, Ghanbari, M, additional, Ikram, M.A, additional, Van Heemst, D, additional, Bos, D, additional, and Kavousi, M, additional

Published
2020
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48. A characterization of cis- and trans-heritability of RNA-Seq-based gene expression

Author

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

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

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

Published
2020
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49. Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson’s disease

Author

Bryois, J. Skene, N.G. Hansen, T.F. Kogelman, L.J.A. Watson, H.J. Liu, Z. Adan, R. Alfredsson, L. Ando, T. Andreassen, O. Baker, J. Bergen, A. Berrettini, W. Birgegård, A. Boden, J. Boehm, I. Boni, C. Boraska Perica, V. Brandt, H. Breen, G. Bryois, J. Buehren, K. Bulik, C. Burghardt, R. Cassina, M. Cichon, S. Clementi, M. Coleman, J. Cone, R. Courtet, P. Crawford, S. Crow, S. Crowley, J. Danner, U. Davis, O. de Zwaan, M. Dedoussis, G. Degortes, D. DeSocio, J. Dick, D. Dikeos, D. Dina, C. Dmitrzak-Weglarz, M. Docampo Martinez, E. Duncan, L. Egberts, K. Ehrlich, S. Escaramís, G. Esko, T. Estivill, X. Farmer, A. Favaro, A. Fernández-Aranda, F. Fichter, M. Fischer, K. Föcker, M. Foretova, L. Forstner, A. Forzan, M. Franklin, C. Gallinger, S. Gaspar, H. Giegling, I. Giuranna, J. Giusti-Rodríquez, P. Gonidakis, F. Gordon, S. Gorwood, P. Gratacos Mayora, M. Grove, J. Guillaume, S. Guo, Y. Hakonarson, H. Halmi, K. Hanscombe, K. Hatzikotoulas, K. Hauser, J. Hebebrand, J. Helder, S. Henders, A. Herms, S. Herpertz-Dahlmann, B. Herzog, W. Hinney, A. Horwood, L.J. Hübel, C. Huckins, L. Hudson, J. Imgart, H. Inoko, H. Janout, V. Jiménez-Murcia, S. Johnson, C. Jordan, J. Julià, A. Juréus, A. Kalsi, G. Kaminská, D. Kaplan, A. Kaprio, J. Karhunen, L. Karwautz, A. Kas, M. Kaye, W. Kennedy, J. Kennedy, M. Keski-Rahkonen, A. Kiezebrink, K. Kim, Y.-R. Kirk, K. Klareskog, L. Klump, K. Knudsen, G.P. La Via, M. Landén, M. Larsen, J. Le Hellard, S. Leppä, V. Levitan, R. Li, D. Lichtenstein, P. Lilenfeld, L. Lin, B.D. Lissowska, J. Luykx, J. Magistretti, P. Maj, M. Mannik, K. Marsal, S. Marshall, C. Martin, N. Mattheisen, M. Mattingsdal, M. McDevitt, S. McGuffin, P. Medland, S. Metspalu, A. Meulenbelt, I. Micali, N. Mitchell, J. Mitchell, K. Monteleone, P. Monteleone, A.M. Montgomery, G. Mortensen, P.B. Munn-Chernoff, M. Nacmias, B. Navratilova, M. Norring, C. Ntalla, I. Olsen, C. Ophoff, R. O’Toole, J. Padyukov, L. Palotie, A. Pantel, J. Papezova, H. Parker, R. Pearson, J. Pedersen, N. Petersen, L. Pinto, D. Purves, K. Rabionet, R. Raevuori, A. Ramoz, N. Reichborn-Kjennerud, T. Ricca, V. Ripatti, S. Ripke, S. Ritschel, F. Roberts, M. Rotondo, A. Rujescu, D. Rybakowski, F. Santonastaso, P. Scherag, A. Scherer, S. Schmidt, U. Schork, N. Schosser, A. Seitz, J. Slachtova, L. Slagboom, P.E. Slof-Op ‘t Landt, M. Slopien, A. Sorbi, S. Strober, M. Stuber, G. Sullivan, P. Świątkowska, B. Szatkiewicz, J. Tachmazidou, I. Tenconi, E. Thornton, L. Tortorella, A. Tozzi, F. Treasure, J. Tsitsika, A. Tyszkiewicz-Nwafor, M. Tziouvas, K. van Elburg, A. van Furth, E. Wade, T. Wagner, G. Walton, E. Watson, H. Werge, T. Whiteman, D. Widen, E. Woodside, D.B. Yao, S. Yilmaz, Z. Zeggini, E. Zerwas, S. Zipfel, S. Anttila, V. Artto, V. Belin, A.C. de Boer, I. Boomsma, D.I. Børte, S. Chasman, D.I. Cherkas, L. Christensen, A.F. Cormand, B. Cuenca-Leon, E. Davey-Smith, G. Dichgans, M. van Duijn, C. Esko, T. Esserlind, A.L. Ferrari, M. Frants, R.R. Freilinger, T. Furlotte, N. Gormley, P. Griffiths, L. Hamalainen, E. Hiekkala, M. Ikram, M.A. Ingason, A. Järvelin, M.-R. Kajanne, R. Kallela, M. Kaprio, J. Kaunisto, M. Kogelman, L.J.A. Kubisch, C. Kurki, M. Kurth, T. Launer, L. Lehtimaki, T. Lessel, D. Ligthart, L. Litterman, N. Maagdenberg, A. Macaya, A. Malik, R. Mangino, M. McMahon, G. Muller-Myhsok, B. Neale, B.M. Northover, C. Nyholt, D.R. Olesen, J. Palotie, A. Palta, P. Pedersen, L. Pedersen, N. Posthuma, D. Pozo-Rosich, P. Pressman, A. Raitakari, O. Schürks, M. Sintas, C. Stefansson, K. Stefansson, H. Steinberg, S. Strachan, D. Terwindt, G. Vila-Pueyo, M. Wessman, M. Winsvold, B.S. Zhao, H. Zwart, J.A. Agee, M. Alipanahi, B. Auton, A. Bell, R. Bryc, K. Elson, S. Fontanillas, P. Furlotte, N. Heilbron, K. Hinds, D. Huber, K. Kleinman, A. Litterman, N. McCreight, J. McIntyre, M. Mountain, J. Noblin, E. Northover, C. Pitts, S. Sathirapongsasuti, J. Sazonova, O. Shelton, J. Shringarpure, S. Tian, C. Tung, J. Vacic, V. Wilson, C. Brueggeman, L. Bulik, C.M. Arenas, E. Hjerling-Leffler, J. Sullivan, P.F. International Headache Genetics Consortium Eating Disorders Working Group of the Psychiatric Genomics Consortium

Abstract

Genome-wide association studies have discovered hundreds of loci associated with complex brain disorders, but it remains unclear in which cell types these loci are active. Here we integrate genome-wide association study results with single-cell transcriptomic data from the entire mouse nervous system to systematically identify cell types underlying brain complex traits. We show that psychiatric disorders are predominantly associated with projecting excitatory and inhibitory neurons. Neurological diseases were associated with different cell types, which is consistent with other lines of evidence. Notably, Parkinson’s disease was genetically associated not only with cholinergic and monoaminergic neurons (which include dopaminergic neurons) but also with enteric neurons and oligodendrocytes. Using post-mortem brain transcriptomic data, we confirmed alterations in these cells, even at the earliest stages of disease progression. Our study provides an important framework for understanding the cellular basis of complex brain maladies, and reveals an unexpected role of oligodendrocytes in Parkinson’s disease. © 2020, The Author(s), under exclusive licence to Springer Nature America, Inc.

Published
2020

50. Heritability estimates for 361 blood metabolites across 40 genome-wide association studies

Author

Hagenbeek, F.A., Pool, R., Dongen, J. van, Draisma, H.H.M., Hottenga, J.J., Willemsen, G., Abdellaoui, A., Fedko, I.O., Braber, A. den, Visser, P.J., Geus, E.J.C.N. de, Dijk, K.W. van, Verhoeven, A., Suchiman, H.E., Beekman, M., Slagboom, P.E., Duijn, C.M. van, Harms, A.C., Hankemeier, T., Bartels, M., Nivard, M.G., Boomsma, D.I., Wolf, J.J.H.B., Cats, D., Amin, N., Beulens, J.W., Bom, J.A. van der, Bomer, N., Demirkan, A., Hilten, J.A. van, Meessen, J.M.T.A., Moed, M.H., Fu, J., Onderwater, G.L.J., Rutters, F., So-Osman, C., Flier, W.M. van der, Heijden, A.A.W.A. van der, Spek, A. van der, Asselbergs, F.W., Boersma, E., Elders, P.M., Geleijnse, J.M., Ikram, M.A., Kloppenburg, M., Meulenbelt, I., Mooijaart, S.P., Nelissen, R.G.H.H., Netea, M.G., Penninx, B.W.J.H., Stehouwer, C.D.A., Teunissen, C.E., Terwindt, G.M., Hart, L.M. 't, Maagdenberg, A.M.J.M. van den, Harst, P. van der, Horst, I.C.C. van der, Kallen, C.J.H. van der, Greevenbroek, M.M.J. van, Spil, W.E. van, Wijmenga, C., Zwinderman, A.H., Zhernikova, A., Jukema, J.W., Mei, H., Slofstra, M., Swertz, M., Akker, E.B. van den, Deelen, J., Reinders, M.J.T., BBMRI Metabolomics Consortium, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Critical care, Anesthesiology, Peri-operative and Emergency medicine (CAPE), Cardiovascular Centre (CVC), Center for Liver, Digestive and Metabolic Diseases (CLDM), Neurology, Epidemiology and Data Science, ACS - Heart failure & arrhythmias, APH - Health Behaviors & Chronic Diseases, General practice, APH - Methodology, Amsterdam Reproduction & Development (AR&D), APH - Mental Health, Amsterdam Neuroscience - Complex Trait Genetics, Psychiatry, Laboratory Medicine, Other Research, APH - Aging & Later Life, APH - Personalized Medicine, APH - Digital Health, ACS - Diabetes & metabolism, Adult Psychiatry, Epidemiology, Cardiology, Radiology & Nuclear Medicine, Biological Psychology, Psychiatrie & Neuropsychologie, RS: MHeNs - R1 - Cognitive Neuropsychiatry and Clinical Neuroscience, MUMC+: HVC Pieken Maastricht Studie (9), MUMC+: MA Interne Geneeskunde (3), Interne Geneeskunde, MUMC+: Centrum voor Chronische Zieken (3), MUMC+: MA Med Staf Artsass Interne Geneeskunde (9), MUMC+: MA Endocrinologie (9), MUMC+: MA Maag Darm Lever (9), MUMC+: MA Hematologie (9), MUMC+: MA Medische Oncologie (9), MUMC+: MA Nefrologie (9), MUMC+: MA Reumatologie (9), and RS: Carim - V01 Vascular complications of diabetes and metabolic syndrome

Subjects
Netherlands Twin Register (NTR), 0301 basic medicine, SELECTION, Quantitative trait loci, Nutrition and Disease, DATABASE, Metabolite, Science, lnfectious Diseases and Global Health Radboud Institute for Molecular Life Sciences [Radboudumc 4], General Physics and Astronomy, Genome-wide association study, VARIANCE-ESTIMATION, Biology, Quantitative trait locus, GENOTYPE IMPUTATION, METABOLOMICS, BIOBANK, Genome-wide association studies, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Metabolomics, All institutes and research themes of the Radboud University Medical Center, Missing heritability problem, MISSING HERITABILITY, Voeding en Ziekte, Genetic variation, Life Science, NETHERLANDS TWIN REGISTER, lcsh:Science, VLAG, Genetics, Multidisciplinary, General Chemistry, Heritability, Genetic architecture, 030104 developmental biology, chemistry, Lipidomics, lcsh:Q, 030217 neurology & neurosurgery
Abstract

Metabolomics examines the small molecules involved in cellular metabolism. Approximately 50% of total phenotypic differences in metabolite levels is due to genetic variance, but heritability estimates differ across metabolite classes. We perform a review of all genome-wide association and (exome-) sequencing studies published between November 2008 and October 2018, and identify >800 class-specific metabolite loci associated with metabolite levels. In a twin-family cohort (N = 5117), these metabolite loci are leveraged to simultaneously estimate total heritability (h2total), and the proportion of heritability captured by known metabolite loci (h2Metabolite-hits) for 309 lipids and 52 organic acids. Our study reveals significant differences in h2Metabolite-hits among different classes of lipids and organic acids. Furthermore, phosphatidylcholines with a high degree of unsaturation have higher h2Metabolite-hits estimates than phosphatidylcholines with low degrees of unsaturation. This study highlights the importance of common genetic variants for metabolite levels, and elucidates the genetic architecture of metabolite classes., Blood metabolite levels are under the influence of environmental and genetic factors. Here, Hagenbeek et al. perform heritability estimations for metabolite measures and determine the contribution of known metabolite loci to metabolite levels using data from 40 genome-wide association studies.

Published
2020
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