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1. Joint Modeling of An Outcome Variable and Integrated Omic Datasets Using GLM-PO2PLS

Author

Gu, Zhujie, Bouhaddani, Said el, Houwing-Duistermaat, Jeanine, and Uh, Hae-Won

Subjects
Statistics - Methodology
Abstract

In many studies of human diseases, multiple omic datasets are measured. Typically, these omic datasets are studied one by one with the disease, thus the relationship between omics are overlooked. Modeling the joint part of multiple omics and its association to the outcome disease will provide insights into the complex molecular base of the disease. In this article, we extend dimension reduction methods which model the joint part of omics to a novel method that jointly models an outcome variable with omics. We establish the model identifiability and develop EM algorithms to obtain maximum likelihood estimators of the parameters for normally and Bernoulli distributed outcomes. Test statistics are proposed to infer the association between the outcome and omics, and their asymptotic distributions are derived. Extensive simulation studies are conducted to evaluate the proposed model. The model is illustrated by a Down syndrome study where Down syndrome and two omics - methylation and glycomics - are jointly modeled.

Published
2022

2. Statistical Integration of Heterogeneous Data with PO2PLS

Author

Bouhaddani, Said el, Uh, Hae-Won, Jongbloed, Geurt, and Houwing-Duistermaat, Jeanine

Subjects
Statistics - Methodology, 62H25
Abstract

The availability of multi-omics data has revolutionized the life sciences by creating avenues for integrated system-level approaches. Data integration links the information across datasets to better understand the underlying biological processes. However, high-dimensionality, correlations and heterogeneity pose statistical and computational challenges. We propose a general framework, probabilistic two-way partial least squares (PO2PLS), which addresses these challenges. PO2PLS models the relationship between two datasets using joint and data-specific latent variables. For maximum likelihood estimation of the parameters, we implement a fast EM algorithm and show that the estimator is asymptotically normally distributed. A global test for testing the relationship between two datasets is proposed, and its asymptotic distribution is derived. Notably, several existing omics integration methods are special cases of PO2PLS. Via extensive simulations, we show that PO2PLS performs better than alternatives in feature selection and prediction performance. In addition, the asymptotic distribution appears to hold when the sample size is sufficiently large. We illustrate PO2PLS with two examples from commonly used study designs: a large population cohort and a small case-control study. Besides recovering known relationships, PO2PLS also identified novel findings. The methods are implemented in our R-package PO2PLS. Supplementary materials for this article are available online., Comment: 36 pages, 4 figures, Submitted to Journal of the American Statistical Association

Published
2021
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3. 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, Brain Disorders, Genetics, Cardiovascular, Prevention, Aging, Cerebrovascular, Stroke, Human Genome, 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

4. Probabilistic partial least squares model: Identifiability, estimation and application

Author

Bouhaddani, Said el, Uh, Hae-Won, Hayward, Caroline, Jongbloed, Geurt, and Houwing-Duistermaat, Jeanine

Subjects
Statistics - Methodology
Abstract

With a rapid increase in volume and complexity of data sets, there is a need for methods that can extract useful information, for example the relationship between two data sets measured for the same persons. The Partial Least Squares (PLS) method can be used for this dimension reduction task. Within life sciences, results across studies are compared and combined. Therefore, parameters need to be identifiable, which is not the case for PLS. In addition, PLS is an algorithm, while epidemiological study designs are often outcome-dependent and methods to analyze such data require a probabilistic formulation. Moreover, a probabilistic model provides a statistical framework for inference. To address these issues, we develop Probabilistic PLS (PPLS). We derive maximum likelihood estimators that satisfy the identifiability conditions by using an EM algorithm with a constrained optimization in the M step. We show that the PPLS parameters are identifiable up to sign. A simulation study is conducted to study the performance of PPLS compared to existing methods. The PPLS estimates performed well in various scenarios, even in high dimensions. Most notably, the estimates seem to be robust against departures from normality. To illustrate our method, we applied it to IgG glycan data from two cohorts. Our PPLS model provided insight as well as interpretable results across the two cohorts., Comment: Accepted in Journal of Multivariate Analysis

Published
2017
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5. Joint modeling of an outcome variable and integrated omics datasets using GLM-PO2PLS.

Author

Gu, Zhujie, Uh, Hae-Won, Houwing-Duistermaat, Jeanine, and el Bouhaddani, Said

Subjects
*MAXIMUM likelihood statistics, *ASYMPTOTIC distribution, *EXPECTATION-maximization algorithms, *DATA integration, *DOWN syndrome
Abstract

In many studies of human diseases, multiple omics datasets are measured. Typically, these omics datasets are studied one by one with the disease, thus the relationship between omics is overlooked. Modeling the joint part of multiple omics and its association to the outcome disease will provide insights into the complex molecular base of the disease. Several dimension reduction methods which jointly model multiple omics and two-stage approaches that model the omics and outcome in separate steps are available. Holistic one-stage models for both omics and outcome are lacking. In this article, we propose a novel one-stage method that jointly models an outcome variable with omics. We establish the model identifiability and develop EM algorithms to obtain maximum likelihood estimators of the parameters for normally and Bernoulli distributed outcomes. Test statistics are proposed to infer the association between the outcome and omics, and their asymptotic distributions are derived. Extensive simulation studies are conducted to evaluate the proposed model. The method is illustrated by modeling Down syndrome as outcome and methylation and glycomics as omics datasets. Here we show that our model provides more insight by jointly considering methylation and glycomics. [ABSTRACT FROM AUTHOR]

Published
2024
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7. Genome Analyses of >200,000 Individuals Identify 58 Loci for Chronic Inflammation and Highlight Pathways that Link Inflammation and Complex Disorders

Author

Ligthart, Symen, Vaez, Ahmad, Võsa, Urmo, Stathopoulou, Maria G, de Vries, Paul S, Prins, Bram P, Van der Most, Peter J, Tanaka, Toshiko, Naderi, Elnaz, Rose, Lynda M, Wu, Ying, Karlsson, Robert, Barbalic, Maja, Lin, Honghuang, Pool, René, Zhu, Gu, Macé, Aurélien, Sidore, Carlo, Trompet, Stella, Mangino, Massimo, Sabater-Lleal, Maria, Kemp, John P, Abbasi, Ali, Kacprowski, Tim, Verweij, Niek, Smith, Albert V, Huang, Tao, Marzi, Carola, Feitosa, Mary F, Lohman, Kurt K, Kleber, Marcus E, Milaneschi, Yuri, Mueller, Christian, Huq, Mahmudul, Vlachopoulou, Efthymia, Lyytikäinen, Leo-Pekka, Oldmeadow, Christopher, Deelen, Joris, Perola, Markus, Zhao, Jing Hua, Feenstra, Bjarke, Amini, Marzyeh, Lahti, Jari, Schraut, Katharina E, Fornage, Myriam, Suktitipat, Bhoom, Chen, Wei-Min, Li, Xiaohui, Nutile, Teresa, Malerba, Giovanni, Luan, Jian’an, Bak, Tom, Schork, Nicholas, Del Greco M., Fabiola, Thiering, Elisabeth, Mahajan, Anubha, Marioni, Riccardo E, Mihailov, Evelin, Eriksson, Johan, Ozel, Ayse Bilge, Zhang, Weihua, Nethander, Maria, Cheng, Yu-Ching, Aslibekyan, Stella, Ang, Wei, Gandin, Ilaria, Yengo, Loïc, Portas, Laura, Kooperberg, Charles, Hofer, Edith, Rajan, Kumar B, Schurmann, Claudia, Hollander, Wouter den, Ahluwalia, Tarunveer Singh, Zhao, Jing, Draisma, Harmen HM, Ford, Ian, Timpson, Nicholas, Teumer, Alexander, Huang, Hongyan, Wahl, Simone, Liu, YongMei, Huang, Jie, Uh, Hae-Won, Geller, Frank, Joshi, Peter K, Yanek, Lisa R, Trabetti, Elisabetta, Lehne, Benjamin, Vozzi, Diego, Verbanck, Marie, Biino, Ginevra, Saba, Yasaman, Meulenbelt, Ingrid, O’Connell, Jeff R, Laakso, Markku, Giulianini, Franco, Magnusson, Patrik KE, Ballantyne, Christie M, and Hottenga, Jouke Jan

Subjects
Epidemiology, Biological Sciences, Health Sciences, Genetics, Human Genome, Prevention, 2.1 Biological and endogenous factors, Aetiology, Inflammatory and immune system, Good Health and Well Being, Adolescent, Adult, Aged, Aged, 80 and over, Biomarkers, Bipolar Disorder, Body Mass Index, C-Reactive Protein, Child, Female, Genetic Loci, Genome-Wide Association Study, Humans, Inflammation, Liver, Male, Mendelian Randomization Analysis, Metabolic Networks and Pathways, Middle Aged, Schizophrenia, Young Adult, LifeLines Cohort Study, CHARGE Inflammation Working Group, C-reactive protein, DEPICT, Mendelian randomization, coronary artery disease, genome-wide association study, inflammation, inflammatory disorders, schizophrenia, system biology, Medical and Health Sciences, Genetics & Heredity, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

C-reactive protein (CRP) is a sensitive biomarker of chronic low-grade inflammation and is associated with multiple complex diseases. The genetic determinants of chronic inflammation remain largely unknown, and the causal role of CRP in several clinical outcomes is debated. We performed two genome-wide association studies (GWASs), on HapMap and 1000 Genomes imputed data, of circulating amounts of CRP by using data from 88 studies comprising 204,402 European individuals. Additionally, we performed in silico functional analyses and Mendelian randomization analyses with several clinical outcomes. The GWAS meta-analyses of CRP revealed 58 distinct genetic loci (p < 5 × 10-8). After adjustment for body mass index in the regression analysis, the associations at all except three loci remained. The lead variants at the distinct loci explained up to 7.0% of the variance in circulating amounts of CRP. We identified 66 gene sets that were organized in two substantially correlated clusters, one mainly composed of immune pathways and the other characterized by metabolic pathways in the liver. Mendelian randomization analyses revealed a causal protective effect of CRP on schizophrenia and a risk-increasing effect on bipolar disorder. Our findings provide further insights into the biology of inflammation and could lead to interventions for treating inflammation and its clinical consequences.

Published
2018

8. Statistical integration of multi-omics and drug screening data from cell lines

Author

Cancer, Biostatistiek Onderzoek, Infection & Immunity, JC onderzoeksprogramma Methodologie, El Bouhaddani, Said, Höllerhage, Matthias, Uh, Hae-Won, Moebius, Claudia, Bickle, Marc, Höglinger, Günter, Houwing-Duistermaat, Jeanine, Cancer, Biostatistiek Onderzoek, Infection & Immunity, JC onderzoeksprogramma Methodologie, El Bouhaddani, Said, Höllerhage, Matthias, Uh, Hae-Won, Moebius, Claudia, Bickle, Marc, Höglinger, Günter, and Houwing-Duistermaat, Jeanine

Published
2024

10. 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, Clinical Research, Aging, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, 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

12. Multiethnic Genome-Wide Association Study of Cerebral White Matter Hyperintensities on MRI

Author

Verhaaren, Benjamin FJ, Debette, Stéphanie, Bis, Joshua C, Smith, Jennifer A, Ikram, M Kamran, Adams, Hieab H, Beecham, Ashley H, Rajan, Kumar B, Lopez, Lorna M, Barral, Sandra, van Buchem, Mark A, van der Grond, Jeroen, Smith, Albert V, Hegenscheid, Katrin, Aggarwal, Neelum T, de Andrade, Mariza, Atkinson, Elizabeth J, Beekman, Marian, Beiser, Alexa S, Blanton, Susan H, Boerwinkle, Eric, Brickman, Adam M, Bryan, R Nick, Chauhan, Ganesh, Chen, Christopher PLH, Chouraki, Vincent, de Craen, Anton JM, Crivello, Fabrice, Deary, Ian J, Deelen, Joris, De Jager, Philip L, Dufouil, Carole, Elkind, Mitchell SV, Evans, Denis A, Freudenberger, Paul, Gottesman, Rebecca F, Guðnason, Vilmundur, Habes, Mohamad, Heckbert, Susan R, Heiss, Gerardo, Hilal, Saima, Hofer, Edith, Hofman, Albert, Ibrahim-Verbaas, Carla A, Knopman, David S, Lewis, Cora E, Liao, Jiemin, Liewald, David CM, Luciano, Michelle, van der Lugt, Aad, Martinez, Oliver O, Mayeux, Richard, Mazoyer, Bernard, Nalls, Mike, Nauck, Matthias, Niessen, Wiro J, Oostra, Ben A, Psaty, Bruce M, Rice, Kenneth M, Rotter, Jerome I, von Sarnowski, Bettina, Schmidt, Helena, Schreiner, Pamela J, Schuur, Maaike, Sidney, Stephen S, Sigurdsson, Sigurdur, Slagboom, P Eline, Stott, David JM, van Swieten, John C, Teumer, Alexander, Töglhofer, Anna Maria, Traylor, Matthew, Trompet, Stella, Turner, Stephen T, Tzourio, Christophe, Uh, Hae-Won, Uitterlinden, André G, Vernooij, Meike W, Wang, Jing J, Wong, Tien Y, Wardlaw, Joanna M, Windham, B Gwen, Wittfeld, Katharina, Wolf, Christiane, Wright, Clinton B, Yang, Qiong, Zhao, Wei, Zijdenbos, Alex, Jukema, J Wouter, Sacco, Ralph L, Kardia, Sharon LR, Amouyel, Philippe, Mosley, Thomas H, Longstreth, WT, DeCarli, Charles C, van Duijn, Cornelia M, Schmidt, Reinhold, Launer, Lenore J, Grabe, Hans J, and Seshadri, Sudha S

Subjects
Aging, Brain Disorders, Neurodegenerative, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Biotechnology, Human Genome, Genetics, Acquired Cognitive Impairment, Clinical Research, Dementia, Alzheimer's Disease, Prevention, Stroke, Neurosciences, Aetiology, 2.1 Biological and endogenous factors, Aged, Aged, 80 and over, Chromosomes, Human, Female, Genetic Loci, Genome-Wide Association Study, Humans, Male, Meta-Analysis as Topic, Middle Aged, Models, Genetic, Racial Groups, White Matter, cerebral small vessel diseases, cerebrovascular disorders, genome-wide association study, hypertension, leukoencephalopathies, polymorphisms, single nucleotide, Medical Biotechnology, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology
Abstract

BackgroundThe burden of cerebral white matter hyperintensities (WMH) is associated with an increased risk of stroke, dementia, and death. WMH are highly heritable, but their genetic underpinnings are incompletely characterized. To identify novel genetic variants influencing WMH burden, we conducted a meta-analysis of multiethnic genome-wide association studies.Methods and resultsWe included 21 079 middle-aged to elderly individuals from 29 population-based cohorts, who were free of dementia and stroke and were of European (n=17 936), African (n=1943), Hispanic (n=795), and Asian (n=405) descent. WMH burden was quantified on MRI either by a validated automated segmentation method or a validated visual grading scale. Genotype data in each study were imputed to the 1000 Genomes reference. Within each ethnic group, we investigated the relationship between each single-nucleotide polymorphism and WMH burden using a linear regression model adjusted for age, sex, intracranial volume, and principal components of ancestry. A meta-analysis was conducted for each ethnicity separately and for the combined sample. In the European descent samples, we confirmed a previously known locus on chr17q25 (P=2.7×10(-19)) and identified novel loci on chr10q24 (P=1.6×10(-9)) and chr2p21 (P=4.4×10(-8)). In the multiethnic meta-analysis, we identified 2 additional loci, on chr1q22 (P=2.0×10(-8)) and chr2p16 (P=1.5×10(-8)). The novel loci contained genes that have been implicated in Alzheimer disease (chr2p21 and chr10q24), intracerebral hemorrhage (chr1q22), neuroinflammatory diseases (chr2p21), and glioma (chr10q24 and chr2p16).ConclusionsWe identified 4 novel genetic loci that implicate inflammatory and glial proliferative pathways in the development of WMH in addition to previously proposed ischemic mechanisms.

Published
2015

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

Author

van Leeuwen, Elisabeth M, Karssen, Lennart C, Deelen, Joris, Isaacs, Aaron, Medina-Gomez, Carolina, Mbarek, Hamdi, Kanterakis, Alexandros, Trompet, Stella, Postmus, Iris, Verweij, Niek, van Enckevort, David J, Huffman, Jennifer E, White, Charles C, Feitosa, Mary F, Bartz, Traci M, Manichaikul, Ani, Joshi, Peter K, Peloso, Gina M, Deelen, Patrick, van Dijk, Freerk, Willemsen, Gonneke, de Geus, Eco J, Milaneschi, Yuri, Penninx, Brenda WJH, Francioli, Laurent C, Menelaou, Androniki, Pulit, Sara L, Rivadeneira, Fernando, Hofman, Albert, Oostra, Ben A, Franco, Oscar H, Mateo Leach, Irene, Beekman, Marian, de Craen, Anton JM, Uh, Hae-Won, Trochet, Holly, Hocking, Lynne J, Porteous, David J, Sattar, Naveed, Packard, Chris J, Buckley, Brendan M, Brody, Jennifer A, Bis, Joshua C, Rotter, Jerome I, Mychaleckyj, Josyf C, Campbell, Harry, Duan, Qing, Lange, Leslie A, Wilson, James F, Hayward, Caroline, Polasek, Ozren, Vitart, Veronique, Rudan, Igor, Wright, Alan F, Rich, Stephen S, Psaty, Bruce M, Borecki, Ingrid B, Kearney, Patricia M, Stott, David J, Adrienne Cupples, L, Genome of The Netherlands Consortium, Jukema, J Wouter, van der Harst, Pim, Sijbrands, Eric J, Hottenga, Jouke-Jan, Uitterlinden, Andre G, Swertz, Morris A, van Ommen, Gert-Jan B, de Bakker, Paul IW, Eline Slagboom, P, Boomsma, Dorret I, Wijmenga, Cisca, and van Duijn, Cornelia M

Subjects
Genome of The Netherlands Consortium, Humans, Cholesterol, ATP-Binding Cassette Transporters, Gene Frequency, Mutation, Missense, Netherlands, Genetic Association Studies, Mutation, Missense
Abstract

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

Published
2015

16. Statistical Analysis of Lipidomics Data in a Case-Control Study

Author

Mertens, Bart J. A., Datta, Susmita, Hankemeier, Thomas, Beekman, Marian, Uh, Hae-Won, Datta, Somnath, Editor-in-chief, Viens, Frederi G., Series editor, Politis, Dimitris N., Series editor, Fokianos, Konstantinos, Series editor, Daniels, Michael, Series editor, Datta, Susmita, editor, and Mertens, Bart J. A., editor

Published
2017
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18. Novel loci for adiponectin levels and their influence on type 2 diabetes and metabolic traits: a multi-ethnic meta-analysis of 45,891 individuals.

Author

Dastani, Zari, Hivert, Marie-France, Timpson, Nicholas, Perry, John RB, Yuan, Xin, Scott, Robert A, Henneman, Peter, Heid, Iris M, Kizer, Jorge R, Lyytikäinen, Leo-Pekka, Fuchsberger, Christian, Tanaka, Toshiko, Morris, Andrew P, Small, Kerrin, Isaacs, Aaron, Beekman, Marian, Coassin, Stefan, Lohman, Kurt, Qi, Lu, Kanoni, Stavroula, Pankow, James S, Uh, Hae-Won, Wu, Ying, Bidulescu, Aurelian, Rasmussen-Torvik, Laura J, Greenwood, Celia MT, Ladouceur, Martin, Grimsby, Jonna, Manning, Alisa K, Liu, Ching-Ti, Kooner, Jaspal, Mooser, Vincent E, Vollenweider, Peter, Kapur, Karen A, Chambers, John, Wareham, Nicholas J, Langenberg, Claudia, Frants, Rune, Willems-Vandijk, Ko, Oostra, Ben A, Willems, Sara M, Lamina, Claudia, Winkler, Thomas W, Psaty, Bruce M, Tracy, Russell P, Brody, Jennifer, Chen, Ida, Viikari, Jorma, Kähönen, Mika, Pramstaller, Peter P, Evans, David M, St Pourcain, Beate, Sattar, Naveed, Wood, Andrew R, Bandinelli, Stefania, Carlson, Olga D, Egan, Josephine M, Böhringer, Stefan, van Heemst, Diana, Kedenko, Lyudmyla, Kristiansson, Kati, Nuotio, Marja-Liisa, Loo, Britt-Marie, Harris, Tamara, Garcia, Melissa, Kanaya, Alka, Haun, Margot, Klopp, Norman, Wichmann, H-Erich, Deloukas, Panos, Katsareli, Efi, Couper, David J, Duncan, Bruce B, Kloppenburg, Margreet, Adair, Linda S, Borja, Judith B, DIAGRAM+ Consortium, MAGIC Consortium, GLGC Investigators, MuTHER Consortium, Wilson, James G, Musani, Solomon, Guo, Xiuqing, Johnson, Toby, Semple, Robert, Teslovich, Tanya M, Allison, Matthew A, Redline, Susan, Buxbaum, Sarah G, Mohlke, Karen L, Meulenbelt, Ingrid, Ballantyne, Christie M, Dedoussis, George V, Hu, Frank B, Liu, Yongmei, Paulweber, Bernhard, Spector, Timothy D, Slagboom, P Eline, Ferrucci, Luigi, and Jula, Antti

Subjects
DIAGRAM+ Consortium, MAGIC Consortium, GLGC Investigators, MuTHER Consortium, DIAGRAM Consortium, GIANT Consortium, Global B Pgen Consortium, Procardis Consortium, MAGIC investigators, GLGC Consortium, Humans, Diabetes Mellitus, Type 2, Insulin Resistance, Genetic Predisposition to Disease, Glucose Tolerance Test, Waist-Hip Ratio, Gene Expression, Polymorphism, Single Nucleotide, African Americans, Asian Continental Ancestry Group, European Continental Ancestry Group, Female, Male, Adiponectin, Cholesterol, HDL, Metabolic Networks and Pathways, Genome-Wide Association Study, Diabetes Mellitus, Type 2, Polymorphism, Single Nucleotide, Cholesterol, HDL, Genetics, Developmental Biology
Abstract

Circulating levels of adiponectin, a hormone produced predominantly by adipocytes, are highly heritable and are inversely associated with type 2 diabetes mellitus (T2D) and other metabolic traits. We conducted a meta-analysis of genome-wide association studies in 39,883 individuals of European ancestry to identify genes associated with metabolic disease. We identified 8 novel loci associated with adiponectin levels and confirmed 2 previously reported loci (P = 4.5×10(-8)-1.2×10(-43)). Using a novel method to combine data across ethnicities (N = 4,232 African Americans, N = 1,776 Asians, and N = 29,347 Europeans), we identified two additional novel loci. Expression analyses of 436 human adipocyte samples revealed that mRNA levels of 18 genes at candidate regions were associated with adiponectin concentrations after accounting for multiple testing (p

Published
2012

20. Digital Twins in Healthcare: Methodological Challenges and Opportunities

Author

Biostatistiek Onderzoek, Infection & Immunity, JC onderzoeksprogramma Methodologie, Cancer, Meijer, Charles, Uh, Hae-Won, El Bouhaddani, Said, Biostatistiek Onderzoek, Infection & Immunity, JC onderzoeksprogramma Methodologie, Cancer, Meijer, Charles, Uh, Hae-Won, and El Bouhaddani, Said

Published
2023

26. Statistical integration of heterogeneous omics data: Probabilistic two-way partial least squares (PO2PLS)

Author

el Bouhaddani, Said (author), Uh, Hae Won (author), Jongbloed, G. (author), Houwing-Duistermaat, Jeanine (author), el Bouhaddani, Said (author), Uh, Hae Won (author), Jongbloed, G. (author), and Houwing-Duistermaat, Jeanine (author)

Abstract

The availability of multi-omics data has revolutionized the life sciences by creating avenues for integrated system-level approaches. Data integration links the information across datasets to better understand the underlying biological processes. However, high dimensionality, correlations and heterogeneity pose statistical and computational challenges. We propose a general framework, probabilistic two-way partial least squares (PO2PLS), that addresses these challenges. PO2PLS models the relationship between two datasets using joint and data-specific latent variables. For maximum likelihood estimation of the parameters, we propose a novel fast EM algorithm and show that the estimator is asymptotically normally distributed. A global test for the relationship between two datasets is proposed, specifically addressing the high dimensionality, and its asymptotic distribution is derived. Notably, several existing data integration methods are special cases of PO2PLS. Via extensive simulations, we show that PO2PLS performs better than alternatives in feature selection and prediction performance. In addition, the asymptotic distribution appears to hold when the sample size is sufficiently large. We illustrate PO2PLS with two examples from commonly used study designs: a large population cohort and a small case–control study. Besides recovering known relationships, PO2PLS also identified novel findings. The methods are implemented in our R-package PO2PLS., Statistics

Published
2022
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28. Smartphone detection of atrial fibrillation using photoplethysmography: a systematic review and meta-analysis

Author

Biostatistiek Onderzoek, JC onderzoeksprogramma Methodologie, Team Medisch, Circulatory Health, Child Health, Infection & Immunity, JC onderzoeksprogramma Infectieziekten, Onderzoek Precision medicine, Gill, Simrat, Bunting, Karina V, Sartini, Claudio, Cardoso, Victor Roth, Ghoreishi, Narges, Uh, Hae-Won, Williams, John A, Suzart-Woischnik, Kiliana, Banerjee, Amitava, Asselbergs, Folkert W, Eijkemans, Mjc, Gkoutos, Georgios V, Kotecha, Dipak, Biostatistiek Onderzoek, JC onderzoeksprogramma Methodologie, Team Medisch, Circulatory Health, Child Health, Infection & Immunity, JC onderzoeksprogramma Infectieziekten, Onderzoek Precision medicine, Gill, Simrat, Bunting, Karina V, Sartini, Claudio, Cardoso, Victor Roth, Ghoreishi, Narges, Uh, Hae-Won, Williams, John A, Suzart-Woischnik, Kiliana, Banerjee, Amitava, Asselbergs, Folkert W, Eijkemans, Mjc, Gkoutos, Georgios V, and Kotecha, Dipak

Published
2022

29. Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare.

Author

Gill, Simrat K, Karwath, Andreas, Uh, Hae-Won, Cardoso, Victor Roth, Gu, Zhujie, Barsky, Andrey, Slater, Luke, Acharjee, Animesh, Duan, Jinming, Dall'Olio, Lorenzo, Bouhaddani, Said el, Chernbumroong, Saisakul, Stanbury, Mary, Haynes, Sandra, Asselbergs, Folkert W, Grobbee, Diederick E, Eijkemans, Marinus J C, Gkoutos, Georgios V, Kotecha, Dipak, and group, BigData@Heart Consortium and the cardAIc

Subjects
ARTIFICIAL intelligence, MEDICAL personnel, MACHINE learning, DISEASE management, MEDICAL care
Abstract

Artificial intelligence (AI) is increasingly being utilized in healthcare. This article provides clinicians and researchers with a step-wise foundation for high-value AI that can be applied to a variety of different data modalities. The aim is to improve the transparency and application of AI methods, with the potential to benefit patients in routine cardiovascular care. Following a clear research hypothesis, an AI-based workflow begins with data selection and pre-processing prior to analysis, with the type of data (structured, semi-structured, or unstructured) determining what type of pre-processing steps and machine-learning algorithms are required. Algorithmic and data validation should be performed to ensure the robustness of the chosen methodology, followed by an objective evaluation of performance. Seven case studies are provided to highlight the wide variety of data modalities and clinical questions that can benefit from modern AI techniques, with a focus on applying them to cardiovascular disease management. Despite the growing use of AI, further education for healthcare workers, researchers, and the public are needed to aid understanding of how AI works and to close the existing gap in knowledge. In addition, issues regarding data access, sharing, and security must be addressed to ensure full engagement by patients and the public. The application of AI within healthcare provides an opportunity for clinicians to deliver a more personalized approach to medical care by accounting for confounders, interactions, and the rising prevalence of multi-morbidity. [ABSTRACT FROM AUTHOR]

Published
2023
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31. Smartphone detection of atrial fibrillation using photoplethysmography: a systematic review and meta-analysis

Author

Gill, Simrat, primary, Bunting, Karina V, additional, Sartini, Claudio, additional, Cardoso, Victor Roth, additional, Ghoreishi, Narges, additional, Uh, Hae-Won, additional, Williams, John A, additional, Suzart-Woischnik, Kiliana, additional, Banerjee, Amitava, additional, Asselbergs, Folkert W, additional, Eijkemans, MJC, additional, Gkoutos, Georgios V, additional, and Kotecha, Dipak, additional

Published
2022
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32. Genetic studies of body mass index yield new insights for obesity biology

Author

Locke, Adam E., Kahali, Bratati, Berndt, Sonja I., Justice, Anne E., Pers, Tune H., Day, Felix R., Powell, Corey, Vedantam, Sailaja, Buchkovich, Martin L., Yang, Jian, Croteau-Chonka, Damien C., Esko, Tonu, Fall, Tove, Ferreira, Teresa, Gustafsson, Stefan, Kutalik, Zoltán, Luan, Jianʼan, Mägi, Reedik, Randall, Joshua C., Winkler, Thomas W., Wood, Andrew R., Workalemahu, Tsegaselassie, Faul, Jessica D., Smith, Jennifer A., Hua Zhao, Jing, Zhao, Wei, Chen, Jin, Fehrmann, Rudolf, Hedman, Åsa K., Karjalainen, Juha, Schmidt, Ellen M., Absher, Devin, Amin, Najaf, Anderson, Denise, Beekman, Marian, Bolton, Jennifer L., Bragg-Gresham, Jennifer L., Buyske, Steven, Demirkan, Ayse, Deng, Guohong, Ehret, Georg B., Feenstra, Bjarke, Feitosa, Mary F., Fischer, Krista, Goel, Anuj, Gong, Jian, Jackson, Anne U., Kanoni, Stavroula, Kleber, Marcus E., Kristiansson, Kati, Lim, Unhee, Lotay, Vaneet, Mangino, Massimo, Mateo Leach, Irene, Medina-Gomez, Carolina, Medland, Sarah E., Nalls, Michael A., Palmer, Cameron D., Pasko, Dorota, Pechlivanis, Sonali, Peters, Marjolein J., Prokopenko, Inga, Shungin, Dmitry, Stančáková, Alena, Strawbridge, Rona J., Ju Sung, Yun, Tanaka, Toshiko, Teumer, Alexander, Trompet, Stella, van der Laan, Sander W., van Setten, Jessica, Van Vliet-Ostaptchouk, Jana V., Wang, Zhaoming, Yengo, Loïc, Zhang, Weihua, Isaacs, Aaron, Albrecht, Eva, Ärnlöv, Johan, Arscott, Gillian M., Attwood, Antony P., Bandinelli, Stefania, Barrett, Amy, Bas, Isabelita N., Bellis, Claire, Bennett, Amanda J., Berne, Christian, Blagieva, Roza, Blüher, Matthias, Böhringer, Stefan, Bonnycastle, Lori L., Böttcher, Yvonne, Boyd, Heather A., Bruinenberg, Marcel, Caspersen, Ida H., Ida Chen, Yii-Der, Clarke, Robert, Warwick Daw, E., de Craen, Anton J. M., Delgado, Graciela, Dimitriou, Maria, Doney, Alex S. F., Eklund, Niina, Estrada, Karol, Eury, Elodie, Folkersen, Lasse, Fraser, Ross M., Garcia, Melissa E., Geller, Frank, Giedraitis, Vilmantas, Gigante, Bruna, Go, Alan S., Golay, Alain, Goodall, Alison H., Gordon, Scott D., Gorski, Mathias, Grabe, Hans-Jörgen, Grallert, Harald, Grammer, Tanja B., Gräler, Jürgen, Grönberg, Henrik, Groves, Christopher J., Gusto, Gaëlle, Haessler, Jeffrey, Hall, Per, Haller, Toomas, Hallmans, Goran, Hartman, Catharina A., Hassinen, Maija, Hayward, Caroline, Heard-Costa, Nancy L., Helmer, Quinta, Hengstenberg, Christian, Holmen, Oddgeir, Hottenga, Jouke-Jan, James, Alan L., Jeff, Janina M., Johansson, Åsa, Jolley, Jennifer, Juliusdottir, Thorhildur, Kinnunen, Leena, Koenig, Wolfgang, Koskenvuo, Markku, Kratzer, Wolfgang, Laitinen, Jaana, Lamina, Claudia, Leander, Karin, Lee, Nanette R., Lichtner, Peter, Lind, Lars, Lindström, Jaana, Sin Lo, Ken, Lobbens, Stéphane, Lorbeer, Roberto, Lu, Yingchang, Mach, François, Magnusson, Patrik K. E., Mahajan, Anubha, McArdle, Wendy L., McLachlan, Stela, Menni, Cristina, Merger, Sigrun, Mihailov, Evelin, Milani, Lili, Moayyeri, Alireza, Monda, Keri L., Morken, Mario A., Mulas, Antonella, Müller, Gabriele, Müller-Nurasyid, Martina, Musk, Arthur W., Nagaraja, Ramaiah, Nöthen, Markus M., Nolte, Ilja M., Pilz, Stefan, Rayner, Nigel W., Renstrom, Frida, Rettig, Rainer, Ried, Janina S., Ripke, Stephan, Robertson, Neil R., Rose, Lynda M., Sanna, Serena, Scharnagl, Hubert, Scholtens, Salome, Schumacher, Fredrick R., Scott, William R., Seufferlein, Thomas, Shi, Jianxin, Vernon Smith, Albert, Smolonska, Joanna, Stanton, Alice V., Steinthorsdottir, Valgerdur, Stirrups, Kathleen, Stringham, Heather M., Sundström, Johan, Swertz, Morris A., Swift, Amy J., Syvänen, Ann-Christine, Tan, Sian-Tsung, Tayo, Bamidele O., Thorand, Barbara, Thorleifsson, Gudmar, Tyrer, Jonathan P., Uh, Hae-Won, Vandenput, Liesbeth, Verhulst, Frank C., Vermeulen, Sita H., Verweij, Niek, Vonk, Judith M., Waite, Lindsay L., Warren, Helen R., Waterworth, Dawn, Weedon, Michael N., Wilkens, Lynne R., Willenborg, Christina, Wilsgaard, Tom, Wojczynski, Mary K., Wong, Andrew, Wright, Alan F., Zhang, Qunyuan, Brennan, Eoin P., Choi, Murim, Dastani, Zari, Drong, Alexander W., Eriksson, Per, Franco-Cereceda, Anders, Gådin, Jesper R., Gharavi, Ali G., Goddard, Michael E., Handsaker, Robert E., Huang, Jinyan, Karpe, Fredrik, Kathiresan, Sekar, Keildson, Sarah, Kiryluk, Krzysztof, Kubo, Michiaki, Lee, Jong-Young, Liang, Liming, Lifton, Richard P., Ma, Baoshan, McCarroll, Steven A., McKnight, Amy J., Min, Josine L., Moffatt, Miriam F., Montgomery, Grant W., Murabito, Joanne M., Nicholson, George, Nyholt, Dale R., Okada, Yukinori, Perry, John R. B., Dorajoo, Rajkumar, Reinmaa, Eva, Salem, Rany M., Sandholm, Niina, Scott, Robert A., Stolk, Lisette, Takahashi, Atsushi, Tanaka, Toshihiro, vanʼt Hooft, Ferdinand M., Vinkhuyzen, Anna A. E., Westra, Harm-Jan, Zheng, Wei, Zondervan, Krina T., Heath, Andrew C., Arveiler, Dominique, Bakker, Stephan J. L., Beilby, John, Bergman, Richard N., Blangero, John, Bovet, Pascal, Campbell, Harry, Caulfield, Mark J., Cesana, Giancarlo, Chakravarti, Aravinda, Chasman, Daniel I., Chines, Peter S., Collins, Francis S., Crawford, Dana C., Adrienne Cupples, L., Cusi, Daniele, Danesh, John, de Faire, Ulf, den Ruijter, Hester M., Dominiczak, Anna F., Erbel, Raimund, Erdmann, Jeanette, Eriksson, Johan G., Farrall, Martin, Felix, Stephan B., Ferrannini, Ele, Ferrières, Jean, Ford, Ian, Forouhi, Nita G., Forrester, Terrence, Franco, Oscar H., Gansevoort, Ron T., Gejman, Pablo V., Gieger, Christian, Gottesman, Omri, Gudnason, Vilmundur, Gyllensten, Ulf, Hall, Alistair S., Harris, Tamara B., Hattersley, Andrew T., Hicks, Andrew A., Hindorff, Lucia A., Hingorani, Aroon D., Hofman, Albert, Homuth, Georg, Kees Hovingh, G., Humphries, Steve E., Hunt, Steven C., Hyppönen, Elina, Illig, Thomas, Jacobs, Kevin B., Jarvelin, Marjo-Riitta, Jöckel, Karl-Heinz, Johansen, Berit, Jousilahti, Pekka, Wouter Jukema, J., Jula, Antti M., Kaprio, Jaakko, Kastelein, John J. P., Keinanen-Kiukaanniemi, Sirkka M., Kiemeney, Lambertus A., Knekt, Paul, Kooner, Jaspal S., Kooperberg, Charles, Kovacs, Peter, Kraja, Aldi T., Kumari, Meena, Kuusisto, Johanna, Lakka, Timo A., Langenberg, Claudia, Le Marchand, Loic, Lehtimäki, Terho, Lyssenko, Valeriya, Männistö, Satu, Marette, André, Matise, Tara C., McKenzie, Colin A., McKnight, Barbara, Moll, Frans L., Morris, Andrew D., Morris, Andrew P., Murray, Jeffrey C., Nelis, Mari, Ohlsson, Claes, Oldehinkel, Albertine J., Ong, Ken K., Madden, Pamela A. F., Pasterkamp, Gerard, Peden, John F., Peters, Annette, Postma, Dirkje S., Pramstaller, Peter P., Price, Jackie F., Qi, Lu, Raitakari, Olli T., Rankinen, Tuomo, Rao, D. C., Rice, Treva K., Ridker, Paul M., Rioux, John D., Ritchie, Marylyn D., Rudan, Igor, Salomaa, Veikko, Samani, Nilesh J., Saramies, Jouko, Sarzynski, Mark A., Schunkert, Heribert, Schwarz, Peter E. H., Sever, Peter, Shuldiner, Alan R., Sinisalo, Juha, Stolk, Ronald P., Strauch, Konstantin, Tönjes, Anke, Trégouët, David-Alexandre, Tremblay, Angelo, Tremoli, Elena, Virtamo, Jarmo, Vohl, Marie-Claude, Völker, Uwe, Waeber, Gérard, Willemsen, Gonneke, Witteman, Jacqueline C., Carola Zillikens, M., Adair, Linda S., Amouyel, Philippe, Asselbergs, Folkert W., Assimes, Themistocles L., Bochud, Murielle, Boehm, Bernhard O., Boerwinkle, Eric, Bornstein, Stefan R., Bottinger, Erwin P., Bouchard, Claude, Cauchi, Stéphane, Chambers, John C., Chanock, Stephen J., Cooper, Richard S., de Bakker, Paul I. W., Dedoussis, George, Ferrucci, Luigi, Franks, Paul W., Froguel, Philippe, Groop, Leif C., Haiman, Christopher A., Hamsten, Anders, Hui, Jennie, Hunter, David J., Hveem, Kristian, Kaplan, Robert C., Kivimaki, Mika, Kuh, Diana, Laakso, Markku, Liu, Yongmei, Martin, Nicholas G., März, Winfried, Melbye, Mads, Metspalu, Andres, Moebus, Susanne, Munroe, Patricia B., Njølstad, Inger, Oostra, Ben A., Palmer, Colin N. A., Pedersen, Nancy L., Perola, Markus, Pérusse, Louis, Peters, Ulrike, Power, Chris, Quertermous, Thomas, Rauramaa, Rainer, Rivadeneira, Fernando, Saaristo, Timo E., Saleheen, Danish, Sattar, Naveed, Schadt, Eric E., Schlessinger, David, Eline Slagboom, P., Snieder, Harold, Spector, Tim D., Thorsteinsdottir, Unnur, Stumvoll, Michael, Tuomilehto, Jaakko, Uitterlinden, André G., Uusitupa, Matti, van der Harst, Pim, Walker, Mark, Wallaschofski, Henri, Wareham, Nicholas J., Watkins, Hugh, Weir, David R., Wichmann, H-Erich, Wilson, James F., Zanen, Pieter, Borecki, Ingrid B., Deloukas, Panos, Fox, Caroline S., Heid, Iris M., OʼConnell, Jeffrey R., Strachan, David P., Stefansson, Kari, van Duijn, Cornelia M., Abecasis, Gonçalo R., Franke, Lude, Frayling, Timothy M., McCarthy, Mark I., Visscher, Peter M., Scherag, André, Willer, Cristen J., Boehnke, Michael, Mohlke, Karen L., Lindgren, Cecilia M., Beckmann, Jacques S., Barroso, Inês, North, Kari E., Ingelsson, Erik, Hirschhorn, Joel N., Loos, Ruth J. F., and Speliotes, Elizabeth K.

Published
2015
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33. Gene set analysis of GWAS data for human longevity highlights the relevance of the insulin/IGF-1 signaling and telomere maintenance pathways

Author

Deelen, Joris, Uh, Hae-Won, Monajemi, Ramin, van Heemst, Diana, Thijssen, Peter E., Böhringer, Stefan, van den Akker, Erik B., de Craen, Anton J. M., Rivadeneira, Fernando, Uitterlinden, André G., Westendorp, Rudi G. J., Goeman, Jelle J., Slagboom, P. Eline, Houwing-Duistermaat, Jeanine J., and Beekman, Marian

Published
2013
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36. Statistical integration of two omics datasets using GO2PLS

Author

Biostatistiek Onderzoek, Onderzoek Precision medicine, Other research (not in main researchprogram), JC onderzoeksprogramma Methodologie, Gu, Zhujie, El Bouhaddani, Said, Pei, Jiayi, Houwing-Duistermaat, Jeanine, Uh, Hae-Won, Biostatistiek Onderzoek, Onderzoek Precision medicine, Other research (not in main researchprogram), JC onderzoeksprogramma Methodologie, Gu, Zhujie, El Bouhaddani, Said, Pei, Jiayi, Houwing-Duistermaat, Jeanine, and Uh, Hae-Won

Published
2021

37. Statistical method for modeling sequencing data from different technologies in longitudinal studies with application to Huntington disease

Author

Biostatistiek Onderzoek, JC onderzoeksprogramma Methodologie, Fuady, Angga M, van Roon-Mom, Willeke M C, Kiełbasa, Szymon M, Uh, Hae-Won, Houwing-Duistermaat, Jeanine J, Biostatistiek Onderzoek, JC onderzoeksprogramma Methodologie, Fuady, Angga M, van Roon-Mom, Willeke M C, Kiełbasa, Szymon M, Uh, Hae-Won, and Houwing-Duistermaat, Jeanine J

Published
2021

38. A meta-analysis of four genome-wide association studies of survival to age 90 years or older: the cohorts for heart and aging research in genomic epidemiology consortium

Author

Newman, Anne B., Walter, Stefan, Lunetta, Kathryn L., Garcia, Melissa E., Slagboom, Eline, Christensen, Kaare, Arnold, Alice M., Aspelund, Thor, Aulchenko, Yurii S., Benjamin, Emelia J., Christiansen, Lene, D'Agostino, Ralph B., Sr., Fitzpatrick, Annette L., Franceschini, Nora, Glazer, Nicole L., Gudnason, Vilmundur, Hofman, Albert, Kaplan, Robert, Karasik, David, Kelly-Hayes, Margaret, Kiel, Douglas E., Launer, Lenore J., Marciante, Kristin D., Massaro, Joseph M., Miljkovic, Iva, Nalls, Michael A., Hernandez, Dena, Psaty, Bruce M., Rivadeneira, Fernando, Rotter, Jerome, Seshadri, Sudha, Smith, Albert V., Taylor, Kent D., Tiemeier, Henning, Uh, Hae-Won, Uitterlinden, Andre G., Vaupel, James W., Walston, Jeremy, Westendorp, Rudi G.J., Harris, Tamara B., Lumley, Thomas, van Duijn, Cornelia M., and Murabito, Joanne M.

Subjects
Genomics -- Research, Single nucleotide polymorphisms -- Research, Longevity -- Health aspects, Longevity -- Genetic aspects, Health, Seniors
Abstract

Background. Genome-wide association studies (GWAS) may yield insights into longevity. Methods. We performed a meta-analysis of GWAS in Caucasians from four prospective cohort studies: the Age, Gene/ Environment Susceptibility-Reykjavik Study, the Cardiovascular Health Study, the Framingham Heart Study, and the Rotterdam Study participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Longevity was defined as survival to age 90 years or older (n = 1,836); the comparison group comprised cohort members who died between the ages of 55 and 80 years (n = 1,955). In a second discovery stage, additional genotyping was conducted in the Leiden Longevity Study cohort and the Danish 1905 cohort. Results. There were 273 single-nucleotide polymorphism (SNP) associations with p < .0001, but none reached the prespecified significance level of 5 x [10.sup.-8]. Of the most significant SNPs, 24 were independent signals, and 16 of these SNPs were successfully genotyped in the second discovery stage, with one association for rs9664222, reaching 6.77 x [10.sup.-7] for the combined meta-analysis of CHARGE and the stage 2 cohorts. The SNP lies in a region near MINPP1 (chromosome 10), a well-conserved gene involved in regulation of cellular proliferation. The minor allele was associated with lower odds of survival past age 90 (odds ratio = 0.82). Associations of interest in a homologue of the longevity assurance gene (LASS3) and PAPPA2 were not strengthened in the second stage. Conclusion. Survival studies of larger size or more extreme or specific phenotypes may support or refine these initial findings. Key Words: Longevity--Genome-wide association study--Meta-analysis. doi: 10.1093/gerona/glq028

Published
2010

39. Genome-wide association meta-analysis of human longevity identifies a novel locus conferring survival beyond 90 years of age

Author

Deelen, Joris, Beekman, Marian, Uh, Hae-Won, Broer, Linda, Ayers, Kristin L., Tan, Qihua, Kamatani, Yoichiro, Bennet, Anna M., Tamm, Riin, Trompet, Stella, Gubjartsson, Daníel F., Flachsbart, Friederike, Rose, Giuseppina, Viktorin, Alexander, Fischer, Krista, Nygaard, Marianne, Cordell, Heather J., Crocco, Paolina, van den Akker, Erik B., Böhringer, Stefan, Helmer, Quinta, Nelson, Christopher P., Saunders, Gary I., Alver, Maris, Andersen-Ranberg, Karen, Breen, Marie E., van der Breggen, Ruud, Caliebe, Amke, Capri, Miriam, Cevenini, Elisa, Collerton, Joanna C., Dato, Serena, Davies, Karen, Ford, Ian, Gampe, Jutta, Garagnani, Paolo, de Geus, Eco J.C., Harrow, Jennifer, van Heemst, Diana, Heijmans, Bastiaan T., Heinsen, Femke-Anouska, Hottenga, Jouke-Jan, Hofman, Albert, Jeune, Bernard, Jonsson, Palmi V., Lathrop, Mark, Lechner, Doris, Martin-Ruiz, Carmen, Mcnerlan, Susan E., Mihailov, Evelin, Montesanto, Alberto, Mooijaart, Simon P., Murphy, Anne, Nohr, Ellen A., Paternoster, Lavinia, Postmus, Iris, Rivadeneira, Fernando, Ross, Owen A., Salvioli, Stefano, Sattar, Naveed, Schreiber, Stefan, Stefánsson, Hreinn, Stott, David J., Tiemeier, Henning, Uitterlinden, André G., Westendorp, Rudi G.J., Willemsen, Gonneke, Samani, Nilesh J., Galan, Pilar, Sørensen, Thorkild I.A., Boomsma, Dorret I., Jukema, J. Wouter, Rea, Irene Maeve, Passarino, Giuseppe, de Craen, Anton J.M., Christensen, Kaare, Nebel, Almut, Stefánsson, Kári, Metspalu, Andres, Magnusson, Patrik, Blanché, Hélène, Christiansen, Lene, Kirkwood, Thomas B.L., van Duijn, Cornelia M., Franceschi, Claudio, Houwing-Duistermaat, Jeanine J., and Slagboom, P. Eline

Published
2014
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40. Glycosylation of immunoglobulin G is regulated by a large network of genes pleiotropic with inflammatory diseases

Author

Klarić, Lucija, Tsepilov, Yakov A, Stanton, Chloe M, Mangino, Massimo, Sikka, Timo Tõnis, Esko, Tõnu, Pakhomov, Eugene, Salo, Perttu, Deelen, Joris, McGurnaghan, Stuart J, Keser, Toma, Vučković, Frano, Ugrina, Ivo, Krištić, Jasminka, Gudelj, Ivan, Štambuk, Jerko, Plomp, Rosina, Pučić-Baković, Maja, Pavić, Tamara, Vilaj, Marija, Trbojević-Akmačić, Irena, Drake, Camilla, Dobrinić, Paula, Mlinarec, Jelena, Jelušić, Barbara, Richmond, Anne, Timofeeva, Maria, Grishchenko, Alexander K, Dmitrieva, Julia, Bermingham, Mairead L, Sharapov, Sodbo Zh, Farrington, Susan M, Theodoratou, Evropi, Uh, Hae-Won, Beekman, Marian, Slagboom, Eline P, Louis, Edouard, Georges, Michel, Wuhrer, Manfred, Colhoun, Helen M, Dunlop, Malcolm G, Perola, Markus, Fischer, Krista, Polasek, Ozren, Campbell, Harry, Rudan, Igor, Wilson, James F, Zoldoš, Vlatka, Vitart, Veronique, Spector, Tim, Aulchenko, Yurii S, Lauc, Gordan, Hayward, Caroline, Klarić, Lucija, Tsepilov, Yakov A, Stanton, Chloe M, Mangino, Massimo, Sikka, Timo Tõnis, Esko, Tõnu, Pakhomov, Eugene, Salo, Perttu, Deelen, Joris, McGurnaghan, Stuart J, Keser, Toma, Vučković, Frano, Ugrina, Ivo, Krištić, Jasminka, Gudelj, Ivan, Štambuk, Jerko, Plomp, Rosina, Pučić-Baković, Maja, Pavić, Tamara, Vilaj, Marija, Trbojević-Akmačić, Irena, Drake, Camilla, Dobrinić, Paula, Mlinarec, Jelena, Jelušić, Barbara, Richmond, Anne, Timofeeva, Maria, Grishchenko, Alexander K, Dmitrieva, Julia, Bermingham, Mairead L, Sharapov, Sodbo Zh, Farrington, Susan M, Theodoratou, Evropi, Uh, Hae-Won, Beekman, Marian, Slagboom, Eline P, Louis, Edouard, Georges, Michel, Wuhrer, Manfred, Colhoun, Helen M, Dunlop, Malcolm G, Perola, Markus, Fischer, Krista, Polasek, Ozren, Campbell, Harry, Rudan, Igor, Wilson, James F, Zoldoš, Vlatka, Vitart, Veronique, Spector, Tim, Aulchenko, Yurii S, Lauc, Gordan, and Hayward, Caroline

Published
2020

41. Glycosylation of immunoglobulin G is regulated by a large network of genes pleiotropic with inflammatory diseases

Author

Biostatistiek Onderzoek, JC onderzoeksprogramma Methodologie, Klarić, Lucija, Tsepilov, Yakov A, Stanton, Chloe M, Mangino, Massimo, Sikka, Timo Tõnis, Esko, Tõnu, Pakhomov, Eugene, Salo, Perttu, Deelen, Joris, McGurnaghan, Stuart J, Keser, Toma, Vučković, Frano, Ugrina, Ivo, Krištić, Jasminka, Gudelj, Ivan, Štambuk, Jerko, Plomp, Rosina, Pučić-Baković, Maja, Pavić, Tamara, Vilaj, Marija, Trbojević-Akmačić, Irena, Drake, Camilla, Dobrinić, Paula, Mlinarec, Jelena, Jelušić, Barbara, Richmond, Anne, Timofeeva, Maria, Grishchenko, Alexander K, Dmitrieva, Julia, Bermingham, Mairead L, Sharapov, Sodbo Zh, Farrington, Susan M, Theodoratou, Evropi, Uh, Hae-Won, Beekman, Marian, Slagboom, Eline P, Louis, Edouard, Georges, Michel, Wuhrer, Manfred, Colhoun, Helen M, Dunlop, Malcolm G, Perola, Markus, Fischer, Krista, Polasek, Ozren, Campbell, Harry, Rudan, Igor, Wilson, James F, Zoldoš, Vlatka, Vitart, Veronique, Spector, Tim, Aulchenko, Yurii S, Lauc, Gordan, Hayward, Caroline, Biostatistiek Onderzoek, JC onderzoeksprogramma Methodologie, Klarić, Lucija, Tsepilov, Yakov A, Stanton, Chloe M, Mangino, Massimo, Sikka, Timo Tõnis, Esko, Tõnu, Pakhomov, Eugene, Salo, Perttu, Deelen, Joris, McGurnaghan, Stuart J, Keser, Toma, Vučković, Frano, Ugrina, Ivo, Krištić, Jasminka, Gudelj, Ivan, Štambuk, Jerko, Plomp, Rosina, Pučić-Baković, Maja, Pavić, Tamara, Vilaj, Marija, Trbojević-Akmačić, Irena, Drake, Camilla, Dobrinić, Paula, Mlinarec, Jelena, Jelušić, Barbara, Richmond, Anne, Timofeeva, Maria, Grishchenko, Alexander K, Dmitrieva, Julia, Bermingham, Mairead L, Sharapov, Sodbo Zh, Farrington, Susan M, Theodoratou, Evropi, Uh, Hae-Won, Beekman, Marian, Slagboom, Eline P, Louis, Edouard, Georges, Michel, Wuhrer, Manfred, Colhoun, Helen M, Dunlop, Malcolm G, Perola, Markus, Fischer, Krista, Polasek, Ozren, Campbell, Harry, Rudan, Igor, Wilson, James F, Zoldoš, Vlatka, Vitart, Veronique, Spector, Tim, Aulchenko, Yurii S, Lauc, Gordan, and Hayward, Caroline

Published
2020

42. Prediction of vascular aging based on smartphone acquired PPG signals

Author

Electrofysiologie, Team Medisch, Verpleegafdeling CTC Team 2, Circulatory Health, Biostatistiek Onderzoek, JC onderzoeksprogramma Methodologie, Dall'Olio, Lorenzo, Curti, Nico, Remondini, Daniel, Safi Harb, Yosef, Asselbergs, Folkert W, Castellani, Gastone, Uh, Hae-Won, Electrofysiologie, Team Medisch, Verpleegafdeling CTC Team 2, Circulatory Health, Biostatistiek Onderzoek, JC onderzoeksprogramma Methodologie, Dall'Olio, Lorenzo, Curti, Nico, Remondini, Daniel, Safi Harb, Yosef, Asselbergs, Folkert W, Castellani, Gastone, and Uh, Hae-Won

Published
2020

43. Low IgA Associated With Oropharyngeal Microbiota Changes and Lung Disease in Primary Antibody Deficiency

Author

MS Reumatologie/Immunologie/Infectie, Infection & Immunity, MS Radiologie, MS Interne Geneeskunde, Immuno/reuma patientenzorg, Child Health, MMB RZN, MMB Research line 2, MMB, Researchgr. Systems Radiology, Circulatory Health, Regenerative Medicine and Stem Cells, Cancer, Biostatistiek Onderzoek, JC onderzoeksprogramma Methodologie, Berbers, Roos Marijn, Mohamed Hoesein, Firdaus A.A., Ellerbroek, Pauline M., van Montfrans, Joris M., Dalm, Virgil A.S.H., van Hagen, P. Martin, Paganelli, Fernanda L., Viveen, Marco C., Rogers, Malbert R.C., de Jong, Pim A., Uh, Hae Won, Willems, Rob J.L., Leavis, Helen L., MS Reumatologie/Immunologie/Infectie, Infection & Immunity, MS Radiologie, MS Interne Geneeskunde, Immuno/reuma patientenzorg, Child Health, MMB RZN, MMB Research line 2, MMB, Researchgr. Systems Radiology, Circulatory Health, Regenerative Medicine and Stem Cells, Cancer, Biostatistiek Onderzoek, JC onderzoeksprogramma Methodologie, Berbers, Roos Marijn, Mohamed Hoesein, Firdaus A.A., Ellerbroek, Pauline M., van Montfrans, Joris M., Dalm, Virgil A.S.H., van Hagen, P. Martin, Paganelli, Fernanda L., Viveen, Marco C., Rogers, Malbert R.C., de Jong, Pim A., Uh, Hae Won, Willems, Rob J.L., and Leavis, Helen L.

Published
2020

44. Choosing proper normalization is essential for discovery of sparse glycan biomarkers

Author

Biostatistiek Onderzoek, JC onderzoeksprogramma Methodologie, Uh, Hae-Won, Klarić, Lucija, Ugrina, Ivo, Lauc, Gordan, Smilde, Age K, Houwing-Duistermaat, Jeanine J, Biostatistiek Onderzoek, JC onderzoeksprogramma Methodologie, Uh, Hae-Won, Klarić, Lucija, Ugrina, Ivo, Lauc, Gordan, Smilde, Age K, and Houwing-Duistermaat, Jeanine J

Published
2020

46. Lipidomics of familial longevity

Author

Gonzalez-Covarrubias, Vanessa, Beekman, Marian, Uh, Hae-Won, Dane, Adrie, Troost, Jorne, Paliukhovich, Iryna, van der Kloet, Frans M., Houwing-Duistermaat, Jeanine, Vreeken, Rob J., Hankemeier, Thomas, and Slagboom, Eline P.

Published
2013
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47. Genome-wide association study identifies a single major locus contributing to survival into old age; the APOE locus revisited

Author

Deelen, Joris, Beekman, Marian, Uh, Hae-Won, Helmer, Quinta, Kuningas, Maris, Christiansen, Lene, Kremer, Dennis, van der Breggen, Ruud, Suchiman, Eka H. D., Lakenberg, Nico, van den Akker, Erik B., Passtoors, Willemijn M., Tiemeier, Henning, van Heemst, Diana, de Craen, Anton J., Rivadeneira, Fernando, de Geus, Eco J, Perola, Markus, van der Ouderaa, Frans J., Gunn, David A., Boomsma, Dorret I., Uitterlinden, André G., Christensen, Kaare, van Duijn, Cornelia M., Heijmans, Bastiaan T., Houwing-Duistermaat, Jeanine J., Westendorp, Rudi G. J., and Slagboom, Eline P.

Published
2011
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49. Low IgA Associated With Oropharyngeal Microbiota Changes and Lung Disease in Primary Antibody Deficiency

Author

Berbers, Roos-Marijn, primary, Mohamed Hoesein, Firdaus A. A., additional, Ellerbroek, Pauline M., additional, van Montfrans, Joris M., additional, Dalm, Virgil A. S. H., additional, van Hagen, P. Martin, additional, Paganelli, Fernanda L., additional, Viveen, Marco C., additional, Rogers, Malbert R. C., additional, de Jong, Pim A., additional, Uh, Hae-Won, additional, Willems, Rob J. L., additional, and Leavis, Helen L., additional

Published
2020
Full Text
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