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

Author

Chauhan, G., Adams, H.H.H., Satizabal, C.L., Bis, J.C., Teumer, A., Sargurupremraj, M., Hofer, E., Trompet, S., Hilal, S., Smith, A.V., Jian, X.Q., Malik, R., Traylor, M., Pulit, S.L., Amouyel, P., Mazoyer, B., Zhu, Y.C., Kaffashian, S., Schilling, S., Beecham, G.W., Montine, T.J., Schellenberg, G.D., Kjartansson, O., Gudnason, V., Knopman, D.S., Griswold, M.E., Windham, B.G., Gottesman, R.F., Mosley, T.H., Schmidt, R., Saba, Y., Schmidt, H., Takeuchi, F., Yamaguchi, S., Nabika, T., Kato, N., Rajan, K.B., Aggarwal, N.T., Jager, P.L. de, Evans, D.A., Psaty, B.M., Rotter, J.I., Rice, K., Lopez, O.L., Liao, J.M., Chen, C., Cheng, C.Y., Wong, T.Y., Ikram, M.K., Lee, S.J. van der, Amin, N., Chouraki, V., DeStefano, A.L., Aparicio, H.J., Romero, J.R., Maillard, P., DeCarli, C., Wardlaw, J.M., Hernandez, M.D.V., Luciano, M., Liewald, D., Deary, I.J., Starr, J.M., Bastin, M.E., Maniega, S.M., Slagboom, P.E., Beekman, M., Deelen, J., Uh, H.W., Lemmens, R., Brodaty, H., Wright, M.J., Ames, D., Boncoraglio, G.B., Hopewell, J.C., Beecham, A.H., Blanton, S.H., Wright, C.B., Sacco, R.L., Wen, W., Thalamuthu, A., Armstrong, N.J., Chong, E., Schofield, P.R., Kwok, J.B., Grond, J. van der, Stott, D.J., Ford, I., Jukema, J.W., Vernooij, M.W., Hofman, A., Uitterlinden, A.G., Lugt, A. van der, Wittfeld, K., Grabe, H.J., Hosten, N., Sarnowski, B. von, Volker, U., Levi, C., Jimenez-Conde, J., Sharma, P., Sudlow, C.L.M., Rosand, J., Woo, D., Cole, J.W., Meschia, J.F., Slowik, A., Thijs, V., Lindgren, A., Melander, O., Grewal, R.P., Rundek, T., Rexrode, K., Rothwell, P.M., Arnett, D.K., Jern, C., Johnson, J.A., Benavente, O.R., Wasssertheil-Smoller, S., Lee, J.M., Wong, Q., Mitchell, B.D., Rich, S.S., McArdle, P.F., Geerlings, M.I., Graaf, Y. van der, Bakker, P.I.W. de, Asselbergs, F.W., Srikanth, V., Thomson, R., McWhirter, R., Moran, C., Callisaya, M., Phan, T., Rutten-Jacobs, L.C.A., Bevan, S., Tzourio, C., Mather, K.A., Sachdev, P.S., Duijn, C.M. van, Worrall, B.B., Dichgans, M., Kittner, S.J., Markus, H.S., Ikram, M.A., Fornage, M., Launer, L.J., Seshadri, S., Longstreth, W.T., Debette, S., Stroke Genetics Network SiGN, ISGC, METASTROKE, ADGC, and CHARGE Consortium

Subjects
Meta-analysis, Mendelian Randomization, Blood Pressure, Polymorphisms, Genome-wide Association, Silent, Insights, Small Vessel Disease, Matter Hyperintensity Volume, Ischemic Stroke, Doenças Cardio e Cérebro-vasculares
Abstract

Collaborators (845): Astrid M. Vicente Free PMC article:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6369905/ Objective: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. Methods: We 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. Results: The 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. Conclusion: In 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. info:eu-repo/semantics/publishedVersion

Published
2019

4. Genomewide meta-analysis identifies loci associated with IGF-I and IGEBP-3 levels with impact on age-related traits (vol 15, pg 811, 2016)

Author

Teumer, A., Qi, Q., Nethander, M., Aschard, H., Bandinelli, S., Beekman, M., Berndt, S.I., Bidlingmaier, M., Broer, L., Cappola, A., Ceda, G.P., Chanock, S., Chen, M.H., Chen, T.C., Chen, Y.D.I., Chung, J., Miglianico, F.D.G., Eriksson, J., Ferrucci, L., Friedrich, N., Gnewuch, C., Goodarzi, M.O., Grarup, N., Guo, T., Hammer, E., Hayes, R.B., Hicks, A.A., Hofman, A., Houwing-Duistermaat, J.J., Hu, F., Hunter, D.J., Husemoen, L.L., Isaacs, A., Jacobs, K.B., Janssen, J.A.M.J.L., Jansson, J.O., Jehmlich, N., Johnson, S., Juul, A., Karlsson, M., Kilpelainen, T.O., Kovacs, P., Kraft, P., Li, C., Linneberg, A., Liu, Y., Loos, R.J.F., Lorentzon, M., Lu, Y., Maggio, M., Magi, R., Meigs, J., Mellstrom, D., Nauck, M., Newman, A.B., Pollak, M.N., Pramstaller, P.P., Prokopenko, I., Psaty, B.M., Reincke, M., Rimm, E.B., Rotter, J.I., Pierre, A.S., Schurmann, C., Seshadri, S., Sjogren, K., Slagboom, P.E., Strickler, H.D., Stumvoll, M., Suh, Y., Sun, Q., Zhang, C., Svensson, J., Tanaka, T., Tare, A., Tonjes, A., Uh, H.W., Duijn, C.M. van, Heemst, D. van, Vandenput, L., Vasan, R.S., Volker, U., Willems, S.M., Ohlsson, C., Wallaschofski, H., Kaplan, R.C., CHARGE Longevity Working Grp, and Body Composition Genetics Consorti

Published
2017

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

Author

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

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

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

Published
2011

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

Author

Leeuwen, E.M. van, Karssen, L.C., Deelen, J., Isaacs, A., Medina-Gomez, C., Mbarek, H., Kanterakis, A., Trompet, S., Postmus, I., Verweij, N., Enckevort, D.J. van, Huffman, J.E., White, C.C., Feitosa, M.F., Bartz, T.M., Manichaikul, A., Joshi, P.K., Peloso, G.M., Deelen, P., Dijk, F. van, Willemsen, G., Geus, E.J. de, Milaneschi, Y., Penninx, B.W., Francioli, L.C., Menelaou, A., Pulit, S.L., Rivadeneira, F., Hofman, A., Oostra, B.A., Franco, O.H., Leach, I. Mateo, Beekman, M., Craen, A.J. de, Uh, H.W., Trochet, H., Hocking, L.J., Porteous, D.J., Sattar, N., Packard, C.J., Buckley, B.M., Brody, J.A., Bis, J.C., Rotter, J.I., Mychaleckyj, J.C., Campbell, H., Duan, Q., Lange, L.A., Wilson, J.F., Hayward, C., Polasek, O., Vitart, V., Rudan, I., Wright, A.F., Rich, S.S., Psaty, B.M., Borecki, I.B., Kearney, P.M., Stott, D.J., Cupples, L. Adrienne, Consortium, G.o.t.N., Jukema, J.W., Harst, P. van der, Sijbrands, E.J., Hottenga, J.J., Uitterlinden, A.G., Swertz, M.A., Ommen, G.J. van, Bakker, P.I. de, Slagboom, P., Boomsma, D.I., Wijmenga, C., Duijn, C.M. van, Hehir-Kwa, J.Y., et al., Leeuwen, E.M. van, Karssen, L.C., Deelen, J., Isaacs, A., Medina-Gomez, C., Mbarek, H., Kanterakis, A., Trompet, S., Postmus, I., Verweij, N., Enckevort, D.J. van, Huffman, J.E., White, C.C., Feitosa, M.F., Bartz, T.M., Manichaikul, A., Joshi, P.K., Peloso, G.M., Deelen, P., Dijk, F. van, Willemsen, G., Geus, E.J. de, Milaneschi, Y., Penninx, B.W., Francioli, L.C., Menelaou, A., Pulit, S.L., Rivadeneira, F., Hofman, A., Oostra, B.A., Franco, O.H., Leach, I. Mateo, Beekman, M., Craen, A.J. de, Uh, H.W., Trochet, H., Hocking, L.J., Porteous, D.J., Sattar, N., Packard, C.J., Buckley, B.M., Brody, J.A., Bis, J.C., Rotter, J.I., Mychaleckyj, J.C., Campbell, H., Duan, Q., Lange, L.A., Wilson, J.F., Hayward, C., Polasek, O., Vitart, V., Rudan, I., Wright, A.F., Rich, S.S., Psaty, B.M., Borecki, I.B., Kearney, P.M., Stott, D.J., Cupples, L. Adrienne, Consortium, G.o.t.N., Jukema, J.W., Harst, P. van der, Sijbrands, E.J., Hottenga, J.J., Uitterlinden, A.G., Swertz, M.A., Ommen, G.J. van, Bakker, P.I. de, Slagboom, P., Boomsma, D.I., Wijmenga, C., Duijn, C.M. van, Hehir-Kwa, J.Y., and et al.

Abstract

Contains fulltext : 155355.pdf (publisher's version ) (Open Access)

Published
2015

8. Decreased Levels of Bisecting GlcNAc Glycoforms of IgG Are Associated with Human Longevity

Author

Ruhaak, L.R., Uh, H.W., Beekman, M., Koeleman, C.A.M., Hokke, C.H., Westendorp, R.G.J., Wuhrer, M., Houwing-Duistermaat, J.J., Slagboom, P.E., and Deelder, A.M.

Subjects
carbohydrates (lipids), human-serum structural-changes leiden longevity ovarian-cancer glycosylation oligosaccharides expression mechanisms biomarkers proteins, lipids (amino acids, peptides, and proteins)
Abstract

Background: Markers for longevity that reflect the health condition and predict healthy aging are extremely scarce. Such markers are, however, valuable in aging research. It has been shown previously that the N-glycosylation pattern of human immunoglobulin G (IgG) is age-dependent. Here we investigate whether N-linked glycans reflect early features of human longevity. Methodology/Principal Findings: The Leiden Longevity Study (LLS) consists of nonagenarian sibling pairs, their offspring, and partners of the offspring serving as control. IgG subclass specific glycosylation patterns were obtained from 1967 participants in the LLS by MALDI-TOF-MS analysis of tryptic IgG Fc glycopeptides. Several regression strategies were applied to evaluate the association of IgG glycosylation with age, sex, and longevity. The degree of galactosylation of IgG decreased with increasing age. For the galactosylated glycoforms the incidence of bisecting GlcNAc increased as a function of age. Sex-related differences were observed at ages below 60 years. Compared to males, younger females had higher galactosylation, which decreased stronger with increasing age, resulting in similar galactosylation for both sexes from 60 onwards. In younger participants (60 years), decreased levels of non-galactosylated glycoforms containing a bisecting GlcNAc reflected early features of longevity. Conclusions/Significance: We here describe IgG glycoforms associated with calendar age at all ages and the propensity for longevity before middle age. As modulation of IgG effector functions has been described for various IgG glycosylation features, a modulatory effect may be expected for the longevity marker described in this study.

Published
2010

9. Asymptotic Normality of Kernel-Type Deconvolution Estimators

Author

van Es, A.J., Uh, H.W., and Stochastics (KDV, FNWI)

Abstract

We derive asymptotic normality of kernel-type deconvolution estimators of the density, the distribution function at a fixed point, and of the probability of an interval. We consider so-called super smooth deconvolution problems where the characteristic function of the known distribution decreases exponentially, but faster than that of the Cauchy distribution. It turns out that the limit behaviour of the pointwise estimators of the density and distribution function is relatively straightforward, while the asymptotic behaviour of the estimator of the probability of an interval depends in a complicated way on the sequence of bandwidths.

Published
2005

10. Gene analysis for longitudinal family data using random-effects models

Author

Houwing-Duistermaat, J.J. (author), Helmer, Q. (author), Balliu, B. (author), Van den Akker, E.B. (author), Tsonaka, R. (author), Uh, H.W. (author), Houwing-Duistermaat, J.J. (author), Helmer, Q. (author), Balliu, B. (author), Van den Akker, E.B. (author), Tsonaka, R. (author), and Uh, H.W. (author)

Abstract

We have extended our recently developed 2-step approach for gene-based analysis to the family design and to the analysis of rare variants. The goal of this approach is to study the joint effect of multiple single-nucleotide polymorphisms that belong to a gene. First, the information in a gene is summarized by 2 variables, namely the empirical Bayes estimate capturing common variation and the number of rare variants. By using random effects for the common variants, our approach acknowledges the within-gene correlations. In the second step, the 2 summaries were included as covariates in linear mixed models. To test the null hypothesis of no association, a multivariate Wald test was applied. We analyzed the simulated data sets to assess the performance of the method. Then we applied the method to the real data set and identified a significant association between FRMD4B and diastolic blood pressure (p-value = 8.3 × 10-12)., Intelligent Systems, Electrical Engineering, Mathematics and Computer Science

Published
2014
Full Text
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11. Kernel Deconvolution

Author

Uh, H.W., Klaassen, Chris, van Es, Bert, and Stochastics (KDV, FNWI)

Published
2003

12. Loci associated with N-glycosylation of human immunoglobulin G show pleiotropy with autoimmune diseases and haematological cancers

Author

Lauc, G., Huffman, J.E., Pucic, M., Zgaga, L., Adamczyk, B., Muzinic, A., Novokmet, M., Polasek, O., Gornik, O., Kristic, J., Keser, T., Vitart, V., Scheijen, B., Uh, H.W., Molokhia, M., Patrick, A.L., McKeigue, P., Kolcic, I., Lukic, I.K., Swann, O., Leeuwen, F.N. van, Ruhaak, L.R., Houwing-Duistermaat, J.J., Slagboom, P.E., Beekman, M., Craen, A.J. de, Deelder, A.M., Zeng, Q., Wang, W., Hastie, N.D., Gyllensten, U., Wilson, J.F., Wuhrer, M., Wright, A.F., Rudd, P.M., Hayward, C., Aulchenko, Y., Campbell, H., Rudan, I., Lauc, G., Huffman, J.E., Pucic, M., Zgaga, L., Adamczyk, B., Muzinic, A., Novokmet, M., Polasek, O., Gornik, O., Kristic, J., Keser, T., Vitart, V., Scheijen, B., Uh, H.W., Molokhia, M., Patrick, A.L., McKeigue, P., Kolcic, I., Lukic, I.K., Swann, O., Leeuwen, F.N. van, Ruhaak, L.R., Houwing-Duistermaat, J.J., Slagboom, P.E., Beekman, M., Craen, A.J. de, Deelder, A.M., Zeng, Q., Wang, W., Hastie, N.D., Gyllensten, U., Wilson, J.F., Wuhrer, M., Wright, A.F., Rudd, P.M., Hayward, C., Aulchenko, Y., Campbell, H., and Rudan, I.

Abstract

Contains fulltext : 118733.pdf (publisher's version ) (Open Access), Glycosylation of immunoglobulin G (IgG) influences IgG effector function by modulating binding to Fc receptors. To identify genetic loci associated with IgG glycosylation, we quantitated N-linked IgG glycans using two approaches. After isolating IgG from human plasma, we performed 77 quantitative measurements of N-glycosylation using ultra-performance liquid chromatography (UPLC) in 2,247 individuals from four European discovery populations. In parallel, we measured IgG N-glycans using MALDI-TOF mass spectrometry (MS) in a replication cohort of 1,848 Europeans. Meta-analysis of genome-wide association study (GWAS) results identified 9 genome-wide significant loci (P<2.27 x 10(-9)) in the discovery analysis and two of the same loci (B4GALT1 and MGAT3) in the replication cohort. Four loci contained genes encoding glycosyltransferases (ST6GAL1, B4GALT1, FUT8, and MGAT3), while the remaining 5 contained genes that have not been previously implicated in protein glycosylation (IKZF1, IL6ST-ANKRD55, ABCF2-SMARCD3, SUV420H1, and SMARCB1-DERL3). However, most of them have been strongly associated with autoimmune and inflammatory conditions (e.g., systemic lupus erythematosus, rheumatoid arthritis, ulcerative colitis, Crohn's disease, diabetes type 1, multiple sclerosis, Graves' disease, celiac disease, nodular sclerosis) and/or haematological cancers (acute lymphoblastic leukaemia, Hodgkin lymphoma, and multiple myeloma). Follow-up functional experiments in haplodeficient Ikzf1 knock-out mice showed the same general pattern of changes in IgG glycosylation as identified in the meta-analysis. As IKZF1 was associated with multiple IgG N-glycan traits, we explored biomarker potential of affected N-glycans in 101 cases with SLE and 183 matched controls and demonstrated substantial discriminative power in a ROC-curve analysis (area under the curve = 0.842). Our study shows that it is possible to identify new loci that control glycosylation of a single plasma protein using GWAS. Th

Published
2013

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

Author

Deelen, J., Uh, H.W., Monajemi, R., Heemst, D. van, Thijssen, P.E., Bohringer, S., Akker, E.B. van den, Craen, A.J. de, Rivadeneira, F., Uitterlinden, A.G., Westendorp, R.G.J., Goeman, J.J., Slagboom, P.E., Houwing-Duistermaat, J.J., Beekman, M., Deelen, J., Uh, H.W., Monajemi, R., Heemst, D. van, Thijssen, P.E., Bohringer, S., Akker, E.B. van den, Craen, A.J. de, Rivadeneira, F., Uitterlinden, A.G., Westendorp, R.G.J., Goeman, J.J., Slagboom, P.E., Houwing-Duistermaat, J.J., and Beekman, M.

Abstract

Item does not contain fulltext, In genome-wide association studies (GWAS) of complex traits, single SNP analysis is still the most applied approach. However, the identified SNPs have small effects and provide limited biological insight. A more appropriate approach to interpret GWAS data of complex traits is to analyze the combined effect of a SNP set grouped per pathway or gene region. We used this approach to study the joint effect on human longevity of genetic variation in two candidate pathways, the insulin/insulin-like growth factor (IGF-1) signaling (IIS) pathway and the telomere maintenance (TM) pathway. For the analyses, we used genotyped GWAS data of 403 unrelated nonagenarians from long-lived sibships collected in the Leiden Longevity Study and 1,670 younger population controls. We analyzed 1,021 SNPs in 68 IIS pathway genes and 88 SNPs in 13 TM pathway genes using four self-contained pathway tests (PLINK set-based test, Global test, GRASS and SNP ratio test). Although we observed small differences between the results of the different pathway tests, they showed consistent significant association of the IIS and TM pathway SNP sets with longevity. Analysis of gene SNP sets from these pathways indicates that the association of the IIS pathway is scattered over several genes (AKT1, AKT3, FOXO4, IGF2, INS, PIK3CA, SGK, SGK2, and YWHAG), while the association of the TM pathway seems to be mainly determined by one gene (POT1). In conclusion, this study shows that genetic variation in genes involved in the IIS and TM pathways is associated with human longevity.

Published
2013

14. 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

DIAGRAM+ Consortium, MAGIC Consortium, GLGC Investigators, MuTHER Consortium, DIAGRAM Consortium, GIANT Consortium, Global B Pgen Consortium, Procardis Consortium, MAGIC investigators, GLGC Consortium, Olson, J., Kronmal, R., Robbins, J., Chaves, PH., Burke, G., Kuller, LH., Tracy, R., Gottdiener, J., Prineas, R., Becker, JT., Enright, P., Klein, R., O'Leary, DH., Dastani, Z., Hivert, M.F., Timpson, N., Perry, J.R., Yuan, X., Scott, R.A., Henneman, P., Heid, I.M., Kizer, J.R., Lyytikäinen, L.P., Fuchsberger, C., Tanaka, T., Morris, A.P., Small, K., Isaacs, A., Beekman, M., Coassin, S., Lohman, K., Qi, L., Kanoni, S., Pankow, J.S., Uh, H.W., Wu, Y., Bidulescu, A., Rasmussen-Torvik, L.J., Greenwood, C.M., Ladouceur, M., Grimsby, J., Manning, A.K., Liu, C.T., Kooner, J., Mooser, V.E., Vollenweider, P., Kapur, K.A., Chambers, J., Wareham, N.J., Langenberg, C., Frants, R., Willems-Vandijk, K., Oostra, B.A., Willems, S.M., Lamina, C., Winkler, T.W., Psaty, B.M., Tracy, R.P., Brody, J., Chen, I., Viikari, J., Kähönen, M., Pramstaller, P.P., Evans, D.M., St Pourcain, B., Sattar, N., Wood, A.R., Bandinelli, S., Carlson, O.D., Egan, J.M., Böhringer, S., van Heemst, D., Kedenko, L., Kristiansson, K., Nuotio, M.L., Loo, B.M., Harris, T., Garcia, M., Kanaya, A., Haun, M., Klopp, N., Wichmann, H.E., Deloukas, P., Katsareli, E., Couper, D.J., Duncan, B.B., Kloppenburg, M., Adair, L.S., Borja, J.B., Wilson, J.G., Musani, S., Guo, X., Johnson, T., Semple, R., Teslovich, T.M., Allison, M.A., Redline, S., Buxbaum, S.G., Mohlke, K.L., Meulenbelt, I., Ballantyne, C.M., Dedoussis, G.V., Hu, F.B., Liu, Y., Paulweber, B., Spector, T.D., Slagboom, P.E., Ferrucci, L., Jula, A., Perola, M., Raitakari, O., Florez, J.C., Salomaa, V., Eriksson, J.G., Frayling, T.M., Hicks, A.A., Lehtimäki, T., Smith, G.D., Siscovick, D.S., Kronenberg, F., van Duijn, C., Loos, R.J., Waterworth, D.M., Meigs, J.B., Dupuis, J., Richards, J.B., Voight, B.F., Scott, L.J., Steinthorsdottir, V., Dina, C., Welch, R.P., Zeggini, E., Huth, C., Aulchenko, Y.S., Thorleifsson, G., McCulloch, L.J., Ferreira, T., Grallert, H., Amin, N., Wu, G., Willer, C.J., Raychaudhuri, S., McCarroll, S.A., Hofmann, O.M., Segrè, A.V., van Hoek, M., Navarro, P., Ardlie, K., Balkau, B., Benediktsson, R., Bennett, A.J., Blagieva, R., Boerwinkle, E., Bonnycastle, L.L., Boström, K.B., Bravenboer, B., Bumpstead, S., Burtt, N.P., Charpentier, G., Chines, P.S., Cornelis, M., Crawford, G., Doney, A.S., Elliott, K.S., Elliott, A.L., Erdos, M.R., Fox, C.S., Franklin, C.S., Ganser, M., Gieger, C., Grarup, N., Green, T., Griffin, S., Groves, C.J., Guiducci, C., Hadjadj, S., Hassanali, N., Herder, C., Isomaa, B., Jackson, A.U., Johnson, P.R., Jørgensen, T., Kao, W.H., Kong, A., Kraft, P., Kuusisto, J., Lauritzen, T., Li, M., Lieverse, A., Lindgren, C.M., Lyssenko, V., Marre, M., Meitinger, T., Midthjell, K., Morken, M.A., Narisu, N., Nilsson, P., Owen, K.R., Payne, F., Petersen, A.K., Platou, C., Proença, C., Prokopenko, I., Rathmann, W., Rayner, N.W., Robertson, N.R., Rocheleau, G., Roden, M., Sampson, M.J., Saxena, R., Shields, B.M., Shrader, P., Sigurdsson, G., Sparsø, T., Strassburger, K., Stringham, H.M., Sun, Q., Swift, A.J., Thorand, B., Tichet, J., Tuomi, T., van Dam, R.M., van Haeften, T.W., van Herpt, T., van Vliet-Ostaptchouk, J.V., Walters, G.B., Weedon, M.N., Wijmenga, C., Witteman, J., Bergman, R.N., Cauchi, S., Collins, F.S., Gloyn, A.L., Gyllensten, U., Hansen, T., Hide, W.A., Hitman, G.A., Hofman, A., Hunter, D.J., Hveem, K., Laakso, M., Morris, A.D., Palmer, C.N., Rudan, I., Sijbrands, E., Stein, L.D., Tuomilehto, J., Uitterlinden, A., Walker, M., Watanabe, R.M., Abecasis, G.R., Boehm, B.O., Campbell, H., Daly, M.J., Hattersley, A.T., Pedersen, O., Barroso, I., Groop, L., Sladek, R., Thorsteinsdottir, U., Wilson, J.F., Illig, T., Froguel, P., van Duijn, C.M., Stefansson, K., Altshuler, D., Boehnke, M., McCarthy, M.I., Soranzo, N., Wheeler, E., Glazer, N.L., Bouatia-Naji, N., Mägi, R., Randall, J., Elliott, P., Rybin, D., Dehghan, A., Hottenga, J.J., Song, K., Goel, A., Lajunen, T., Doney, A., Cavalcanti-Proença, C., Kumari, M., Timpson, N.J., Zabena, C., Ingelsson, E., An, P., O'Connell, J., Luan, J., Elliott, A., Roccasecca, R.M., Pattou, F., Sethupathy, P., Ariyurek, Y., Barter, P., Beilby, J.P., Ben-Shlomo, Y., Bergmann, S., Bochud, M., Bonnefond, A., Borch-Johnsen, K., Böttcher, Y., Brunner, E., Bumpstead, S.J., Chen, Y.D., Chines, P., Clarke, R., Coin, L.J., Cooper, M.N., Crisponi, L., Day, I.N., de Geus, E.J., Delplanque, J., Fedson, A.C., Fischer-Rosinsky, A., Forouhi, N.G., Franzosi, M.G., Galan, P., Goodarzi, M.O., Graessler, J., Grundy, S., Gwilliam, R., Hallmans, G., Hammond, N., Han, X., Hartikainen, A.L., Hayward, C., Heath, S.C., Hercberg, S., Hillman, D.R., Hingorani, A.D., Hui, J., Hung, J., Kaakinen, M., Kaprio, J., Kesaniemi, Y.A., Kivimaki, M., Knight, B., Koskinen, S., Kovacs, P., Kyvik, K.O., Lathrop, G.M., Lawlor, D.A., Le Bacquer, O., Lecoeur, C., Li, Y., Mahley, R., Mangino, M., Martínez-Larrad, M.T., McAteer, J.B., McPherson, R., Meisinger, C., Melzer, D., Meyre, D., Mitchell, B.D., Mukherjee, S., Naitza, S., Neville, M.J., Orrù, M., Pakyz, R., Paolisso, G., Pattaro, C., Pearson, D., Peden, J.F., Pedersen, N.L., Pfeiffer, A.F., Pichler, I., Polasek, O., Posthuma, D., Potter, S.C., Pouta, A., Province, M.A., Rice, K., Ripatti, S., Rivadeneira, F., Rolandsson, O., Sandbaek, A., Sandhu, M., Sanna, S., Sayer, A.A., Scheet, P., Seedorf, U., Sharp, S.J., Shields, B., Sigurðsson, G., Sijbrands, E.J., Silveira, A., Simpson, L., Singleton, A., Smith, N.L., Sovio, U., Swift, A., Syddall, H., Syvänen, A.C., Tönjes, A., Uitterlinden, A.G., van Dijk, K.W., Varma, D., Visvikis-Siest, S., Vitart, V., Vogelzangs, N., Waeber, G., Wagner, P.J., Walley, A., Ward, K.L., Watkins, H., Wild, S.H., Willemsen, G., Witteman, J.C., Yarnell, J.W., Zelenika, D., Zethelius, B., Zhai, G., Zhao, J.H., Zillikens, M.C., Borecki, I.B., Meneton, P., Magnusson, P.K., Nathan, D.M., Williams, G.H., Silander, K., Bornstein, S.R., Schwarz, P., Spranger, J., Karpe, F., Shuldiner, A.R., Cooper, C., Serrano-Ríos, M., Lind, L., Palmer, L.J., Franks, P.W., Ebrahim, S., Marmot, M., Wright, A.F., Stumvoll, M., Hamsten, A., Buchanan, T.A., Valle, T.T., Rotter, J.I., Penninx, B.W., Boomsma, D.I., Cao, A., Scuteri, A., Schlessinger, D., Uda, M., Ruokonen, A., Jarvelin, M.R., Peltonen, L., Mooser, V., Musunuru, K., Smith, A.V., Edmondson, A.C., Stylianou, I.M., Koseki, M., Pirruccello, J.P., Chasman, D.I., Johansen, C.T., Fouchier, S.W., Peloso, G.M., Barbalic, M., Ricketts, S.L., Bis, J.C., Feitosa, M.F., Orho-Melander, M., Melander, O., Li, X., Cho, Y.S., Go, M.J., Kim, Y.J., Lee, J.Y., Park, T., Kim, K., Sim, X., Ong, R.T., Croteau-Chonka, D.C., Lange, L.A., Smith, J.D., Ziegler, A., Zhang, W., Zee, R.Y., Whitfield, J.B., Thompson, J.R., Surakka, I., Smit, J.H., Sinisalo, J., Scott, J., Saharinen, J., Sabatti, C., Rose, L.M., Roberts, R., Rieder, M., Parker, A.N., Pare, G., O'Donnell, C.J., Nieminen, M.S., Nickerson, D.A., Montgomery, G.W., McArdle, W., Masson, D., Martin, N.G., Marroni, F., Lucas, G., Luben, R., Lokki, M.L., Lettre, G., Launer, L.J., Lakatta, E.G., Laaksonen, R., König, I.R., Khaw, K.T., Kaplan, L.M., Johansson, Å., Janssens, A.C., Igl, W., Hovingh, G.K., Hengstenberg, C., Havulinna, A.S., Hastie, N.D., Harris, T.B., Haritunians, T., Hall, A.S., Groop, L.C., Gonzalez, E., Freimer, N.B., Erdmann, J., Ejebe, K.G., Döring, A., Dominiczak, A.F., Demissie, S., de Faire, U., Caulfield, M.J., Boekholdt, S.M., Assimes, T.L., Quertermous, T., Seielstad, M., Wong, T.Y., Tai, E.S., Feranil, A.B., Kuzawa, C.W., Taylor, H.A., Gabriel, S.B., Holm, H., Gudnason, V., Krauss, R.M., Ordovas, J.M., Munroe, P.B., Kooner, J.S., Tall, A.R., Hegele, R.A., Kastelein, J.J., Schadt, E.E., Strachan, D.P., Reilly, M.P., Samani, N.J., Schunkert, H., Cupples, L.A., Sandhu, M.S., Ridker, P.M., Rader, D.J., Kathiresan, S., DIAGRAM+ Consortium, MAGIC Consortium, GLGC Investigators, MuTHER Consortium, DIAGRAM Consortium, GIANT Consortium, Global B Pgen Consortium, Procardis Consortium, MAGIC investigators, GLGC Consortium, Olson, J., Kronmal, R., Robbins, J., Chaves, PH., Burke, G., Kuller, LH., Tracy, R., Gottdiener, J., Prineas, R., Becker, JT., Enright, P., Klein, R., O'Leary, DH., Dastani, Z., Hivert, M.F., Timpson, N., Perry, J.R., Yuan, X., Scott, R.A., Henneman, P., Heid, I.M., Kizer, J.R., Lyytikäinen, L.P., Fuchsberger, C., Tanaka, T., Morris, A.P., Small, K., Isaacs, A., Beekman, M., Coassin, S., Lohman, K., Qi, L., Kanoni, S., Pankow, J.S., Uh, H.W., Wu, Y., Bidulescu, A., Rasmussen-Torvik, L.J., Greenwood, C.M., Ladouceur, M., Grimsby, J., Manning, A.K., Liu, C.T., Kooner, J., Mooser, V.E., Vollenweider, P., Kapur, K.A., Chambers, J., Wareham, N.J., Langenberg, C., Frants, R., Willems-Vandijk, K., Oostra, B.A., Willems, S.M., Lamina, C., Winkler, T.W., Psaty, B.M., Tracy, R.P., Brody, J., Chen, I., Viikari, J., Kähönen, M., Pramstaller, P.P., Evans, D.M., St Pourcain, B., Sattar, N., Wood, A.R., Bandinelli, S., Carlson, O.D., Egan, J.M., Böhringer, S., van Heemst, D., Kedenko, L., Kristiansson, K., Nuotio, M.L., Loo, B.M., Harris, T., Garcia, M., Kanaya, A., Haun, M., Klopp, N., Wichmann, H.E., Deloukas, P., Katsareli, E., Couper, D.J., Duncan, B.B., Kloppenburg, M., Adair, L.S., Borja, J.B., Wilson, J.G., Musani, S., Guo, X., Johnson, T., Semple, R., Teslovich, T.M., Allison, M.A., Redline, S., Buxbaum, S.G., Mohlke, K.L., Meulenbelt, I., Ballantyne, C.M., Dedoussis, G.V., Hu, F.B., Liu, Y., Paulweber, B., Spector, T.D., Slagboom, P.E., Ferrucci, L., Jula, A., Perola, M., Raitakari, O., Florez, J.C., Salomaa, V., Eriksson, J.G., Frayling, T.M., Hicks, A.A., Lehtimäki, T., Smith, G.D., Siscovick, D.S., Kronenberg, F., van Duijn, C., Loos, R.J., Waterworth, D.M., Meigs, J.B., Dupuis, J., Richards, J.B., Voight, B.F., Scott, L.J., Steinthorsdottir, V., Dina, C., Welch, R.P., Zeggini, E., Huth, C., Aulchenko, Y.S., Thorleifsson, G., McCulloch, L.J., Ferreira, T., Grallert, H., Amin, N., Wu, G., Willer, C.J., Raychaudhuri, S., McCarroll, S.A., Hofmann, O.M., Segrè, A.V., van Hoek, M., Navarro, P., Ardlie, K., Balkau, B., Benediktsson, R., Bennett, A.J., Blagieva, R., Boerwinkle, E., Bonnycastle, L.L., Boström, K.B., Bravenboer, B., Bumpstead, S., Burtt, N.P., Charpentier, G., Chines, P.S., Cornelis, M., Crawford, G., Doney, A.S., Elliott, K.S., Elliott, A.L., Erdos, M.R., Fox, C.S., Franklin, C.S., Ganser, M., Gieger, C., Grarup, N., Green, T., Griffin, S., Groves, C.J., Guiducci, C., Hadjadj, S., Hassanali, N., Herder, C., Isomaa, B., Jackson, A.U., Johnson, P.R., Jørgensen, T., Kao, W.H., Kong, A., Kraft, P., Kuusisto, J., Lauritzen, T., Li, M., Lieverse, A., Lindgren, C.M., Lyssenko, V., Marre, M., Meitinger, T., Midthjell, K., Morken, M.A., Narisu, N., Nilsson, P., Owen, K.R., Payne, F., Petersen, A.K., Platou, C., Proença, C., Prokopenko, I., Rathmann, W., Rayner, N.W., Robertson, N.R., Rocheleau, G., Roden, M., Sampson, M.J., Saxena, R., Shields, B.M., Shrader, P., Sigurdsson, G., Sparsø, T., Strassburger, K., Stringham, H.M., Sun, Q., Swift, A.J., Thorand, B., Tichet, J., Tuomi, T., van Dam, R.M., van Haeften, T.W., van Herpt, T., van Vliet-Ostaptchouk, J.V., Walters, G.B., Weedon, M.N., Wijmenga, C., Witteman, J., Bergman, R.N., Cauchi, S., Collins, F.S., Gloyn, A.L., Gyllensten, U., Hansen, T., Hide, W.A., Hitman, G.A., Hofman, A., Hunter, D.J., Hveem, K., Laakso, M., Morris, A.D., Palmer, C.N., Rudan, I., Sijbrands, E., Stein, L.D., Tuomilehto, J., Uitterlinden, A., Walker, M., Watanabe, R.M., Abecasis, G.R., Boehm, B.O., Campbell, H., Daly, M.J., Hattersley, A.T., Pedersen, O., Barroso, I., Groop, L., Sladek, R., Thorsteinsdottir, U., Wilson, J.F., Illig, T., Froguel, P., van Duijn, C.M., Stefansson, K., Altshuler, D., Boehnke, M., McCarthy, M.I., Soranzo, N., Wheeler, E., Glazer, N.L., Bouatia-Naji, N., Mägi, R., Randall, J., Elliott, P., Rybin, D., Dehghan, A., Hottenga, J.J., Song, K., Goel, A., Lajunen, T., Doney, A., Cavalcanti-Proença, C., Kumari, M., Timpson, N.J., Zabena, C., Ingelsson, E., An, P., O'Connell, J., Luan, J., Elliott, A., Roccasecca, R.M., Pattou, F., Sethupathy, P., Ariyurek, Y., Barter, P., Beilby, J.P., Ben-Shlomo, Y., Bergmann, S., Bochud, M., Bonnefond, A., Borch-Johnsen, K., Böttcher, Y., Brunner, E., Bumpstead, S.J., Chen, Y.D., Chines, P., Clarke, R., Coin, L.J., Cooper, M.N., Crisponi, L., Day, I.N., de Geus, E.J., Delplanque, J., Fedson, A.C., Fischer-Rosinsky, A., Forouhi, N.G., Franzosi, M.G., Galan, P., Goodarzi, M.O., Graessler, J., Grundy, S., Gwilliam, R., Hallmans, G., Hammond, N., Han, X., Hartikainen, A.L., Hayward, C., Heath, S.C., Hercberg, S., Hillman, D.R., Hingorani, A.D., Hui, J., Hung, J., Kaakinen, M., Kaprio, J., Kesaniemi, Y.A., Kivimaki, M., Knight, B., Koskinen, S., Kovacs, P., Kyvik, K.O., Lathrop, G.M., Lawlor, D.A., Le Bacquer, O., Lecoeur, C., Li, Y., Mahley, R., Mangino, M., Martínez-Larrad, M.T., McAteer, J.B., McPherson, R., Meisinger, C., Melzer, D., Meyre, D., Mitchell, B.D., Mukherjee, S., Naitza, S., Neville, M.J., Orrù, M., Pakyz, R., Paolisso, G., Pattaro, C., Pearson, D., Peden, J.F., Pedersen, N.L., Pfeiffer, A.F., Pichler, I., Polasek, O., Posthuma, D., Potter, S.C., Pouta, A., Province, M.A., Rice, K., Ripatti, S., Rivadeneira, F., Rolandsson, O., Sandbaek, A., Sandhu, M., Sanna, S., Sayer, A.A., Scheet, P., Seedorf, U., Sharp, S.J., Shields, B., Sigurðsson, G., Sijbrands, E.J., Silveira, A., Simpson, L., Singleton, A., Smith, N.L., Sovio, U., Swift, A., Syddall, H., Syvänen, A.C., Tönjes, A., Uitterlinden, A.G., van Dijk, K.W., Varma, D., Visvikis-Siest, S., Vitart, V., Vogelzangs, N., Waeber, G., Wagner, P.J., Walley, A., Ward, K.L., Watkins, H., Wild, S.H., Willemsen, G., Witteman, J.C., Yarnell, J.W., Zelenika, D., Zethelius, B., Zhai, G., Zhao, J.H., Zillikens, M.C., Borecki, I.B., Meneton, P., Magnusson, P.K., Nathan, D.M., Williams, G.H., Silander, K., Bornstein, S.R., Schwarz, P., Spranger, J., Karpe, F., Shuldiner, A.R., Cooper, C., Serrano-Ríos, M., Lind, L., Palmer, L.J., Franks, P.W., Ebrahim, S., Marmot, M., Wright, A.F., Stumvoll, M., Hamsten, A., Buchanan, T.A., Valle, T.T., Rotter, J.I., Penninx, B.W., Boomsma, D.I., Cao, A., Scuteri, A., Schlessinger, D., Uda, M., Ruokonen, A., Jarvelin, M.R., Peltonen, L., Mooser, V., Musunuru, K., Smith, A.V., Edmondson, A.C., Stylianou, I.M., Koseki, M., Pirruccello, J.P., Chasman, D.I., Johansen, C.T., Fouchier, S.W., Peloso, G.M., Barbalic, M., Ricketts, S.L., Bis, J.C., Feitosa, M.F., Orho-Melander, M., Melander, O., Li, X., Cho, Y.S., Go, M.J., Kim, Y.J., Lee, J.Y., Park, T., Kim, K., Sim, X., Ong, R.T., Croteau-Chonka, D.C., Lange, L.A., Smith, J.D., Ziegler, A., Zhang, W., Zee, R.Y., Whitfield, J.B., Thompson, J.R., Surakka, I., Smit, J.H., Sinisalo, J., Scott, J., Saharinen, J., Sabatti, C., Rose, L.M., Roberts, R., Rieder, M., Parker, A.N., Pare, G., O'Donnell, C.J., Nieminen, M.S., Nickerson, D.A., Montgomery, G.W., McArdle, W., Masson, D., Martin, N.G., Marroni, F., Lucas, G., Luben, R., Lokki, M.L., Lettre, G., Launer, L.J., Lakatta, E.G., Laaksonen, R., König, I.R., Khaw, K.T., Kaplan, L.M., Johansson, Å., Janssens, A.C., Igl, W., Hovingh, G.K., Hengstenberg, C., Havulinna, A.S., Hastie, N.D., Harris, T.B., Haritunians, T., Hall, A.S., Groop, L.C., Gonzalez, E., Freimer, N.B., Erdmann, J., Ejebe, K.G., Döring, A., Dominiczak, A.F., Demissie, S., de Faire, U., Caulfield, M.J., Boekholdt, S.M., Assimes, T.L., Quertermous, T., Seielstad, M., Wong, T.Y., Tai, E.S., Feranil, A.B., Kuzawa, C.W., Taylor, H.A., Gabriel, S.B., Holm, H., Gudnason, V., Krauss, R.M., Ordovas, J.M., Munroe, P.B., Kooner, J.S., Tall, A.R., Hegele, R.A., Kastelein, J.J., Schadt, E.E., Strachan, D.P., Reilly, M.P., Samani, N.J., Schunkert, H., Cupples, L.A., Sandhu, M.S., Ridker, P.M., Rader, D.J., and Kathiresan, S.

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<3×10(-4)). We next developed a multi-SNP genotypic risk score to test the association of adiponectin decreasing risk alleles on metabolic traits and diseases using consortia-level meta-analytic data. This risk score was associated with increased risk of T2D (p = 4.3×10(-3), n = 22,044), increased triglycerides (p = 2.6×10(-14), n = 93,440), increased waist-to-hip ratio (p = 1.8×10(-5), n = 77,167), increased glucose two hours post oral glucose tolerance testing (p = 4.4×10(-3), n = 15,234), increased fasting insulin (p = 0.015, n = 48,238), but with lower in HDL-cholesterol concentrations (p = 4.5×10(-13), n = 96,748) and decreased BMI (p = 1.4×10(-4), n = 121,335). These findings identify novel genetic determinants of adiponectin levels, which, taken together, influence risk of T2D and markers of insulin resistance.

Published
2012

15. 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, Z., Hivert, M.F., Timpson, N.J., Perry, J.R., Yuan, X., Scott, R.A., Henneman, P., Heid, I.M., Kizer, J.R., Lyytikäinen, L.P., Fuchsberger, C., Tanaka, T., Morris, A.P., Small, K., Isaacs, A., Beekman, M., Coassin, S., Lohman, K., Qi, L., Kanoni, S., Pankow, J.S., Uh, H.W., Wu, Y., Bidulescu, A., Rasmussen-Torvik, L.J., Greenwood, C.M., Ladouceur, M., Grimsby, J., Manning, A.K., Liu, C.T., Kooner, J., Mooser, V.E., Vollenweider, P., Kapur, K.A., Chambers, J., Wareham, N.J., Langenberg, C., Frants, R., Willems van Dijk, K., Oostra, B.A., Willems, S.M., Lamina, C., Winkler, T.W., Psaty, B.M., Tracy, R.P., Brody, J., Chen, I., Viikari, J., Kähönen, M., Pramstaller, P.P., Evans, D.M., St Pourcain, B., Sattar, N., Wood, A.R., Bandinelli, S., Carlson, O.D., Egan, J.M., Böhringer, S., van Heemst, D., Kedenko, L., Kristiansson, K., Nuotio, M.L., Loo, B.M., Harris, T., Garcia, M., Kanaya, A., Haun, M., Klopp, N., Wichmann, H.E., Deloukas, P., Katsareli, E., Couper, D.J., Duncan, B.B., Kloppenburg, M., Adair, L.S., Borja, J.B., Hottenga, J.J., de Geus, E.J.C., Willemsen, G., Boomsma, D.I., Wilson, J.G., Musani, G., Guo, X., Johnson, T., Semple, R., Teslovich, T.M., Allison, M.A., Redline, S., Buxbaum, S.G., Mohlke, K.L., Meulenbelt, I., Ballantyne, C.M., Dedoussis, G.V., Hu, F.B., Liu, Y., Paulweber, B., Spector, T.D., Slagboom, P.E., Ferrucci, L., Jula, A., Perola, M., Raitakari, O., Florez, J.C., Salomaa, V., Eriksson, J.G., Frayling, T.M., Hicks, A.A., Lehtimäki, T., Smith, G.D., Siscovick, D.S., Kronenberg, F., van Duijn, C.M., Loos, R.J., Waterworth, D., Meigs, J.B., Dupuis, J., Richards, J.B., Posthuma, D., Penninx, B.W.J.H., Vogelzangs, N., Kathiresan, S., Dastani, Z., Hivert, M.F., Timpson, N.J., Perry, J.R., Yuan, X., Scott, R.A., Henneman, P., Heid, I.M., Kizer, J.R., Lyytikäinen, L.P., Fuchsberger, C., Tanaka, T., Morris, A.P., Small, K., Isaacs, A., Beekman, M., Coassin, S., Lohman, K., Qi, L., Kanoni, S., Pankow, J.S., Uh, H.W., Wu, Y., Bidulescu, A., Rasmussen-Torvik, L.J., Greenwood, C.M., Ladouceur, M., Grimsby, J., Manning, A.K., Liu, C.T., Kooner, J., Mooser, V.E., Vollenweider, P., Kapur, K.A., Chambers, J., Wareham, N.J., Langenberg, C., Frants, R., Willems van Dijk, K., Oostra, B.A., Willems, S.M., Lamina, C., Winkler, T.W., Psaty, B.M., Tracy, R.P., Brody, J., Chen, I., Viikari, J., Kähönen, M., Pramstaller, P.P., Evans, D.M., St Pourcain, B., Sattar, N., Wood, A.R., Bandinelli, S., Carlson, O.D., Egan, J.M., Böhringer, S., van Heemst, D., Kedenko, L., Kristiansson, K., Nuotio, M.L., Loo, B.M., Harris, T., Garcia, M., Kanaya, A., Haun, M., Klopp, N., Wichmann, H.E., Deloukas, P., Katsareli, E., Couper, D.J., Duncan, B.B., Kloppenburg, M., Adair, L.S., Borja, J.B., Hottenga, J.J., de Geus, E.J.C., Willemsen, G., Boomsma, D.I., Wilson, J.G., Musani, G., Guo, X., Johnson, T., Semple, R., Teslovich, T.M., Allison, M.A., Redline, S., Buxbaum, S.G., Mohlke, K.L., Meulenbelt, I., Ballantyne, C.M., Dedoussis, G.V., Hu, F.B., Liu, Y., Paulweber, B., Spector, T.D., Slagboom, P.E., Ferrucci, L., Jula, A., Perola, M., Raitakari, O., Florez, J.C., Salomaa, V., Eriksson, J.G., Frayling, T.M., Hicks, A.A., Lehtimäki, T., Smith, G.D., Siscovick, D.S., Kronenberg, F., van Duijn, C.M., Loos, R.J., Waterworth, D., Meigs, J.B., Dupuis, J., Richards, J.B., Posthuma, D., Penninx, B.W.J.H., Vogelzangs, N., and Kathiresan, S.

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

Published
2012
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16. Does treatment of intestinal helminth infections influence malaria? Background and methodology of a longitudinal study of clinical, parasitological and immunological parameters in Nangapanda, Flores, Indonesia (ImmunoSPIN Study).

Author

Wiria, A.E., Prasetyani, M.A., Hamid, F., Wammes, L.J., Lell, B., Ariawan, I., Uh, H.W., Wibowo, H., Djuardi, Y., Wahyuni, S., Sutanto, I., May, L., Luty, A.J.F., Verweij, J.J., Sartono, E., Yazdanbakhsh, M., Supali, T., Wiria, A.E., Prasetyani, M.A., Hamid, F., Wammes, L.J., Lell, B., Ariawan, I., Uh, H.W., Wibowo, H., Djuardi, Y., Wahyuni, S., Sutanto, I., May, L., Luty, A.J.F., Verweij, J.J., Sartono, E., Yazdanbakhsh, M., and Supali, T.

Abstract

Contains fulltext : 88856.pdf (publisher's version ) (Open Access), BACKGROUND: Given that helminth infections are thought to have strong immunomodulatory activity, the question whether helminth infections might affect responses to malaria antigens needs to be addressed. Different cross-sectional studies using diverse methodologies have reported that helminth infections might either exacerbate or reduce the severity of malaria attacks. The same discrepancies have been reported for parasitemia. METHODS/DESIGN: To determine the effect of geohelminth infections and their treatment on malaria infection and disease outcome, as well as on immunological parameters, the area of Nangapanda on Flores Island, Indonesia, where malaria and helminth parasites are co-endemic was selected for a longitudinal study. Here a Double-blind randomized trial will be performed, incorporating repeated treatment with albendazole (400 mg) or placebo at three monthly intervals. Household characteristic data, anthropometry, the presence of intestinal helminth and Plasmodium spp infections, and the incidence of malaria episodes are recorded. In vitro cultures of whole blood, stimulated with a number of antigens, mitogens and toll like receptor ligands provide relevant immunological parameters at baseline and following 1 and 2 years of treatment rounds. The primary outcome of the study is the prevalence of Plasmodium falciparum and P. vivax infection. The secondary outcome will be incidence and severity of malaria episodes detected via both passive and active follow-up. The tertiary outcome is the inflammatory cytokine profile in response to parasite antigens. The project also facilitates the transfer of state of the art methodologies and technologies, molecular diagnosis of parasitic diseases, immunology and epidemiology from Europe to Indonesia. DISCUSSION: The study will provide data on the effect of helminth infections on malaria. It will also give information on anthelminthic treatment efficacy and effectiveness and could help develop evidence-based policymak

Published
2010

18. Statistical modeling of an outcome variable with integrated omics data

Author

Gu, Zhujie, Sturkenboom, M.C.J.M., Houwing-Duistermaat, J.J., Uh, H.W., and Bouhaddani, S. el

Subjects
Omics research, Omics heterogeneity, Data integration, High dimensional statistics, Dimension reduction, Partial least squares, Two-stage modelling, Joint modelling, Generalized linear models, GLM-PO2PLS
Abstract

In human disease studies, it has become common to collect multiple omics datasets measured on various molecular levels. The aim is to study the underlying mechanisms of disease from different perspectives by jointly analyzing these datasets. This thesis develops statistical methodologies to model a disease outcome with two omics datasets. We consider latent variable methods for constructing low-dimensional components representing the two omics, and linear models for associating the components to a disease. The latent variable methods address the statistical challenges of high dimensionality, correlations within and between omics, and systematic differences between datasets. The linear models provide flexibility for various study designs and different distributions of disease outcomes. Both two-stage methods where latent variable model and linear model are fitted separately and one-stage methods where the two are fitted simultaneously are developed. The two-stage methods are computationally fast and offer more flexibility in the linear models, while the one-stage models provide unbiased inference results. The methods are all validated and can be used in a wide range of disease studies.

Published
2023

19. Statistical integration of diverse omics data

Author

Bouhaddani, S. el, Houwing-Duistermaat, J.J., Uh, H.W., Jongbloed, G., Slagboom, P.E., Kent, J.T., Burzykowski, T., and Leiden University

Subjects
Joint principal components, O2PLS, Dimension reduction, Latent variable methods, Omics data integration, High dimensional statistics, Probabilistic Partial Least Squares, Structural Equation Modeling
Abstract

This thesis is concerned with statistical methodology for jointly analyzing multiple types of omics data. These datasets provide information on several biological levels, and an integrated analysis can lead to a better understanding of whole biological system. Due to the strong correlations within and between datasets, high dimensionality, and systematic differences between datasets, novel methods are needed. We consider latent variable modeling where strong correlations are incorporated, dimension reduction is performed, and heterogeneity between omics data is modeled. The first part of the thesis studies current data integration methods applied to population cohorts and their software implementations. In the second part, we propose a novel probabilistic data integration framework to model the relation between omics data: PO2PLS. This framework allows for statistical inference and helps reduce overfitting. The PO2PLS framework can be used to integrate multiple omics data with various study designs.

Published
2020

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