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1. Skewed X-inactivation is common in the general female population

Author

Shvetsova, E. (Ekaterina), Sofronova, A. (Alina), Monajemi, R. (Ramin), Gagalova, K. (Kristina), Draisma, H.H.M. (Harmen), White, S.J. (Stefan), Santen, G.W.E. (Gijs), Chuva de Sousa Lopes, S.M. (Susana), Heijmans, B.T. (Bastiaan), Meurs, J. (Joyce) van, Jansen, R. (Rick), Franke, L. (Lude), Kiełbasa, S.M. (Szymon), Dunnen, J.T. (Johan) den, Hoen, P.A.C. (Peter) ‘t, BIOS consortium, GoNL consortium, Shvetsova, E. (Ekaterina), Sofronova, A. (Alina), Monajemi, R. (Ramin), Gagalova, K. (Kristina), Draisma, H.H.M. (Harmen), White, S.J. (Stefan), Santen, G.W.E. (Gijs), Chuva de Sousa Lopes, S.M. (Susana), Heijmans, B.T. (Bastiaan), Meurs, J. (Joyce) van, Jansen, R. (Rick), Franke, L. (Lude), Kiełbasa, S.M. (Szymon), Dunnen, J.T. (Johan) den, Hoen, P.A.C. (Peter) ‘t, BIOS consortium, and GoNL consortium

Abstract

X-inactivation is a well-established dosage compensation mechanism ensuring that X-chromosomal genes are expressed at comparable levels in males and females. Skewed X-inactivation is often explained by negative selection of one of the alleles. We demonstrate that imbalanced expression of the paternal and maternal X-chromosomes is common in the general population and that the random nature of the X-inactivation mechanism can be sufficient to explain the imbalance. To this end, we analyzed blood-derived RNA and whole-genome sequencing data from 79 female children and their parents from the Genome of the Netherlands project. We calculated the median ratio of the paternal over total counts at all X-chromosomal heterozygous single-nucleotide variants with coverage ≥10. We identified two individuals where the same X-chromosome was inactivated in all cells. Imbalanced expression of the two X-chromosomes (ratios ≤0.35 or ≥0.65) was observed in nearly 50% of the population. The empirically observed skewing is explained by a theoretical model where X-inactivation takes place in an embryonic stage in which eight cells give rise to the hematopoietic compartment. Genes escaping X-inactivation are expressed from both alleles and therefore demonstrate less skewing than inactivated genes. Using this characteristic, we identified three novel escapee genes (SSR4, REPS2, and SEPT6), but did not find support for many previously reported escapee genes in blood. Our collective data suggest that skewed X-inactivation is common in the general population. This may contribute to manifestation of symptoms in carriers of recessive X-linked disorders. We recommend that X-inactivation results should not be used lightly in the interpretation of X-linked variants.

Published
2018
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2. Skewed X-inactivation is common in the general female population

Author

Shvetsova, E. (Ekaterina), Sofronova, A. (Alina), Monajemi, R. (Ramin), Gagalova, K. (Kristina), Draisma, H.H.M. (Harmen), White, S.J. (Stefan), Santen, G.W.E. (Gijs), Chuva de Sousa Lopes, S.M. (Susana), Heijmans, B.T. (Bastiaan), Meurs, J. (Joyce) van, Jansen, R. (Rick), Franke, L. (Lude), Kiełbasa, S.M. (Szymon), Dunnen, J.T. (Johan) den, Hoen, P.A.C. (Peter) ‘t, BIOS consortium, GoNL consortium, Shvetsova, E. (Ekaterina), Sofronova, A. (Alina), Monajemi, R. (Ramin), Gagalova, K. (Kristina), Draisma, H.H.M. (Harmen), White, S.J. (Stefan), Santen, G.W.E. (Gijs), Chuva de Sousa Lopes, S.M. (Susana), Heijmans, B.T. (Bastiaan), Meurs, J. (Joyce) van, Jansen, R. (Rick), Franke, L. (Lude), Kiełbasa, S.M. (Szymon), Dunnen, J.T. (Johan) den, Hoen, P.A.C. (Peter) ‘t, BIOS consortium, and GoNL consortium

Abstract

X-inactivation is a well-established dosage compensation mechanism ensuring that X-chromosomal genes are expressed at comparable levels in males and females. Skewed X-inactivation is often explained by negative selection of one of the alleles. We demonstrate that imbalanced expression of the paternal and maternal X-chromosomes is common in the general population and that the random nature of the X-inactivation mechanism can be sufficient to explain the imbalance. To this end, we analyzed blood-derived RNA and whole-genome sequencing data from 79 female children and their parents from the Genome of the Netherlands project. We calculated the median ratio of the paternal over total counts at all X-chromosomal heterozygous single-nucleotide variants with coverage ≥10. We identified two individuals where the same X-chromosome was inactivated in all cells. Imbalanced expression of the two X-chromosomes (ratios ≤0.35 or ≥0.65) was observed in nearly 50% of the population. The empirically observed skewing is explained by a theoretical model where X-inactivation takes place in an embryonic stage in which eight cells give rise to the hematopoietic compartment. Genes escaping X-inactivation are expressed from both alleles and therefore demonstrate less skewing than inactivated genes. Using this characteristic, we identified three novel escapee genes (SSR4, REPS2, and SEPT6), but did not find support for many previously reported escapee genes in blood. Our collective data suggest that skewed X-inactivation is common in the general population. This may contribute to manifestation of symptoms in carriers of recessive X-linked disorders. We recommend that X-inactivation results should not be used lightly in the interpretation of X-linked variants.

Published
2018
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3. Age-related accrual of methylomic variability is linked to fundamental ageing mechanisms

Author

Slieker, R. (Roderick), Iterson, M. (Maarten) van, Luijk, R. (René), Beekman, M. (Marian), Zhernakova, A. (Alexandra), Moed, H. (Heleen), Mei, S. (Shan), Van Galen, M. (Michiel), Deelen, P. (Patrick), Bonder, M.J. (Marc), Uitterlinden, A.G. (André), Tigchelaar, E.F. (Ettje F.), Stehouwer, C.D.A. (Coen D.A.), Schalkwijk, C.G. (Casper), Kallen, C.J. van der, Hofman, A. (Albert), Heemst, D. (Diana) van, Geus, E.J.C. (Eco) de, Dongen, J. (Jenny) van, Deelen, J. (Joris), Berg, L.H. (Leonard) van den, Meurs, J.B.J. (Joyce) van, Jansen, R., Hoen, P.A.C. (Peter) 't, Franke, L. (Lude), Wijmenga, C. (Cisca), Veldink, J.H. (Jan), Swertz, M. (Morris), Greevenbroek, M.M. van, Duijn, C.M. (Cornelia) van, Boomsma, D.I. (Dorret), Slagboom, P.E. (Eline), Heijmans, B.T. (Bastiaan), Slieker, R. (Roderick), Iterson, M. (Maarten) van, Luijk, R. (René), Beekman, M. (Marian), Zhernakova, A. (Alexandra), Moed, H. (Heleen), Mei, S. (Shan), Van Galen, M. (Michiel), Deelen, P. (Patrick), Bonder, M.J. (Marc), Uitterlinden, A.G. (André), Tigchelaar, E.F. (Ettje F.), Stehouwer, C.D.A. (Coen D.A.), Schalkwijk, C.G. (Casper), Kallen, C.J. van der, Hofman, A. (Albert), Heemst, D. (Diana) van, Geus, E.J.C. (Eco) de, Dongen, J. (Jenny) van, Deelen, J. (Joris), Berg, L.H. (Leonard) van den, Meurs, J.B.J. (Joyce) van, Jansen, R., Hoen, P.A.C. (Peter) 't, Franke, L. (Lude), Wijmenga, C. (Cisca), Veldink, J.H. (Jan), Swertz, M. (Morris), Greevenbroek, M.M. van, Duijn, C.M. (Cornelia) van, Boomsma, D.I. (Dorret), Slagboom, P.E. (Eline), and Heijmans, B.T. (Bastiaan)

Abstract

Background: Epigenetic change is a hallmark of ageing but its link to ageing mechanisms in humans remains poorly understood. While DNA methylation at many CpG sites closely tracks chronological age, DNA methylation changes relevant to biological age are expected to gradually dissociate from chronological age, mirroring the increased heterogeneity in health status at older ages. Results: Here, we report on the large-scale identification of 6366 age-related variably methylated positions (aVMPs) identified in 3295 whole blood DNA methylation profiles, 2044 of which have a matching RNA-seq gene expression profile. aVMPs are enriched at polycomb repressed regions and, accordingly, methylation at those positions is associated with the expression of genes encoding components of polycomb repressive complex 2 (PRC2) in trans. Further analysis revealed trans-associations for 1816 aVMPs with an additional 854 genes. These trans-associated aVMPs are characterized by either an age-related g

Published
2016
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4. The FAIR Guiding Principles for scientific data management and stewardship

Author

Wilkinson, J.M. (Mark), Dumontier, M. (Michel), Aalbersberg, I.J. (Ijsbrand Jan), Appleton, G. (Gabrielle), Axton, M. (Myles), Baak, A. (Arie), Blomberg, N. (Niklas), Boiten, J.W. (Jan-Willem), Silva Santos, L.B. (Luiz Bonino) da, Bourne, P.E. (Philip), Bouwman, J. (Jildau), Brookes, A.J. (Anthony), Clark, T. (Tim), Crosas, M. (Mercè), Dillo, I. (Ingrid), Dumon, O. (Olivier), Edmunts, S. (Scott), Evelo, C.T. (Chris), Finkers, R. (Richard), Gonzalez-Beltran, A. (Alejandra), Gray, A. (Alastair), Groth, P. (Paul), Goble, C.A. (Carole Ann), Grethe, S. (Jeffrey), Heringa, J. (Jaap), Hoen, P.A.C. (Peter) 't, Hooft, R. (Rob), Kuhn, T. (Tobias), Kok, R. (Ruben), Kok, J. (Joost), Lusher, S.J. (Scott), Martone, M.E. (Maryann), Mons, A. (Albert), Packer, A. (Abel), Persson, B. (Bengt), Roca-Serra, P. (Philippe), Roos, M. (Marco), Schaik, R. (Rene) van, Sansone, S.A. (Susanna-Assunta), Schultes, E. (Erik), Sengstag, T. (Thierry), Slater, T. (Ted), Strawn, G. (George), Swertz, M. (Morris), Thompson, M. (Mark), Lei, J. (Johan) van der, Mulligen, E.M. (Erik) van, Velterop, J. (Jan), Waagmeester, A. (Andra), Wittenburg, P. (Peter), Wolstencroft, K. (Katherine), Zhao, J. (Jun), Mons, B. (Barend), Wilkinson, J.M. (Mark), Dumontier, M. (Michel), Aalbersberg, I.J. (Ijsbrand Jan), Appleton, G. (Gabrielle), Axton, M. (Myles), Baak, A. (Arie), Blomberg, N. (Niklas), Boiten, J.W. (Jan-Willem), Silva Santos, L.B. (Luiz Bonino) da, Bourne, P.E. (Philip), Bouwman, J. (Jildau), Brookes, A.J. (Anthony), Clark, T. (Tim), Crosas, M. (Mercè), Dillo, I. (Ingrid), Dumon, O. (Olivier), Edmunts, S. (Scott), Evelo, C.T. (Chris), Finkers, R. (Richard), Gonzalez-Beltran, A. (Alejandra), Gray, A. (Alastair), Groth, P. (Paul), Goble, C.A. (Carole Ann), Grethe, S. (Jeffrey), Heringa, J. (Jaap), Hoen, P.A.C. (Peter) 't, Hooft, R. (Rob), Kuhn, T. (Tobias), Kok, R. (Ruben), Kok, J. (Joost), Lusher, S.J. (Scott), Martone, M.E. (Maryann), Mons, A. (Albert), Packer, A. (Abel), Persson, B. (Bengt), Roca-Serra, P. (Philippe), Roos, M. (Marco), Schaik, R. (Rene) van, Sansone, S.A. (Susanna-Assunta), Schultes, E. (Erik), Sengstag, T. (Thierry), Slater, T. (Ted), Strawn, G. (George), Swertz, M. (Morris), Thompson, M. (Mark), Lei, J. (Johan) van der, Mulligen, E.M. (Erik) van, Velterop, J. (Jan), Waagmeester, A. (Andra), Wittenburg, P. (Peter), Wolstencroft, K. (Katherine), Zhao, J. (Jun), and Mons, B. (Barend)

Abstract

There is an urgent need to improve the infrastructure supporting the reuse of scholarly data. A diverse set of stakeholders—representing academia, industry, funding agencies, and scholarly publishers—have come together to design and jointly endorse a concise and measureable set of principles that we refer to as the FAIR Data Principles. The intent is that these may act as a guideline for those wishing to enhance the reusability of their data holdings. Distinct from peer initiatives that focus on the human scholar, the FAIR Principles put specific emphasis on enhancing the ability of machines to automatically find and use the data, in addition to supporting its reuse by individuals. This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community.

Published
2016
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5. The implicitome: A resource for rationalizing gene-disease associations

Author

Hettne, K.M. (Kristina), Thompson, M. (Mark), Haagen, H.H.H.B.M. (Herman) van, Van Der Horst, E. (Eelke), Kaliyaperumal, R. (Rajaram), Mina, E. (Eleni), Tatum, Z. (Zuotian), Laros, J.F.J. (Jeroen F.), Van Mulligen, E.M. (Erik M.), Schuemie, M.J. (Martijn), Emmelien, A. (Aten), Li, T.S. (Tong Shu), Bruskiewich, R. (Richard), Good, B.M. (Benjamin M.), Su, A.I. (Andrew I.), Kors, J.A. (Jan), Dunnen, J.T. (Johan) den, Van Ommen, G.-J.B. (Gert-Jan B.), Roos, M. (Marco), Hoen, P.A.C. (Peter) 't, Mons, B. (Barend), Schultes, E. (Erik), Hettne, K.M. (Kristina), Thompson, M. (Mark), Haagen, H.H.H.B.M. (Herman) van, Van Der Horst, E. (Eelke), Kaliyaperumal, R. (Rajaram), Mina, E. (Eleni), Tatum, Z. (Zuotian), Laros, J.F.J. (Jeroen F.), Van Mulligen, E.M. (Erik M.), Schuemie, M.J. (Martijn), Emmelien, A. (Aten), Li, T.S. (Tong Shu), Bruskiewich, R. (Richard), Good, B.M. (Benjamin M.), Su, A.I. (Andrew I.), Kors, J.A. (Jan), Dunnen, J.T. (Johan) den, Van Ommen, G.-J.B. (Gert-Jan B.), Roos, M. (Marco), Hoen, P.A.C. (Peter) 't, Mons, B. (Barend), and Schultes, E. (Erik)

Abstract

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome). Implicit relations are largely unknown to, or are even unintended by the original authors, but they vastly extend the reach of existing b

Published
2016
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6. Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels

Author

Draisma, G. (Gerrit), Pool, R. (Reńe), Kobl, M. (Michael), Jansen, R. (Rick), Petersen, A.K., Vaarhorst, A.A.M. (Anika), Yet, I. (Idil), Haller, T. (Toomas), Demirkan, A. (Ayşe), Esko, T. (Tõnu), Zhu, G. (Gu), Böhringer, S. (Stefan), Beekman, M. (Marian), Klinken, J.B. (Jan Bert) van, Römisch-Margl, W. (Werner), Prehn, C. (Cornelia), Adamski, J. (Jerzy), De Craen, A.J.M. (Anton J. M.), Leeuwen, E.M. (Elisa) van, Amin, N. (Najaf), Dharuri, H. (Harish), Westra, H.J. (Harm-Jan), Franke, L. (Lude), Geus, E.J.C. (Eco) de, Hottenga, J.J. (Jouke Jan), Willemsen, G.A.H.M. (Gonneke), Henders, A.K. (Anjali), Montgomery, G.W. (Grant), Dimas, A.S. (Antigone), Whitfield, J.B. (John B.), Penninx, B.W.J.H. (Brenda), Spector, T.D. (Timothy), Metspalu, A. (Andres), Slagboom, P.E. (Eline), Van Dijk, K.W. (Ko Willems), Hoen, P.A.C. (Peter) 't, Strauch, K. (Konstantin), Martin, N.G. (Nicholas), Ommen, G.J. (Gert) van, Illig, T. (Thomas), Bell, J.T. (Jordana), Mangino, M. (Massimo), Suhre, K. (Karsten), McCarthy, M.I. (Mark), Gieger, C. (Christian), Isaacs, A. (Aaron), Duijn, C.M. (Cornelia) van, Boomsma, D.I. (Dorret), Draisma, G. (Gerrit), Pool, R. (Reńe), Kobl, M. (Michael), Jansen, R. (Rick), Petersen, A.K., Vaarhorst, A.A.M. (Anika), Yet, I. (Idil), Haller, T. (Toomas), Demirkan, A. (Ayşe), Esko, T. (Tõnu), Zhu, G. (Gu), Böhringer, S. (Stefan), Beekman, M. (Marian), Klinken, J.B. (Jan Bert) van, Römisch-Margl, W. (Werner), Prehn, C. (Cornelia), Adamski, J. (Jerzy), De Craen, A.J.M. (Anton J. M.), Leeuwen, E.M. (Elisa) van, Amin, N. (Najaf), Dharuri, H. (Harish), Westra, H.J. (Harm-Jan), Franke, L. (Lude), Geus, E.J.C. (Eco) de, Hottenga, J.J. (Jouke Jan), Willemsen, G.A.H.M. (Gonneke), Henders, A.K. (Anjali), Montgomery, G.W. (Grant), Dimas, A.S. (Antigone), Whitfield, J.B. (John B.), Penninx, B.W.J.H. (Brenda), Spector, T.D. (Timothy), Metspalu, A. (Andres), Slagboom, P.E. (Eline), Van Dijk, K.W. (Ko Willems), Hoen, P.A.C. (Peter) 't, Strauch, K. (Konstantin), Martin, N.G. (Nicholas), Ommen, G.J. (Gert) van, Illig, T. (Thomas), Bell, J.T. (Jordana), Mangino, M. (Massimo), Suhre, K. (Karsten), McCarthy, M.I. (Mark), Gieger, C. (Christian), Isaacs, A. (Aaron), Duijn, C.M. (Cornelia) van, and Boomsma, D.I. (Dorret)

Abstract

Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platfo

Published
2015
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7. Insight in Genome-Wide Association of Metabolite Quantitative Traits by Exome Sequence Analyses

Author

Demirkan, A. (Ayşe), Henneman, P. (Peter), Verhoeven, A. (Aswin), Dharuri, H. (Harish), Amin, N. (Najaf), Klinken, J.B. (Jan Bert) van, Karssen, L.C. (Lennart), Stam, A.H. (Anine), Meissner, A. (Axel), Goraler, S. (Sibel), Maagdenberg, A.M.J.M. (Arn), Deelder, A.M. (André), Hoen, P.A.C. (Peter) 't, Duijn, C.M. (Cornelia) van, Willems van Dijk, J.A.P. (Ko), Demirkan, A. (Ayşe), Henneman, P. (Peter), Verhoeven, A. (Aswin), Dharuri, H. (Harish), Amin, N. (Najaf), Klinken, J.B. (Jan Bert) van, Karssen, L.C. (Lennart), Stam, A.H. (Anine), Meissner, A. (Axel), Goraler, S. (Sibel), Maagdenberg, A.M.J.M. (Arn), Deelder, A.M. (André), Hoen, P.A.C. (Peter) 't, Duijn, C.M. (Cornelia) van, and Willems van Dijk, J.A.P. (Ko)

Abstract

Metabolite quantitative traits carry great promise for epidemiological studies, and their genetic background has been addressed using Genome-Wide Association Studies (GWAS). Thus far, the role of less common variants has not been exhaustively studied. Here, we set out a GWAS for metabolite quantitative traits in serum, followed by exome sequence analysis to zoom in on putative causal variants in the associated genes. 1H Nuclear Magnetic Resonance (1H-NMR) spectroscopy experiments yielded successful quantification of 42 unique metabolites in 2,482 individuals from The Erasmus Rucphen Family (ERF) study. Heritability of metabolites were estimated by SOLAR. GWAS was performed by linear mixed models, using HapMap imputations. Based on physical vicinity and pathway analyses, candidate genes were screened for coding region variation using exome sequence data. Heritability estimates for metabolites ranged between 10% and 52%. GWAS replicated three known loci in the metabolome wide significance: CPS1 with glycine (P-value = 1.27×10−32), PRODH with proline (P-value = 1.11×10−19), SLC16A9 with carnitine level (P-value = 4.81×10−14) and uncovered a novel association between DMGDH and dimethyl-glycine (P-value = 1.65×10−19) level. In addition, we found three novel, suggestively significant loci: TNP1 with pyruvate (P-value = 1.26×10−8), KCNJ16 with 3-hydroxybutyrate (P-value = 1.65×10−8) and 2p12 locus with valine (P-value = 3.49×10−8). Exome sequence analysis identified potentially causal coding and regulatory variants located in the genes CPS1, KCNJ2 and PRODH, and revealed allelic heterogeneity for CPS1 and PRODH. Combined GWAS and exome analyses of metabolites detected by high-resolution 1H-NMR is a robust approach to uncover metabolite quantitative trait loci (mQTL), and the likely causative variants in these loci. It is anticipated that insight in the genetics of intermediate phenotypes will provide additional insight into the genetics of complex traits.

Published
2015
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8. Systematic identification of trans eQTLs as putative drivers of known disease associations

Author

Westra, H.J. (Harm-Jan), Peters, M.J. (Marjolein), Esko, T. (Tõnu), Yaghootkar, H. (Hanieh), Schurmann, C. (Claudia), Kettunen, J. (Johannes), Christiansen, M.W. (Mark), Fairfax, B.P. (Benjamin), Schramm, K. (Katharina), Powell, J.E. (Joseph), Zhernakova, A. (Alexandra), Veldink, J.H. (Jan), Berg, L.H. (Leonard) van den, Karjalainen, J. (Juha), Withoff, S. (Sebo), Uitterlinden, A.G. (André), Hofman, B., Rivadeneira Ramirez, F. (Fernando), Hoen, P.A.C. (Peter) 't, Reinmaa, E. (Eva), Fischer, K. (Kathelijn), Nelis, M. (Mari), Milani, A.L. (Alfredo), Melzer, D. (David), Ferrucci, L. (Luigi), Singleton, A.B. (Andrew), Hernandez, D.G. (Dena), Nalls, M.A. (Michael), Homuth, G. (Georg), Nauck, M.A. (Matthias), Radke, D. (Dörte), Vol̈ker, U. (Uwe), Perola, M. (Markus), Salomaa, V. (Veikko), Brody, J.A. (Jennifer A.), Suchy-Dicey, A. (Astrid), Gharib, S.A. (Sina), Enquobahrie, D., Lumley, T. (Thomas), Montgomery, G.W., Makino, S. (Seiko), Prokisch, H. (Holger), Herder, C. (Christian), Roden, M. (Michael), Grallert, H. (Harald), Meitinger, T. (Thomas), Strauch, K. (Konstantin), Li, Y. (Yunmin), Jansen, J.R.C. (Jos), Visscher, P.M. (Peter M.), Knight, J.C. (Julian), Psaty, B.M. (Bruce), Ripatti, S. (Samuli), Teumer, A. (Alexander), Frayling, T.M. (Timothy), Metspalu, A. (Andres), Meurs, J.B.J. (Joyce) van, Franke, L. (Lude), Westra, H.J. (Harm-Jan), Peters, M.J. (Marjolein), Esko, T. (Tõnu), Yaghootkar, H. (Hanieh), Schurmann, C. (Claudia), Kettunen, J. (Johannes), Christiansen, M.W. (Mark), Fairfax, B.P. (Benjamin), Schramm, K. (Katharina), Powell, J.E. (Joseph), Zhernakova, A. (Alexandra), Veldink, J.H. (Jan), Berg, L.H. (Leonard) van den, Karjalainen, J. (Juha), Withoff, S. (Sebo), Uitterlinden, A.G. (André), Hofman, B., Rivadeneira Ramirez, F. (Fernando), Hoen, P.A.C. (Peter) 't, Reinmaa, E. (Eva), Fischer, K. (Kathelijn), Nelis, M. (Mari), Milani, A.L. (Alfredo), Melzer, D. (David), Ferrucci, L. (Luigi), Singleton, A.B. (Andrew), Hernandez, D.G. (Dena), Nalls, M.A. (Michael), Homuth, G. (Georg), Nauck, M.A. (Matthias), Radke, D. (Dörte), Vol̈ker, U. (Uwe), Perola, M. (Markus), Salomaa, V. (Veikko), Brody, J.A. (Jennifer A.), Suchy-Dicey, A. (Astrid), Gharib, S.A. (Sina), Enquobahrie, D., Lumley, T. (Thomas), Montgomery, G.W., Makino, S. (Seiko), Prokisch, H. (Holger), Herder, C. (Christian), Roden, M. (Michael), Grallert, H. (Harald), Meitinger, T. (Thomas), Strauch, K. (Konstantin), Li, Y. (Yunmin), Jansen, J.R.C. (Jos), Visscher, P.M. (Peter M.), Knight, J.C. (Julian), Psaty, B.M. (Bruce), Ripatti, S. (Samuli), Teumer, A. (Alexander), Frayling, T.M. (Timothy), Metspalu, A. (Andres), Meurs, J.B.J. (Joyce) van, and Franke, L. (Lude)

Abstract

Identifying the downstream effects of disease-associated SNPs is challenging. To help overcome this problem, we performed expression quantitative trait locus (eQTL) meta-analysis in non-transformed peripheral blood samples from 5,311 individuals with replication in 2,775 individuals. We identified and replicated trans eQTLs for 233 SNPs (reflecting 103 independent loci) that were previously associated with complex traits at genome-wide significance. Some of these SNPs affect multiple genes in trans that are known to be altered in individuals with disease: rs4917014, previously associated with systemic lupus erythematosus (SLE), altered gene expression of C1QB and five type I interferon response genes, both hallmarks of SLE. DeepSAGE RNA sequencing showed that rs4917014 strongly alters the 3′ UTR levels of IKZF1 in cis, and chromatin immunoprecipitation and sequencing analysis of the trans-regulated genes implicated IKZF1 as the causal gene. Variants associated with cholesterol metabolism and type 1 diabetes showed similar phenomena, indicating that large-scale eQTL mapping provides insight into the downstream effects of many trait-associated variants.

Published
2013
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9. Automated workflow-based exploitation of pathway databases provides new insights into genetic associations of metabolite profiles

Author

Dharuri, H. (Harish), Henneman, P. (Peter), Demirkan, A. (Ayşe), Klinken, J.B. (Jan) van, Mook-Kanamori, D.O. (Dennis), Wang-Sattler, R. (Rui), Gieger, C. (Christian), Adamski, J. (Jerzy), Hettne, K.M. (Kristina), Roos, M. (Marco), Suhre, K. (Karsten), Duijn, C.M. (Cornelia) van, Willems van Dijk, J.A.P. (Ko), Hoen, P.A.C. (Peter) 't, Ugocsai, P. (Peter), Isaacs, A.J. (Aaron), Pramstaller, P.P. (Peter Paul), Liebisch, G. (Gerhard), Wilson, J.F. (James F), Johansson, A. (Åsa), Rudan, I. (Igor), Aulchenko, Y.S. (Yurii), Kirichenko, A.V. (Anatoly), Janssens, A.C.J.W. (Cécile), Jansen, R.C. (Ritsert), Gnewuch, C. (Carsten), Domingues, I. (Inês), Pattaro, C. (Cristian), Wild, S.H. (Sarah), Jonasson, I. (Inger), Polasek, O. (Ozren), Zorkoltseva, I.V. (Irina), Hofman, A. (Albert), Karssen, L.C. (Lennart), Struchalin, M.V. (Maksim), Floyd, J. (Jamie), Igl, W. (Wilmar), Biloglav, Z. (Zrinka), Broer, L. (Linda), Pfeufer, A. (Arne), Pichler, I. (Irene), Campbell, S. (Susan), Zaboli, G. (Ghazal), Kolcic, I. (Ivana), Rivadeneira Ramirez, F. (Fernando), Huffman, J.E. (Jennifer), Hastie, N. (Nick), Uitterlinden, A.G. (André), Franke, L. (Lude), Franklin, C.S. (Christopher), Vitart, V. (Veronique), Witteman, J.C.M. (Jacqueline), Axenovich, T.I. (Tatiana), Oostra, B.A. (Ben), Meitinger, T. (Thomas), Hicks, A.A. (Andrew), Hayward, C. (Caroline), Wright, A.F. (Alan), Gyllensten, U. (Ulf), Campbell, H. (Harry), Schmitz, G. (Gerd), Dharuri, H. (Harish), Henneman, P. (Peter), Demirkan, A. (Ayşe), Klinken, J.B. (Jan) van, Mook-Kanamori, D.O. (Dennis), Wang-Sattler, R. (Rui), Gieger, C. (Christian), Adamski, J. (Jerzy), Hettne, K.M. (Kristina), Roos, M. (Marco), Suhre, K. (Karsten), Duijn, C.M. (Cornelia) van, Willems van Dijk, J.A.P. (Ko), Hoen, P.A.C. (Peter) 't, Ugocsai, P. (Peter), Isaacs, A.J. (Aaron), Pramstaller, P.P. (Peter Paul), Liebisch, G. (Gerhard), Wilson, J.F. (James F), Johansson, A. (Åsa), Rudan, I. (Igor), Aulchenko, Y.S. (Yurii), Kirichenko, A.V. (Anatoly), Janssens, A.C.J.W. (Cécile), Jansen, R.C. (Ritsert), Gnewuch, C. (Carsten), Domingues, I. (Inês), Pattaro, C. (Cristian), Wild, S.H. (Sarah), Jonasson, I. (Inger), Polasek, O. (Ozren), Zorkoltseva, I.V. (Irina), Hofman, A. (Albert), Karssen, L.C. (Lennart), Struchalin, M.V. (Maksim), Floyd, J. (Jamie), Igl, W. (Wilmar), Biloglav, Z. (Zrinka), Broer, L. (Linda), Pfeufer, A. (Arne), Pichler, I. (Irene), Campbell, S. (Susan), Zaboli, G. (Ghazal), Kolcic, I. (Ivana), Rivadeneira Ramirez, F. (Fernando), Huffman, J.E. (Jennifer), Hastie, N. (Nick), Uitterlinden, A.G. (André), Franke, L. (Lude), Franklin, C.S. (Christopher), Vitart, V. (Veronique), Witteman, J.C.M. (Jacqueline), Axenovich, T.I. (Tatiana), Oostra, B.A. (Ben), Meitinger, T. (Thomas), Hicks, A.A. (Andrew), Hayward, C. (Caroline), Wright, A.F. (Alan), Gyllensten, U. (Ulf), Campbell, H. (Harry), and Schmitz, G. (Gerd)

Abstract

Background: Genome-wide association studies (GWAS) have identified many common single nucleotide polymorphisms (SNPs) that associate with clinical phenotypes, but these SNPs usually explain just a small part of the heritability and have relatively modest effect sizes. In contrast, SNPs that associate with metabolite levels generally explain a higher percentage of the genetic variation and demonstrate larger effect sizes. Still, the discovery of SNPs associated with metabolite levels is challenging since testing all metabolites measured in typical metabolomics studies with all SNPs comes with a severe multiple testing penalty. We have developed an automated workflow approach that utilizes prior knowledge of biochemical pathways present in databases like KEGG and BioCyc to generate a smaller SNP set relevant to the metabolite. This paper explores the opportunities and challenges in the analysis of GWAS of metabolomic phenotypes and provides novel insights into the genetic basis of metabolic variation through the re-analysis of published GWAS datasets. Results: Re-analysis of the published GWAS dataset from Illig et al. (Nature Genetics, 2010) using a pathway-based workflow (http://www.myexperiment.org/packs/319.html), confirmed previously identified hits and identified a new locus of human metabolic individuality, associating Aldehyde dehydrogenase family1 L1 (ALDH1L1) with serine/glycine ratios in blood. Replication in an independent GWAS dataset of phospholipids (Demirkan et al., PLoS Genetics, 2012) identified two novel loci supported by additional literature evidence: GPAM (Glycerol-3 phosphate acyltransferase) and CBS (Cystathionine beta-synthase). In addition, the workflow approach provided novel insight into the affected pathways and relevance of some of these gene-metabolite pairs in disease development and progression. Conclusions: We demonstrate the utility of automated exploitation of background knowledge present in pathway databases for the analysis of GWA

Published
2013
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10. Integrated analysis of microRNA and mRNA expression: Adding biological significance to microRNA target predictions

Author

Iterson, M. (Mat) van, Bervoets, S. (Sander), Meijer, E.J. (Emile) de, Buermans, H.P. (Henk), Hoen, P.A.C. (Peter) 't, Menezes, R.X. (Renée), Boer, J.M. (Judith), Iterson, M. (Mat) van, Bervoets, S. (Sander), Meijer, E.J. (Emile) de, Buermans, H.P. (Henk), Hoen, P.A.C. (Peter) 't, Menezes, R.X. (Renée), and Boer, J.M. (Judith)

Abstract

Current microRNA target predictions are based on sequence information and empirically derived rules but do not make use of the expression of microRNAs and their targets. This study aimed to improve microRNA target predictions in a given biological context, using in silico predictions, microRNA and mRNA expression. We used target prediction tools to produce lists of predicted targets and used a gene set test designed to detect consistent effects of microRNAs on the joint expression of multiple targets. In a single test, association between microRNA expression and target gene set expression as well as the contribution of the individual target genes on the association are determined. The strongest negatively associated mRNAs as measured by the test were prioritized. We applied our integration method to a well-defined muscle differentiation model. Validation of our predictions in C2C12 cells confirmed predicted targets of known as well as novel muscle-related microRNAs. We further studied associations between microRNA-mRNA pairs in human prostate cancer, finding some pairs that have been recently experimentally validated by others. Using the same study, we showed the advantages of the global test over Pearson correlation and lasso. We conclude that our integrated approach successfully identifies regulated microRNAs and their targets.

Published
2013
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11. Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure

Author

Wain, L.V. (Louise), Verwoert, G.C. (Germaine), O'Reilly, P.F. (Paul), Shi, G. (Gang), Johnson, T. (Toby), Bochud, M. (Murielle), Rice, K. (Kenneth), Henneman, P. (Peter), Smith, A.V. (Albert Vernon), Ehret, G.B. (Georg), Amin, N. (Najaf), Larson, M.G. (Martin), Mooser, V. (Vincent), Hadley, D. (David), Dörr, M. (Marcus), Bis, J.C. (Joshua), Aspelund, T. (Thor), Esko, T. (Tõnu), Janssens, A.C.J.W. (Cécile), Zhao, J.H. (Jing Hua), Heath, S.C. (Simon), Laan, M. (Maris), Fu, J. (Jingyuan), Pistis, G. (Giorgio), Luan, J., Lucas, G. (Gavin), Pirastu, N. (Nicola), Pichler, I. (Irene), Jackson, A.U. (Anne), Webster, R.J. (Rebecca J.), Zhang, F.F., Peden, J. (John), Schmidt, R. (Reinhold), Tanaka, T. (Toshiko), Campbell, H. (Harry), Igl, W. (Wilmar), Milaneschi, Y. (Yuri), Hottenga, J.J. (Jouke Jan), Vitart, V. (Veronique), Chasman, D.I. (Daniel), Trompet, S. (Stella), Bragg-Gresham, J.L. (Jennifer L.), Alizadeh, B.Z. (Behrooz), Chambers, J.C. (John), Guo, X. (Xiuqing), Lehtimäki, T. (Terho), Kuhnel, B. (Brigitte), Lopez, L.M., Polasek, O. (Ozren), Boban, M. (Mladen), Nelson, C.P. (Christopher P.), Morrison, A.C. (Alanna), Pihur, V. (Vasyl), Ganesh, S.K. (Santhi), Hofman, A. (Albert), Kundu, S. (Suman), Mattace Raso, F.U.S. (Francesco), Rivadeneira Ramirez, F. (Fernando), Sijbrands, E.J.G. (Eric), Uitterlinden, A.G. (André), Hwang, S.J., Vasan, R.S. (Ramachandran Srini), Wang, Y.A. (Ying), Bergmann, S.M. (Sven), Vollenweider, P. (Peter), Waeber, G. (Gérard), Laitinen, J. (Jaana), Pouta, A. (Anneli), Zitting, P. (Paavo), McArdle, W.L. (Wendy), Kroemer, H.K. (Heyo), Völker, U. (Uwe), Völzke, H. (Henry), Glazer, N.L. (Nicole), Taylor, K.D. (Kent), Harris, T.B. (Tamara), Alavere, H. (Helene), Haller, T. (Toomas), Keis, A. (Aime), Tammesoo, M.L., Aulchenko, Y.S. (Yurii), Khaw, K-T. (Kay-Tee), Galan, P. (Pilar), Hercberg, S. (Serge), Lathrop, G.M. (Mark), Eyheramendy, S. (Susana), Org, E. (Elin), Sõber, S. (Siim), Lu, X. (Xiaowen), Nolte, I.M. (Ilja), Penninx, B.W.J.H. (Brenda), Corre, T. (Tanguy), Masciullo, C. (Corrado), Sala, C. (Cinzia), Groop, L. (Leif), Voight, B.F. (Benjamin), Melander, O. (Olle), O'Donnell, C.J. (Christopher), Salomaa, V. (Veikko), Adamo, P. (Pio) d', Fabretto, A. (Antonella), Faletra, F. (Flavio), Ulivi, S. (Shelia), Del Greco, F. (Fabiola), Facheris, M.F. (Maurizio), Collins, F.S. (Francis), Bergman, R.N. (Richard), Beilby, J.P. (John), Hung, J. (Judy), Musk, A.W. (Arthur), Mangino, M. (Massimo), Shin, S.Y. (So Youn), Soranzo, N. (Nicole), Watkins, H. (Hugh), Goel, A. (Anuj), Hamsten, A. (Anders), Gider, P. (Pierre), Loitfelder, M. (Marisa), Zeginigg, M. (Marion), Hernandez, D.G. (Dena), Najjar, S.S. (Samer), Navarro, P. (Pau), Wild, S.H. (Sarah), Corsi, A.M. (Anna Maria), Singleton, A. (Andrew), Geus, E.J.C. (Eco) de, Willemsen, G.A.H.M. (Gonneke), Parker, A.N. (Alex), Rose, L.M. (Lynda), Buckley, B.M. (Brendan M.), Stott, D.J. (David. J.), Orrù, M. (Marco), Uda, M. (Manuela), Klauw, M.M. (Melanie) van der, Scott, J. (James), Chen, Y.D.I. (Yii-Der Ida), Burke, G.L. (Greg), Kähönen, M. (Mika), Viikari, J. (Jorma), Döring, A. (Angela), Meitinger, T. (Thomas), Davis, G.S., Starr, J.M. (John), Emilsson, V. (Valur), Plump, A.S. (Andrew), Lindeman, J.H. (Jan H.), Hoen, P.A.C. (Peter) 't, König, I.R. (Inke), Felix, J.F. (Janine), Clarke, R., Hopewell, J., Ongen, H. (Halit), Breteler, M.M.B. (Monique), Debette, S. (Stéphanie), DeStefano, A.L. (Anita), Fornage, M. (Myriam), Mitchell, G.F. (Gary), Holm, H. (Hilma), Stefansson, K. (Kari), Thorleifsson, G. (Gudmar), Thorsteinsdottir, U. (Unnur), Samani, N.J. (Nilesh), Preuss, M. (Michael), Rudan, I. (Igor), Hayward, C. (Caroline), Deary, I.J. (Ian), Wichmann, H.E. (Heinz Erich), Raitakari, O. (Olli), Palmas, W. (Walter), Kooner, J.S. (Jaspal), Stolk, R.P. (Ronald), Jukema, J.W. (Jan Wouter), Wright, A.F. (Alan), Boomsma, D.I. (Dorret), Bandinelli, S. (Stefania), Gyllensten, U. (Ulf), Wilson, J.F. (James), Ferrucci, L. (Luigi), Farrall, M. (Martin), Spector, T.D. (Timothy), Palmer, L.J., Tuomilehto, J. (Jaakko), Pfeufer, A. (Arne), Gasparini, P. (Paolo), Siscovick, D.S. (David), Altshuler, D. (David), Loos, R.J.F. (Ruth), Toniolo, D. (Daniela), Snieder, H. (Harold), Gieger, C. (Christian), Meneton, P. (Pierre), Wareham, N.J. (Nick), Oostra, B.A. (Ben), Metspalu, A. (Andres), Launer, L.J. (Lenore), Rettig, R. (Rainer), Strachan, D.P. (David), Beckmann, J.S. (Jacques), Witteman, J.C.M. (Jacqueline), Willems van Dijk, J.A.P. (Ko), Boerwinkle, E.A. (Eric), Boehnke, M. (Michael), Ridker, P.M. (Paul), Järvelin, M.R., Chakravarti, A. (Aravinda), Erdmann, J. (Jeanette), Gudnason, V. (Vilmundur), Newton-Cheh, C. (Christopher), Levy, D. (Daniel), Arora, P. (Pankaj), Munroe, P. (Patricia), Psaty, B.M. (Bruce), Caulfield, M. (Mark), Li, X. (Xinzhong), Rao, D.C. (Dabeeru C.), Elliott, P. (Paul), Tikka-Kleemola, P. (Päivi), Abecasis, G.R. (Gonçalo), Barroso, I.E. (Inês), Wain, L.V. (Louise), Verwoert, G.C. (Germaine), O'Reilly, P.F. (Paul), Shi, G. (Gang), Johnson, T. (Toby), Bochud, M. (Murielle), Rice, K. (Kenneth), Henneman, P. (Peter), Smith, A.V. (Albert Vernon), Ehret, G.B. (Georg), Amin, N. (Najaf), Larson, M.G. (Martin), Mooser, V. (Vincent), Hadley, D. (David), Dörr, M. (Marcus), Bis, J.C. (Joshua), Aspelund, T. (Thor), Esko, T. (Tõnu), Janssens, A.C.J.W. (Cécile), Zhao, J.H. (Jing Hua), Heath, S.C. (Simon), Laan, M. (Maris), Fu, J. (Jingyuan), Pistis, G. (Giorgio), Luan, J., Lucas, G. (Gavin), Pirastu, N. (Nicola), Pichler, I. (Irene), Jackson, A.U. (Anne), Webster, R.J. (Rebecca J.), Zhang, F.F., Peden, J. (John), Schmidt, R. (Reinhold), Tanaka, T. (Toshiko), Campbell, H. (Harry), Igl, W. (Wilmar), Milaneschi, Y. (Yuri), Hottenga, J.J. (Jouke Jan), Vitart, V. (Veronique), Chasman, D.I. (Daniel), Trompet, S. (Stella), Bragg-Gresham, J.L. (Jennifer L.), Alizadeh, B.Z. (Behrooz), Chambers, J.C. (John), Guo, X. (Xiuqing), Lehtimäki, T. (Terho), Kuhnel, B. (Brigitte), Lopez, L.M., Polasek, O. (Ozren), Boban, M. (Mladen), Nelson, C.P. (Christopher P.), Morrison, A.C. (Alanna), Pihur, V. (Vasyl), Ganesh, S.K. (Santhi), Hofman, A. (Albert), Kundu, S. (Suman), Mattace Raso, F.U.S. (Francesco), Rivadeneira Ramirez, F. (Fernando), Sijbrands, E.J.G. (Eric), Uitterlinden, A.G. (André), Hwang, S.J., Vasan, R.S. (Ramachandran Srini), Wang, Y.A. (Ying), Bergmann, S.M. (Sven), Vollenweider, P. (Peter), Waeber, G. (Gérard), Laitinen, J. (Jaana), Pouta, A. (Anneli), Zitting, P. (Paavo), McArdle, W.L. (Wendy), Kroemer, H.K. (Heyo), Völker, U. (Uwe), Völzke, H. (Henry), Glazer, N.L. (Nicole), Taylor, K.D. (Kent), Harris, T.B. (Tamara), Alavere, H. (Helene), Haller, T. (Toomas), Keis, A. (Aime), Tammesoo, M.L., Aulchenko, Y.S. (Yurii), Khaw, K-T. (Kay-Tee), Galan, P. (Pilar), Hercberg, S. (Serge), Lathrop, G.M. (Mark), Eyheramendy, S. (Susana), Org, E. (Elin), Sõber, S. (Siim), Lu, X. (Xiaowen), Nolte, I.M. (Ilja), Penninx, B.W.J.H. (Brenda), Corre, T. (Tanguy), Masciullo, C. (Corrado), Sala, C. (Cinzia), Groop, L. (Leif), Voight, B.F. (Benjamin), Melander, O. (Olle), O'Donnell, C.J. (Christopher), Salomaa, V. (Veikko), Adamo, P. (Pio) d', Fabretto, A. (Antonella), Faletra, F. (Flavio), Ulivi, S. (Shelia), Del Greco, F. (Fabiola), Facheris, M.F. (Maurizio), Collins, F.S. (Francis), Bergman, R.N. (Richard), Beilby, J.P. (John), Hung, J. (Judy), Musk, A.W. (Arthur), Mangino, M. (Massimo), Shin, S.Y. (So Youn), Soranzo, N. (Nicole), Watkins, H. (Hugh), Goel, A. (Anuj), Hamsten, A. (Anders), Gider, P. (Pierre), Loitfelder, M. (Marisa), Zeginigg, M. (Marion), Hernandez, D.G. (Dena), Najjar, S.S. (Samer), Navarro, P. (Pau), Wild, S.H. (Sarah), Corsi, A.M. (Anna Maria), Singleton, A. (Andrew), Geus, E.J.C. (Eco) de, Willemsen, G.A.H.M. (Gonneke), Parker, A.N. (Alex), Rose, L.M. (Lynda), Buckley, B.M. (Brendan M.), Stott, D.J. (David. J.), Orrù, M. (Marco), Uda, M. (Manuela), Klauw, M.M. (Melanie) van der, Scott, J. (James), Chen, Y.D.I. (Yii-Der Ida), Burke, G.L. (Greg), Kähönen, M. (Mika), Viikari, J. (Jorma), Döring, A. (Angela), Meitinger, T. (Thomas), Davis, G.S., Starr, J.M. (John), Emilsson, V. (Valur), Plump, A.S. (Andrew), Lindeman, J.H. (Jan H.), Hoen, P.A.C. (Peter) 't, König, I.R. (Inke), Felix, J.F. (Janine), Clarke, R., Hopewell, J., Ongen, H. (Halit), Breteler, M.M.B. (Monique), Debette, S. (Stéphanie), DeStefano, A.L. (Anita), Fornage, M. (Myriam), Mitchell, G.F. (Gary), Holm, H. (Hilma), Stefansson, K. (Kari), Thorleifsson, G. (Gudmar), Thorsteinsdottir, U. (Unnur), Samani, N.J. (Nilesh), Preuss, M. (Michael), Rudan, I. (Igor), Hayward, C. (Caroline), Deary, I.J. (Ian), Wichmann, H.E. (Heinz Erich), Raitakari, O. (Olli), Palmas, W. (Walter), Kooner, J.S. (Jaspal), Stolk, R.P. (Ronald), Jukema, J.W. (Jan Wouter), Wright, A.F. (Alan), Boomsma, D.I. (Dorret), Bandinelli, S. (Stefania), Gyllensten, U. (Ulf), Wilson, J.F. (James), Ferrucci, L. (Luigi), Farrall, M. (Martin), Spector, T.D. (Timothy), Palmer, L.J., Tuomilehto, J. (Jaakko), Pfeufer, A. (Arne), Gasparini, P. (Paolo), Siscovick, D.S. (David), Altshuler, D. (David), Loos, R.J.F. (Ruth), Toniolo, D. (Daniela), Snieder, H. (Harold), Gieger, C. (Christian), Meneton, P. (Pierre), Wareham, N.J. (Nick), Oostra, B.A. (Ben), Metspalu, A. (Andres), Launer, L.J. (Lenore), Rettig, R. (Rainer), Strachan, D.P. (David), Beckmann, J.S. (Jacques), Witteman, J.C.M. (Jacqueline), Willems van Dijk, J.A.P. (Ko), Boerwinkle, E.A. (Eric), Boehnke, M. (Michael), Ridker, P.M. (Paul), Järvelin, M.R., Chakravarti, A. (Aravinda), Erdmann, J. (Jeanette), Gudnason, V. (Vilmundur), Newton-Cheh, C. (Christopher), Levy, D. (Daniel), Arora, P. (Pankaj), Munroe, P. (Patricia), Psaty, B.M. (Bruce), Caulfield, M. (Mark), Li, X. (Xinzhong), Rao, D.C. (Dabeeru C.), Elliott, P. (Paul), Tikka-Kleemola, P. (Päivi), Abecasis, G.R. (Gonçalo), and Barroso, I.E. (Inês)

Abstract

Numerous genetic loci have been associated with systolic blood pressure (SBP) and diastolic blood pressure (DBP) in Europeans. We now report genome-wide association studies of pulse pressure (PP) and mean arterial pressure (MAP). In discovery (N = 74,064) and follow-up studies (N = 48,607), we identified at genome-wide significance (P = 2.7 × 10 -8 to P = 2.3 × 10 -13) four new PP loci (at 4q12 near CHIC2, 7q22.3 near PIK3CG, 8q24.12 in NOV and 11q24.3 near ADAMTS8), two new MAP loci (3p21.31 in MAP4 and 10q25.3 near ADRB1) and one locus associated with both of these traits (2q24.3 near FIGN) that has also recently been associated with SBP in east Asians. For three of the new PP loci, the estimated effect for SBP was opposite of that for DBP, in contrast to the majority of common SBP- and DBP-associated variants, which show concordant effects on both traits. These findings suggest new genetic pathways underlying blood pressure variation, some of which may differentially influence SBP and DBP.

Published
2011
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12. Novel protein-protein interactions inferred from literature context

Author

Haagen, H.H.H.B.M. (Herman) van, Hoen, P.A.C. (Peter) 't, Bovo, A.B., Morrée, A. (Antoine) de, Mulligen, E.M. (Erik) van, Chichester, C. (Christine), Kors, J.A. (Jan), Dunnen, J.T. (Johan) den, Ommen, G.J.B. van, Maarel, S.M. (Silvère) van der, Kern, V.M., Mons, B. (Barend), Schuemie, M.J. (Martijn), Haagen, H.H.H.B.M. (Herman) van, Hoen, P.A.C. (Peter) 't, Bovo, A.B., Morrée, A. (Antoine) de, Mulligen, E.M. (Erik) van, Chichester, C. (Christine), Kors, J.A. (Jan), Dunnen, J.T. (Johan) den, Ommen, G.J.B. van, Maarel, S.M. (Silvère) van der, Kern, V.M., Mons, B. (Barend), and Schuemie, M.J. (Martijn)

Abstract

We have developed a method that predicts Protein-Protein Interactions (PPIs) based on the similarity of the context in which proteins appear in literature. This method outperforms previously developed PPI prediction algorithms that rely on the conjunction of two protein names in MEDLINE abstracts. We show significant increases in coverage (76% versus 32%) and sensitivity (66% versus 41% at a specificity of 95%) for the prediction of PPIs currently archived in 6 PPI databases. A retrospective analysis shows that PPIs can efficiently be predicted before they enter PPI databases and before their interaction is explicitly described in the literature. The practical value of the method for discovery of novel PPIs is illustrated by the experimental confirmation of the inferred physical interaction between CAPN3 and PARVB, which was based on frequent co-occurrence of both proteins with concepts like Z-disc, dysferlin, and alpha-actinin. The relationships between proteins predicted by our method are broader than PPIs, and include proteins in the same complex or pathway. Dependent on the type of relationships deemed useful, the precision of our method can be as high as 90%. The full set of predicted interactions is available in a downloadable matrix and through the webtool Nermal, which lists the most likely interaction partners for a given protein. Our framework can be used for prioritizing potential interaction partners, hitherto undiscovered, for follow-up studies and to aid the generation of accurate protein interaction maps.

Published
2009
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13. Literature-aided meta-analysis of microarray data: A compendium study on muscle development and disease

Author

Jelier, R. (Rob), Hoen, P.A.C. (Peter) 't, Sterrenburg, E. (Ellen), Dunnen, J.T. (Johan) den, Ommen, G.J. (Gert) van, Kors, J.A. (Jan), Mons, B. (Barend), Jelier, R. (Rob), Hoen, P.A.C. (Peter) 't, Sterrenburg, E. (Ellen), Dunnen, J.T. (Johan) den, Ommen, G.J. (Gert) van, Kors, J.A. (Jan), and Mons, B. (Barend)

Abstract

Background: Comparative analysis of expression microarray studies is difficult due to the large influence of technical factors on experimental outcome. Still, the identified differentially expressed genes may hint at the same biological processes. However, manually curated assignment of genes to biological processes, such as pursued by the Gene Ontology (GO) consortium, is incomplete and limited. We hypothesised that automatic association of genes with biological processes through thesaurus-controlled mining of Medline abstracts would be more effective. Therefore, we developed a novel algorithm (LAMA: Literature-Aided Meta-Analysis) to quantify the similarity between transcriptomics studies. We evaluated our algorithm on a large compendium of 102 microarray studies published in the field of muscle development and disease, and compared it to similarity measures based on gene overlap and over-representation of biological processes assigned by GO. Results: While the overlap in both genes and overrepresented GO-terms was poor, LAMA retrieved many more biologically meaningful links between studies, with substantially lower influence of technical factors. LAMA correctly grouped muscular dystrophy, regeneration and myositis studies, and linked patient and corresponding mouse model studies. LAMA also retrieves the connecting biological concepts. Among other new discoveries, we associated cullin proteins, a class of ubiquitinylation proteins, with genes down-regulated during muscle regeneration, whereas ubiquitinylation was previously reported to be activated during the inverse process: muscle atrophy. Conclusion: Our literature-based association analysis is capable of finding hidden common biological denominators in microarray studies, and circumvents the need for raw data analysis or curated gene annotation databases.

Published
2008
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