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1. MRI-based and metabolomics-based age scores act synergetically for mortality prediction shown by multi-cohort federated learning

Author

Mateus, Pedro, Garst, Swier, Yu, Jing, Cats, Davy, Harms, Alexander G. J., Birhanu, Mahlet, Beekman, Marian, Slagboom, P. Eline, Reinders, Marcel, van der Grond, Jeroen, Dekker, Andre, Jansen, Jacobus F. A., Beran, Magdalena, Schram, Miranda T., Visser, Pieter Jelle, Moonen, Justine, Ghanbari, Mohsen, Roshchupkin, Gennady, Vojinovic, Dina, Bermejo, Inigo, Mei, Hailiang, and Bron, Esther E.

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Machine Learning, I.2.1
Abstract

Biological age scores are an emerging tool to characterize aging by estimating chronological age based on physiological biomarkers. Various scores have shown associations with aging-related outcomes. This study assessed the relation between an age score based on brain MRI images (BrainAge) and an age score based on metabolomic biomarkers (MetaboAge). We trained a federated deep learning model to estimate BrainAge in three cohorts. The federated BrainAge model yielded significantly lower error for age prediction across the cohorts than locally trained models. Harmonizing the age interval between cohorts further improved BrainAge accuracy. Subsequently, we compared BrainAge with MetaboAge using federated association and survival analyses. The results showed a small association between BrainAge and MetaboAge as well as a higher predictive value for the time to mortality of both scores combined than for the individual scores. Hence, our study suggests that both aging scores capture different aspects of the aging process.

Published
2024

2. The performance of metabolomics-based prediction scores for mortality in older patients with solid tumors

Author

van Holstein, Yara, Mooijaart, Simon P., van Oevelen, Mathijs, van Deudekom, Floor J., Vojinovic, Dina, Bizzarri, Daniele, van den Akker, Erik B., Noordam, Raymond, Deelen, Joris, van Heemst, Diana, de Glas, Nienke A., Holterhues, Cynthia, Labots, Geert, van den Bos, Frederiek, Beekman, Marian, Slagboom, P. Eline, van Munster, Barbara C., Portielje, Johanneke E. A., and Trompet, Stella

Published
2024
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8. Epigenome-wide association study of dietary fatty acid intake

Author

Lange de Luna, Julia, Nounu, Aayah, Neumeyer, Sonja, Sinke, Lucy, Wilson, Rory, Hellbach, Fabian, Matías-García, Pamela R., Delerue, Thomas, Winkelmann, Juliane, Peters, Annette, Thorand, Barbara, Beekman, Marian, Heijmans, Bastiaan T., Slagboom, Eline, Gieger, Christian, Linseisen, Jakob, and Waldenberger, Melanie

Published
2024
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9. Multi-trait analysis characterizes the genetics of thyroid function and identifies causal associations with clinical implications

Author

Sterenborg, Rosalie B. T. M., Steinbrenner, Inga, Li, Yong, Bujnis, Melissa N., Naito, Tatsuhiko, Marouli, Eirini, Galesloot, Tessel E., Babajide, Oladapo, Andreasen, Laura, Astrup, Arne, Åsvold, Bjørn Olav, Bandinelli, Stefania, Beekman, Marian, Beilby, John P., Bork-Jensen, Jette, Boutin, Thibaud, Brody, Jennifer A., Brown, Suzanne J., Brumpton, Ben, Campbell, Purdey J., Cappola, Anne R., Ceresini, Graziano, Chaker, Layal, Chasman, Daniel I., Concas, Maria Pina, Coutinho de Almeida, Rodrigo, Cross, Simone M., Cucca, Francesco, Deary, Ian J., Kjaergaard, Alisa Devedzic, Echouffo Tcheugui, Justin B., Ellervik, Christina, Eriksson, Johan G., Ferrucci, Luigi, Freudenberg, Jan, Fuchsberger, Christian, Gieger, Christian, Giulianini, Franco, Gögele, Martin, Graham, Sarah E., Grarup, Niels, Gunjača, Ivana, Hansen, Torben, Harding, Barbara N., Harris, Sarah E., Haunsø, Stig, Hayward, Caroline, Hui, Jennie, Ittermann, Till, Jukema, J. Wouter, Kajantie, Eero, Kanters, Jørgen K., Kårhus, Line L., Kiemeney, Lambertus A. L. M., Kloppenburg, Margreet, Kühnel, Brigitte, Lahti, Jari, Langenberg, Claudia, Lapauw, Bruno, Leese, Graham, Li, Shuo, Liewald, David C. M., Linneberg, Allan, Lominchar, Jesus V. T., Luan, Jian’an, Martin, Nicholas G., Matana, Antonela, Meima, Marcel E., Meitinger, Thomas, Meulenbelt, Ingrid, Mitchell, Braxton D., Møllehave, Line T., Mora, Samia, Naitza, Silvia, Nauck, Matthias, Netea-Maier, Romana T., Noordam, Raymond, Nursyifa, Casia, Okada, Yukinori, Onano, Stefano, Papadopoulou, Areti, Palmer, Colin N. A., Pattaro, Cristian, Pedersen, Oluf, Peters, Annette, Pietzner, Maik, Polašek, Ozren, Pramstaller, Peter P., Psaty, Bruce M., Punda, Ante, Ray, Debashree, Redmond, Paul, Richards, J. Brent, Ridker, Paul M., Russ, Tom C., Ryan, Kathleen A., Olesen, Morten Salling, Schultheiss, Ulla T., Selvin, Elizabeth, Siddiqui, Moneeza K., Sidore, Carlo, Slagboom, P. Eline, Sørensen, Thorkild I. A., Soto-Pedre, Enrique, Spector, Tim D., Spedicati, Beatrice, Srinivasan, Sundararajan, Starr, John M., Stott, David J., Tanaka, Toshiko, Torlak, Vesela, Trompet, Stella, Tuhkanen, Johanna, Uitterlinden, André G., van den Akker, Erik B., van den Eynde, Tibbert, van der Klauw, Melanie M., van Heemst, Diana, Verroken, Charlotte, Visser, W. Edward, Vojinovic, Dina, Völzke, Henry, Waldenberger, Melanie, Walsh, John P., Wareham, Nicholas J., Weiss, Stefan, Willer, Cristen J., Wilson, Scott G., Wolffenbuttel, Bruce H. R., Wouters, Hanneke J. C. M., Wright, Margaret J., Yang, Qiong, Zemunik, Tatijana, Zhou, Wei, Zhu, Gu, Zöllner, Sebastian, Smit, Johannes W. A., Peeters, Robin P., Köttgen, Anna, Teumer, Alexander, and Medici, Marco

Published
2024
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10. Multi-ancestry genome-wide study identifies effector genes and druggable pathways for coronary artery calcification

Author

Kavousi, Maryam, Bos, Maxime M, Barnes, Hanna J, Lino Cardenas, Christian L, Wong, Doris, Lu, Haojie, Hodonsky, Chani J, Landsmeer, Lennart PL, Turner, Adam W, Kho, Minjung, Hasbani, Natalie R, de Vries, Paul S, Bowden, Donald W, Chopade, Sandesh, Deelen, Joris, Benavente, Ernest Diez, Guo, Xiuqing, Hofer, Edith, Hwang, Shih-Jen, Lutz, Sharon M, Lyytikäinen, Leo-Pekka, Slenders, Lotte, Smith, Albert V, Stanislawski, Maggie A, van Setten, Jessica, Wong, Quenna, Yanek, Lisa R, Becker, Diane M, Beekman, Marian, Budoff, Matthew J, Feitosa, Mary F, Finan, Chris, Hilliard, Austin T, Kardia, Sharon LR, Kovacic, Jason C, Kral, Brian G, Langefeld, Carl D, Launer, Lenore J, Malik, Shaista, Hoesein, Firdaus AA Mohamed, Mokry, Michal, Schmidt, Reinhold, Smith, Jennifer A, Taylor, Kent D, Terry, James G, van der Grond, Jeroen, van Meurs, Joyce, Vliegenthart, Rozemarijn, Xu, Jianzhao, Young, Kendra A, Zilhão, Nuno R, Zweiker, Robert, Assimes, Themistocles L, Becker, Lewis C, Bos, Daniel, Carr, J Jeffrey, Cupples, L Adrienne, de Kleijn, Dominique PV, de Winther, Menno, den Ruijter, Hester M, Fornage, Myriam, Freedman, Barry I, Gudnason, Vilmundur, Hingorani, Aroon D, Hokanson, John E, Ikram, M Arfan, Išgum, Ivana, Jacobs, David R, Kähönen, Mika, Lange, Leslie A, Lehtimäki, Terho, Pasterkamp, Gerard, Raitakari, Olli T, Schmidt, Helena, Slagboom, P Eline, Uitterlinden, André G, Vernooij, Meike W, Bis, Joshua C, Franceschini, Nora, Psaty, Bruce M, Post, Wendy S, Rotter, Jerome I, Björkegren, Johan LM, O’Donnell, Christopher J, Bielak, Lawrence F, Peyser, Patricia A, Malhotra, Rajeev, van der Laan, Sander W, and Miller, Clint L

Subjects
Biological Sciences, Genetics, Prevention, Heart Disease - Coronary Heart Disease, Atherosclerosis, Heart Disease, Human Genome, Cardiovascular, 2.1 Biological and endogenous factors, Humans, Black People, Coronary Artery Disease, Genome-Wide Association Study, Risk Factors, European People, Medical and Health Sciences, Developmental Biology, Agricultural biotechnology, Bioinformatics and computational biology
Abstract

Coronary artery calcification (CAC), a measure of subclinical atherosclerosis, predicts future symptomatic coronary artery disease (CAD). Identifying genetic risk factors for CAC may point to new therapeutic avenues for prevention. Currently, there are only four known risk loci for CAC identified from genome-wide association studies (GWAS) in the general population. Here we conducted the largest multi-ancestry GWAS meta-analysis of CAC to date, which comprised 26,909 individuals of European ancestry and 8,867 individuals of African ancestry. We identified 11 independent risk loci, of which eight were new for CAC and five had not been reported for CAD. These new CAC loci are related to bone mineralization, phosphate catabolism and hormone metabolic pathways. Several new loci harbor candidate causal genes supported by multiple lines of functional evidence and are regulators of smooth muscle cell-mediated calcification ex vivo and in vitro. Together, these findings help refine the genetic architecture of CAC and extend our understanding of the biological and potential druggable pathways underlying CAC.

Published
2023

11. Genome-wide characterization of circulating metabolic biomarkers

Author

Karjalainen, Minna K., Karthikeyan, Savita, Oliver-Williams, Clare, Sliz, Eeva, Allara, Elias, Fung, Wing Tung, Surendran, Praveen, Zhang, Weihua, Jousilahti, Pekka, Kristiansson, Kati, Salomaa, Veikko, Goodwin, Matt, Hughes, David A., Boehnke, Michael, Fernandes Silva, Lilian, Yin, Xianyong, Mahajan, Anubha, Neville, Matt J., van Zuydam, Natalie R., de Mutsert, Renée, Li-Gao, Ruifang, Mook-Kanamori, Dennis O., Demirkan, Ayse, Liu, Jun, Noordam, Raymond, Trompet, Stella, Chen, Zhengming, Kartsonaki, Christiana, Li, Liming, Lin, Kuang, Hagenbeek, Fiona A., Hottenga, Jouke Jan, Pool, René, Ikram, M. Arfan, van Meurs, Joyce, Haller, Toomas, Milaneschi, Yuri, Kähönen, Mika, Mishra, Pashupati P., Joshi, Peter K., Macdonald-Dunlop, Erin, Mangino, Massimo, Zierer, Jonas, Acar, Ilhan E., Hoyng, Carel B., Lechanteur, Yara T. E., Franke, Lude, Kurilshikov, Alexander, Zhernakova, Alexandra, Beekman, Marian, van den Akker, Erik B., Kolcic, Ivana, Polasek, Ozren, Rudan, Igor, Gieger, Christian, Waldenberger, Melanie, Asselbergs, Folkert W., Hayward, Caroline, Fu, Jingyuan, den Hollander, Anneke I., Menni, Cristina, Spector, Tim D., Wilson, James F., Lehtimäki, Terho, Raitakari, Olli T., Penninx, Brenda W. J. H., Esko, Tonu, Walters, Robin G., Jukema, J. Wouter, Sattar, Naveed, Ghanbari, Mohsen, Willems van Dijk, Ko, Karpe, Fredrik, McCarthy, Mark I., Laakso, Markku, Järvelin, Marjo-Riitta, Timpson, Nicholas J., Perola, Markus, Kooner, Jaspal S., Chambers, John C., van Duijn, Cornelia, Slagboom, P. Eline, Boomsma, Dorret I., Danesh, John, Ala-Korpela, Mika, Butterworth, Adam S., and Kettunen, Johannes

Published
2024
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13. Associations of circulating GDF15 with combined cognitive frailty and depression in older adults of the MARK-AGE study

Author

Kochlik, Bastian, Herpich, Catrin, Moreno-Villanueva, María, Klaus, Susanne, Müller-Werdan, Ursula, Weinberger, Birgit, Fiegl, Simone, Toussaint, Olivier, Debacq-Chainiaux, Florence, Schön, Christiane, Bernhard, Jürgen, Breusing, Nicolle, Gonos, Efstathios S., Franceschi, Claudio, Capri, Miriam, Sikora, Ewa, Hervonen, Antti, Hurme, Mikko, Slagboom, P. Eline, Dollé, Martijn E. T., Jansen, Eugene, Grune, Tilman, Bürkle, Alexander, and Norman, Kristina

Published
2024
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14. Validation of biomarkers of aging

Author

Moqri, Mahdi, Herzog, Chiara, Poganik, Jesse R., Ying, Kejun, Justice, Jamie N., Belsky, Daniel W., Higgins-Chen, Albert T., Chen, Brian H., Cohen, Alan A., Fuellen, Georg, Hägg, Sara, Marioni, Riccardo E., Widschwendter, Martin, Fortney, Kristen, Fedichev, Peter O., Zhavoronkov, Alex, Barzilai, Nir, Lasky-Su, Jessica, Kiel, Douglas P., Kennedy, Brian K., Cummings, Steven, Slagboom, P. Eline, Verdin, Eric, Maier, Andrea B., Sebastiano, Vittorio, Snyder, Michael P., Gladyshev, Vadim N., Horvath, Steve, and Ferrucci, Luigi

Published
2024
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15. Percutaneous coronary intervention in patients undergoing transcatheter aortic valve implantation: a systematic review and meta-analysis

Author

Aarts, Hugo M., van Hemert, Nicole D., Meijs, Timion A., van Nieuwkerk, Astrid C., Berg, Jurriën M. ten, Wykrzykowska, Joanna J., van Royen, Niels, Schotborgh, Carl E., Tonino, Pim A. L., IJsselmuiden, Alexander, Vossenberg, Tessel N., van Houwelingen, Gert K., Slagboom, Ton, Voskuil, Michiel, and Delewi, Ronak

Published
2023
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16. Understanding the complex genetic architecture connecting rheumatoid arthritis, osteoporosis and inflammation: discovering causal pathways

Author

Kasher, Melody, Williams, Frances MK, Freidin, Maxim B, Malkin, Ida, Cherny, Stacey S, Benjamin, Emelia, Chasman, Daniel I, Dehghan, Abbas, Ahluwalia, Tarunveer Singh, Meigs, James, Tracy, Russell, Alizadeh, Behrooz Z, Ligthart, Symen, Bis, Josh, Eiriksdottir, Gudny, Pankratz, Nathan, Gross, Myron, Rainer, Alex, Snieder, Harold, Wilson, James G, Psaty, Bruce M, Dupuis, Josee, Prins, Bram, Vaso, Urmo, Stathopoulou, Maria, Franke, Lude, Lehtimaki, Terho, Koenig, Wolfgang, Jamshidi, Yalda, Siest, Sophie, Abbasi, Ali, Uitterlinden, Andre G, Abdollahi, Mohammadreza, Schnabel, Renate, Schick, Ursula M, Nolte, Ilja M, Kraja, Aldi, Hsu, Yi-Hsiang, Tylee, Daniel S, Zwicker, Alyson, Uher, Rudolf, Davey-Smith, George, Morrison, Alanna C, Hicks, Andrew, van Duijn, Cornelia M, Ward-Caviness, Cavin, Boerwinkle, Eric, Rotter, J, Rice, Ken, Lange, Leslie, Perola, Markus, de Geus, Eco, Morris, Andrew P, Makela, Kari Matti, Stacey, David, Eriksson, Johan, Frayling, Tim M, Slagboom, Eline P, and Livshits, Gregory

Subjects
Aging, Autoimmune Disease, Arthritis, Osteoporosis, Human Genome, Heart Disease, Cardiovascular, Rheumatoid Arthritis, Genetics, 2.1 Biological and endogenous factors, Aetiology, Inflammatory and immune system, Arthritis, Rheumatoid, C-Reactive Protein, Genome-Wide Association Study, Humans, Inflammation, Mendelian Randomization Analysis, Polymorphism, Single Nucleotide, CHARGE Inflammation Working Group, Biological Sciences, Medical and Health Sciences, Genetics & Heredity
Abstract

Rheumatoid arthritis (RA) and osteoporosis (OP) are two comorbid complex inflammatory conditions with evidence of shared genetic background and causal relationships. We aimed to clarify the genetic architecture underlying RA and various OP phenotypes while additionally considering an inflammatory component, C-reactive protein (CRP). Genome-wide association study summary statistics were acquired from the GEnetic Factors for OSteoporosis Consortium, Cohorts for Heart and Aging Research Consortium and UK Biobank. Mendelian randomization (MR) was used to detect the presence of causal relationships. Colocalization analysis was performed to determine shared genetic variants between CRP and OP phenotypes. Analysis of pleiotropy between traits owing to shared causal single nucleotide polymorphisms (SNPs) was performed using PL eiotropic A nalysis under CO mposite null hypothesis (PLACO). MR analysis was suggestive of horizontal pleiotropy between RA and OP traits. RA was a significant causal risk factor for CRP (β = 0.027, 95% confidence interval = 0.016-0.038). There was no evidence of CRP→OP causal relationship, but horizontal pleiotropy was apparent. Colocalization established shared genomic regions between CRP and OP, including GCKR and SERPINA1 genes. Pleiotropy arising from shared causal SNPs revealed through the colocalization analysis was all confirmed by PLACO. These genes were found to be involved in the same molecular function 'protein binding' (GO:0005515) associated with RA, OP and CRP. We identified three major components explaining the epidemiological relationship among RA, OP and inflammation: (1) Pleiotropy explains a portion of the shared genetic relationship between RA and OP, albeit polygenically; (2) RA contributes to CRP elevation and (3) CRP, which is influenced by RA, demonstrated pleiotropy with OP.

Published
2022

17. RNNs on Monitoring Physical Activity Energy Expenditure in Older People

Author

Paraschiakos, Stylianos, de Sá, Cláudio Rebelo, Okai, Jeremiah, Slagboom, Eline P., Beekman, Marian, and Knobbe, Arno

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
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 monitoring environment, 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. 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 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. In this paper, we describe our efforts to go beyond the standard facilities of a GRU-based RNN, with the aim of achieving accuracy surpassing the state of the art. These efforts include switching aggregation function from mean to dispersion measures (SD, IQR, ...), combining temporal and static data (person-specific details such as age, weight, BMI) and adding symbolic activity data as predicted by a previously trained ML model. The resulting architecture manages to increase its performance by approximatelly 10% while decreasing training input by a factor of 10. It can thus be employed to investigate associations of PAEE with vitality parameters related to metabolic and cognitive health and mental well-being., Comment: For a revised, updated and published version (Jan 2022, open access) refer to the Journal of Data Mining and Knowledge Discovery, DOI https://doi.org/10.1007/s10618-021-00817-w. To make our experiments, scripts and results accessible to other researchers (open-source) we shared our scripts with the published version (Jan 2022)

Published
2020
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18. Metabolomic biomarkers of habitual B vitamin intakes unveil novel differentially methylated positions in the human epigenome

Author

Costeira, Ricardo, Evangelista, Laila, Wilson, Rory, Yan, Xinyu, Hellbach, Fabian, Sinke, Lucy, Christiansen, Colette, Villicaña, Sergio, Masachs, Olatz M., Tsai, Pei-Chien, Mangino, Massimo, Menni, Cristina, Berry, Sarah E., Beekman, Marian, van Heemst, Diana, Slagboom, P. Eline, Heijmans, Bastiaan T., Suhre, Karsten, Kastenmüller, Gabi, Gieger, Christian, Peters, Annette, Small, Kerrin S., Linseisen, Jakob, Waldenberger, Melanie, and Bell, Jordana T.

Published
2023
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21. Association of common genetic variants with brain microbleeds: A Genome-wide Association Study

Author

Knol, Maria J, Lu, Dongwei, Traylor, Matthew, Adams, Hieab HH, Romero, José Rafael J, Smith, Albert V, Fornage, Myriam, Hofer, Edith, Liu, Junfeng, Hostettler, Isabel C, Luciano, Michelle, Trompet, Stella, Giese, Anne-Katrin, Hilal, Saima, van den Akker, Erik B, Vojinovic, Dina, Li, Shuo, Sigurdsson, Sigurdur, van der Lee, Sven J, Jack, Clifford R, Wilson, Duncan, Yilmaz, Pinar, Satizabal, Claudia L, Liewald, David CM, van der Grond, Jeroen, Chen, Christopher, Saba, Yasaman, van der Lugt, Aad, Bastin, Mark E, Windham, B Gwen, Cheng, Ching Yu, Pirpamer, Lukas, Kantarci, Kejal, Himali, Jayandra J, Yang, Qiong, Morris, Zoe, Beiser, Alexa S, Tozer, Daniel J, Vernooij, Meike W, Amin, Najaf, Beekman, Marian, Koh, Jia Yu, Stott, David J, Houlden, Henry, Schmidt, Reinhold, Gottesman, Rebecca F, MacKinnon, Andrew D, DeCarli, Charles, Gudnason, Vilmundur, Deary, Ian J, van Duijn, Cornelia M, Slagboom, P Eline, Wong, Tien Yin, Rost, Natalia S, Jukema, J Wouter, Mosley, Thomas H, Werring, David J, Schmidt, Helena, Wardlaw, Joanna M, Ikram, M Arfan, Seshadri, Sudha, Launer, Lenore J, Markus, Hugh S, and Initiative, for the Alzheimer’s Disease Neuroimaging

Subjects
Stroke, Human Genome, Neurosciences, Brain Disorders, Genetics, Prevention, Aetiology, 2.1 Biological and endogenous factors, Aged, Aged, 80 and over, Alleles, Apolipoprotein E2, Apolipoprotein E4, Apolipoproteins E, Case-Control Studies, Cerebral Hemorrhage, Cerebral Small Vessel Diseases, Cohort Studies, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Polymorphism, Single Nucleotide, Risk, White Matter, Alzheimer's Disease Neuroimaging Initiative, Clinical Sciences, Cognitive Sciences, Neurology & Neurosurgery
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

22. Cerebral small vessel disease genomics and its implications across the lifespan.

Author

Sargurupremraj, Muralidharan, Suzuki, Hideaki, Jian, Xueqiu, Sarnowski, Chloé, Evans, Tavia E, Bis, Joshua C, Eiriksdottir, Gudny, Sakaue, Saori, Terzikhan, Natalie, Habes, Mohamad, Zhao, Wei, Armstrong, Nicola J, Hofer, Edith, Yanek, Lisa R, Hagenaars, Saskia P, Kumar, Rajan B, van den Akker, Erik B, McWhirter, Rebekah E, Trompet, Stella, Mishra, Aniket, Saba, Yasaman, Satizabal, Claudia L, Beaudet, Gregory, Petit, Laurent, Tsuchida, Ami, Zago, Laure, Schilling, Sabrina, Sigurdsson, Sigurdur, Gottesman, Rebecca F, Lewis, Cora E, Aggarwal, Neelum T, Lopez, Oscar L, Smith, Jennifer A, Valdés Hernández, Maria C, van der Grond, Jeroen, Wright, Margaret J, Knol, Maria J, Dörr, Marcus, Thomson, Russell J, Bordes, Constance, Le Grand, Quentin, Duperron, Marie-Gabrielle, Smith, Albert V, Knopman, David S, Schreiner, Pamela J, Evans, Denis A, Rotter, Jerome I, Beiser, Alexa S, Maniega, Susana Muñoz, Beekman, Marian, Trollor, Julian, Stott, David J, Vernooij, Meike W, Wittfeld, Katharina, Niessen, Wiro J, Soumaré, Aicha, Boerwinkle, Eric, Sidney, Stephen, Turner, Stephen T, Davies, Gail, Thalamuthu, Anbupalam, Völker, Uwe, van Buchem, Mark A, Bryan, R Nick, Dupuis, Josée, Bastin, Mark E, Ames, David, Teumer, Alexander, Amouyel, Philippe, Kwok, John B, Bülow, Robin, Deary, Ian J, Schofield, Peter R, Brodaty, Henry, Jiang, Jiyang, Tabara, Yasuharu, Setoh, Kazuya, Miyamoto, Susumu, Yoshida, Kazumichi, Nagata, Manabu, Kamatani, Yoichiro, Matsuda, Fumihiko, Psaty, Bruce M, Bennett, David A, De Jager, Philip L, Mosley, Thomas H, Sachdev, Perminder S, Schmidt, Reinhold, Warren, Helen R, Evangelou, Evangelos, Trégouët, David-Alexandre, International Network against Thrombosis (INVENT) Consortium, International Headache Genomics Consortium (IHGC), Ikram, Mohammad A, Wen, Wei, DeCarli, Charles, Srikanth, Velandai K, Jukema, J Wouter, Slagboom, Eline P, and Kardia, Sharon LR

Subjects
International Network against Thrombosis (INVENT) Consortium, International Headache Genomics Consortium, Humans, Alzheimer Disease, Hypertension, Medical History Taking, Risk Assessment, Risk Factors, Adult, Aged, Aged, 80 and over, Middle Aged, Female, Male, Stroke, Genome-Wide Association Study, Young Adult, Diffusion Tensor Imaging, Genetic Loci, Mendelian Randomization Analysis, Cerebral Small Vessel Diseases, White Matter, and over
Abstract

White matter hyperintensities (WMH) are the most common brain-imaging feature of cerebral small vessel disease (SVD), hypertension being the main known risk factor. Here, we identify 27 genome-wide loci for WMH-volume in a cohort of 50,970 older individuals, accounting for modification/confounding by hypertension. Aggregated WMH risk variants were associated with altered white matter integrity (p = 2.5×10-7) in brain images from 1,738 young healthy adults, providing insight into the lifetime impact of SVD genetic risk. Mendelian randomization suggested causal association of increasing WMH-volume with stroke, Alzheimer-type dementia, and of increasing blood pressure (BP) with larger WMH-volume, notably also in persons without clinical hypertension. Transcriptome-wide colocalization analyses showed association of WMH-volume with expression of 39 genes, of which four encode known drug targets. Finally, we provide insight into BP-independent biological pathways underlying SVD and suggest potential for genetic stratification of high-risk individuals and for genetically-informed prioritization of drug targets for prevention trials.

Published
2020

23. Correction to: A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer’s disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity

Author

van der Lee, Sven J, Conway, Olivia J, Jansen, Iris, Carrasquillo, Minerva M, Kleineidam, Luca, van den Akker, Erik, Hernández, Isabel, van Eijk, Kristel R, Stringa, Najada, Chen, Jason A, Zettergren, Anna, Andlauer, Till FM, Diez-Fairen, Monica, Simon-Sanchez, Javier, Lleó, Alberto, Zetterberg, Henrik, Nygaard, Marianne, Blauwendraat, Cornelis, Savage, Jeanne E, Mengel-From, Jonas, Moreno-Grau, Sonia, Wagner, Michael, Fortea, Juan, Keogh, Michael J, Blennow, Kaj, Skoog, Ingmar, Friese, Manuel A, Pletnikova, Olga, Zulaica, Miren, Lage, Carmen, de Rojas, Itziar, Riedel-Heller, Steffi, Illán-Gala, Ignacio, Wei, Wei, Jeune, Bernard, Orellana, Adelina, Then Bergh, Florian, Wang, Xue, Hulsman, Marc, Beker, Nina, Tesi, Niccolo, Morris, Christopher M, Indakoetxea, Begoña, Collij, Lyduine E, Scherer, Martin, Morenas-Rodríguez, Estrella, Ironside, James W, van Berckel, Bart NM, Alcolea, Daniel, Wiendl, Heinz, Strickland, Samantha L, Pastor, Pau, Rodríguez Rodríguez, Eloy, Boeve, Bradley F, Petersen, Ronald C, Ferman, Tanis J, van Gerpen, Jay A, Reinders, Marcel JT, Uitti, Ryan J, Tárraga, Lluís, Maier, Wolfgang, Dols-Icardo, Oriol, Kawalia, Amit, Dalmasso, Maria Carolina, Boada, Mercè, Zettl, Uwe K, van Schoor, Natasja M, Beekman, Marian, Allen, Mariet, Masliah, Eliezer, de Munain, Adolfo López, Pantelyat, Alexander, Wszolek, Zbigniew K, Ross, Owen A, Dickson, Dennis W, Graff-Radford, Neill R, Knopman, David, Rademakers, Rosa, Lemstra, Afina W, Pijnenburg, Yolande AL, Scheltens, Philip, Gasser, Thomas, Chinnery, Patrick F, Hemmer, Bernhard, Huisman, Martijn A, Troncoso, Juan, Moreno, Fermin, Nohr, Ellen A, Sørensen, Thorkild IA, Heutink, Peter, Sánchez-Juan, Pascual, Posthuma, Danielle, Clarimón, Jordi, Christensen, Kaare, Ertekin-Taner, Nilüfer, Scholz, Sonja W, Ramirez, Alfredo, Ruiz, Agustín, Slagboom, Eline, and van der Flier, Wiesje M

Subjects
Biomedical and Clinical Sciences, Neurosciences, Neurodegenerative, Dementia, Acquired Cognitive Impairment, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Lewy Body Dementia, Genetics, Aging, Alzheimer's Disease Related Dementias (ADRD), Parkinson's Disease, Prevention, Brain Disorders, Alzheimer's Disease, Neurological, DESGESCO, EADB, IFGC, IPDGC, RiMod-FTD, Netherlands Brain Bank, GIFT (Genetic Investigation in Frontotemporal Dementia and Alzheimer’s Disease) Study Group, Clinical Sciences, Neurology & Neurosurgery
Abstract

The IPDGC (The International Parkinson Disease Genomics Consortium) and EADB (Alzheimer Disease European DNA biobank) are listed correctly as an author to the article, however, they were incorrectly listed more than once.

Published
2020

24. Circulating cell-free DNA in health and disease — the relationship to health behaviours, ageing phenotypes and metabolomics

Author

Kananen, Laura, Hurme, Mikko, Bürkle, Alexander, Moreno-Villanueva, Maria, Bernhardt, Jürgen, Debacq-Chainiaux, Florence, Grubeck-Loebenstein, Beatrix, Malavolta, Marco, Basso, Andrea, Piacenza, Francesco, Collino, Sebastiano, Gonos, Efstathios S., Sikora, Ewa, Gradinaru, Daniela, Jansen, Eugene H. J. M., Dollé, Martijn E. T., Salmon, Michel, Stuetz, Wolfgang, Weber, Daniela, Grune, Tilman, Breusing, Nicolle, Simm, Andreas, Capri, Miriam, Franceschi, Claudio, Slagboom, Eline, Talbot, Duncan, Libert, Claude, Raitanen, Jani, Koskinen, Seppo, Härkänen, Tommi, Stenholm, Sari, Ala-Korpela, Mika, Lehtimäki, Terho, Raitakari, Olli T., Ukkola, Olavi, Kähönen, Mika, Jylhä, Marja, and Jylhävä, Juulia

Published
2023
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25. Metabolomic profiles predict individual multidisease outcomes

Author

Buergel, Thore, Steinfeldt, Jakob, Ruyoga, Greg, Pietzner, Maik, Bizzarri, Daniele, Vojinovic, Dina, Upmeier zu Belzen, Julius, Loock, Lukas, Kittner, Paul, Christmann, Lara, Hollmann, Noah, Strangalies, Henrik, Braunger, Jana M., Wild, Benjamin, Chiesa, Scott T., Spranger, Joachim, Klostermann, Fabian, van den Akker, Erik B., Trompet, Stella, Mooijaart, Simon P., Sattar, Naveed, Jukema, J. Wouter, Lavrijssen, Birgit, Kavousi, Maryam, Ghanbari, Mohsen, Ikram, Mohammad A., Slagboom, Eline, Kivimaki, Mika, Langenberg, Claudia, Deanfield, John, Eils, Roland, and Landmesser, Ulf

Published
2022
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26. A meta-analysis of genome-wide association studies identifies multiple longevity genes.

Author

Deelen, Joris, Evans, Daniel S, Arking, Dan E, Tesi, Niccolò, Nygaard, Marianne, Liu, Xiaomin, Wojczynski, Mary K, Biggs, Mary L, van der Spek, Ashley, Atzmon, Gil, Ware, Erin B, Sarnowski, Chloé, Smith, Albert V, Seppälä, Ilkka, Cordell, Heather J, Dose, Janina, Amin, Najaf, Arnold, Alice M, Ayers, Kristin L, Barzilai, Nir, Becker, Elizabeth J, Beekman, Marian, Blanché, Hélène, Christensen, Kaare, Christiansen, Lene, Collerton, Joanna C, Cubaynes, Sarah, Cummings, Steven R, Davies, Karen, Debrabant, Birgit, Deleuze, Jean-François, Duncan, Rachel, Faul, Jessica D, Franceschi, Claudio, Galan, Pilar, Gudnason, Vilmundur, Harris, Tamara B, Huisman, Martijn, Hurme, Mikko A, Jagger, Carol, Jansen, Iris, Jylhä, Marja, Kähönen, Mika, Karasik, David, Kardia, Sharon LR, Kingston, Andrew, Kirkwood, Thomas BL, Launer, Lenore J, Lehtimäki, Terho, Lieb, Wolfgang, Lyytikäinen, Leo-Pekka, Martin-Ruiz, Carmen, Min, Junxia, Nebel, Almut, Newman, Anne B, Nie, Chao, Nohr, Ellen A, Orwoll, Eric S, Perls, Thomas T, Province, Michael A, Psaty, Bruce M, Raitakari, Olli T, Reinders, Marcel JT, Robine, Jean-Marie, Rotter, Jerome I, Sebastiani, Paola, Smith, Jennifer, Sørensen, Thorkild IA, Taylor, Kent D, Uitterlinden, André G, van der Flier, Wiesje, van der Lee, Sven J, van Duijn, Cornelia M, van Heemst, Diana, Vaupel, James W, Weir, David, Ye, Kenny, Zeng, Yi, Zheng, Wanlin, Holstege, Henne, Kiel, Douglas P, Lunetta, Kathryn L, Slagboom, P Eline, and Murabito, Joanne M

Subjects
Humans, Heat-Shock Proteins, Longevity, Apolipoprotein E2, Apolipoprotein E4, Genome-Wide Association Study
Abstract

Human longevity is heritable, but genome-wide association (GWA) studies have had limited success. Here, we perform two meta-analyses of GWA studies of a rigorous longevity phenotype definition including 11,262/3484 cases surviving at or beyond the age corresponding to the 90th/99th survival percentile, respectively, and 25,483 controls whose age at death or at last contact was at or below the age corresponding to the 60th survival percentile. Consistent with previous reports, rs429358 (apolipoprotein E (ApoE) ε4) is associated with lower odds of surviving to the 90th and 99th percentile age, while rs7412 (ApoE ε2) shows the opposite. Moreover, rs7676745, located near GPR78, associates with lower odds of surviving to the 90th percentile age. Gene-level association analysis reveals a role for tissue-specific expression of multiple genes in longevity. Finally, genetic correlation of the longevity GWA results with that of several disease-related phenotypes points to a shared genetic architecture between health and longevity.

Published
2019

27. A genome-wide association study identifies genetic loci associated with specific lobar brain volumes

Author

van der Lee, Sven J, Knol, Maria J, Chauhan, Ganesh, Satizabal, Claudia L, Smith, Albert Vernon, Hofer, Edith, Bis, Joshua C, Hibar, Derrek P, Hilal, Saima, van den Akker, Erik B, Arfanakis, Konstantinos, Bernard, Manon, Yanek, Lisa R, Amin, Najaf, Crivello, Fabrice, Cheung, Josh W, Harris, Tamara B, Saba, Yasaman, Lopez, Oscar L, Li, Shuo, van der Grond, Jeroen, Yu, Lei, Paus, Tomas, Roshchupkin, Gennady V, Amouyel, Philippe, Jahanshad, Neda, Taylor, Kent D, Yang, Qiong, Mathias, Rasika A, Boehringer, Stefan, Mazoyer, Bernard, Rice, Ken, Cheng, Ching Yu, Maillard, Pauline, van Heemst, Diana, Wong, Tien Yin, Niessen, Wiro J, Beiser, Alexa S, Beekman, Marian, Zhao, Wanting, Nyquist, Paul A, Chen, Christopher, Launer, Lenore J, Psaty, Bruce M, Ikram, M Kamran, Vernooij, Meike W, Schmidt, Helena, Pausova, Zdenka, Becker, Diane M, De Jager, Philip L, Thompson, Paul M, van Duijn, Cornelia M, Bennett, David A, Slagboom, P Eline, Schmidt, Reinhold, Longstreth, WT, Ikram, M Arfan, Seshadri, Sudha, Debette, Stéphanie, Gudnason, Vilmundur, Adams, Hieab HH, and DeCarli, Charles

Subjects
Biological Sciences, Genetics, Mental Health, Human Genome, Brain Disorders, Neurosciences, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Neurological, Frontal Lobe, Gene Expression Regulation, Developmental, Genetic Loci, Genetic Variation, Genome-Wide Association Study, Genotype, Heredity, Humans, Magnetic Resonance Imaging, Occipital Lobe, Organ Size, Parietal Lobe, Phenotype, Temporal Lobe, United Kingdom, Biomarkers, Genome-wide association studies, Neurology, Biological sciences, Biomedical and clinical sciences
Abstract

Brain lobar volumes are heritable but genetic studies are limited. We performed genome-wide association studies of frontal, occipital, parietal and temporal lobe volumes in 16,016 individuals, and replicated our findings in 8,789 individuals. We identified six genetic loci associated with specific lobar volumes independent of intracranial volume. Two loci, associated with occipital (6q22.32) and temporal lobe volume (12q14.3), were previously reported to associate with intracranial and hippocampal volume, respectively. We identified four loci previously unknown to affect brain volumes: 3q24 for parietal lobe volume, and 1q22, 4p16.3 and 14q23.1 for occipital lobe volume. The associated variants were located in regions enriched for histone modifications (DAAM1 and THBS3), or close to genes causing Mendelian brain-related diseases (ZIC4 and FGFRL1). No genetic overlap between lobar volumes and neurological or psychiatric diseases was observed. Our findings reveal part of the complex genetics underlying brain development and suggest a role for regulatory regions in determining brain volumes.

Published
2019

28. A nonsynonymous mutation in PLCG2 reduces the risk of Alzheimer’s disease, dementia with Lewy bodies and frontotemporal dementia, and increases the likelihood of longevity

Author

van der Lee, Sven J, Conway, Olivia J, Jansen, Iris, Carrasquillo, Minerva M, Kleineidam, Luca, van den Akker, Erik, Hernández, Isabel, van Eijk, Kristel R, Stringa, Najada, Chen, Jason A, Zettergren, Anna, Andlauer, Till FM, Diez-Fairen, Monica, Simon-Sanchez, Javier, Lleó, Alberto, Zetterberg, Henrik, Nygaard, Marianne, Blauwendraat, Cornelis, Savage, Jeanne E, Mengel-From, Jonas, Moreno-Grau, Sonia, Wagner, Michael, Fortea, Juan, Keogh, Michael J, Blennow, Kaj, Skoog, Ingmar, Friese, Manuel A, Pletnikova, Olga, Zulaica, Miren, Lage, Carmen, de Rojas, Itziar, Riedel-Heller, Steffi, Illán-Gala, Ignacio, Wei, Wei, Jeune, Bernard, Orellana, Adelina, Then Bergh, Florian, Wang, Xue, Hulsman, Marc, Beker, Nina, Tesi, Niccolo, Morris, Christopher M, Indakoetxea, Begoña, Collij, Lyduine E, Scherer, Martin, Morenas-Rodríguez, Estrella, Ironside, James W, van Berckel, Bart NM, Alcolea, Daniel, Wiendl, Heinz, Strickland, Samantha L, Pastor, Pau, Rodríguez Rodríguez, Eloy, Boeve, Bradley F, Petersen, Ronald C, Ferman, Tanis J, van Gerpen, Jay A, Reinders, Marcel JT, Uitti, Ryan J, Tárraga, Lluís, Maier, Wolfgang, Dols-Icardo, Oriol, Kawalia, Amit, Dalmasso, Maria Carolina, Boada, Mercè, Zettl, Uwe K, van Schoor, Natasja M, Beekman, Marian, Allen, Mariet, Masliah, Eliezer, de Munain, Adolfo López, Pantelyat, Alexander, Wszolek, Zbigniew K, Ross, Owen A, Dickson, Dennis W, Graff-Radford, Neill R, Knopman, David, Rademakers, Rosa, Lemstra, Afina W, Pijnenburg, Yolande AL, Scheltens, Philip, Gasser, Thomas, Chinnery, Patrick F, Hemmer, Bernhard, Huisman, Martijn A, Troncoso, Juan, Moreno, Fermin, Nohr, Ellen A, Sørensen, Thorkild IA, Heutink, Peter, Sánchez-Juan, Pascual, Posthuma, Danielle, Clarimón, Jordi, Christensen, Kaare, Ertekin-Taner, Nilüfer, Scholz, Sonja W, Ramirez, Alfredo, Ruiz, Agustín, Slagboom, Eline, and van der Flier, Wiesje M

Subjects
Biomedical and Clinical Sciences, Neurosciences, Rare Diseases, Acquired Cognitive Impairment, Alzheimer's Disease, Neurodegenerative, Alzheimer's Disease Related Dementias (ADRD), Brain Disorders, Dementia, Clinical Research, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Prevention, Aging, Genetics, Aetiology, 2.1 Biological and endogenous factors, Neurological, Alleles, Alzheimer Disease, Amyotrophic Lateral Sclerosis, Brain, Frontotemporal Dementia, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Lewy Body Disease, Longevity, Microglia, Multiple Sclerosis, Mutation, Neuroimaging, Parkinson Disease, Phospholipase C gamma, Risk, Alzheimer's disease, Frontotemporal dementia, Dementia with Lewy bodies, Progressive supranuclear palsy, Parkinson's disease, Amyotrophic lateral sclerosis, Multiple sclerosis, Neurodegenerative disease, PLCG2, Phospholipase C Gamma 2, DESGESCO (Dementia Genetics Spanish Consortium), EADB, EADB, IFGC (International FTD-Genomics Consortium), IPDGC, IPDGC, RiMod-FTD, Netherlands Brain Bank, GIFT (Genetic Investigation in Frontotemporal Dementia and Alzheimer’s Disease) Study Group, Alzheimer’s disease, Parkinson’s disease, frontotemporal dementia, dementia with Lewy-bodies, progressive suprauclear palsy, Parkinson's Disease, amyotrophic lateral sclerosis, multiple sclerosis, neurodegenerative disease, longevity, Clinical Sciences, Neurology & Neurosurgery
Abstract

The genetic variant rs72824905-G (minor allele) in the PLCG2 gene was previously associated with a reduced Alzheimer's disease risk (AD). The role of PLCG2 in immune system signaling suggests it may also protect against other neurodegenerative diseases and possibly associates with longevity. We studied the effect of the rs72824905-G on seven neurodegenerative diseases and longevity, using 53,627 patients, 3,516 long-lived individuals and 149,290 study-matched controls. We replicated the association of rs72824905-G with reduced AD risk and we found an association with reduced risk of dementia with Lewy bodies (DLB) and frontotemporal dementia (FTD). We did not find evidence for an effect on Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) risks, despite adequate sample sizes. Conversely, the rs72824905-G allele was associated with increased likelihood of longevity. By-proxy analyses in the UK Biobank supported the associations with both dementia and longevity. Concluding, rs72824905-G has a protective effect against multiple neurodegenerative diseases indicating shared aspects of disease etiology. Our findings merit studying the PLCγ2 pathway as drug-target.

Published
2019

29. Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting

Author

Chauhan, Ganesh, Adams, Hieab HH, Satizabal, Claudia L, Bis, Joshua C, Teumer, Alexander, Sargurupremraj, Muralidharan, Hofer, Edith, Trompet, Stella, Hilal, Saima, Smith, Albert Vernon, Jian, Xueqiu, Malik, Rainer, Traylor, Matthew, Pulit, Sara L, Amouyel, Philippe, Mazoyer, Bernard, Zhu, Yi-Cheng, Kaffashian, Sara, Schilling, Sabrina, Beecham, Gary W, Montine, Thomas J, Schellenberg, Gerard D, Kjartansson, Olafur, Guðnason, Vilmundur, Knopman, David S, Griswold, Michael E, Windham, B Gwen, Gottesman, Rebecca F, Mosley, Thomas H, Schmidt, Reinhold, Saba, Yasaman, Schmidt, Helena, Takeuchi, Fumihiko, Yamaguchi, Shuhei, Nabika, Toru, Kato, Norihiro, Rajan, Kumar B, Aggarwal, Neelum T, De Jager, Philip L, Evans, Denis A, Psaty, Bruce M, Rotter, Jerome I, Rice, Kenneth, Lopez, Oscar L, Liao, Jiemin, Chen, Christopher, Cheng, Ching-Yu, Wong, Tien Y, Ikram, Mohammad K, van der Lee, Sven J, Amin, Najaf, Chouraki, Vincent, DeStefano, Anita L, Aparicio, Hugo J, Romero, Jose R, Maillard, Pauline, DeCarli, Charles, Wardlaw, Joanna M, del C. Valdés Hernández, Maria, Luciano, Michelle, Liewald, David, Deary, Ian J, Starr, John M, Bastin, Mark E, Maniega, Susana Muñoz, Slagboom, P Eline, Beekman, Marian, Deelen, Joris, Uh, Hae-Won, Lemmens, Robin, Brodaty, Henry, Wright, Margaret J, Ames, David, Boncoraglio, Giorgio B, Hopewell, Jemma C, Beecham, Ashley H, Blanton, Susan H, Wright, Clinton B, Sacco, Ralph L, Wen, Wei, Thalamuthu, Anbupalam, Armstrong, Nicola J, Chong, Elizabeth, Schofield, Peter R, Kwok, John B, van der Grond, Jeroen, Stott, David J, Ford, Ian, Jukema, J Wouter, Vernooij, Meike W, Hofman, Albert, Uitterlinden, André G, van der Lugt, Aad, Wittfeld, Katharina, Grabe, Hans J, Hosten, Norbert, von Sarnowski, Bettina, Völker, Uwe, Levi, Christopher, and Jimenez-Conde, Jordi

Subjects
Biomedical and Clinical Sciences, Neurosciences, Clinical Sciences, Genetics, Stroke, Cardiovascular, Aging, Human Genome, Prevention, Cerebrovascular, Brain Disorders, 2.1 Biological and endogenous factors, Good Health and Well Being, Stroke Genetics Network (SiGN), the International Stroke Genetics Consortium (ISGC), METASTROKE, Alzheimer's Disease Genetics Consortium (ADGC), and the Neurology Working Group of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, Cognitive Sciences, Neurology & Neurosurgery, Clinical sciences
Abstract

ObjectiveTo explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts.MethodsWe performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI.ResultsThe mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p [BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p [BI] = 4.4 × 10-10; p [SSBI] = 1.2 × 10-4), diabetes (p [BI] = 1.7 × 10-8; p [SSBI] = 2.8 × 10-3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10-24), and MRI-defined white matter hyperintensity burden (p [BI] = 1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy.ConclusionIn this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI.

Published
2019

32. Secondary Phenotype Analysis in Ascertained Family Designs: Application to the Leiden Longevity Study

Author

Tissier, Renaud, Tsonaka, Roula, Mooijaart, Simon P., Slagboom, Eline, and Houwing-Duistermaat, Jeanine J.

Subjects
Statistics - Applications
Abstract

The case-control design is often used to test associations between the case-control status and genetic variants. In addition to this primary phenotype a number of additional traits, known as secondary phenotypes, are routinely recorded and typically associations between genetic factors and these secondary traits are studied too. Analysing secondary phenotypes in case-control studies may lead to biased genetic effect estimates, especially when the marker tested is associated with the primary phenotype and when the primary and secondary phenotypes tested are correlated. Several methods have been proposed in the literature to overcome the problem but they are limited to case-control studies and not directly applicable to more complex designs, such as the multiple-cases family studies. A proper secondary phenotype analysis, in this case, is complicated by the within families correlations on top of the biased sampling design. We propose a novel approach to accommodate the ascertainment process while explicitly modelling the familial relationships. Our approach pairs existing methods for mixed-effects models with the retrospective likelihood framework and uses a multivariate probit model to capture the association between the mixed type primary and secondary phenotypes. To examine the efficiency and bias of the estimates we performed simulations under several scenarios for the association between the primary phenotype, secondary phenotype, and genetic markers. We will illustrate the method by analysing the association between triglyceride levels and glucose (secondary phenotypes) and genetic markers from the Leiden Longevity study, a multiple-cases family study that investigates longevity.

Published
2016

33. Genome-wide association study in 79,366 European-ancestry individuals informs the genetic architecture of 25-hydroxyvitamin D levels

Author

Jiang, Xia, O’Reilly, Paul F, Aschard, Hugues, Hsu, Yi-Hsiang, Richards, J Brent, Dupuis, Josée, Ingelsson, Erik, Karasik, David, Pilz, Stefan, Berry, Diane, Kestenbaum, Bryan, Zheng, Jusheng, Luan, Jianan, Sofianopoulou, Eleni, Streeten, Elizabeth A, Albanes, Demetrius, Lutsey, Pamela L, Yao, Lu, Tang, Weihong, Econs, Michael J, Wallaschofski, Henri, Völzke, Henry, Zhou, Ang, Power, Chris, McCarthy, Mark I, Michos, Erin D, Boerwinkle, Eric, Weinstein, Stephanie J, Freedman, Neal D, Huang, Wen-Yi, Van Schoor, Natasja M, van der Velde, Nathalie, Groot, Lisette CPGM de, Enneman, Anke, Cupples, L Adrienne, Booth, Sarah L, Vasan, Ramachandran S, Liu, Ching-Ti, Zhou, Yanhua, Ripatti, Samuli, Ohlsson, Claes, Vandenput, Liesbeth, Lorentzon, Mattias, Eriksson, Johan G, Shea, M Kyla, Houston, Denise K, Kritchevsky, Stephen B, Liu, Yongmei, Lohman, Kurt K, Ferrucci, Luigi, Peacock, Munro, Gieger, Christian, Beekman, Marian, Slagboom, Eline, Deelen, Joris, Heemst, Diana van, Kleber, Marcus E, März, Winfried, de Boer, Ian H, Wood, Alexis C, Rotter, Jerome I, Rich, Stephen S, Robinson-Cohen, Cassianne, den Heijer, Martin, Jarvelin, Marjo-Riitta, Cavadino, Alana, Joshi, Peter K, Wilson, James F, Hayward, Caroline, Lind, Lars, Michaëlsson, Karl, Trompet, Stella, Zillikens, M Carola, Uitterlinden, Andre G, Rivadeneira, Fernando, Broer, Linda, Zgaga, Lina, Campbell, Harry, Theodoratou, Evropi, Farrington, Susan M, Timofeeva, Maria, Dunlop, Malcolm G, Valdes, Ana M, Tikkanen, Emmi, Lehtimäki, Terho, Lyytikäinen, Leo-Pekka, Kähönen, Mika, Raitakari, Olli T, Mikkilä, Vera, Ikram, M Arfan, Sattar, Naveed, Jukema, J Wouter, Wareham, Nicholas J, Langenberg, Claudia, Forouhi, Nita G, Gundersen, Thomas E, Khaw, Kay-Tee, Butterworth, Adam S, Danesh, John, and Spector, Timothy

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Clinical Research, 1.1 Normal biological development and functioning, Inflammatory and immune system, Amidohydrolases, Autoimmune Diseases, Cohort Studies, Female, Genome-Wide Association Study, Humans, Male, Polymorphism, Single Nucleotide, Vesicular Transport Proteins, Vitamin D, White People
Abstract

Vitamin D is a steroid hormone precursor that is associated with a range of human traits and diseases. Previous GWAS of serum 25-hydroxyvitamin D concentrations have identified four genome-wide significant loci (GC, NADSYN1/DHCR7, CYP2R1, CYP24A1). In this study, we expand the previous SUNLIGHT Consortium GWAS discovery sample size from 16,125 to 79,366 (all European descent). This larger GWAS yields two additional loci harboring genome-wide significant variants (P = 4.7×10-9 at rs8018720 in SEC23A, and P = 1.9×10-14 at rs10745742 in AMDHD1). The overall estimate of heritability of 25-hydroxyvitamin D serum concentrations attributable to GWAS common SNPs is 7.5%, with statistically significant loci explaining 38% of this total. Further investigation identifies signal enrichment in immune and hematopoietic tissues, and clustering with autoimmune diseases in cell-type-specific analysis. Larger studies are required to identify additional common SNPs, and to explore the role of rare or structural variants and gene-gene interactions in the heritability of circulating 25-hydroxyvitamin D levels.

Published
2018

34. Large-scale pharmacogenomic study of sulfonylureas and the QT, JT and QRS intervals: CHARGE Pharmacogenomics Working Group

Author

Floyd, JS, Sitlani, CM, Avery, CL, Noordam, R, Li, X, Smith, AV, Gogarten, SM, Li, J, Broer, L, Evans, DS, Trompet, S, Brody, JA, Stewart, JD, Eicher, JD, Seyerle, AA, Roach, J, Lange, LA, Lin, HJ, Kors, JA, Harris, TB, Li-Gao, R, Sattar, N, Cummings, SR, Wiggins, KL, Napier, MD, Stürmer, T, Bis, JC, Kerr, KF, Uitterlinden, AG, Taylor, KD, Stott, DJ, de Mutsert, R, Launer, LJ, Busch, EL, Méndez-Giráldez, R, Sotoodehnia, N, Soliman, EZ, Li, Y, Duan, Q, Rosendaal, FR, Slagboom, PE, Wilhelmsen, KC, Reiner, AP, Chen, Y-DI, Heckbert, SR, Kaplan, RC, Rice, KM, Jukema, JW, Johnson, AD, Liu, Y, Mook-Kanamori, DO, Gudnason, V, Wilson, JG, Rotter, JI, Laurie, CC, Psaty, BM, Whitsel, EA, Cupples, LA, and Stricker, BH

Subjects
Pharmacology and Pharmaceutical Sciences, Biomedical and Clinical Sciences, Cardiovascular, Human Genome, Patient Safety, Heart Disease, Genetics, Prevention, Good Health and Well Being, Aged, Cardiovascular Diseases, Cytochrome P-450 CYP2C9, Diabetes Mellitus, Type 2, Drug-Related Side Effects and Adverse Reactions, Electrocardiography, Ethnicity, Female, Genetic Variation, Genome-Wide Association Study, Humans, Male, Middle Aged, Pharmacogenetics, Pharmacogenomic Testing, Sulfonylurea Compounds, Pharmacology & Pharmacy, Pharmacology and pharmaceutical sciences
Abstract

Sulfonylureas, a commonly used class of medication used to treat type 2 diabetes, have been associated with an increased risk of cardiovascular disease. Their effects on QT interval duration and related electrocardiographic phenotypes are potential mechanisms for this adverse effect. In 11 ethnically diverse cohorts that included 71 857 European, African-American and Hispanic/Latino ancestry individuals with repeated measures of medication use and electrocardiogram (ECG) measurements, we conducted a pharmacogenomic genome-wide association study of sulfonylurea use and three ECG phenotypes: QT, JT and QRS intervals. In ancestry-specific meta-analyses, eight novel pharmacogenomic loci met the threshold for genome-wide significance (P

Published
2018

35. DNA Methylation Analysis Identifies Loci for Blood Pressure Regulation

Author

Richard, Melissa A, Huan, Tianxiao, Ligthart, Symen, Gondalia, Rahul, Jhun, Min A, Brody, Jennifer A, Irvin, Marguerite R, Marioni, Riccardo, Shen, Jincheng, Tsai, Pei-Chien, Montasser, May E, Jia, Yucheng, Syme, Catriona, Salfati, Elias L, Boerwinkle, Eric, Guan, Weihua, Mosley, Thomas H, Bressler, Jan, Morrison, Alanna C, Liu, Chunyu, Mendelson, Michael M, Uitterlinden, André G, van Meurs, Joyce B, Consortium, BIOS, Heijmans, Bastiaan T, Hoen, Peter AC ’t, van Meurs, Joyce, Isaacs, Aaron, Jansen, Rick, Franke, Lude, Boomsma, Dorret I, Pool, René, van Dongen, Jenny, Hottenga, Jouke J, van Greevenbroek, Marleen MJ, Stehouwer, Coen DA, van der Kallen, Carla JH, Schalkwijk, Casper G, Wijmenga, Cisca, Zhernakova, Alexandra, Tigchelaar, Ettje F, Slagboom, P Eline, Beekman, Marian, Deelen, Joris, van Heemst, Diana, Veldink, Jan H, van den Berg, Leonard H, van Duijn, Cornelia M, Hofman, Albert, Jhamai, P Mila, Verbiest, Michael, Suchiman, H Eka D, Verkerk, Marijn, van der Breggen, Ruud, van Rooij, Jeroen, Lakenberg, Nico, Mei, Hailiang, van Iterson, Maarten, van Galen, Michiel, Bot, Jan, van ’t Hof, Peter, Deelen, Patrick, Nooren, Irene, Moed, Matthijs, Vermaat, Martijn, Zhernakova, Dasha V, Luijk, René, Bonder, Marc Jan, van Dijk, Freerk, Arindrarto, Wibowo, Kielbasa, Szymon M, Swertz, Morris A, van Zwet, Erik W, Franco, Oscar H, Zhang, Guosheng, Li, Yun, Stewart, James D, Bis, Joshua C, Psaty, Bruce M, Chen, Yii-Der Ida, Kardia, Sharon LR, Zhao, Wei, Turner, Stephen T, Absher, Devin, Aslibekyan, Stella, Starr, John M, McRae, Allan F, Hou, Lifang, Just, Allan C, Schwartz, Joel D, Vokonas, Pantel S, Menni, Cristina, Spector, Tim D, Shuldiner, Alan, Damcott, Coleen M, Rotter, Jerome I, Palmas, Walter, Liu, Yongmei, and Paus, Tomáš

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Hypertension, Human Genome, 2.1 Biological and endogenous factors, Cardiovascular, Aged, Blood Pressure, CpG Islands, Cross-Sectional Studies, DNA Methylation, Epigenesis, Genetic, Genetic Variation, Genome-Wide Association Study, Humans, Mendelian Randomization Analysis, Middle Aged, Nerve Tissue Proteins, Quantitative Trait Loci, Tetraspanins, BIOS Consortium, DNA methylation, Mendelian randomization, blood pressure, epigenome-wide association study, gene expression, sequence variation, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

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

Published
2017

36. A genome-wide interaction analysis of tricyclic/tetracyclic antidepressants and RR and QT intervals: a pharmacogenomics study from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium

Author

Noordam, Raymond, Sitlani, Colleen M, Avery, Christy L, Stewart, James D, Gogarten, Stephanie M, Wiggins, Kerri L, Trompet, Stella, Warren, Helen R, Sun, Fangui, Evans, Daniel S, Li, Xiaohui, Li, Jin, Smith, Albert V, Bis, Joshua C, Brody, Jennifer A, Busch, Evan L, Caulfield, Mark J, Chen, Yii-Der I, Cummings, Steven R, Cupples, L Adrienne, Duan, Qing, Franco, Oscar H, Méndez-Giráldez, Rául, Harris, Tamara B, Heckbert, Susan R, van Heemst, Diana, Hofman, Albert, Floyd, James S, Kors, Jan A, Launer, Lenore J, Li, Yun, Li-Gao, Ruifang, Lange, Leslie A, Lin, Henry J, de Mutsert, Renée, Napier, Melanie D, Newton-Cheh, Christopher, Poulter, Neil, Reiner, Alexander P, Rice, Kenneth M, Roach, Jeffrey, Rodriguez, Carlos J, Rosendaal, Frits R, Sattar, Naveed, Sever, Peter, Seyerle, Amanda A, Slagboom, P Eline, Soliman, Elsayed Z, Sotoodehnia, Nona, Stott, David J, Stürmer, Til, Taylor, Kent D, Thornton, Timothy A, Uitterlinden, André G, Wilhelmsen, Kirk C, Wilson, James G, Gudnason, Vilmundur, Jukema, J Wouter, Laurie, Cathy C, Liu, Yongmei, Mook-Kanamori, Dennis O, Munroe, Patricia B, Rotter, Jerome I, Vasan, Ramachandran S, Psaty, Bruce M, Stricker, Bruno H, and Whitsel, Eric A

Subjects
Pharmacology and Pharmaceutical Sciences, Biological Sciences, Biomedical and Clinical Sciences, Genetics, Minority Health, Cardiovascular, Human Genome, Health Disparities, Heart Disease, Good Health and Well Being, Aged, Aging, Antidepressive Agents, Tricyclic, Electrocardiography, Female, Genetic Loci, Genome-Wide Association Study, Heart, Humans, Male, Middle Aged, Pharmacogenetics, Genome-wide, QT interval electrocardiography, RR interval, drug-gene interaction, tri/tetracyclic antidepressants, Medical and Health Sciences, Genetics & Heredity, Clinical sciences
Abstract

BackgroundIncreased heart rate and a prolonged QT interval are important risk factors for cardiovascular morbidity and mortality, and can be influenced by the use of various medications, including tricyclic/tetracyclic antidepressants (TCAs). We aim to identify genetic loci that modify the association between TCA use and RR and QT intervals.Methods and resultsWe conducted race/ethnic-specific genome-wide interaction analyses (with HapMap phase II imputed reference panel imputation) of TCAs and resting RR and QT intervals in cohorts of European (n=45 706; n=1417 TCA users), African (n=10 235; n=296 TCA users) and Hispanic/Latino (n=13 808; n=147 TCA users) ancestry, adjusted for clinical covariates. Among the populations of European ancestry, two genome-wide significant loci were identified for RR interval: rs6737205 in BRE (β=56.3, pinteraction=3.9e-9) and rs9830388 in UBE2E2 (β=25.2, pinteraction=1.7e-8). In Hispanic/Latino cohorts, rs2291477 in TGFBR3 significantly modified the association between TCAs and QT intervals (β=9.3, pinteraction=2.55e-8). In the meta-analyses of the other ethnicities, these loci either were excluded from the meta-analyses (as part of quality control), or their effects did not reach the level of nominal statistical significance (pinteraction>0.05). No new variants were identified in these ethnicities. No additional loci were identified after inverse-variance-weighted meta-analysis of the three ancestries.ConclusionsAmong Europeans, TCA interactions with variants in BRE and UBE2E2 were identified in relation to RR intervals. Among Hispanic/Latinos, variants in TGFBR3 modified the relation between TCAs and QT intervals. Future studies are required to confirm our results.

Published
2017

37. Ageing affects subtelomeric DNA methylation in blood cells from a large European population enrolled in the MARK-AGE study

Author

Bacalini, Maria Giulia, Reale, Anna, Malavolta, Marco, Ciccarone, Fabio, Moreno-Villanueva, María, Dollé, Martijn E. T., Jansen, Eugène, Grune, Tilman, Gonos, Efstathios S., Schön, Christiane, Bernhardt, Jürgen, Grubeck-Loebenstein, Beatrix, Sikora, Ewa, Toussaint, Olivier, Debacq-Chainiaux, Florence, Capri, Miriam, Hervonen, Antti, Hurme, Mikko, Slagboom, P. Eline, Breusing, Nicolle, Aversano, Valentina, Tagliatesta, Stefano, Franceschi, Claudio, Blasco, Maria A., Bürkle, Alexander, Caiafa, Paola, and Zampieri, Michele

Published
2021
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38. Comparison of HapMap and 1000 Genomes Reference Panels in a Large-Scale Genome-Wide Association Study

Author

de Vries, Paul S, Sabater-Lleal, Maria, Chasman, Daniel I, Trompet, Stella, Ahluwalia, Tarunveer S, Teumer, Alexander, Kleber, Marcus E, Chen, Ming-Huei, Wang, Jie Jin, Attia, John R, Marioni, Riccardo E, Steri, Maristella, Weng, Lu-Chen, Pool, Rene, Grossmann, Vera, Brody, Jennifer A, Venturini, Cristina, Tanaka, Toshiko, Rose, Lynda M, Oldmeadow, Christopher, Mazur, Johanna, Basu, Saonli, Frånberg, Mattias, Yang, Qiong, Ligthart, Symen, Hottenga, Jouke J, Rumley, Ann, Mulas, Antonella, de Craen, Anton JM, Grotevendt, Anne, Taylor, Kent D, Delgado, Graciela E, Kifley, Annette, Lopez, Lorna M, Berentzen, Tina L, Mangino, Massimo, Bandinelli, Stefania, Morrison, Alanna C, Hamsten, Anders, Tofler, Geoffrey, de Maat, Moniek PM, Draisma, Harmen HM, Lowe, Gordon D, Zoledziewska, Magdalena, Sattar, Naveed, Lackner, Karl J, Völker, Uwe, McKnight, Barbara, Huang, Jie, Holliday, Elizabeth G, McEvoy, Mark A, Starr, John M, Hysi, Pirro G, Hernandez, Dena G, Guan, Weihua, Rivadeneira, Fernando, McArdle, Wendy L, Slagboom, P Eline, Zeller, Tanja, Psaty, Bruce M, Uitterlinden, André G, de Geus, Eco JC, Stott, David J, Binder, Harald, Hofman, Albert, Franco, Oscar H, Rotter, Jerome I, Ferrucci, Luigi, Spector, Tim D, Deary, Ian J, März, Winfried, Greinacher, Andreas, Wild, Philipp S, Cucca, Francesco, Boomsma, Dorret I, Watkins, Hugh, Tang, Weihong, Ridker, Paul M, Jukema, Jan W, Scott, Rodney J, Mitchell, Paul, Hansen, Torben, O'Donnell, Christopher J, Smith, Nicholas L, Strachan, David P, and Dehghan, Abbas

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Genome-Wide Association Study, HapMap Project, Humans, General Science & Technology
Abstract

An increasing number of genome-wide association (GWA) studies are now using the higher resolution 1000 Genomes Project reference panel (1000G) for imputation, with the expectation that 1000G imputation will lead to the discovery of additional associated loci when compared to HapMap imputation. In order to assess the improvement of 1000G over HapMap imputation in identifying associated loci, we compared the results of GWA studies of circulating fibrinogen based on the two reference panels. Using both HapMap and 1000G imputation we performed a meta-analysis of 22 studies comprising the same 91,953 individuals. We identified six additional signals using 1000G imputation, while 29 loci were associated using both HapMap and 1000G imputation. One locus identified using HapMap imputation was not significant using 1000G imputation. The genome-wide significance threshold of 5×10-8 is based on the number of independent statistical tests using HapMap imputation, and 1000G imputation may lead to further independent tests that should be corrected for. When using a stricter Bonferroni correction for the 1000G GWA study (P-value < 2.5×10-8), the number of loci significant only using HapMap imputation increased to 4 while the number of loci significant only using 1000G decreased to 5. In conclusion, 1000G imputation enabled the identification of 20% more loci than HapMap imputation, although the advantage of 1000G imputation became less clear when a stricter Bonferroni correction was used. More generally, our results provide insights that are applicable to the implementation of other dense reference panels that are under development.

Published
2017

39. Genome-wide Trans-ethnic Meta-analysis Identifies Seven Genetic Loci Influencing Erythrocyte Traits and a Role for RBPMS in Erythropoiesis

Author

van Rooij, Frank JA, Qayyum, Rehan, Smith, Albert V, Zhou, Yi, Trompet, Stella, Tanaka, Toshiko, Keller, Margaux F, Chang, Li-Ching, Schmidt, Helena, Yang, Min-Lee, Chen, Ming-Huei, Hayes, James, Johnson, Andrew D, Yanek, Lisa R, Mueller, Christian, Lange, Leslie, Floyd, James S, Ghanbari, Mohsen, Zonderman, Alan B, Jukema, J Wouter, Hofman, Albert, van Duijn, Cornelia M, Desch, Karl C, Saba, Yasaman, Ozel, Ayse B, Snively, Beverly M, Wu, Jer-Yuarn, Schmidt, Reinhold, Fornage, Myriam, Klein, Robert J, Fox, Caroline S, Matsuda, Koichi, Kamatani, Naoyuki, Wild, Philipp S, Stott, David J, Ford, Ian, Slagboom, P Eline, Yang, Jaden, Chu, Audrey Y, Lambert, Amy J, Uitterlinden, André G, Franco, Oscar H, Hofer, Edith, Ginsburg, David, Hu, Bella, Keating, Brendan, Schick, Ursula M, Brody, Jennifer A, Li, Jun Z, Chen, Zhao, Zeller, Tanja, Guralnik, Jack M, Chasman, Daniel I, Peters, Luanne L, Kubo, Michiaki, Becker, Diane M, Li, Jin, Eiriksdottir, Gudny, Rotter, Jerome I, Levy, Daniel, Grossmann, Vera, Patel, Kushang V, Chen, Chien-Hsiun, Project, The BioBank Japan, Ridker, Paul M, Tang, Hua, Launer, Lenore J, Rice, Kenneth M, Li-Gao, Ruifang, Ferrucci, Luigi, Evans, Michelle K, Choudhuri, Avik, Trompouki, Eirini, Abraham, Brian J, Yang, Song, Takahashi, Atsushi, Kamatani, Yoichiro, Kooperberg, Charles, Harris, Tamara B, Jee, Sun Ha, Coresh, Josef, Tsai, Fuu-Jen, Longo, Dan L, Chen, Yuan-Tsong, Felix, Janine F, Yang, Qiong, Psaty, Bruce M, Boerwinkle, Eric, Becker, Lewis C, Mook-Kanamori, Dennis O, Wilson, James G, Gudnason, Vilmundur, O'Donnell, Christopher J, Dehghan, Abbas, Cupples, L Adrienne, Nalls, Michael A, Morris, Andrew P, Okada, Yukinori, Reiner, Alexander P, and Zon, Leonard I

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Hematology, Human Genome, Minority Health, 2.1 Biological and endogenous factors, Africa, Alleles, Animals, Bayes Theorem, Erythrocytes, Erythropoiesis, Ethnicity, Europe, Asia, Eastern, Female, Genome-Wide Association Study, Humans, Linkage Disequilibrium, Male, RNA-Binding Proteins, Racial Groups, Zebrafish, BioBank Japan Project, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Genome-wide association studies (GWASs) have identified loci for erythrocyte traits in primarily European ancestry populations. We conducted GWAS meta-analyses of six erythrocyte traits in 71,638 individuals from European, East Asian, and African ancestries using a Bayesian approach to account for heterogeneity in allelic effects and variation in the structure of linkage disequilibrium between ethnicities. We identified seven loci for erythrocyte traits including a locus (RBPMS/GTF2E2) associated with mean corpuscular hemoglobin and mean corpuscular volume. Statistical fine-mapping at this locus pointed to RBPMS at this locus and excluded nearby GTF2E2. Using zebrafish morpholino to evaluate loss of function, we observed a strong in vivo erythropoietic effect for RBPMS but not for GTF2E2, supporting the statistical fine-mapping at this locus and demonstrating that RBPMS is a regulator of erythropoiesis. Our findings show the utility of trans-ethnic GWASs for discovery and characterization of genetic loci influencing hematologic traits.

Published
2017

40. Meta-analysis of genome-wide association studies of HDL cholesterol response to statins

Author

Postmus, Iris, Warren, Helen R, Trompet, Stella, Arsenault, Benoit J, Avery, Christy L, Bis, Joshua C, Chasman, Daniel I, de Keyser, Catherine E, Deshmukh, Harshal A, Evans, Daniel S, Feng, QiPing, Li, Xiaohui, Smit, Roelof AJ, Smith, Albert V, Sun, Fangui, Taylor, Kent D, Arnold, Alice M, Barnes, Michael R, Barratt, Bryan J, Betteridge, John, Boekholdt, S Matthijs, Boerwinkle, Eric, Buckley, Brendan M, Chen, Y-D Ida, de Craen, Anton JM, Cummings, Steven R, Denny, Joshua C, Dubé, Marie Pierre, Durrington, Paul N, Eiriksdottir, Gudny, Ford, Ian, Guo, Xiuqing, Harris, Tamara B, Heckbert, Susan R, Hofman, Albert, Hovingh, G Kees, Kastelein, John JP, Launer, Leonore J, Liu, Ching-Ti, Liu, Yongmei, Lumley, Thomas, McKeigue, Paul M, Munroe, Patricia B, Neil, Andrew, Nickerson, Deborah A, Nyberg, Fredrik, O'Brien, Eoin, O'Donnell, Christopher J, Post, Wendy, Poulter, Neil, Vasan, Ramachandran S, Rice, Kenneth, Rich, Stephen S, Rivadeneira, Fernando, Sattar, Naveed, Sever, Peter, Shaw-Hawkins, Sue, Shields, Denis C, Slagboom, P Eline, Smith, Nicholas L, Smith, Joshua D, Sotoodehnia, Nona, Stanton, Alice, Stott, David J, Stricker, Bruno H, Stürmer, Til, Uitterlinden, André G, Wei, Wei-Qi, Westendorp, Rudi GJ, Whitsel, Eric A, Wiggins, Kerri L, Wilke, Russell A, Ballantyne, Christie M, Colhoun, Helen M, Cupples, L Adrienne, Franco, Oscar H, Gudnason, Vilmundur, Hitman, Graham, Palmer, Colin NA, Psaty, Bruce M, Ridker, Paul M, Stafford, Jeanette M, Stein, Charles M, Tardif, Jean-Claude, Caulfield, Mark J, Jukema, J Wouter, Rotter, Jerome I, and Krauss, Ronald M

Subjects
Pharmacology and Pharmaceutical Sciences, Biological Sciences, Biomedical and Clinical Sciences, Genetics, Atherosclerosis, Human Genome, Cardiovascular, Stroke, Good Health and Well Being, Cholesterol Ester Transfer Proteins, Cholesterol, HDL, Female, Genome-Wide Association Study, Humans, Hydroxymethylglutaryl-CoA Reductase Inhibitors, Male, Pharmacogenomic Variants, Polymorphism, Single Nucleotide, Treatment Outcome, White People, Genome-wide association study, HDL-cholesterol, Statins, pharmacogenetics, Medical and Health Sciences, Genetics & Heredity, Clinical sciences
Abstract

BackgroundIn addition to lowering low density lipoprotein cholesterol (LDL-C), statin therapy also raises high density lipoprotein cholesterol (HDL-C) levels. Inter-individual variation in HDL-C response to statins may be partially explained by genetic variation.Methods and resultsWe performed a meta-analysis of genome-wide association studies (GWAS) to identify variants with an effect on statin-induced high density lipoprotein cholesterol (HDL-C) changes. The 123 most promising signals with p

Published
2016

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

Author

Hehir-Kwa, Jayne Y, Marschall, Tobias, Kloosterman, Wigard P, Francioli, Laurent C, Baaijens, Jasmijn A, Dijkstra, Louis J, Abdellaoui, Abdel, Koval, Vyacheslav, Thung, Djie Tjwan, Wardenaar, René, Renkens, Ivo, Coe, Bradley P, Deelen, Patrick, de Ligt, Joep, Lameijer, Eric-Wubbo, van Dijk, Freerk, Hormozdiari, Fereydoun, Genome of the Netherlands Consortium, Uitterlinden, André G, van Duijn, Cornelia M, Eichler, Evan E, de Bakker, Paul IW, Swertz, Morris A, Wijmenga, Cisca, van Ommen, Gert-Jan B, Slagboom, P Eline, Boomsma, Dorret I, Schönhuth, Alexander, Ye, Kai, and Guryev, Victor

Subjects
Genome of the Netherlands Consortium, Chromosomes, Humans, RNA, Polymerase Chain Reaction, Sequence Analysis, DNA, Sequence Analysis, RNA, Computational Biology, Genomics, Gene Deletion, Genotype, Haplotypes, Linkage Disequilibrium, Polymorphism, Single Nucleotide, Genome, Human, Algorithms, Software, Netherlands, INDEL Mutation, Genomic Structural Variation, Sequence Analysis, DNA, Polymorphism, Single Nucleotide, Genome, Human
Abstract

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

Published
2016

42. Novel Genetic Variants for Cartilage Thickness and Hip Osteoarthritis.

Author

Castaño-Betancourt, Martha C, Evans, Dan S, Ramos, Yolande FM, Boer, Cindy G, Metrustry, Sarah, Liu, Youfang, den Hollander, Wouter, van Rooij, Jeroen, Kraus, Virginia B, Yau, Michelle S, Mitchell, Braxton D, Muir, Kenneth, Hofman, Albert, Doherty, Michael, Doherty, Sally, Zhang, Weiya, Kraaij, Robert, Rivadeneira, Fernando, Barrett-Connor, Elizabeth, Maciewicz, Rose A, Arden, Nigel, Nelissen, Rob GHH, Kloppenburg, Margreet, Jordan, Joanne M, Nevitt, Michael C, Slagboom, Eline P, Hart, Deborah J, Lafeber, Floris, Styrkarsdottir, Unnur, Zeggini, Eleftheria, Evangelou, Evangelos, Spector, Tim D, Uitterlinden, Andre G, Lane, Nancy E, Meulenbelt, Ingrid, Valdes, Ana M, and van Meurs, Joyce BJ

Subjects
Cartilage, Hip Joint, Humans, Osteoarthritis, Hip, Genetic Predisposition to Disease, Trehalase, Transforming Growth Factor alpha, Regulatory Sequences, Nucleic Acid, Genetic Heterogeneity, Polymorphism, Single Nucleotide, Aged, Aged, 80 and over, Middle Aged, Female, Male, Receptor, Fibroblast Growth Factor, Type 3, Genome-Wide Association Study, Phosphatidylinositol 3-Kinases, Class Ia Phosphatidylinositol 3-Kinase, Osteoarthritis, Hip, Regulatory Sequences, Nucleic Acid, Polymorphism, Single Nucleotide, and over, Receptor, Fibroblast Growth Factor, Type 3, Genetics, Developmental Biology
Abstract

Osteoarthritis is one of the most frequent and disabling diseases of the elderly. Only few genetic variants have been identified for osteoarthritis, which is partly due to large phenotype heterogeneity. To reduce heterogeneity, we here examined cartilage thickness, one of the structural components of joint health. We conducted a genome-wide association study of minimal joint space width (mJSW), a proxy for cartilage thickness, in a discovery set of 13,013 participants from five different cohorts and replication in 8,227 individuals from seven independent cohorts. We identified five genome-wide significant (GWS, P≤5·0×10-8) SNPs annotated to four distinct loci. In addition, we found two additional loci that were significantly replicated, but results of combined meta-analysis fell just below the genome wide significance threshold. The four novel associated genetic loci were located in/near TGFA (rs2862851), PIK3R1 (rs10471753), SLBP/FGFR3 (rs2236995), and TREH/DDX6 (rs496547), while the other two (DOT1L and SUPT3H/RUNX2) were previously identified. A systematic prioritization for underlying causal genes was performed using diverse lines of evidence. Exome sequencing data (n = 2,050 individuals) indicated that there were no rare exonic variants that could explain the identified associations. In addition, TGFA, FGFR3 and PIK3R1 were differentially expressed in OA cartilage lesions versus non-lesioned cartilage in the same individuals. In conclusion, we identified four novel loci (TGFA, PIK3R1, FGFR3 and TREH) and confirmed two loci known to be associated with cartilage thickness.The identified associations were not caused by rare exonic variants. This is the first report linking TGFA to human OA, which may serve as a new target for future therapies.

Published
2016

43. Genomewide meta‐analysis identifies loci associated with IGF‐I and IGFBP‐3 levels with impact on age‐related traits

Author

Teumer, Alexander, Qi, Qibin, Nethander, Maria, Aschard, Hugues, Bandinelli, Stefania, Beekman, Marian, Berndt, Sonja I, Bidlingmaier, Martin, Broer, Linda, Group, CHARGE Longevity Working, Cappola, Anne, Ceda, Gian Paolo, Chanock, Stephen, Chen, Ming‐Huei, Chen, Tai C, Chen, Yii‐Der Ida, Chung, Jonathan, Del Greco Miglianico, Fabiola, Eriksson, Joel, Ferrucci, Luigi, Friedrich, Nele, Gnewuch, Carsten, Goodarzi, Mark O, Grarup, Niels, Guo, Tingwei, Hammer, Elke, Hayes, Richard B, Hicks, Andrew A, Hofman, Albert, Houwing‐Duistermaat, Jeanine J, Hu, Frank, Hunter, David J, Husemoen, Lise L, Isaacs, Aaron, Jacobs, Kevin B, Janssen, Joop AMJL, Jansson, John‐Olov, Jehmlich, Nico, Johnson, Simon, Juul, Anders, Karlsson, Magnus, Kilpelainen, Tuomas O, Kovacs, Peter, Kraft, Peter, Li, Chao, Linneberg, Allan, Liu, Yongmei, Loos, Ruth JF, Consortium, Body Composition Genetics, Lorentzon, Mattias, Lu, Yingchang, Maggio, Marcello, Magi, Reedik, Meigs, James, Mellström, Dan, Nauck, Matthias, Newman, Anne B, Pollak, Michael N, Pramstaller, Peter P, Prokopenko, Inga, Psaty, Bruce M, Reincke, Martin, Rimm, Eric B, Rotter, Jerome I, Pierre, Aude Saint, Schurmann, Claudia, Seshadri, Sudha, Sjögren, Klara, Slagboom, P Eline, Strickler, Howard D, Stumvoll, Michael, Suh, Yousin, Sun, Qi, Zhang, Cuilin, Svensson, Johan, Tanaka, Toshiko, Tare, Archana, Tönjes, Anke, Uh, Hae‐Won, van Duijn, Cornelia M, van Heemst, Diana, Vandenput, Liesbeth, Vasan, Ramachandran S, Völker, Uwe, Willems, Sara M, Ohlsson, Claes, Wallaschofski, Henri, and Kaplan, Robert C

Subjects
Biological Sciences, Biomedical and Clinical Sciences, Genetics, Aging, Clinical Research, Human Genome, 2.1 Biological and endogenous factors, Cardiovascular, Adult, Female, Gene Expression Regulation, Genome-Wide Association Study, Humans, Insulin-Like Growth Factor Binding Protein 3, Insulin-Like Growth Factor I, Male, Metabolome, Quantitative Trait Loci, Quantitative Trait, Heritable, Regulatory Sequences, Nucleic Acid, aging, genomewide association study, growth hormone axis, IGF-I, IGFBP-3, longevity, CHARGE Longevity Working Group, Body Composition Genetics Consortium, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

The growth hormone/insulin-like growth factor (IGF) axis can be manipulated in animal models to promote longevity, and IGF-related proteins including IGF-I and IGF-binding protein-3 (IGFBP-3) have also been implicated in risk of human diseases including cardiovascular diseases, diabetes, and cancer. Through genomewide association study of up to 30 884 adults of European ancestry from 21 studies, we confirmed and extended the list of previously identified loci associated with circulating IGF-I and IGFBP-3 concentrations (IGF1, IGFBP3, GCKR, TNS3, GHSR, FOXO3, ASXL2, NUBP2/IGFALS, SORCS2, and CELSR2). Significant sex interactions, which were characterized by different genotype-phenotype associations between men and women, were found only for associations of IGFBP-3 concentrations with SNPs at the loci IGFBP3 and SORCS2. Analyses of SNPs, gene expression, and protein levels suggested that interplay between IGFBP3 and genes within the NUBP2 locus (IGFALS and HAGH) may affect circulating IGF-I and IGFBP-3 concentrations. The IGF-I-decreasing allele of SNP rs934073, which is an eQTL of ASXL2, was associated with lower adiposity and higher likelihood of survival beyond 90 years. The known longevity-associated variant rs2153960 (FOXO3) was observed to be a genomewide significant SNP for IGF-I concentrations. Bioinformatics analysis suggested enrichment of putative regulatory elements among these IGF-I- and IGFBP-3-associated loci, particularly of rs646776 at CELSR2. In conclusion, this study identified several loci associated with circulating IGF-I and IGFBP-3 concentrations and provides clues to the potential role of the IGF axis in mediating effects of known (FOXO3) and novel (ASXL2) longevity-associated loci.

Published
2016

44. Meta-analysis of 49 549 individuals imputed with the 1000 Genomes Project reveals an exonic damaging variant in ANGPTL4 determining fasting TG levels

Author

van Leeuwen, Elisabeth M, Sabo, Aniko, Bis, Joshua C, Huffman, Jennifer E, Manichaikul, Ani, Smith, Albert V, Feitosa, Mary F, Demissie, Serkalem, Joshi, Peter K, Duan, Qing, Marten, Jonathan, van Klinken, Jan B, Surakka, Ida, Nolte, Ilja M, Zhang, Weihua, Mbarek, Hamdi, Li-Gao, Ruifang, Trompet, Stella, Verweij, Niek, Evangelou, Evangelos, Lyytikäinen, Leo-Pekka, Tayo, Bamidele O, Deelen, Joris, van der Most, Peter J, van der Laan, Sander W, Arking, Dan E, Morrison, Alanna, Dehghan, Abbas, Franco, Oscar H, Hofman, Albert, Rivadeneira, Fernando, Sijbrands, Eric J, Uitterlinden, Andre G, Mychaleckyj, Josyf C, Campbell, Archie, Hocking, Lynne J, Padmanabhan, Sandosh, Brody, Jennifer A, Rice, Kenneth M, White, Charles C, Harris, Tamara, Isaacs, Aaron, Campbell, Harry, Lange, Leslie A, Rudan, Igor, Kolcic, Ivana, Navarro, Pau, Zemunik, Tatijana, Salomaa, Veikko, Study, The LifeLines Cohort, Kooner, Angad S, Kooner, Jaspal S, Lehne, Benjamin, Scott, William R, Tan, Sian-Tsung, de Geus, Eco J, Milaneschi, Yuri, Penninx, Brenda WJH, Willemsen, Gonneke, de Mutsert, Renée, Ford, Ian, Gansevoort, Ron T, Segura-Lepe, Marcelo P, Raitakari, Olli T, Viikari, Jorma S, Nikus, Kjell, Forrester, Terrence, McKenzie, Colin A, de Craen, Anton JM, de Ruijter, Hester M, Group, CHARGE Lipids Working, Pasterkamp, Gerard, Snieder, Harold, Oldehinkel, Albertine J, Slagboom, P Eline, Cooper, Richard S, Kähönen, Mika, Lehtimäki, Terho, Elliott, Paul, van der Harst, Pim, Jukema, J Wouter, Mook-Kanamori, Dennis O, Boomsma, Dorret I, Chambers, John C, Swertz, Morris, Ripatti, Samuli, van Dijk, Ko Willems, Vitart, Veronique, Polasek, Ozren, Hayward, Caroline, Wilson, James G, Wilson, James F, Gudnason, Vilmundur, Rich, Stephen S, Psaty, Bruce M, Borecki, Ingrid B, Boerwinkle, Eric, Rotter, Jerome I, Cupples, L Adrienne, and van Duijn, Cornelia M

Subjects
Biological Sciences, Genetics, Human Genome, Clinical Research, 2.1 Biological and endogenous factors, Angiopoietin-Like Protein 4, Angiopoietins, Exons, Fasting, Female, Genome, Human, Genome-Wide Association Study, Genotype, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, LifeLines Cohort Study, CHARGE Lipids Working Group, Complex traits, Epidemiology, Genome-wide, circulating lipid levels, Medical and Health Sciences, Genetics & Heredity, Clinical sciences
Abstract

BackgroundSo far, more than 170 loci have been associated with circulating lipid levels through genome-wide association studies (GWAS). These associations are largely driven by common variants, their function is often not known, and many are likely to be markers for the causal variants. In this study we aimed to identify more new rare and low-frequency functional variants associated with circulating lipid levels.MethodsWe used the 1000 Genomes Project as a reference panel for the imputations of GWAS data from ∼60 000 individuals in the discovery stage and ∼90 000 samples in the replication stage.ResultsOur study resulted in the identification of five new associations with circulating lipid levels at four loci. All four loci are within genes that can be linked biologically to lipid metabolism. One of the variants, rs116843064, is a damaging missense variant within the ANGPTL4 gene.ConclusionsThis study illustrates that GWAS with high-scale imputation may still help us unravel the biological mechanism behind circulating lipid levels.

Published
2016

45. Cerebral small vessel disease genomics and its implications across the lifespan

Author

Muralidharan Sargurupremraj, Hideaki Suzuki, Xueqiu Jian, Chloé Sarnowski, Tavia E. Evans, Joshua C. Bis, Gudny Eiriksdottir, Saori Sakaue, Natalie Terzikhan, Mohamad Habes, Wei Zhao, Nicola J. Armstrong, Edith Hofer, Lisa R. Yanek, Saskia P. Hagenaars, Rajan B. Kumar, Erik B. van den Akker, Rebekah E. McWhirter, Stella Trompet, Aniket Mishra, Yasaman Saba, Claudia L. Satizabal, Gregory Beaudet, Laurent Petit, Ami Tsuchida, Laure Zago, Sabrina Schilling, Sigurdur Sigurdsson, Rebecca F. Gottesman, Cora E. Lewis, Neelum T. Aggarwal, Oscar L. Lopez, Jennifer A. Smith, Maria C. Valdés Hernández, Jeroen van der Grond, Margaret J. Wright, Maria J. Knol, Marcus Dörr, Russell J. Thomson, Constance Bordes, Quentin Le Grand, Marie-Gabrielle Duperron, Albert V. Smith, David S. Knopman, Pamela J. Schreiner, Denis A. Evans, Jerome I. Rotter, Alexa S. Beiser, Susana Muñoz Maniega, Marian Beekman, Julian Trollor, David J. Stott, Meike W. Vernooij, Katharina Wittfeld, Wiro J. Niessen, Aicha Soumaré, Eric Boerwinkle, Stephen Sidney, Stephen T. Turner, Gail Davies, Anbupalam Thalamuthu, Uwe Völker, Mark A. van Buchem, R. Nick Bryan, Josée Dupuis, Mark E. Bastin, David Ames, Alexander Teumer, Philippe Amouyel, John B. Kwok, Robin Bülow, Ian J. Deary, Peter R. Schofield, Henry Brodaty, Jiyang Jiang, Yasuharu Tabara, Kazuya Setoh, Susumu Miyamoto, Kazumichi Yoshida, Manabu Nagata, Yoichiro Kamatani, Fumihiko Matsuda, Bruce M. Psaty, David A. Bennett, Philip L. De Jager, Thomas H. Mosley, Perminder S. Sachdev, Reinhold Schmidt, Helen R. Warren, Evangelos Evangelou, David-Alexandre Trégouët, International Network against Thrombosis (INVENT) Consortium, International Headache Genomics Consortium (IHGC), Mohammad A. Ikram, Wei Wen, Charles DeCarli, Velandai K. Srikanth, J. Wouter Jukema, Eline P. Slagboom, Sharon L. R. Kardia, Yukinori Okada, Bernard Mazoyer, Joanna M. Wardlaw, Paul A. Nyquist, Karen A. Mather, Hans J. Grabe, Helena Schmidt, Cornelia M. Van Duijn, Vilmundur Gudnason, William T. Longstreth, Lenore J. Launer, Mark Lathrop, Sudha Seshadri, Christophe Tzourio, Hieab H. Adams, Paul M. Matthews, Myriam Fornage, and Stéphanie Debette

Subjects
Science
Abstract

White matter hyperintensities (WMH) are a common brain-imaging feature of cerebral small vessel disease. Here, the authors carry out a GWAS and followup analyses for WMH-volume, implicating several variants with potential for risk stratification and drug targeting.

Published
2020
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46. GWAS for executive function and processing speed suggests involvement of the CADM2 gene

Author

Ibrahim-Verbaas, CA, Bressler, J, Debette, S, Schuur, M, Smith, AV, Bis, JC, Davies, G, Trompet, S, Smith, JA, Wolf, C, Chibnik, LB, Liu, Y, Vitart, V, Kirin, M, Petrovic, K, Polasek, O, Zgaga, L, Fawns-Ritchie, C, Hoffmann, P, Karjalainen, J, Lahti, J, Llewellyn, DJ, Schmidt, CO, Mather, KA, Chouraki, V, Sun, Q, Resnick, SM, Rose, LM, Oldmeadow, C, Stewart, M, Smith, BH, Gudnason, V, Yang, Q, Mirza, SS, Jukema, JW, deJager, PL, Harris, TB, Liewald, DC, Amin, N, Coker, LH, Stegle, O, Lopez, OL, Schmidt, R, Teumer, A, Ford, I, Karbalai, N, Becker, JT, Jonsdottir, MK, Au, R, Fehrmann, RSN, Herms, S, Nalls, M, Zhao, W, Turner, ST, Yaffe, K, Lohman, K, van Swieten, JC, Kardia, SLR, Knopman, DS, Meeks, WM, Heiss, G, Holliday, EG, Schofield, PW, Tanaka, T, Stott, DJ, Wang, J, Ridker, P, Gow, AJ, Pattie, A, Starr, JM, Hocking, LJ, Armstrong, NJ, McLachlan, S, Shulman, JM, Pilling, LC, Eiriksdottir, G, Scott, RJ, Kochan, NA, Palotie, A, Hsieh, Y-C, Eriksson, JG, Penman, A, Gottesman, RF, Oostra, BA, Yu, L, DeStefano, AL, Beiser, A, Garcia, M, Rotter, JI, Nöthen, MM, Hofman, A, Slagboom, PE, Westendorp, RGJ, Buckley, BM, Wolf, PA, Uitterlinden, AG, Psaty, BM, Grabe, HJ, Bandinelli, S, and Chasman, DI

Subjects
Biological Psychology, Clinical and Health Psychology, Psychology, Genetics, Basic Behavioral and Social Science, Aging, Human Genome, Brain Disorders, Behavioral and Social Science, Mental Health, Neurosciences, Aged, Aged, 80 and over, Cell Adhesion Molecules, Cognition, Cohort Studies, Executive Function, Female, Genetic Association Studies, Genetic Variation, Genome-Wide Association Study, Genomics, Humans, Introns, Male, Middle Aged, Neuropsychological Tests, Polymorphism, Single Nucleotide, White People, gamma-Aminobutyric Acid, Generation Scotland, Biological Sciences, Medical and Health Sciences, Psychology and Cognitive Sciences, Psychiatry, Clinical sciences, Biological psychology, Clinical and health psychology
Abstract

To identify common variants contributing to normal variation in two specific domains of cognitive functioning, we conducted a genome-wide association study (GWAS) of executive functioning and information processing speed in non-demented older adults from the CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology) consortium. Neuropsychological testing was available for 5429-32,070 subjects of European ancestry aged 45 years or older, free of dementia and clinical stroke at the time of cognitive testing from 20 cohorts in the discovery phase. We analyzed performance on the Trail Making Test parts A and B, the Letter Digit Substitution Test (LDST), the Digit Symbol Substitution Task (DSST), semantic and phonemic fluency tests, and the Stroop Color and Word Test. Replication was sought in 1311-21860 subjects from 20 independent cohorts. A significant association was observed in the discovery cohorts for the single-nucleotide polymorphism (SNP) rs17518584 (discovery P-value=3.12 × 10(-8)) and in the joint discovery and replication meta-analysis (P-value=3.28 × 10(-9) after adjustment for age, gender and education) in an intron of the gene cell adhesion molecule 2 (CADM2) for performance on the LDST/DSST. Rs17518584 is located about 170 kb upstream of the transcription start site of the major transcript for the CADM2 gene, but is within an intron of a variant transcript that includes an alternative first exon. The variant is associated with expression of CADM2 in the cingulate cortex (P-value=4 × 10(-4)). The protein encoded by CADM2 is involved in glutamate signaling (P-value=7.22 × 10(-15)), gamma-aminobutyric acid (GABA) transport (P-value=1.36 × 10(-11)) and neuron cell-cell adhesion (P-value=1.48 × 10(-13)). Our findings suggest that genetic variation in the CADM2 gene is associated with individual differences in information processing speed.

Published
2016

47. A meta-analysis of 120 246 individuals identifies 18 new loci for fibrinogen concentration

Author

de Vries, Paul S, Chasman, Daniel I, Sabater-Lleal, Maria, Chen, Ming-Huei, Huffman, Jennifer E, Steri, Maristella, Tang, Weihong, Teumer, Alexander, Marioni, Riccardo E, Grossmann, Vera, Hottenga, Jouke J, Trompet, Stella, Müller-Nurasyid, Martina, Zhao, Jing Hua, Brody, Jennifer A, Kleber, Marcus E, Guo, Xiuqing, Wang, Jie Jin, Auer, Paul L, Attia, John R, Yanek, Lisa R, Ahluwalia, Tarunveer S, Lahti, Jari, Venturini, Cristina, Tanaka, Toshiko, Bielak, Lawrence F, Joshi, Peter K, Rocanin-Arjo, Ares, Kolcic, Ivana, Navarro, Pau, Rose, Lynda M, Oldmeadow, Christopher, Riess, Helene, Mazur, Johanna, Basu, Saonli, Goel, Anuj, Yang, Qiong, Ghanbari, Mohsen, Willemsen, Gonneke, Rumley, Ann, Fiorillo, Edoardo, de Craen, Anton JM, Grotevendt, Anne, Scott, Robert, Taylor, Kent D, Delgado, Graciela E, Yao, Jie, Kifley, Annette, Kooperberg, Charles, Qayyum, Rehan, Lopez, Lorna M, Berentzen, Tina L, Räikkönen, Katri, Mangino, Massimo, Bandinelli, Stefania, Peyser, Patricia A, Wild, Sarah, Trégouët, David-Alexandre, Wright, Alan F, Marten, Jonathan, Zemunik, Tatijana, Morrison, Alanna C, Sennblad, Bengt, Tofler, Geoffrey, de Maat, Moniek PM, de Geus, Eco JC, Lowe, Gordon D, Zoledziewska, Magdalena, Sattar, Naveed, Binder, Harald, Völker, Uwe, Waldenberger, Melanie, Khaw, Kay-Tee, Mcknight, Barbara, Huang, Jie, Jenny, Nancy S, Holliday, Elizabeth G, Qi, Lihong, Mcevoy, Mark G, Becker, Diane M, Starr, John M, Sarin, Antti-Pekka, Hysi, Pirro G, Hernandez, Dena G, Jhun, Min A, Campbell, Harry, Hamsten, Anders, Rivadeneira, Fernando, Mcardle, Wendy L, Slagboom, P Eline, Zeller, Tanja, Koenig, Wolfgang, Psaty, Bruce M, Haritunians, Talin, Liu, Jingmin, Palotie, Aarno, Uitterlinden, André G, Stott, David J, Hofman, Albert, and Franco, Oscar H

Subjects
Biological Sciences, Genetics, Human Genome, 2.1 Biological and endogenous factors, Adult, Aged, Aged, 80 and over, Female, Fibrinogen, Genetic Loci, Genome-Wide Association Study, Humans, INDEL Mutation, Male, Middle Aged, Polymorphism, Single Nucleotide, White People, Medical and Health Sciences, Genetics & Heredity
Abstract

Genome-wide association studies have previously identified 23 genetic loci associated with circulating fibrinogen concentration. These studies used HapMap imputation and did not examine the X-chromosome. 1000 Genomes imputation provides better coverage of uncommon variants, and includes indels. We conducted a genome-wide association analysis of 34 studies imputed to the 1000 Genomes Project reference panel and including ∼120 000 participants of European ancestry (95 806 participants with data on the X-chromosome). Approximately 10.7 million single-nucleotide polymorphisms and 1.2 million indels were examined. We identified 41 genome-wide significant fibrinogen loci; of which, 18 were newly identified. There were no genome-wide significant signals on the X-chromosome. The lead variants of five significant loci were indels. We further identified six additional independent signals, including three rare variants, at two previously characterized loci: FGB and IRF1. Together the 41 loci explain 3% of the variance in plasma fibrinogen concentration.

Published
2016

48. White Matter Lesion Progression

Author

Hofer, Edith, Cavalieri, Margherita, Bis, Joshua C, DeCarli, Charles, Fornage, Myriam, Sigurdsson, Sigurdur, Srikanth, Velandai, Trompet, Stella, Verhaaren, Benjamin FJ, Wolf, Christiane, Yang, Qiong, Adams, Hieab HH, Amouyel, Philippe, Beiser, Alexa, Buckley, Brendan M, Callisaya, Michele, Chauhan, Ganesh, de Craen, Anton JM, Dufouil, Carole, van Duijn, Cornelia M, Ford, Ian, Freudenberger, Paul, Gottesman, Rebecca F, Gudnason, Vilmundur, Heiss, Gerardo, Hofman, Albert, Lumley, Thomas, Martinez, Oliver, Mazoyer, Bernard, Moran, Chris, Niessen, Wiro J, Phan, Thanh, Psaty, Bruce M, Satizabal, Claudia L, Sattar, Naveed, Schilling, Sabrina, Shibata, Dean K, Slagboom, P Eline, Smith, Albert, Stott, David J, Taylor, Kent D, Thomson, Russell, Töglhofer, Anna M, Tzourio, Christophe, van Buchem, Mark, Wang, Jing, Westendorp, Rudi GJ, Windham, B Gwen, Vernooij, Meike W, Zijdenbos, Alex, Beare, Richard, Debette, Stéphanie, Ikram, M Arfan, Jukema, J Wouter, Launer, Lenore J, Longstreth, WT, Mosley, Thomas H, Seshadri, Sudha, Schmidt, Helena, and Schmidt, Reinhold

Subjects
Epidemiology, Biomedical and Clinical Sciences, Health Sciences, Prevention, Genetics, Cardiovascular, Cerebrovascular, Clinical Research, Human Genome, Aging, 2.1 Biological and endogenous factors, Stroke, Adult, Aged, Cohort Studies, Disease Progression, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Leukoencephalopathies, Male, Middle Aged, Prospective Studies, White Matter, Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium, aging, biological factors, cerebral small vessel diseases, magnetic resonance imaging, white matter lesions, Cardiorespiratory Medicine and Haematology, Clinical Sciences, Neurosciences, Neurology & Neurosurgery, Clinical sciences, Allied health and rehabilitation science
Abstract

Background and purposeWhite matter lesion (WML) progression on magnetic resonance imaging is related to cognitive decline and stroke, but its determinants besides baseline WML burden are largely unknown. Here, we estimated heritability of WML progression, and sought common genetic variants associated with WML progression in elderly participants from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium.MethodsHeritability of WML progression was calculated in the Framingham Heart Study. The genome-wide association study included 7773 elderly participants from 10 cohorts. To assess the relative contribution of genetic factors to progression of WML, we compared in 7 cohorts risk models including demographics, vascular risk factors plus single-nucleotide polymorphisms that have been shown to be associated cross-sectionally with WML in the current and previous association studies.ResultsA total of 1085 subjects showed WML progression. The heritability estimate for WML progression was low at 6.5%, and no single-nucleotide polymorphisms achieved genome-wide significance (P

Published
2015

49. The transcriptional landscape of age in human peripheral blood.

Author

Peters, Marjolein J, Joehanes, Roby, Pilling, Luke C, Schurmann, Claudia, Conneely, Karen N, Powell, Joseph, Reinmaa, Eva, Sutphin, George L, Zhernakova, Alexandra, Schramm, Katharina, Wilson, Yana A, Kobes, Sayuko, Tukiainen, Taru, NABEC/UKBEC Consortium, Ramos, Yolande F, Göring, Harald HH, Fornage, Myriam, Liu, Yongmei, Gharib, Sina A, Stranger, Barbara E, De Jager, Philip L, Aviv, Abraham, Levy, Daniel, Murabito, Joanne M, Munson, Peter J, Huan, Tianxiao, Hofman, Albert, Uitterlinden, André G, Rivadeneira, Fernando, van Rooij, Jeroen, Stolk, Lisette, Broer, Linda, Verbiest, Michael MPJ, Jhamai, Mila, Arp, Pascal, Metspalu, Andres, Tserel, Liina, Milani, Lili, Samani, Nilesh J, Peterson, Pärt, Kasela, Silva, Codd, Veryan, Peters, Annette, Ward-Caviness, Cavin K, Herder, Christian, Waldenberger, Melanie, Roden, Michael, Singmann, Paula, Zeilinger, Sonja, Illig, Thomas, Homuth, Georg, Grabe, Hans-Jörgen, Völzke, Henry, Steil, Leif, Kocher, Thomas, Murray, Anna, Melzer, David, Yaghootkar, Hanieh, Bandinelli, Stefania, Moses, Eric K, Kent, Jack W, Curran, Joanne E, Johnson, Matthew P, Williams-Blangero, Sarah, Westra, Harm-Jan, McRae, Allan F, Smith, Jennifer A, Kardia, Sharon LR, Hovatta, Iiris, Perola, Markus, Ripatti, Samuli, Salomaa, Veikko, Henders, Anjali K, Martin, Nicholas G, Smith, Alicia K, Mehta, Divya, Binder, Elisabeth B, Nylocks, K Maria, Kennedy, Elizabeth M, Klengel, Torsten, Ding, Jingzhong, Suchy-Dicey, Astrid M, Enquobahrie, Daniel A, Brody, Jennifer, Rotter, Jerome I, Chen, Yii-Der I, Houwing-Duistermaat, Jeanine, Kloppenburg, Margreet, Slagboom, P Eline, Helmer, Quinta, den Hollander, Wouter, Bean, Shannon, Raj, Towfique, Bakhshi, Noman, Wang, Qiao Ping, Oyston, Lisa J, Psaty, Bruce M, Tracy, Russell P, Montgomery, Grant W, and Turner, Stephen T

Subjects
NABEC/UKBEC Consortium, Humans, Gene Expression Profiling, DNA Methylation, Aging, European Continental Ancestry Group, Transcriptome, Biomarkers
Abstract

Disease incidences increase with age, but the molecular characteristics of ageing that lead to increased disease susceptibility remain inadequately understood. Here we perform a whole-blood gene expression meta-analysis in 14,983 individuals of European ancestry (including replication) and identify 1,497 genes that are differentially expressed with chronological age. The age-associated genes do not harbor more age-associated CpG-methylation sites than other genes, but are instead enriched for the presence of potentially functional CpG-methylation sites in enhancer and insulator regions that associate with both chronological age and gene expression levels. We further used the gene expression profiles to calculate the 'transcriptomic age' of an individual, and show that differences between transcriptomic age and chronological age are associated with biological features linked to ageing, such as blood pressure, cholesterol levels, fasting glucose, and body mass index. The transcriptomic prediction model adds biological relevance and complements existing epigenetic prediction models, and can be used by others to calculate transcriptomic age in external cohorts.

Published
2015

50. Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation.

Author

Horikoshi, Momoko, Mӓgi, Reedik, van de Bunt, Martijn, Surakka, Ida, Sarin, Antti-Pekka, Mahajan, Anubha, Marullo, Letizia, Thorleifsson, Gudmar, Hӓgg, Sara, Hottenga, Jouke-Jan, Ladenvall, Claes, Ried, Janina S, Winkler, Thomas W, Willems, Sara M, Pervjakova, Natalia, Esko, Tõnu, Beekman, Marian, Nelson, Christopher P, Willenborg, Christina, Wiltshire, Steven, Ferreira, Teresa, Fernandez, Juan, Gaulton, Kyle J, Steinthorsdottir, Valgerdur, Hamsten, Anders, Magnusson, Patrik KE, Willemsen, Gonneke, Milaneschi, Yuri, Robertson, Neil R, Groves, Christopher J, Bennett, Amanda J, Lehtimӓki, Terho, Viikari, Jorma S, Rung, Johan, Lyssenko, Valeriya, Perola, Markus, Heid, Iris M, Herder, Christian, Grallert, Harald, Müller-Nurasyid, Martina, Roden, Michael, Hypponen, Elina, Isaacs, Aaron, van Leeuwen, Elisabeth M, Karssen, Lennart C, Mihailov, Evelin, Houwing-Duistermaat, Jeanine J, de Craen, Anton JM, Deelen, Joris, Havulinna, Aki S, Blades, Matthew, Hengstenberg, Christian, Erdmann, Jeanette, Schunkert, Heribert, Kaprio, Jaakko, Tobin, Martin D, Samani, Nilesh J, Lind, Lars, Salomaa, Veikko, Lindgren, Cecilia M, Slagboom, P Eline, Metspalu, Andres, van Duijn, Cornelia M, Eriksson, Johan G, Peters, Annette, Gieger, Christian, Jula, Antti, Groop, Leif, Raitakari, Olli T, Power, Chris, Penninx, Brenda WJH, de Geus, Eco, Smit, Johannes H, Boomsma, Dorret I, Pedersen, Nancy L, Ingelsson, Erik, Thorsteinsdottir, Unnur, Stefansson, Kari, Ripatti, Samuli, Prokopenko, Inga, McCarthy, Mark I, Morris, Andrew P, and ENGAGE Consortium

Subjects
ENGAGE Consortium, Humans, Obesity, Genetic Predisposition to Disease, Glucose-6-Phosphatase, Protein-Serine-Threonine Kinases, Thrombospondins, Body Mass Index, Chromosome Mapping, Glycemic Index, Gene Frequency, Polymorphism, Single Nucleotide, Quantitative Trait Loci, Genome-Wide Association Study, Germinal Center Kinases, Polymorphism, Single Nucleotide, Genetics, Developmental Biology
Abstract

Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated.

Published
2015

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