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1. An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis

Author

Liu, Jun, Carnero-Montoro, Elena, van Dongen, Jenny, Lent, Samantha, Nedeljkovic, Ivana, Ligthart, Symen, Tsai, Pei-Chien, Martin, Tiphaine C, Mandaviya, Pooja R, Jansen, Rick, Peters, Marjolein J, Duijts, Liesbeth, Jaddoe, Vincent WV, Tiemeier, Henning, Felix, Janine F, Willemsen, Gonneke, de Geus, Eco JC, Chu, Audrey Y, Levy, Daniel, Hwang, Shih-Jen, Bressler, Jan, Gondalia, Rahul, Salfati, Elias L, Herder, Christian, Hidalgo, Bertha A, Tanaka, Toshiko, Moore, Ann Zenobia, Lemaitre, Rozenn N, Jhun, Min A, Smith, Jennifer A, Sotoodehnia, Nona, Bandinelli, Stefania, Ferrucci, Luigi, Arnett, Donna K, Grallert, Harald, Assimes, Themistocles L, Hou, Lifang, Baccarelli, Andrea, Whitsel, Eric A, van Dijk, Ko Willems, Amin, Najaf, Uitterlinden, André G, Sijbrands, Eric JG, Franco, Oscar H, Dehghan, Abbas, Spector, Tim D, Dupuis, Josée, Hivert, Marie-France, Rotter, Jerome I, Meigs, James B, Pankow, James S, van Meurs, Joyce BJ, Isaacs, Aaron, Boomsma, Dorret I, Bell, Jordana T, Demirkan, Ayşe, and van Duijn, Cornelia M

Subjects
Biological Sciences, Genetics, Nutrition, Diabetes, Human Genome, Obesity, 2.1 Biological and endogenous factors, Aetiology, Metabolic and endocrine, Adult, Aged, Aged, 80 and over, Computer Simulation, CpG Islands, DNA Methylation, Diabetes Mellitus, Type 2, Epigenesis, Genetic, Epigenomics, Female, Gene Expression Profiling, Gene Expression Regulation, Genome-Wide Association Study, Glucose, Homeostasis, Humans, Insulin, Male, Metabolic Networks and Pathways, Middle Aged, Polymorphism, Single Nucleotide, Young Adult
Abstract

Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D.

Published
2019

2. DNA methylation signature of chronic low-grade inflammation and its role in cardio-respiratory diseases

Author

Wielscher, Matthias, Mandaviya, Pooja R., Kuehnel, Brigitte, Joehanes, Roby, Mustafa, Rima, Robinson, Oliver, Zhang, Yan, Bodinier, Barbara, Walton, Esther, Mishra, Pashupati P., Schlosser, Pascal, Wilson, Rory, Tsai, Pei-Chien, Palaniswamy, Saranya, Marioni, Riccardo E., Fiorito, Giovanni, Cugliari, Giovanni, Karhunen, Ville, Ghanbari, Mohsen, Psaty, Bruce M., Loh, Marie, Bis, Joshua C., Lehne, Benjamin, Sotoodehnia, Nona, Deary, Ian J., Chadeau-Hyam, Marc, Brody, Jennifer A., Cardona, Alexia, Selvin, Elizabeth, Smith, Alicia K., Miller, Andrew H., Torres, Mylin A., Marouli, Eirini, Gào, Xin, van Meurs, Joyce B. J., Graf-Schindler, Johanna, Rathmann, Wolfgang, Koenig, Wolfgang, Peters, Annette, Weninger, Wolfgang, Farlik, Matthias, Zhang, Tao, Chen, Wei, Xia, Yujing, Teumer, Alexander, Nauck, Matthias, Grabe, Hans J., Doerr, Macus, Lehtimäki, Terho, Guan, Weihua, Milani, Lili, Tanaka, Toshiko, Fisher, Krista, Waite, Lindsay L., Kasela, Silva, Vineis, Paolo, Verweij, Niek, van der Harst, Pim, Iacoviello, Licia, Sacerdote, Carlotta, Panico, Salvatore, Krogh, Vittorio, Tumino, Rosario, Tzala, Evangelia, Matullo, Giuseppe, Hurme, Mikko A., Raitakari, Olli T., Colicino, Elena, Baccarelli, Andrea A., Kähönen, Mika, Herzig, Karl-Heinz, Li, Shengxu, Conneely, Karen N., Kooner, Jaspal S., Köttgen, Anna, Heijmans, Bastiaan T., Deloukas, Panos, Relton, Caroline, Ong, Ken K., Bell, Jordana T., Boerwinkle, Eric, Elliott, Paul, Brenner, Hermann, Beekman, Marian, Levy, Daniel, Waldenberger, Melanie, Chambers, John C., Dehghan, Abbas, and Järvelin, Marjo-Riitta

Published
2022
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3. Autosomal genetic variation is associated with DNA methylation in regions variably escaping X-chromosome inactivation

Author

Luijk, René, Wu, Haoyu, Ward-Caviness, Cavin K, Hannon, Eilis, Carnero-Montoro, Elena, Min, Josine L., Mandaviya, Pooja, Müller-Nurasyid, Martina, Mei, Hailiang, van der Maarel, Silvere M., BIOS Consortium, Relton, Caroline, Mill, Jonathan, Waldenberger, Melanie, Bell, Jordana T., Jansen, Rick, Zhernakova, Alexandra, Franke, Lude, ‘t Hoen, Peter A. C., Boomsma, Dorret I., van Duijn, Cornelia M., van Greevenbroek, Marleen M. J., Veldink, Jan H., Wijmenga, Cisca, van Meurs, Joyce, Daxinger, Lucia, Slagboom, P. Eline, van Zwet, Erik W., and Heijmans, Bastiaan T.

Published
2018
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6. An epigenome-wide association study meta-analysis of educational attainment

Author

Linnér, R Karlsson, Marioni, R E, Rietveld, C A, Simpkin, A J, Davies, N M, Watanabe, K, Armstrong, N J, Auro, K, Baumbach, C, Bonder, M J, Buchwald, J, Fiorito, G, Ismail, K, Iurato, S, Joensuu, A, Karell, P, Kasela, S, Lahti, J, McRae, A F, Mandaviya, P R, Seppälä, I, Wang, Y, Baglietto, L, Binder, E B, Harris, S E, Hodge, A M, Horvath, S, Hurme, M, Johannesson, M, Latvala, A, Mather, K A, Medland, S E, Metspalu, A, Milani, L, Milne, R L, Pattie, A, Pedersen, N L, Peters, A, Polidoro, S, Räikkönen, K, Severi, G, Starr, J M, Stolk, L, Waldenberger, M, Consortium, B IOS, Eriksson, J G, Esko, T, Franke, L, Gieger, C, Giles, G G, Hägg, S, Jousilahti, P, Kaprio, J, Kähönen, M, Lehtimäki, T, Martin, N G, van Meurs, J BC, Ollikainen, M, Perola, M, Posthuma, D, Raitakari, O T, Sachdev, P S, Taskesen, E, Uitterlinden, A G, Vineis, P, Wijmenga, C, Wright, M J, Relton, C, Smith, G Davey, Deary, I J, Koellinger, P D, and Benjamin, D J

Published
2017
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7. DNA methylation signature of chronic low-grade inflammation and its role in cardio-respiratory diseases

Author

Wielscher, M. (Matthias), Mandaviya, P. R. (Pooja R.), Kuehnel, B. (Brigitte), Joehanes, R. (Roby), Mustafa, R. (Rima), Robinson, O. (Oliver), Zhang, Y. (Yan), Bodinier, B. (Barbara), Walton, E. (Esther), Mishra, P. P. (Pashupati P.), Schlosser, P. (Pascal), Wilson, R. (Rory), Tsai, P.-C. (Pei-Chien), Palaniswamy, S. (Saranya), Marioni, R. E. (Riccardo E.), Fiorito, G. (Giovanni), Cugliari, G. (Giovanni), Karhunen, V. (Ville), Ghanbari, M. (Mohsen), Psaty, B. M. (Bruce M.), Loh, M. (Marie), Bis, J. C. (Joshua C.), Lehne, B. (Benjamin), Sotoodehnia, N. (Nona), Deary, I. J. (Ian J.), Chadeau-Hyam, M. (Marc), Brody, J. A. (Jennifer A.), Cardona, A. (Alexia), Selvin, E. (Elizabeth), Smith, A. K. (Alicia K.), Miller, A. H. (Andrew H.), Torres, M. A. (Mylin A.), Marouli, E. (Eirini), Gao, X. (Xin), van Meurs, J. B. (Joyce B. J.), Graf-Schindler, J. (Johanna), Rathmann, W. (Wolfgang), Koenig, W. (Wolfgang), Peters, A. (Annette), Weninger, W. (Wolfgang), Farlik, M. (Matthias), Zhang, T. (Tao), Chen, W. (Wei), Xia, Y. (Yujing), Teumer, A. (Alexander), Nauck, M. (Matthias), Grabe, H. J. (Hans J.), Doerr, M. (Macus), Lehtimaki, T. (Terho), Guan, W. (Weihua), Milani, L. (Lili), Tanaka, T. (Toshiko), Fisher, K. (Krista), Waite, L. L. (Lindsay L.), Kasela, S. (Silva), Vineis, P. (Paolo), Verweij, N. (Niek), van der Harst, P. (Pim), Iacoviello, L. (Licia), Sacerdote, C. (Carlotta), Panico, S. (Salvatore), Krogh, V. (Vittorio), Tumino, R. (Rosario), Tzala, E. (Evangelia), Matullo, G. (Giuseppe), Hurme, M. A. (Mikko A.), Raitakari, O. T. (Olli T.), Colicino, E. (Elena), Baccarelli, A. A. (Andrea A.), Kahonen, M. (Mika), Herzig, K.-H. (Karl-Heinz), Li, S. (Shengxu), BIOS consortium, Conneely, K. N. (Karen N.), Kooner, J. S. (Jaspal S.), Kottgen, A. (Anna), Heijmans, B. T. (Bastiaan T.), Deloukas, P. (Panos), Relton, C. (Caroline), Ong, K. K. (Ken K.), Bell, J. T. (Jordana T.), Boerwinkle, E. (Eric), Elliott, P. (Paul), Brenner, H. (Hermann), Beekman, M. (Marian), Levy, D. (Daniel), Waldenberger, M. (Melanie), Chambers, J. C. (John C.), Dehghan, A. (Abbas), Järvelin, M.-R. (Marjo-Riitta), Wielscher, M. (Matthias), Mandaviya, P. R. (Pooja R.), Kuehnel, B. (Brigitte), Joehanes, R. (Roby), Mustafa, R. (Rima), Robinson, O. (Oliver), Zhang, Y. (Yan), Bodinier, B. (Barbara), Walton, E. (Esther), Mishra, P. P. (Pashupati P.), Schlosser, P. (Pascal), Wilson, R. (Rory), Tsai, P.-C. (Pei-Chien), Palaniswamy, S. (Saranya), Marioni, R. E. (Riccardo E.), Fiorito, G. (Giovanni), Cugliari, G. (Giovanni), Karhunen, V. (Ville), Ghanbari, M. (Mohsen), Psaty, B. M. (Bruce M.), Loh, M. (Marie), Bis, J. C. (Joshua C.), Lehne, B. (Benjamin), Sotoodehnia, N. (Nona), Deary, I. J. (Ian J.), Chadeau-Hyam, M. (Marc), Brody, J. A. (Jennifer A.), Cardona, A. (Alexia), Selvin, E. (Elizabeth), Smith, A. K. (Alicia K.), Miller, A. H. (Andrew H.), Torres, M. A. (Mylin A.), Marouli, E. (Eirini), Gao, X. (Xin), van Meurs, J. B. (Joyce B. J.), Graf-Schindler, J. (Johanna), Rathmann, W. (Wolfgang), Koenig, W. (Wolfgang), Peters, A. (Annette), Weninger, W. (Wolfgang), Farlik, M. (Matthias), Zhang, T. (Tao), Chen, W. (Wei), Xia, Y. (Yujing), Teumer, A. (Alexander), Nauck, M. (Matthias), Grabe, H. J. (Hans J.), Doerr, M. (Macus), Lehtimaki, T. (Terho), Guan, W. (Weihua), Milani, L. (Lili), Tanaka, T. (Toshiko), Fisher, K. (Krista), Waite, L. L. (Lindsay L.), Kasela, S. (Silva), Vineis, P. (Paolo), Verweij, N. (Niek), van der Harst, P. (Pim), Iacoviello, L. (Licia), Sacerdote, C. (Carlotta), Panico, S. (Salvatore), Krogh, V. (Vittorio), Tumino, R. (Rosario), Tzala, E. (Evangelia), Matullo, G. (Giuseppe), Hurme, M. A. (Mikko A.), Raitakari, O. T. (Olli T.), Colicino, E. (Elena), Baccarelli, A. A. (Andrea A.), Kahonen, M. (Mika), Herzig, K.-H. (Karl-Heinz), Li, S. (Shengxu), BIOS consortium, Conneely, K. N. (Karen N.), Kooner, J. S. (Jaspal S.), Kottgen, A. (Anna), Heijmans, B. T. (Bastiaan T.), Deloukas, P. (Panos), Relton, C. (Caroline), Ong, K. K. (Ken K.), Bell, J. T. (Jordana T.), Boerwinkle, E. (Eric), Elliott, P. (Paul), Brenner, H. (Hermann), Beekman, M. (Marian), Levy, D. (Daniel), Waldenberger, M. (Melanie), Chambers, J. C. (John C.), Dehghan, A. (Abbas), and Järvelin, M.-R. (Marjo-Riitta)

Abstract

We performed a multi-ethnic Epigenome Wide Association study on 22,774 individuals to describe the DNA methylation signature of chronic low-grade inflammation as measured by C-Reactive protein (CRP). We find 1,511 independent differentially methylated loci associated with CRP. These CpG sites show correlation structures across chromosomes, and are primarily situated in euchromatin, depleted in CpG islands. These genomic loci are predominantly situated in transcription factor binding sites and genomic enhancer regions. Mendelian randomization analysis suggests altered CpG methylation is a consequence of increased blood CRP levels. Mediation analysis reveals obesity and smoking as important underlying driving factors for changed CpG methylation. Finally, we find that an activated CpG signature significantly increases the risk for cardiometabolic diseases and COPD.

Published
2022

8. Does Gender Matter? Job Stress, Work-Life Balance, Health and Job Satisfaction among University Teachers in India.

Author

Solanki, Sandip and Mandaviya, Meeta

Subjects
JOB stress, WORK-life balance, JOB satisfaction, PSYCHOLOGY of college teachers, INDIAN women (Asians)
Abstract

This study investigates gender differences in the perceived level of stress of university instructors in India. An online cross-sectional survey was completed with 86 respondents comprised of 51 males and 35 females in the state of Gujarat. Results indicate that job stress on work-life balance is significantly stronger for females. Additionally, male respondents scored higher in managing anger at work compared with female respondents and reveal a stronger detachment with work. Further, male respondents have more health-related issues compared with females due to job stress and imbalance in work life, while females exhibit lower career resilience due to family characteristics and responsibilities. This research contributes to the research on work-life balance specific to the teaching profession. Originality/value: To the best of the author's knowledge this study is unique and different from other studies as this is the first study concerning India. [ABSTRACT FROM AUTHOR]

Published
2021

9. An epigenome-wide association study meta-analysis of educational attainment

Author

Karlsson Linnér, R, Marioni, R E, Rietveld, C A, Simpkin, A J, Davies, N M, Watanabe, K, Armstrong, N J, Auro, K, Baumbach, C, Bonder, M J, Buchwald, J, Fiorito, G, Ismail, K, Iurato, S, Joensuu, A, Karell, P, Kasela, S, Lahti, J, McRae, A F, Mandaviya, P R, Seppälä, I, Wang, Y, Baglietto, L, Binder, E B, Harris, S E, Hodge, A M, Horvath, S, Hurme, M, Johannesson, M, Latvala, A, Mather, K A, Medland, S E, Metspalu, A, Milani, L, Milne, R L, Pattie, A, Pedersen, N L, Peters, A, Polidoro, S, Räikkönen, K, Severi, G, Starr, J M, Stolk, L, Waldenberger, M, Eriksson, J G, Esko, T, Franke, L, Gieger, C, Relton, C, Davey Smith, G, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Department of Health and Life Sciences, and Stem Cell Aging Leukemia and Lymphoma (SALL)

Subjects
EPIGENETIC CLOCK, AGE, STRESS, TISSUE, MORTALITY, PATTERNS, SOCIOECONOMIC-STATUS, MATERNAL SMOKING, EXPOSURE, DNA METHYLATION
Abstract

The epigenome is associated with biological factors, such as disease status, and environmental factors, such as smoking, alcohol consumption and body mass index. Although there is a widespread perception that environmental influences on the epigenome are pervasive and profound, there has been little evidence to date in humans with respect to environmental factors that are biologically distal. Here we provide evidence on the associations between epigenetic modifications-in our case, CpG methylation-and educational attainment (EA), a biologically distal environmental factor that is arguably among the most important life-shaping experiences for individuals. Specifically, we report the results of an epigenome-wide association study meta-analysis of EA based on data from 27 cohort studies with a total of 10 767 individuals. We find nine CpG probes significantly associated with EA. However, robustness analyses show that all nine probes have previously been found to be associated with smoking. Only two associations remain when we perform a sensitivity analysis in the subset of never-smokers, and these two probes are known to be strongly associated with maternal smoking during pregnancy, and thus their association with EA could be due to correlation between EA and maternal smoking. Moreover, the effect sizes of the associations with EA are far smaller than the known associations with the biologically proximal environmental factors alcohol consumption, body mass index, smoking and maternal smoking during pregnancy. Follow-up analyses that combine the effects of many probes also point to small methylation associations with EA that are highly correlated with the combined effects of smoking. If our findings regarding EA can be generalized to other biologically distal environmental factors, then they cast doubt on the hypothesis that such factors have large effects on the epigenome.Molecular Psychiatry advance online publication, 31 October 2017; doi:10.1038/mp.2017.210.

Published
2017
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10. Association of maternal prenatal smoking GFI1-locus and cardio-metabolic phenotypes in 18,212 adults

Author

Parmar, P. (Priyanka), Lowry, E. (Estelle), Cugliari, G. (Giovanni), Suderman, M. (Matthew), Wilson, R. (Rory), Karhunen, V. (Ville), Andrew, T. (Toby), Wiklund, P. (Petri), Wielscher, M. (Matthias), Guarrera, S. (Simonetta), Teumer, A. (Alexander), Lehne, B. (Benjamin), Milani, L. (Lili), de Klein, N. (Niek), Mishra, P. P. (Pashupati P.), Melton, P. E. (Phillip E.), Mandaviya, P. R. (Pooja R.), Kasela, S. (Silva), Nano, J. (Jana), Zhang, W. (Weihua), Zhang, Y. (Yan), Uitterlinden, A. G. (Andre G.), Peters, A. (Annette), Schoettker, B. (Ben), Gieger, C. (Christian), Anderson, D. (Denise), Boomsma, D. I. (Dorret, I), Grabe, H. J. (Hans J.), Panico, S. (Salvatore), Veldink, J. H. (Jan H.), van Meurs, J. B. (Joyce B. J.), van den Berg, L. (Leonard), Beilin, L. J. (Lawrence J.), Franke, L. (Lude), Loh, M. (Marie), van Greevenbroek, M. M. (Marleen M. J.), Nauck, M. (Matthias), Kahonen, M. (Mika), Hurme, M. A. (Mikko A.), Raitakari, O. T. (Olli T.), Franco, O. H. (Oscar H.), Slagboom, P. E. (P. Eline), van der Harst, P. (Pim), Kunze, S. (Sonja), Felix, S. B. (Stephan B.), Zhang, T. (Tao), Chen, W. (Wei), Mori, T. A. (Trevor A.), Bonnefond, A. (Amelie), Heijmans, B. T. (Bastiaan T.), Muka, T. (Taulant), Kooner, J. S. (Jaspal S.), Fischer, K. (Krista), Waldenberger, M. (Melanie), Froguel, P. (Philippe), Huang, R.-C. (Rae-Chi), Lehtimaki, T. (Terho), Rathmann, W. (Wolfgang), Relton, C. L. (Caroline L.), Matullo, G. (Giuseppe), Brenner, H. (Hermann), Verweij, N. (Niek), Li, S. (Shengxu), Chambers, J. C. (John C.), Jarvelin, M.-R. (Marjo-Riitta), Sebert, S. (Sylvain), Parmar, P. (Priyanka), Lowry, E. (Estelle), Cugliari, G. (Giovanni), Suderman, M. (Matthew), Wilson, R. (Rory), Karhunen, V. (Ville), Andrew, T. (Toby), Wiklund, P. (Petri), Wielscher, M. (Matthias), Guarrera, S. (Simonetta), Teumer, A. (Alexander), Lehne, B. (Benjamin), Milani, L. (Lili), de Klein, N. (Niek), Mishra, P. P. (Pashupati P.), Melton, P. E. (Phillip E.), Mandaviya, P. R. (Pooja R.), Kasela, S. (Silva), Nano, J. (Jana), Zhang, W. (Weihua), Zhang, Y. (Yan), Uitterlinden, A. G. (Andre G.), Peters, A. (Annette), Schoettker, B. (Ben), Gieger, C. (Christian), Anderson, D. (Denise), Boomsma, D. I. (Dorret, I), Grabe, H. J. (Hans J.), Panico, S. (Salvatore), Veldink, J. H. (Jan H.), van Meurs, J. B. (Joyce B. J.), van den Berg, L. (Leonard), Beilin, L. J. (Lawrence J.), Franke, L. (Lude), Loh, M. (Marie), van Greevenbroek, M. M. (Marleen M. J.), Nauck, M. (Matthias), Kahonen, M. (Mika), Hurme, M. A. (Mikko A.), Raitakari, O. T. (Olli T.), Franco, O. H. (Oscar H.), Slagboom, P. E. (P. Eline), van der Harst, P. (Pim), Kunze, S. (Sonja), Felix, S. B. (Stephan B.), Zhang, T. (Tao), Chen, W. (Wei), Mori, T. A. (Trevor A.), Bonnefond, A. (Amelie), Heijmans, B. T. (Bastiaan T.), Muka, T. (Taulant), Kooner, J. S. (Jaspal S.), Fischer, K. (Krista), Waldenberger, M. (Melanie), Froguel, P. (Philippe), Huang, R.-C. (Rae-Chi), Lehtimaki, T. (Terho), Rathmann, W. (Wolfgang), Relton, C. L. (Caroline L.), Matullo, G. (Giuseppe), Brenner, H. (Hermann), Verweij, N. (Niek), Li, S. (Shengxu), Chambers, J. C. (John C.), Jarvelin, M.-R. (Marjo-Riitta), and Sebert, S. (Sylvain)

Abstract

Background: DNA methylation at the GFI1-locus has been repeatedly associated with exposure to smoking from the foetal period onwards. We explored whether DNA methylation may be a mechanism that links exposure to maternal prenatal smoking with offspring’s adult cardio-metabolic health. Methods: We meta-analysed the association between DNA methylation at GFI1-locus with maternal prenatal smoking, adult own smoking, and cardio-metabolic phenotypes in 22 population-based studies from Europe, Australia, and USA (n = 18,212). DNA methylation at the GFI1-locus was measured in whole-blood. Multivariable regression models were fitted to examine its association with exposure to prenatal and own adult smoking. DNA methylation levels were analysed in relation to body mass index (BMI), waist circumference (WC), fasting glucose (FG), high-density lipoprotein cholesterol (HDL—C), triglycerides (TG), diastolic, and systolic blood pressure (BP). Findings: Lower DNA methylation at three out of eight GFI1-CpGs was associated with exposure to maternal prenatal smoking, whereas, all eight CpGs were associated with adult own smoking. Lower DNA methylation at cg14179389, the strongest maternal prenatal smoking locus, was associated with increased WC and BP when adjusted for sex, age, and adult smoking with Bonferroni-corrected P < 0·012. In contrast, lower DNA methylation at cg09935388, the strongest adult own smoking locus, was associated with decreased BMI, WC, and BP (adjusted 1 × 10⁻⁷ < P < 0.01). Similarly, lower DNA methylation at cg12876356, cg18316974, cg09662411, and cg18146737 was associated with decreased BMI and WC (5 × 10⁻⁸ < P < 0.001). Lower DNA methylation at all the CpGs was consistently associated with higher TG levels. Interpretation: Epigenetic changes at the GFI1 were linked to smoking exposure in-utero/in-adulthood and robustly associated with cardio-metabolic risk factors. Fund: European Union’s Horizon 2020 research and innovation programme

Published
2018

12. Evaluation of commonly used analysis strategies for epigenome- and transcriptome-wide association studies through replication of large-scale population studies

Author

van Rooij, Jeroen, Mandaviya, Pooja R., Claringbould, Annique, Felix, Janine F., van Dongen, Jenny, Jansen, Rick, Franke, Lude, ’t Hoen, Peter A. C., Heijmans, Bas, and van Meurs, Joyce B. J.

Abstract

Background: A large number of analysis strategies are available for DNA methylation (DNAm) array and RNA-seq datasets, but it is unclear which strategies are best to use. We compare commonly used strategies and report how they influence results in large cohort studies. Results: We tested the associations of DNAm and RNA expression with age, BMI, and smoking in four different cohorts (n= ~ 2900). By comparing strategies against the base model on the number and percentage of replicated CpGs for DNAm analyses or genes for RNA-seq analyses in a leave-one-out cohort replication approach, we find the choice of the normalization method and statistical test does not strongly influence the results for DNAm array data. However, adjusting for cell counts or hidden confounders substantially decreases the number of replicated CpGs for age and increases the number of replicated CpGs for BMI and smoking. For RNA-seq data, the choice of the normalization method, gene expression inclusion threshold, and statistical test does not strongly influence the results. Including five principal components or excluding correction of technical covariates or cell counts decreases the number of replicated genes. Conclusions: Results were not influenced by the normalization method or statistical test. However, the correction method for cell counts, technical covariates, principal components, and/or hidden confounders does influence the results.

Published
2019
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13. Medication Use is Associated with Distinct Microbial Features in Anxiety and Depression.

Author

Dilmore AH, Kuplicki R, McDonald D, Kumar M, Estaki M, Youngblut N, Tyakht A, Ackermann G, Blach C, MahmoudianDehkordi S, Dunlop BW, Bhattacharyya S, Guinjoan S, Mandaviya P, Ley RE, Kaddaruh-Dauok R, Paulus MP, and Knight R

Abstract

This study investigated the relationship between gut microbiota and neuropsychiatric disorders (NPDs), specifically anxiety disorder (ANXD) and/or major depressive disorder (MDD), as defined by DSM-IV or V criteria. The study also examined the influence of medication use, particularly antidepressants and/or anxiolytics, classified through the Anatomical Therapeutic Chemical (ATC) Classification System, on the gut microbiota. Both 16S rRNA gene amplicon sequencing and shallow shotgun sequencing were performed on DNA extracted from 666 fecal samples from the Tulsa-1000 and NeuroMAP CoBRE cohorts. The results highlight the significant influence of medication use; antidepressant use is associated with significant differences in gut microbiota beta diversity and has a larger effect size than NPD diagnosis. Next, specific microbes were associated with ANXD and MDD, highlighting their potential for non-pharmacological intervention. Finally, the study demonstrated the capability of Random Forest classifiers to predict diagnoses of NPD and medication use from microbial profiles, suggesting a promising direction for the use of gut microbiota as biomarkers for NPD. The findings suggest that future research on the gut microbiota's role in NPD and its interactions with pharmacological treatments are needed., Competing Interests: 9.Conflicts of Interest: Daniel McDonald is a consultant for, and has equity in, BiomeSense, Inc. Mehrbod Estaki is the chief science officer and has equity at Innovate Phytoceuticals Inc. He is a scientific advisor and holds equity at Melius Microbiomics Inc. Rima Kaddurah-Daouk is an inventor on key patents in the field of Metabolomics and holds equity in Metabolon. In addition, she holds patents licensed to Chymia LLC and PsyProtix with royalties and ownership. Rob Knight is a scientific advisory board member, and consultant for BiomeSense, Inc., has equity and receives income. He is a scientific advisory board member and has equity in GenCirq. He is a consultant and scientific advisory board member for DayTwo, and receives income. He has equity in and acts as a consultant for Cybele. He is a co-founder of Biota, Inc., and has equity. He is a cofounder of Micronoma, and has equity and is a scientific advisory board member. The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. The companies listed here had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the paper; and decision to submit the paper for publication. Pieter Dorrestein (of the AGMP consortium) is an advisor and holds equity in Cybele and Sirenas and a Scientific co-founder, advisor and holds equity to Ometa, Enveda, and Arome with prior approval by UC San Diego. Pieter Dorrestein also consulted for DSM animal health in 2023.

Published
2024
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14. CyTargetLinker: a cytoscape app to integrate regulatory interactions in network analysis.

Author

Kutmon M, Kelder T, Mandaviya P, Evelo CT, and Coort SL

Subjects
Animals, Humans, Internet, Mice, Databases, Genetic, Diabetes Mellitus genetics, Diabetes Mellitus metabolism, Epistasis, Genetic, Gene Expression Regulation, Neural Networks, Computer, Transcription, Genetic
Abstract

Introduction: The high complexity and dynamic nature of the regulation of gene expression, protein synthesis, and protein activity pose a challenge to fully understand the cellular machinery. By deciphering the role of important players, including transcription factors, microRNAs, or small molecules, a better understanding of key regulatory processes can be obtained. Various databases contain information on the interactions of regulators with their targets for different organisms, data recently being extended with the results of the ENCODE (Encyclopedia of DNA Elements) project. A systems biology approach integrating our understanding on different regulators is essential in interpreting the regulation of molecular biological processes., Implementation: We developed CyTargetLinker (http://projects.bigcat.unimaas.nl/cytargetlinker), a Cytoscape app, for integrating regulatory interactions in network analysis. Recently we released CyTargetLinker as one of the first apps for Cytoscape 3. It provides a user-friendly and flexible interface to extend biological networks with regulatory interactions, such as microRNA-target, transcription factor-target and/or drug-target. Importantly, CyTargetLinker employs identifier mapping to combine various interaction data resources that use different types of identifiers., Results: Three case studies demonstrate the strength and broad applicability of CyTargetLinker, (i) extending a mouse molecular interaction network, containing genes linked to diabetes mellitus, with validated and predicted microRNAs, (ii) enriching a molecular interaction network, containing DNA repair genes, with ENCODE transcription factor and (iii) building a regulatory meta-network in which a biological process is extended with information on transcription factor, microRNA and drug regulation., Conclusions: CyTargetLinker provides a simple and extensible framework for biologists and bioinformaticians to integrate different regulatory interactions into their network analysis approaches. Visualization options enable biological interpretation of complex regulatory networks in a graphical way. Importantly the incorporation of our tool into the Cytoscape framework allows the application of CyTargetLinker in combination with a wide variety of other apps for state-of-the-art network analysis.

Published
2013
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