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1. Fine-mapping analysis including over 254,000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes

Author

Zhishan Chen, Xingyi Guo, Ran Tao, Jeroen R. Huyghe, Philip J. Law, Ceres Fernandez-Rozadilla, Jie Ping, Guochong Jia, Jirong Long, Chao Li, Quanhu Shen, Yuhan Xie, Maria N. Timofeeva, Minta Thomas, Stephanie L. Schmit, Virginia Díez-Obrero, Matthew Devall, Ferran Moratalla-Navarro, Juan Fernandez-Tajes, Claire Palles, Kitty Sherwood, Sarah E. W. Briggs, Victoria Svinti, Kevin Donnelly, Susan M. Farrington, James Blackmur, Peter G. Vaughan-Shaw, Xiao-Ou Shu, Yingchang Lu, Peter Broderick, James Studd, Tabitha A. Harrison, David V. Conti, Fredrick R. Schumacher, Marilena Melas, Gad Rennert, Mireia Obón-Santacana, Vicente Martín-Sánchez, Jae Hwan Oh, Jeongseon Kim, Sun Ha Jee, Keum Ji Jung, Sun-Seog Kweon, Min-Ho Shin, Aesun Shin, Yoon-Ok Ahn, Dong-Hyun Kim, Isao Oze, Wanqing Wen, Keitaro Matsuo, Koichi Matsuda, Chizu Tanikawa, Zefang Ren, Yu-Tang Gao, Wei-Hua Jia, John L. Hopper, Mark A. Jenkins, Aung Ko Win, Rish K. Pai, Jane C. Figueiredo, Robert W. Haile, Steven Gallinger, Michael O. Woods, Polly A. Newcomb, David Duggan, Jeremy P. Cheadle, Richard Kaplan, Rachel Kerr, David Kerr, Iva Kirac, Jan Böhm, Jukka-Pekka Mecklin, Pekka Jousilahti, Paul Knekt, Lauri A. Aaltonen, Harri Rissanen, Eero Pukkala, Johan G. Eriksson, Tatiana Cajuso, Ulrika Hänninen, Johanna Kondelin, Kimmo Palin, Tomas Tanskanen, Laura Renkonen-Sinisalo, Satu Männistö, Demetrius Albanes, Stephanie J. Weinstein, Edward Ruiz-Narvaez, Julie R. Palmer, Daniel D. Buchanan, Elizabeth A. Platz, Kala Visvanathan, Cornelia M. Ulrich, Erin Siegel, Stefanie Brezina, Andrea Gsur, Peter T. Campbell, Jenny Chang-Claude, Michael Hoffmeister, Hermann Brenner, Martha L. Slattery, John D. Potter, Kostas K. Tsilidis, Matthias B. Schulze, Marc J. Gunter, Neil Murphy, Antoni Castells, Sergi Castellví-Bel, Leticia Moreira, Volker Arndt, Anna Shcherbina, D. Timothy Bishop, Graham G. Giles, Melissa C. Southey, Gregory E. Idos, Kevin J. McDonnell, Zomoroda Abu-Ful, Joel K. Greenson, Katerina Shulman, Flavio Lejbkowicz, Kenneth Offit, Yu-Ru Su, Robert Steinfelder, Temitope O. Keku, Bethany van Guelpen, Thomas J. Hudson, Heather Hampel, Rachel Pearlman, Sonja I. Berndt, Richard B. Hayes, Marie Elena Martinez, Sushma S. Thomas, Paul D. P. Pharoah, Susanna C. Larsson, Yun Yen, Heinz-Josef Lenz, Emily White, Li Li, Kimberly F. Doheny, Elizabeth Pugh, Tameka Shelford, Andrew T. Chan, Marcia Cruz-Correa, Annika Lindblom, David J. Hunter, Amit D. Joshi, Clemens Schafmayer, Peter C. Scacheri, Anshul Kundaje, Robert E. Schoen, Jochen Hampe, Zsofia K. Stadler, Pavel Vodicka, Ludmila Vodickova, Veronika Vymetalkova, Christopher K. Edlund, W. James Gauderman, David Shibata, Amanda Toland, Sanford Markowitz, Andre Kim, Stephen J. Chanock, Franzel van Duijnhoven, Edith J. M. Feskens, Lori C. Sakoda, Manuela Gago-Dominguez, Alicja Wolk, Barbara Pardini, Liesel M. FitzGerald, Soo Chin Lee, Shuji Ogino, Stephanie A. Bien, Charles Kooperberg, Christopher I. Li, Yi Lin, Ross Prentice, Conghui Qu, Stéphane Bézieau, Taiki Yamaji, Norie Sawada, Motoki Iwasaki, Loic Le Marchand, Anna H. Wu, Chenxu Qu, Caroline E. McNeil, Gerhard Coetzee, Caroline Hayward, Ian J. Deary, Sarah E. Harris, Evropi Theodoratou, Stuart Reid, Marion Walker, Li Yin Ooi, Ken S. Lau, Hongyu Zhao, Li Hsu, Qiuyin Cai, Malcolm G. Dunlop, Stephen B. Gruber, Richard S. Houlston, Victor Moreno, Graham Casey, Ulrike Peters, Ian Tomlinson, and Wei Zheng

Subjects
Science
Abstract

Abstract Genome-wide association studies (GWAS) have identified more than 200 common genetic variants independently associated with colorectal cancer (CRC) risk, but the causal variants and target genes are mostly unknown. We sought to fine-map all known CRC risk loci using GWAS data from 100,204 cases and 154,587 controls of East Asian and European ancestry. Our stepwise conditional analyses revealed 238 independent association signals of CRC risk, each with a set of credible causal variants (CCVs), of which 28 signals had a single CCV. Our cis-eQTL/mQTL and colocalization analyses using colorectal tissue-specific transcriptome and methylome data separately from 1299 and 321 individuals, along with functional genomic investigation, uncovered 136 putative CRC susceptibility genes, including 56 genes not previously reported. Analyses of single-cell RNA-seq data from colorectal tissues revealed 17 putative CRC susceptibility genes with distinct expression patterns in specific cell types. Analyses of whole exome sequencing data provided additional support for several target genes identified in this study as CRC susceptibility genes. Enrichment analyses of the 136 genes uncover pathways not previously linked to CRC risk. Our study substantially expanded association signals for CRC and provided additional insight into the biological mechanisms underlying CRC development.

Published
2024
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2. A prospective study of the relationships between movement and glycemic control during day and night in pregnancy

Author

Masoud Behravesh, Juan Fernandez-Tajes, Angela C. Estampador, Tibor V. Varga, Ómar S. Gunnarsson, Helena Strevens, Simon Timpka, and Paul W. Franks

Subjects
Medicine, Science
Abstract

Abstract Both disturbed sleep and lack of exercise can disrupt metabolism in pregnancy. Accelerometery was used to objectively assess movement during waking (physical activity) and movement during sleeping (sleep disturbance) periods and evaluated relationships with continuous blood glucose variation during pregnancy. Data was analysed prospectively. 15-women without pre-existing diabetes mellitus wore continuous glucose monitors and triaxial accelerometers from February through June 2018 in Sweden. The relationships between physical activity and sleep disturbance with blood glucose rate of change were assessed. An interaction term was fitted to determine difference in the relationship between movement and glucose variation, conditional on waking/sleeping. Total movement was inversely related to glucose rate of change (p

Published
2021
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3. Association analyses identify 31 new risk loci for colorectal cancer susceptibility

Author

Philip J. Law, Maria Timofeeva, Ceres Fernandez-Rozadilla, Peter Broderick, James Studd, Juan Fernandez-Tajes, Susan Farrington, Victoria Svinti, Claire Palles, Giulia Orlando, Amit Sud, Amy Holroyd, Steven Penegar, Evropi Theodoratou, Peter Vaughan-Shaw, Harry Campbell, Lina Zgaga, Caroline Hayward, Archie Campbell, Sarah Harris, Ian J. Deary, John Starr, Laura Gatcombe, Maria Pinna, Sarah Briggs, Lynn Martin, Emma Jaeger, Archana Sharma-Oates, James East, Simon Leedham, Roland Arnold, Elaine Johnstone, Haitao Wang, David Kerr, Rachel Kerr, Tim Maughan, Richard Kaplan, Nada Al-Tassan, Kimmo Palin, Ulrika A. Hänninen, Tatiana Cajuso, Tomas Tanskanen, Johanna Kondelin, Eevi Kaasinen, Antti-Pekka Sarin, Johan G. Eriksson, Harri Rissanen, Paul Knekt, Eero Pukkala, Pekka Jousilahti, Veikko Salomaa, Samuli Ripatti, Aarno Palotie, Laura Renkonen-Sinisalo, Anna Lepistö, Jan Böhm, Jukka-Pekka Mecklin, Daniel D. Buchanan, Aung-Ko Win, John Hopper, Mark E. Jenkins, Noralane M. Lindor, Polly A. Newcomb, Steven Gallinger, David Duggan, Graham Casey, Per Hoffmann, Markus M. Nöthen, Karl-Heinz Jöckel, Douglas F. Easton, Paul D. P. Pharoah, Julian Peto, Federico Canzian, Anthony Swerdlow, Rosalind A. Eeles, Zsofia Kote-Jarai, Kenneth Muir, Nora Pashayan, The PRACTICAL consortium, Andrea Harkin, Karen Allan, John McQueen, James Paul, Timothy Iveson, Mark Saunders, Katja Butterbach, Jenny Chang-Claude, Michael Hoffmeister, Hermann Brenner, Iva Kirac, Petar Matošević, Philipp Hofer, Stefanie Brezina, Andrea Gsur, Jeremy P. Cheadle, Lauri A. Aaltonen, Ian Tomlinson, Richard S. Houlston, and Malcolm G. Dunlop

Subjects
Science
Abstract

In colorectal cancer (CRC), finding loci associated with risk may give insight into disease aetiology. Here, the authors report a genome-wide association analysis in Europeans of 34,627 CRC cases and 71,379 controls, and find 31 new risk loci and 17 new risk SNPs at previously reported loci.

Published
2019
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4. Correction: Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria.

Author

Felix Day, Tugce Karaderi, Michelle R Jones, Cindy Meun, Chunyan He, Alex Drong, Peter Kraft, Nan Lin, Hongyan Huang, Linda Broer, Reedik Magi, Richa Saxena, Triin Laisk, Margrit Urbanek, M Geoffrey Hayes, Gudmar Thorleifsson, Juan Fernandez-Tajes, Anubha Mahajan, Benjamin H Mullin, Bronwyn G A Stuckey, Timothy D Spector, Scott G Wilson, Mark O Goodarzi, Lea Davis, Barbara Obermayer-Pietsch, André G Uitterlinden, Verneri Anttila, Benjamin M Neale, Marjo-Riitta Jarvelin, Bart Fauser, Irina Kowalska, Jenny A Visser, Marianne Andersen, Ken Ong, Elisabet Stener-Victorin, David Ehrmann, Richard S Legro, Andres Salumets, Mark I McCarthy, Laure Morin-Papunen, Unnur Thorsteinsdottir, Kari Stefansson, andMe Research Team, Unnur Styrkarsdottir, John R B Perry, Andrea Dunaif, Joop Laven, Steve Franks, Cecilia M Lindgren, and Corrine K Welt

Subjects
Genetics, QH426-470
Abstract

[This corrects the article DOI: 10.1371/journal.pgen.1007813.].

Published
2019
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5. Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria.

Author

Felix Day, Tugce Karaderi, Michelle R Jones, Cindy Meun, Chunyan He, Alex Drong, Peter Kraft, Nan Lin, Hongyan Huang, Linda Broer, Reedik Magi, Richa Saxena, Triin Laisk, Margrit Urbanek, M Geoffrey Hayes, Gudmar Thorleifsson, Juan Fernandez-Tajes, Anubha Mahajan, Benjamin H Mullin, Bronwyn G A Stuckey, Timothy D Spector, Scott G Wilson, Mark O Goodarzi, Lea Davis, Barbara Obermayer-Pietsch, André G Uitterlinden, Verneri Anttila, Benjamin M Neale, Marjo-Riitta Jarvelin, Bart Fauser, Irina Kowalska, Jenny A Visser, Marianne Andersen, Ken Ong, Elisabet Stener-Victorin, David Ehrmann, Richard S Legro, Andres Salumets, Mark I McCarthy, Laure Morin-Papunen, Unnur Thorsteinsdottir, Kari Stefansson, andMe Research Team, Unnur Styrkarsdottir, John R B Perry, Andrea Dunaif, Joop Laven, Steve Franks, Cecilia M Lindgren, and Corrine K Welt

Subjects
Genetics, QH426-470
Abstract

Polycystic ovary syndrome (PCOS) is a disorder characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. Affected women frequently have metabolic disturbances including insulin resistance and dysregulation of glucose homeostasis. PCOS is diagnosed with two different sets of diagnostic criteria, resulting in a phenotypic spectrum of PCOS cases. The genetic similarities between cases diagnosed based on the two criteria have been largely unknown. Previous studies in Chinese and European subjects have identified 16 loci associated with risk of PCOS. We report a fixed-effect, inverse-weighted-variance meta-analysis from 10,074 PCOS cases and 103,164 controls of European ancestry and characterisation of PCOS related traits. We identified 3 novel loci (near PLGRKT, ZBTB16 and MAPRE1), and provide replication of 11 previously reported loci. Only one locus differed significantly in its association by diagnostic criteria; otherwise the genetic architecture was similar between PCOS diagnosed by self-report and PCOS diagnosed by NIH or non-NIH Rotterdam criteria across common variants at 13 loci. Identified variants were associated with hyperandrogenism, gonadotropin regulation and testosterone levels in affected women. Linkage disequilibrium score regression analysis revealed genetic correlations with obesity, fasting insulin, type 2 diabetes, lipid levels and coronary artery disease, indicating shared genetic architecture between metabolic traits and PCOS. Mendelian randomization analyses suggested variants associated with body mass index, fasting insulin, menopause timing, depression and male-pattern balding play a causal role in PCOS. The data thus demonstrate 3 novel loci associated with PCOS and similar genetic architecture for all diagnostic criteria. The data also provide the first genetic evidence for a male phenotype for PCOS and a causal link to depression, a previously hypothesized comorbid disease. Thus, the genetics provide a comprehensive view of PCOS that encompasses multiple diagnostic criteria, gender, reproductive potential and mental health.

Published
2018
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6. Unbiased high-throughput characterization of mussel transcriptomic responses to sublethal concentrations of the biotoxin okadaic acid

Author

Victoria Suarez-Ulloa, Juan Fernandez-Tajes, Vanessa Aguiar-Pulido, M. Veronica Prego-Faraldo, Fernanda Florez-Barros, Alexia Sexto-Iglesias, Josefina Mendez, and Jose M. Eirin-Lopez

Subjects
Okadaic acid, Marine pollution, Transcriptomics, Diarrhetic shellfish poisoning, Biomonitoring, Harmful algal blooms, Medicine, Biology (General), QH301-705.5
Abstract

Background. Harmful Algal Blooms (HABs) responsible for Diarrhetic Shellfish Poisoning (DSP) represent a major threat for human consumers of shellfish. The biotoxin Okadaic Acid (OA), a well-known phosphatase inhibitor and tumor promoter, is the primary cause of acute DSP intoxications. Although several studies have described the molecular effects of high OA concentrations on sentinel organisms (e.g., bivalve molluscs), the effect of prolonged exposures to low (sublethal) OA concentrations is still unknown. In order to fill this gap, this work combines Next-Generation sequencing and custom-made microarray technologies to develop an unbiased characterization of the transcriptomic response of mussels during early stages of a DSP bloom.Methods. Mussel specimens were exposed to a HAB episode simulating an early stage DSP bloom (200 cells/L of the dinoflagellate Prorocentrum lima for 24 h). The unbiased characterization of the transcriptomic responses triggered by OA was carried out using two complementary methods of cDNA library preparation: normalized and Suppression Subtractive Hybridization (SSH). Libraries were sequenced and read datasets were mapped to Gene Ontology and KEGG databases. A custom-made oligonucleotide microarray was developed based on these data, completing the expression analysis of digestive gland and gill tissues.Results. Our findings show that exposure to sublethal concentrations of OA is enough to induce gene expression modifications in the mussel Mytilus. Transcriptomic analyses revealed an increase in proteasomal activity, molecular transport, cell cycle regulation, energy production and immune activity in mussels. Oppositely, a number of transcripts hypothesized to be responsive to OA (notably the Serine/Threonine phosphatases PP1 and PP2A) failed to show substantial modifications. Both digestive gland and gill tissues responded similarly to OA, although expression modifications were more dramatic in the former, supporting the choice of this tissue for future biomonitoring studies.Discussion. Exposure to OA concentrations within legal limits for safe consumption of shellfish is enough to disrupt important cellular processes in mussels, eliciting sharp transcriptional changes as a result. By combining the study of cDNA libraries and a custom-made OA-specific microarray, our work provides a comprehensive characterization of the OA-specific transcriptome, improving the accuracy of the analysis of expresion profiles compared to single-replicated RNA-seq methods. The combination of our data with related studies helps understanding the molecular mechanisms underlying molecular responses to DSP episodes in marine organisms, providing useful information to develop a new generation of tools for the monitoring of OA pollution.

Published
2015
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7. Investigating the causal relationships between excess adiposity and cardiometabolic health in men and women

Author

Pascal M. Mutie, Hugo Pomares-Milan, Naeimeh Atabaki-Pasdar, Daniel Coral, Hugo Fitipaldi, Neli Tsereteli, Juan Fernandez Tajes, Paul W. Franks, and Giuseppe N. Giordano

Subjects
Male, Endocrinology, Diabetes and Metabolism, Coronary Artery Disease, Cholesterol, LDL, Mendelian Randomization Analysis, Body Mass Index, Diabetes Mellitus, Type 2, Risk Factors, Cardiovascular Diseases, Internal Medicine, Humans, Female, Obesity, Biomarkers, Adiposity
Abstract

Aims/hypothesis Excess adiposity is differentially associated with increased risk of cardiometabolic disease in men and women, according to observational studies. Causal inference studies largely assume a linear relationship between BMI and cardiometabolic outcomes, which may not be the case. In this study, we investigated the shapes of the causal relationships between BMI and cardiometabolic diseases and risk factors. We further investigated sex differences within the causal framework. Methods To assess causal relationships between BMI and the outcomes, we used two-stage least-squares Mendelian randomisation (MR), with a polygenic risk score for BMI as the instrumental variable. To elucidate the shapes of the causal relationships, we used a non-linear MR fractional polynomial method, and used piecewise MR to investigate threshold relationships and confirm the shapes. Results BMI was associated with type 2 diabetes (OR 3.10; 95% CI 2.73, 3.53), hypertension (OR 1.53; 95% CI 1.44, 1.62) and coronary artery disease (OR 1.20; 95% CI 1.08, 1.33), but not chronic kidney disease (OR 1.08; 95% CI 0.67, 1.72) or stroke (OR 1.08; 95% CI 0.92, 1.28). The data suggest that these relationships are non-linear. For cardiometabolic risk factors, BMI was positively associated with glucose, HbA1c, triacylglycerol levels and both systolic and diastolic BP. BMI had an inverse causal relationship with total cholesterol, LDL-cholesterol and HDL-cholesterol. The data suggest a non-linear causal relationship between BMI and BP and other biomarkers (pp Conclusions/interpretation We describe the shapes of causal effects of BMI on cardiometabolic diseases and risk factors, and report sex differences in the causal effects of BMI on LDL-cholesterol. We found evidence of non-linearity in the causal effect of BMI on diseases and risk factor biomarkers. Reducing excess adiposity is highly beneficial for health, but there is greater need to consider biological sex in the management of adiposity. Graphical abstract

Published
2022
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8. A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes

Author

Daniel E. Coral, Juan Fernandez-Tajes, Neli Tsereteli, Hugo Pomares-Millan, Hugo Fitipaldi, Pascal M. Mutie, Naeimeh Atabaki-Pasdar, Sebastian Kalamajski, Alaitz Poveda, Tyne W. Miller-Fleming, Xue Zhong, Giuseppe N. Giordano, Ewan R. Pearson, Nancy J. Cox, and Paul W. Franks

Subjects
Physiology (medical), Endocrinology, Diabetes and Metabolism, Internal Medicine, Cell Biology
Abstract

Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.

Published
2023

9. Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries

Author

Ceres Fernandez-Rozadilla, Maria Timofeeva, Zhishan Chen, Philip Law, Minta Thomas, Stephanie Schmit, Virginia Díez-Obrero, Li Hsu, Juan Fernandez-Tajes, Claire Palles, Kitty Sherwood, Sarah Briggs, Victoria Svinti, Kevin Donnelly, Susan Farrington, James Blackmur, Peter Vaughan-Shaw, Xiao-ou Shu, Jirong Long, Qiuyin Cai, Xingyi Guo, Yingchang Lu, Peter Broderick, James Studd, Jeroen Huyghe, Tabitha Harrison, David Conti, Christopher Dampier, Mathew Devall, Fredrick Schumacher, Marilena Melas, Gad Rennert, Mireia Obón-Santacana, Vicente Martín-Sánchez, Ferran Moratalla-Navarro, Jae Hwan Oh, Jeongseon Kim, Sun Ha Jee, Keum Ji Jung, Sun-Seog Kweon, Min-Ho Shin, Aesun Shin, Yoon-Ok Ahn, Dong-Hyun Kim, Isao Oze, Wanqing Wen, Keitaro Matsuo, Koichi Matsuda, Chizu Tanikawa, Zefang Ren, Yu-Tang Gao, Wei-Hua Jia, John Hopper, Mark Jenkins, Aung Ko Win, Rish Pai, Jane Figueiredo, Robert Haile, Steven Gallinger, Michael Woods, Polly Newcomb, David Duggan, Jeremy Cheadle, Richard Kaplan, Timothy Maughan, Rachel Kerr, David Kerr, Iva Kirac, Jan Böhm, Lukka-Pekka Mecklin, Pekka Jousilahti, Paul Knekt, Lauri Aaltonen, Harri Rissanen, Eero Pukkala, Johan Eriksson, Tatiana Cajuso, Ulrika Hänninen, Johanna Kondelin, Kimmo Palin, Tomas Tanskanen, Laura Renkonen-Sinisalo, Brent Zanke, Satu Männistö, Demetrius Albanes, Stephanie Weinstein, Edward Ruiz-Narvaez, Julie Palmer, Daniel Buchanan, Elizabeth Platz, Kala Visvanathan, Cornelia Ulrich, Erin Siegel, Stefanie Brezina, Andrea Gsur, Peter Campbell, Jenny Chang-Claude, Michael Hoffmeister, Hermann Brenner, Martha Slattery, John Potter, Konstantinos Tsilidis, Matthias Schulze, Marc Gunter, Neil Murphy, Antoni Castells, Sergi Castellví-Bel, Leticia Moreira, Volker Arndt, Anna Shcherbina, Mariana Stern, Bens Pardamean, Timothy Bishop, Graham Giles, Melissa Southey, Gregory Idos, Kevin McDonnell, Zomoroda Abu-Ful, Joel Greenson, Katerina Shulman, Flavio Lejbkowicz, Kenneth Offit, Yu-Ru Su, Robert Steinfelder, Temitope Keku, Bethany van Guelpen, Thomas Hudson, Heather Hampel, Rachel Pearlman, Sonja Berndt, Richard Hayes, Marie Elena Martinez, Sushma Thomas, Douglas Corley, Paul Pharoah, Susanna Larsson, Yun Yen, Heinz-Josef Lenz, Emily White, Li Li, Kimberly Doheny, Elizabeth Pugh, Tameka Shelford, Andrew Chan, Marcia Cruz-Correa, Annika Lindblom, David Hunter, Amit Joshi, Clemens Schafmayer, Peter Scacheri, Anshul Kundaje, Deborah Nickerson, Robert Schoen, Jochen Hampe, Zsofia Stadler, Pavel Vodicka, Ludmila Vodickova, Veronika Vymetalkova, Nickolas Papadopoulos, Chistopher Edlund, William Gauderman, Duncan Thomas, David Shibata, Amanda Toland, Sanford Markowitz, Andre Kim, Stephen Chanock, Franzel van Duijnhoven, Edith Feskens, Lori Sakoda, Manuela Gago-Dominguez, Alicja Wolk, Alessio Naccarati, Barbara Pardini, Liesel FitzGerald, Soo Chin Lee, Shuji Ogino, Stephanie Bien, Charles Kooperberg, Christopher Li, Yi Lin, Ross Prentice, Conghui Qu, Stéphane Bézieau, Catherine Tangen, Elaine Mardis, Taiki Yamaji, Norie Sawada, Motoki Iwasaki, Christopher Haiman, Loic Le Marchand, Anna Wu, Chenxu Qu, Caroline McNeil, Gerhard Coetzee, Caroline Hayward, Ian Deary, Sarah Harris, Evropi Theodoratou, Stuart Reid, Marion Walker, Li Yin Ooi, Victor Moreno, Graham Casey, Stephen Gruber, Ian Tomlinson, Wei Zheng, Malcolm Dunlop, Richard Houlston, and Ulrike Peters

Subjects
Global Nutrition, Wereldvoeding, Nutrition and Disease, Voeding en Ziekte, Genetics, Life Science, Article, VLAG
Abstract

Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and East Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions, and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Cross-tissue analyses indicated that over a third of effector genes most likely act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis, and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.

Published
2023
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10. Impact of insufficient sleep on dysregulated blood glucose control under standardised meal conditions

Author

Matthew P. Walker, Andrew T. Chan, Raphael Vallat, Jonathan Wolf, Juan Fernandez-Tajes, Linda M. Delahanty, Ana M. Valdes, David A. Drew, Tim D. Spector, Paul W. Franks, Nicola Segata, Neli Tsereteli, Sarah Berry, and Jose M. Ordovas

Subjects
Adult, Blood Glucose, Male, medicine.medical_specialty, Adolescent, Metabolic health, Endocrinology, Diabetes and Metabolism, 030209 endocrinology & metabolism, Glycemic Control, Bedtime, Article, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, Internal Medicine, medicine, Humans, Person-centring, Aged, Breakfast, 030304 developmental biology, Morning, 2. Zero hunger, 0303 health sciences, Meal, Diet, Postprandial glucose, Sleep, Female, Glycemic Index, Middle Aged, Postprandial Period, Sleep Deprivation, business.industry, Repeated measures design, Actigraphy, Sleep in non-human animals, Postprandial, business
Abstract

Aims/hypothesis Sleep, diet and exercise are fundamental to metabolic homeostasis. In this secondary analysis of a repeated measures, nutritional intervention study, we tested whether an individual’s sleep quality, duration and timing impact glycaemic response to a breakfast meal the following morning. Methods Healthy adults’ data (N = 953 [41% twins]) were analysed from the PREDICT dietary intervention trial. Participants consumed isoenergetic standardised meals over 2 weeks in the clinic and at home. Actigraphy was used to assess sleep variables (duration, efficiency, timing) and continuous glucose monitors were used to measure glycaemic variation (>8000 meals). Results Sleep variables were significantly associated with postprandial glycaemic control (2 h incremental AUC), at both between- and within-person levels. Sleep period time interacted with meal type, with a smaller effect of poor sleep on postprandial blood glucose levels when high-carbohydrate (low fat/protein) (pinteraction = 0.02) and high-fat (pinteraction = 0.03) breakfasts were consumed compared with a reference 75 g OGTT. Within-person sleep period time had a similar interaction (high carbohydrate: pinteraction = 0.001, high fat: pinteraction = 0.02). Within- and between-person sleep efficiency were significantly associated with lower postprandial blood glucose levels irrespective of meal type (both p p = 0.035 and p = 0.051, respectively). Conclusions/interpretation Poor sleep efficiency and later bedtime routines are associated with more pronounced postprandial glycaemic responses to breakfast the following morning. A person’s deviation from their usual sleep pattern was also associated with poorer postprandial glycaemic control. These findings underscore sleep as a modifiable, non-pharmacological therapeutic target for the optimal regulation of human metabolic health. Trial registrationClinicalTrials.gov NCT03479866. Graphical abstract

Published
2021
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11. Subtyping of obesity and type 2 diabetes using genetic discordance: a phenome-wide comparative analysis

Author

Daniel Coral, Juan Fernandez Tajes, Neli Tsereteli, Giuseppe Giordano, Hugo Pomares-Millan, Pascal Mutie, Naeimeh Atabaki-Pasdar, Sebastian Kalamajski, Alaitz Poveda, Tyne Miller-Fleming, Xue Zhong, Nancy Cox, and Paul Franks

Abstract

Obesity and type 2 diabetes are causally related, yet the underlying mechanisms are poorly defined. We aimed to subclassify obesity into etiological subtypes using a genetic-driven agnostic approach. Genetic instruments for body mass index and type 2 diabetes were classified into profiles that convey highly concordant (48 SNPs) and discordant (19 SNPs) diabetogenic effects. We annotated association signals for these instruments across clinical and molecular phenotypic layers, spanning > 5,000 traits. Using a combination of machine-learning techniques, key differences were identified in fat distribution, liver metabolism, blood pressure, lipids and branched-chain amino-acids, as well as the sulfotransferase HS6ST2 and several bacterial taxa belonging to the Bacteroidetes and Firmicutes phyla. We reproduced the clinical differences with polygenic scores specific to each profile in an independent cohort, which revealed differential cardiovascular mortality by obesity subtype. Mendelian randomization analyses revealed prominent roles of waist-to-hip ratio, concentration and cholesterol content of HDL particles, and blood pressure in the causal processes leading to diabetes in obesity; through these analyses, we prioritized 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity.

Published
2022
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12. An exploratory analysis of associations of genetic variation with the efficacy of tocilizumab in severe COVID-19 patients. A pharmacogenetic study based on next-generation sequencing

Author

Alejandro Durán-Sotuela, Jorge Vázquez-García, Sara Relaño-Fernández, Vanesa Balboa-Barreiro, Juan Fernández-Tajes, Francisco J. Blanco, and Ignacio Rego-Pérez

Subjects
pharmacogenetics, tocilizumab, COVID-19, next-generation sequencing, interleukin, Therapeutics. Pharmacology, RM1-950
Abstract

BackgroundIn the context of the cytokine storm the takes place in severe COVID-19 patients, the Interleukin 6 (IL6) pathway emerges as one of the key pathways involved in the pathogenesis of this hyperinflammatory state. The strategy of blocking the inflammatory storm by targeting the IL6 is a promising therapy to mitigate mortality. The use of Tocilizumab was recommended by the World Health Organization (WHO) to treat severe COVID-19 patients. However, the efficacy of Tocilizumab is variable. We hypothesize that the genetic background could be behind the efficacy of Tocilizumab in terms of mortality.MethodsWe performed a targeted-next generation sequencing of 287 genes, of which 264 belong to a community panel of ThermoFisher for the study of genetic causes of primary immunodeficiency disorders, and 23 additional genes mostly related to inflammation, not included in the original community panel. This panel was sequenced in an initial cohort of 425 COVID-19 patients, of which 232 were treated with Tocilizumab and standard therapy, and 193 with standard therapy only. Selected genetic variants were genotyped by single base extension in additional 245 patients (95 treated with Tocilizumab and 150 non-treated with Tocilizumab). Appropriate statistical analyses and internal validation, including logistic regression models, with the interaction between Tocilizumab and genetic variants, were applied to assess the impact of these genetic variants in the efficacy of Tocilizumab in terms of mortality.ResultsAge (p < 0.001) and cardiovascular disease (p < 0.001) are risk factors for mortality in COVID-19 patients. The presence of GG and TT genotypes at IL10Rβ (rs2834167) and IL1β (rs1143633) genes significantly associates with a reduced risk of mortality in patients treated with Tocilizumab (OR = 0.111; 95%CI = 0.015–0.829; p = 0.010 and OR = 0.378; 95%CI = 0.154–0.924; p = 0.028 respectively). The presence of CC genotype at IL1RN (rs2234679) significantly associates with an increased risk of mortality, but only in patients not treated with Tocilizumab (OR = 3.200; 95%CI = 1.512–6.771; p = 0.002). Exhaustive internal validation using a bootstrap method (B = 500 replicates) validated the accuracy of the predictive models.ConclusionWe developed a series of predictive models based on three genotypes in genes with a strong implication in the etiopathogenesis of COVID-19 disease capable of predicting the risk of mortality in patients treated with Tocilizumab.

Published
2024
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13. Inferring causal pathways between metabolic processes and liver fat accumulation: an IMI DIRECT study

Author

Jochen M. Schwenk, Ana Viñuela, Ian M Forgie, Hugo Pomares-Millan, Timothy J. McDonald, Andrea Mari, Jerzy Adamski, Juan Fernandez Tajes, Søren Brunak, Konstantinos D. Tsirigos, Harriet Teare, Andrea Tura, Hartmut Ruetten, E. Louise Thomas, Mattias Ohlsson, Robert W. Koivula, Federico De Masi, Henrik Vestergaard, Ramneek Gupta, Petra J. M. Elders, Giuseppe N. Giordano, Joline W. Beulens, Anubha Mahajan, Torben Hansen, Tarja Kokkola, Andrew T. Hattersley, Paul W. Franks, Alison Heggie, Martin Ridderstråle, Elizaveta Chabanova, Mark Walker, Daniel Coral, Henna Cederberg, Tue H. Hansen, Jagadish Vangipurapu, Naeimeh Atabaki-Pasdar, Leandro Z. Agudelo, Emmanouil T. Dermitzakis, Imre Pavo, Andrew A. Brown, Jimmy D. Bell, Markku Laakso, Femke Rutters, Ewan R. Pearson, Oluf Pedersen, Kristine H. Allin, Sabine van Oort, Mark I. McCarthy, Angus G. Jones, and Donna McEvoy

Subjects
0303 health sciences, business.industry, Insulin, medicine.medical_treatment, Fatty liver, nutritional and metabolic diseases, 030209 endocrinology & metabolism, Disease, Anthropometry, medicine.disease, Bioinformatics, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, Diabetes mellitus, Causal inference, Mendelian randomization, medicine, Biomarker (medicine), business, 030304 developmental biology
Abstract

Type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD) often co-occur. Defining causal pathways underlying this relationship may help optimize the prevention and treatment of both diseases. Thus, we assessed the strength and magnitude of the putative causal pathways linking dysglycemia and fatty liver, using a combination of causal inference methods.Measures of glycemia, insulin dynamics, magnetic resonance imaging (MRI)-derived abdominal and liver fat content, serological biomarkers, lifestyle, and anthropometry were obtained in participants from the IMI DIRECT cohorts (n=795 with new onset T2D and 2234 individuals free from diabetes). UK Biobank (n=3641) was used for modelling and replication purposes. Bayesian networks were employed to infer causal pathways, with causal validation using two-sample Mendelian randomization.Bayesian networks fitted to IMI DIRECT data identified higher basal insulin secretion rate (BasalISR) and MRI-derived excess visceral fat (VAT) accumulation as the features of dysmetabolism most likely to cause liver fat accumulation; the unconditional probability of fatty liver (>5%) increased significantly when conditioning on high levels of BasalISR and VAT (by 23%, 32% respectively; 40% for both). Analyses in UK Biobank yielded comparable results. MR confirmed most causal pathways predicted by the Bayesian networks.Here, BasalISR had the highest causal effect on fatty liver predisposition, providing mechanistic evidence underpinning the established association of NAFLD and T2D. BasalISR may represent a pragmatic biomarker for NAFLD prediction in clinical practice.

Published
2021
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14. Whole blood co-expression modules associate with metabolic traits and type 2 diabetes : an IMI-DIRECT study

Author

Mark Walker, Ramneek Gupta, Cecilia Engel Thomas, Mun-Gwan Hong, Femke Rutters, Tarja Kokkola, Andrew T. Hattersley, Ali Syed, Andrea Mari, Elizaveta Chabanova, Joline W.J. Beulens, Helle Krogh Pedersen, Henna Cederberg, Petra J. M. Elders, Torben Hansen, Tue H. Hansen, Caroline Brorsson, Jerzy Adamski, Harald Grallert, Jagadish Vangipurapu, Karina Banasik, Emmanouil T. Dermitzakis, Agata Wesolowska-Andersen, Henrik S. Thomsen, Juan Fernandez Tajes, Donna McEvoy, Valborg Gudmundsdottir, Jochen M. Schwenk, Gianluca Mazzoni, Kristine H. Allin, Ian M Forgie, Søren Brunak, Petra B. Musholt, Timothy J. McDonald, Henrik Vestergaard, Federico De Masi, Cornelia Prehn, Ana Viñuela, Martin Ridderstråle, Roderick C. Slieker, Alison Heggie, Giuseppe N. Giordano, Ewan R. Pearson, Oluf Pedersen, Anna Artati, Mark Haid, Peter Løngreen, Markku Laakso, Imre Pavo, Paul W. Franks, Mark I. McCarthy, Angus G. Jones, Hartmut Ruetten, Robert W. Koivula, Sapna Sharma, Anubha Mahajan, HUS Abdominal Center, Clinicum, Department of Medicine, Helsinki University Hospital Area, Endokrinologian yksikkö, Epidemiology and Data Science, ACS - Diabetes & metabolism, ACS - Heart failure & arrhythmias, APH - Health Behaviors & Chronic Diseases, General practice, and APH - Aging & Later Life

Subjects
0301 basic medicine, COUNT, lcsh:Medicine, Genome-wide association study, Cohort Studies, 0302 clinical medicine, DRIVERS, Leukocytes, Insulin, Genetics (clinical), Whole blood, RISK, INSULIN-RESISTANCE, 1184 Genetics, developmental biology, physiology, Type 2 diabetes, Omics data integration, Phenotype, medicine.anatomical_structure, Endokrinologi och diabetes, Molecular Medicine, lcsh:QH426-470, BIOMARKERS, 030209 endocrinology & metabolism, Computational biology, Biology, Endocrinology and Diabetes, 03 medical and health sciences, Insulin resistance, Metabolomics, SDG 3 - Good Health and Well-being, INFLAMMATION, White blood cell, Genetics, medicine, Humans, GENOME-WIDE ASSOCIATION, TRANSCRIPTOME, Transcriptomics, Molecular Biology, Gene, MONONUCLEAR-CELLS, Research, lcsh:R, medicine.disease, Human genetics, Co-expression modules, lcsh:Genetics, 030104 developmental biology, Diabetes Mellitus, Type 2, Gene Expression Regulation, GENE-EXPRESSION PROFILES, 3121 General medicine, internal medicine and other clinical medicine, 3111 Biomedicine, Insulin Resistance, Co-expression Modules, Omics Data Integration, Type 2 Diabetes, Genome-Wide Association Study
Abstract

BackgroundThe rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D.MethodsClusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts.ResultsWe identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling.ConclusionsOur results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.

Published
2020
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15. Genetic predisposition to coronary artery disease in type 2 diabetes mellitus

Author

Natalie R. van Zuydam, Claes Ladenvall, Benjamin F. Voight, Rona J. Strawbridge, Juan Fernandez-Tajes, N. William Rayner, Neil R. Robertson, Anubha Mahajan, Efthymia Vlachopoulou, Anuj Goel, Marcus E. Kleber, Christopher P. Nelson, Lydia Coulter Kwee, Tõnu Esko, Evelin Mihailov, Reedik Mägi, Lili Milani, Krista Fischer, Stavroula Kanoni, Jitender Kumar, Ci Song, Jaana A. Hartiala, Nancy L. Pedersen, Markus Perola, Christian Gieger, Annette Peters, Liming Qu, Sara M. Willems, Alex S.F. Doney, Andrew D. Morris, Yan Zheng, Giorgio Sesti, Frank B. Hu, Lu Qi, Markku Laakso, Unnur Thorsteinsdottir, Harald Grallert, Cornelia van Duijn, Muredach P. Reilly, Erik Ingelsson, Panos Deloukas, Sek Kathiresan, Andres Metspalu, Svati H. Shah, Juha Sinisalo, Veikko Salomaa, Anders Hamsten, Nilesh J. Samani, Winfried März, Stanley L. Hazen, Hugh Watkins, Danish Saleheen, Andrew P. Morris, Helen M. Colhoun, Leif Groop, Mark I. McCarthy, Colin N.A. Palmer, John Danesh, Jeanette Erdmann, Dongfeng Gu, Jaspal S. Kooner, Robert Roberts, Heribert Schunkert, Themistocles L. Assimes, Stefan Blankenberg, Bernhard O. Boehm, John C. Chambers, Robert Clarke, Rory Collins, George Dedoussis, Paul W. Franks, G. Kees Hovingh, Bong-Jo Kim, Terho Lehtimäki, Ruth McPherson, Markku S Nieminen, Christopher O’Donnell, Samuli Ripatti, Manjinder S Sandhu, Stefan Schreiber, Agneta Siegbahn, Cristen J. Willer, Pierre A. Zalloua, Michael Mark, Timo Kanninen, Barbara Thorand, Giuseppe Remuzzi, David Dunger, Angela Shore, Ulf Smith, Per-Henrik Groop, Seppo Ylä-Herttuala, Claudio Cobelli, Riccardo Bellazzi, Ele Ferrannini, Carlo Patrono, Pirjo Nuutila, Paul McKeague, Birgit Steckel-Hamann, Li-ming Gan, Everson Nogoceke, Piero Tortoli, Bernd Jablonka, Mary-Julia Brosnan, Tampere University, Department of Clinical Chemistry, Clinical Medicine, Transplantation Laboratory, University of Helsinki, Research Programs Unit, CAMM - Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, Department of Medicine, Doctoral Programme in Clinical Research, Clinicum, HUS Heart and Lung Center, Centre of Excellence in Complex Disease Genetics, HUS Abdominal Center, Institute for Molecular Medicine Finland, Department of Public Health, Samuli Olli Ripatti / Principal Investigator, Complex Disease Genetics, University Management, Biostatistics Helsinki, and Epidemiology

Subjects
0301 basic medicine, Heart disease, endocrine system diseases, LOCI, Genome-wide association study, CAD, 030204 cardiovascular system & hematology, GLUCOSE, Coronary artery disease, 0302 clinical medicine, risk factors, blood pressure, coronary artery disease, diabetes mellitus, genome-wide association study, RISK, 1184 Genetics, developmental biology, physiology, COMMON VARIANTS, General Medicine, INSIGHTS, CARDIOVASCULAR-DISEASE, Cardiology, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, Medical genetics, Medical Genetics, medicine.medical_specialty, endocrine system, Blood Pressure, Coronary Artery Disease, Diabetes Mellitus, Genome-wide Association Study, Risk Factors, HEART-DISEASE, 3121 Internal medicine, 03 medical and health sciences, SDG 3 - Good Health and Well-being, Meta-Analysis as Topic, Internal medicine, Genetic predisposition, medicine, GLYCEMIC TRAITS, Humans, Genetic Predisposition to Disease, cardiovascular diseases, GENOME-WIDE ASSOCIATION, Medicinsk genetik, Polymorphism, Genetic, business.industry, Type 2 Diabetes Mellitus, PATHWAYS, nutritional and metabolic diseases, Original Articles, medicine.disease, Genetic architecture, 030104 developmental biology, Diabetes Mellitus, Type 2, Case-Control Studies, 3121 General medicine, internal medicine and other clinical medicine, 3111 Biomedicine, business, human activities
Abstract

Supplemental Digital Content is available in the text., Background: Coronary artery disease (CAD) is accelerated in subjects with type 2 diabetes mellitus (T2D). Methods: To test whether this reflects differential genetic influences on CAD risk in subjects with T2D, we performed a systematic assessment of genetic overlap between CAD and T2D in 66 643 subjects (27 708 with CAD and 24 259 with T2D). Variants showing apparent association with CAD in stratified analyses or evidence of interaction were evaluated in a further 117 787 subjects (16 694 with CAD and 11 537 with T2D). Results: None of the previously characterized CAD loci was found to have specific effects on CAD in T2D individuals, and a genome-wide interaction analysis found no new variants for CAD that could be considered T2D specific. When we considered the overall genetic correlations between CAD and its risk factors, we found no substantial differences in these relationships by T2D background. Conclusions: This study found no evidence that the genetic architecture of CAD differs in those with T2D compared with those without T2D.

Published
2020
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16. Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

Author

Agata Wesolowska-Andersen, Caroline A. Brorsson, Roberto Bizzotto, Andrea Mari, Andrea Tura, Robert Koivula, Anubha Mahajan, Ana Vinuela, Juan Fernandez Tajes, Sapna Sharma, Mark Haid, Cornelia Prehn, Anna Artati, Mun-Gwan Hong, Petra B. Musholt, Azra Kurbasic, Federico De Masi, Kostas Tsirigos, Helle Krogh Pedersen, Valborg Gudmundsdottir, Cecilia Engel Thomas, Karina Banasik, Chrisopher Jennison, Angus Jones, Gwen Kennedy, Jimmy Bell, Louise Thomas, Gary Frost, Henrik Thomsen, Kristine Allin, Tue Haldor Hansen, Henrik Vestergaard, Torben Hansen, Femke Rutters, Petra Elders, Leen t’Hart, Amelie Bonnefond, Mickaël Canouil, Soren Brage, Tarja Kokkola, Alison Heggie, Donna McEvoy, Andrew Hattersley, Timothy McDonald, Harriet Teare, Martin Ridderstrale, Mark Walker, Ian Forgie, Giuseppe N. Giordano, Philippe Froguel, Imre Pavo, Hartmut Ruetten, Oluf Pedersen, Emmanouil Dermitzakis, Paul W. Franks, Jochen M. Schwenk, Jerzy Adamski, Ewan Pearson, Mark I. McCarthy, Søren Brunak, Epidemiology and Data Science, ACS - Diabetes & metabolism, APH - Aging & Later Life, APH - Health Behaviors & Chronic Diseases, General practice, Brage, Soren [0000-0002-1265-7355], and Apollo - University of Cambridge Repository

Subjects
Adult, Male, precision medicine, soft-clustering, glycaemic deterioration, archetypes, Genomics, patient stratification, multi-omics, Middle Aged, Article, General Biochemistry, Genetics and Molecular Biology, disease progression, Phenotype, Diabetes Mellitus, Type 2, SDG 3 - Good Health and Well-being, Risk Factors, Humans, Female, Genetic Predisposition to Disease, type 2 diabetes, patient clustering, Follow-Up Studies
Abstract

Summary The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments., Graphical abstract, Highlights • Soft clustering based on 32 phenotypes identified 4 quantitative archetypes • These reflect different patterns of dysfunction across T2D etiological processes • The four archetypes are different in disease progression, GRSs, and omics signals • Some patients are dominated by one archetype, but many have etiological combinations, Wesolowska-Andersen et al. represent the clinical heterogeneity of newly diagnosed T2D as four quantitative archetype profiles reflecting patterns of dysfunction in disease etiological processes, rather than clustering individuals into categorical subgroups as attempted by others. The archetype profiles differ in genetic risk scores, disease progression, and circulating omics biomarkers.

Published
2022
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17. The genetic architecture of type 2 diabetes

Author

Clement Ma, Liisa Hakaste, Claes Ladenvall, Massimo Mangino, Lars Lind, Aaron G. Day-Williams, Ryan Poplin, Thomas Schwarzmayr, Annette Peters, Thomas Meitinger, Satish Kumar, Kyong Soo Park, Ching-Yu Cheng, Farook Thameem, Kyle J. Gaulton, Olov Rolandsson, Yossi Farjoun, Neil Robertson, Mauricio O. Carneiro, Mark A. DePristo, Pierre Fontanillas, Ruth J. F. Loos, Heather M. Highland, Vilmantas Giedraitis, Gemma Buck, Fredrik Karpe, Mohammad Kamran Ikram, Alex S. F. Doney, Hae Kyung Im, Eric S. Lander, Johanna Kuusisto, Dorothée Thuillier, Denis Rybin, Jacquelyn Murphy, Benjamin M. Neale, Martina Müller-Nurasyid, Eric R. Gamazon, Johanne Marie Justesen, Tin Aung, Vincent K. L. Lam, John Danesh, Donna M. Lehman, Goo Jun, Marit E. Jørgensen, Christian Herder, Edmund Chan, Pamela J. Hicks, Todd Green, Claudia Langenberg, Yoon Shin Cho, James G. Wilson, Daniel E. Hale, Rector Arya, Giriraj R. Chandak, Juan Fernandez Tajes, Jaakko Tuomilehto, Christopher P. Jenkinson, Anders Rosengren, Torsten Lauritzen, Michael Griswold, Karen L. Mohlke, Amy J. Swift, Alena Stančáková, Christian Fuchsberger, Iksoo Huh, Nikhil Tandon, João Fadista, Christa Meisinger, Gonçalo R. Abecasis, Pål R. Njølstad, Dwaipayan Bharadwaj, Richard M. Watanabe, Shaun Purcell, Veikko Salomaa, Tim D. Spector, Paul W. Franks, Adam E. Locke, Sharon P. Fowler, Panos Deloukas, Reedik Mägi, Solomon K. Musani, Kee Seng Chia, Barbara Thorand, Soo Heon Kwak, Annemari Käräjämäki, Evelin Mihailov, John Blangero, Thomas Wieland, Christian Gieger, E. Shyong Tai, Jianjun Liu, James B. Meigs, Teemu Kuulasmaa, Martin Hrabé de Angelis, Francis S. Collins, Tim M. Strom, Tibor V. Varga, Juyoung Lee, Mark I. McCarthy, Domenico Palli, Jong-Young Lee, Benjamin Glaser, Tasha E. Fingerlin, Stephen O'Rahilly, Andrew T. Hattersley, Gil Atzmon, Manuel A. Rivas, Noël P. Burtt, Ivan Brandslund, Young-Jin Kim, Dorairaj Prabhakaran, Xu Wang, Taylor J. Maxwell, Valeriya Lyssenko, Frank B. Hu, Adolfo Correa, Khalid Shakir, Kathleen A. Jablonski, Yingchang Lu, Wei-Yen Lim, Min Jin Go, William R. Scott, Hanna E. Abboud, Leena Kinnunen, Inês Barroso, Gabriela L. Surdulescu, Peng Chen, Robert Sladek, Marju Orho-Melander, Xueling Sim, Robert A. Scott, Joanna M. M. Howson, Markku Laakso, David Altshuler, Vidya S. Farook, Harald Grallert, Janina S. Ried, Vineeta Agarwala, Philippe Froguel, Nicholas J. Wareham, Tiinamaija Tuomi, Joon Yoon, Heikki A. Koistinen, Toni I. Pollin, Heiner Boeing, Marie Loh, Nicola L. Beer, Joanne E. Curran, Lars Lannfelt, Dorota Pasko, Taesung Park, David Buck, Nir Barzilai, Stephen C. J. Parker, Michael Roden, Bok-Ghee Han, David Aguilar, Timothy Fennell, Sian-Tsung Tan, Gregory P. Wilson, Momoko Horikoshi, Cramer Christensen, Craig L. Hanis, Gilean McVean, Benjamin F. Voight, Wolfgang Rathmann, Jared Maguire, Ashok Kumar, Donald W. Bowden, Michael L. Stitzel, Yongkang Kim, Christine Blancher, Jason Carey, Min-Seok Kwon, Nicholette D. Palmer, Oluf Pedersen, Niels Grarup, Peter S. Chines, Cornelia Huth, James Scott, Torben Hansen, Konstantin Strauch, Erwin P. Bottinger, Andrew D. Morris, Anette P. Gjesing, Erik Ingelsson, Colin N. A. Palmer, Claudia H. T. Tam, Stacey Gabriel, Pablo Cingolani, Beverley Balkau, Michael Boehnke, Peter M. Nilsson, Wing-Yee So, Andrew R. Wood, Lili Milani, Ravindranath Duggirala, Qibin Qi, Mark Walker, Weiping Jia, Mark Seielstad, Narisu Narisu, Tien Yin Wong, Martijn van de Bunt, Anubha Mahajan, Davis J. McCarthy, Ann-Christine Syvänen, Jennifer Kriebel, N. William Rayner, Han Chen, Jinyan Huang, Chiea Chuen Khor, Richard N. Bergman, Shah Ebrahim, Dylan Hodgkiss, Weihua Zhang, Manjinder S. Sandhu, Richard D. Pearson, Juliana C.N. Chan, Rainer Rauramaa, Ralph A. DeFronzo, Andrew Farmer, Yoshihiko Nagai, Allan Linneberg, Herman A. Taylor, Jose C. Florez, Jaspal S. Kooner, Lori L. Bonnycastle, Jeroen R. Huyghe, Leif Groop, Joseph Trakalo, Sobha Puppala, Bong-Jo Kim, Kathleen Stirrups, Kerrin S. Small, Cecilia M. Lindgren, Jennifer E. Below, Heung Man Lee, Inga Prokopenko, Liming Liang, Timothy M. Frayling, Uzma Afzal, Mark J. Daly, Yik Ying Teo, Andrew P. Morris, Phoenix Kwan, Yvonne T. van der Schouw, Cheng Hu, Katharine R. Owen, Alisa K. Manning, Christopher J. Groves, Josée Dupuis, Ryan P. Welch, Loukas Moutsianas, Joshua D. Smith, Tanya M. Teslovich, Robert C. Onofrio, Maggie C.Y. Ng, Peter Donnelly, Tõnu Esko, Matt Neville, Bo Isomaa, Anne U. Jackson, Jette Bork-Jensen, Barry I. Freedman, Yi Chen, Danish Saleheen, Lu Qi, Laura J. Scott, Jasmina Kravic, Paul Elliott, Mette Hollensted, Carmen Navarro, Selyeong Lee, Benjamin Lehne, Thomas Illig, Nancy J. Cox, Jonathan C. Levy, George B. Grant, John C. Chambers, Adam S. Butterworth, Ronald C.W. Ma, Teresa Ferreira, John R. B. Perry, Eleftheria Zeggini, Hyun Min Kang, Loic Yengo, Eric Banks, Jae-Hoon Lee, Anna L. Gloyn, Christopher Hartl, Wei Zhao, Andres Metspalu, Keng-Han Lin, Graeme I. Bell, Jason Flannick, Torben Jørgensen, Thomas W. Blackwell, Heather M. Stringham, Olle Melander, Omri Gottesman, Other departments, British Heart Foundation, Wellcome Trust, Medical Research Council (MRC), Massachusetts Institute of Technology. Department of Biology, Altshuler, David M, Internal Medicine, Perry, John [0000-0001-6483-3771], Butterworth, Adam [0000-0002-6915-9015], Howson, Joanna [0000-0001-7618-0050], Danesh, John [0000-0003-1158-6791], Johnson, Kathleen [0000-0002-6823-3252], O'Rahilly, Stephen [0000-0003-2199-4449], Langenberg, Claudia [0000-0002-5017-7344], Sandhu, Manjinder [0000-0002-2725-142X], Barroso, Ines [0000-0001-5800-4520], Wareham, Nicholas [0000-0003-1422-2993], and Apollo - University of Cambridge Repository

Subjects
0301 basic medicine, PROTEIN INTERACTION NETWORKS, SUSCEPTIBILITY LOCI, Genotyping Techniques, General Science & Technology, DNA Mutational Analysis, SEQUENCING ASSOCIATION, 030209 endocrinology & metabolism, Genome-wide association study, Biology, Research Support, N.I.H, 03 medical and health sciences, 0302 clinical medicine, Research Support, N.I.H., Extramural, SDG 3 - Good Health and Well-being, MD Multidisciplinary, Genetic predisposition, Journal Article, Humans, Exome, Genetic Predisposition to Disease, GENOME-WIDE ASSOCIATION, Non-U.S. Gov't, Exome sequencing, Alleles, Genetic association, Genetics, RISK, Multidisciplinary, HERITABILITY, Research Support, Non-U.S. Gov't, COMPLEX TRAITS, Extramural, Genetic Variation, Genetic architecture, HUMAN-DISEASE, Europe, 030104 developmental biology, Diabetes Mellitus, Type 2, RARE VARIANTS, Sample Size, LOW-FREQUENCY, Imputation (genetics), Common disease-common variant, Genome-Wide Association Study
Abstract

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.

Published
2016
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18. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes

Author

Ching-Ti Liu, Michael Boehnke, George Dedoussis, Sophie V. Eastwood, Ruth J. F. Loos, Keng-Hung Lin, Denis Rybin, Fredrik Karpe, Martina Müller-Nurasyid, Michael A. Province, Alison D. Murray, Bo Isomaa, Eirini Marouli, Konstantin Strauch, Michael Preuss, Paul M. Ridker, Jette Bork-Jensen, Albert V. Smith, Hugoline G. de Haan, Wayne Huey-Herng Sheu, Barbara Thorand, Wolfgang Rathmann, Lawrence F. Bielak, Peter Kovacs, Marit E. Jørgensen, Jennifer Wessel, Danish Saleheen, Jung-Jin Lee, James B. Meigs, Veikko Salomaa, Alena Stančáková, Tibor V. Varga, Hidetoshi Kitajima, Inês Barroso, Kent D. Taylor, Claudia Langenberg, Yoon Shin Cho, Joanna M. M. Howson, Andrew T. Hattersley, Marie-France Hivert, Markku Laakso, Kai-Uwe Eckardt, Bram P. Prins, Matthias B. Schulze, Andrew D. Morris, Susanne Jäger, Francis S. Collins, Kristi Läll, Xu Lin, Anette Varbo, Benjamin Lehne, Girish N. Nadkarni, Jonathan Marchini, Daniel I. Chasman, Michael Stumvoll, Mark O. Goodarzi, Cécile Lecoeur, Philippe M. Frossard, Noël P. Burtt, Frank Kee, Jasmina Kravic, Alain G. Bertoni, Ivan Brandslund, Najaf Amin, Lenore J. Launer, Oluf Pedersen, Johanna Kuusisto, Line Rode, Eleftheria Zeggini, Yingchang Lu, Markus Perola, Helen R. Warren, André G. Uitterlinden, Hanieh Yaghootkar, Torben Hansen, Harald Grallert, Annemari Käräjämäki, Abbas Dehghan, Gina M. Peloso, Yii-Der Ida Chen, Man Li, Shaofeng Huo, Lars Lind, Karen L. Mohlke, Adrienne Tin, Yang Hai, Renée de Mutsert, Gudmar Thorleifsson, Marie Moitry, Sune F. Nielsen, Sara M. Willems, Matthias Wuttke, Weihua Zhang, Young-Jin Kim, Giovanni Malerba, Richard A. Jensen, Loic Yengo, Mickaël Canouil, Kurt Lohman, Robert A. Scott, Tamara B. Harris, Ruifang Li-Gao, Florian Kronenberg, Anke Tönjes, Bok-Ghee Han, Krista Fischer, Thomas Meitinger, James S. Pankow, Jaakko Tuomilehto, Adam S. Butterworth, Jerome I. Rotter, Olov Rolandsson, Xiuqing Guo, Cramer Christensen, Marie Loh, Elizabeth Selvin, Bong-Jo Kim, Audrey Y. Chu, Reedik Mägi, Josée Dupuis, Anna Köttgen, Jean Ferrières, Jin Li, Robert Sladek, Leslie A. Lange, Niels Grarup, Roberta McKean-Cowdin, Cristen J. Willer, Jose C. Florez, Valgerdur Steinthorsdottir, Karina Meidtner, Annette Peters, Børge G. Nordestgaard, Rajiv Chowdhury, Ioanna Ntalla, Emma Ahlqvist, Leif Groop, Nicholas J. Wareham, Kerrin S. Small, Tiinamaija Tuomi, Cecilia M. Lindgren, Katharine R. Owen, Giovanni Gambaro, Cornelia M. van Duijn, Dennis O. Mook-Kanamori, Kenneth Rice, Erik Ingelsson, Colin N. A. Palmer, Sharon L.R. Kardia, Neil R. Robertson, Dajiang J. Liu, Sebastian Schönherr, Daniel Taliun, Sekar Kathiresan, James G. Wilson, Ping An, Patricia A. Peyser, Matthias Blüher, Frits R. Rosendaal, John C. Chambers, Caroline Hayward, Shoaib Afzal, Fernando Rivadineira, Marielisa Graff, Pranav Yajnik, Vasiliki Mamakou, Juan Fernandez Tajes, Stefan Gustafsson, Heather M. Highland, Vilmantas Giedraitis, Andrew R. Wood, Saima Afaq, Jaspal S. Kooner, Megan L. Grove, Jennifer A. Brody, Andrew P. Morris, James P. Cook, Praveen Surendran, Jennifer Kriebel, Heikki A. Koistinen, Kari Stefansson, Anders Rosengren, Rainer Rauramaa, Satu Männistö, Oscar H. Franco, Yongmei Liu, N. William Rayner, Blair H. Smith, Erwin P. Bottinger, Ayse Demirkan, Allan Linneberg, Jonathan Marten, Huaixing Li, Sung Soo Kim, Sophie Hackinger, Cristina Bombieri, Lia B. Bang, Jun Liu, Asif Rasheed, Tim D. Spector, Paul W. Franks, Mark I. McCarthy, Heiner Boeing, Anne E. Justice, Vilmundur Gudnason, Sohee Han, Unnur Thorsteinsdottir, Panos Deloukas, Naveed Sattar, Eric Boerwinkle, Martin Ingelsson, John Danesh, Vassily Trubetskoy, Marco M Ferrario, Marju Orho-Melander, Wei Gan, Philippe Froguel, Symen Ligthart, Susan R. Heckbert, Jie Yao, Anne Tybjærg-Hansen, Robin Young, Daniel R. Witte, Anubha Mahajan, Peter Almgren, Timothy M. Frayling, Tanya M. Teslovich, Matt Neville, Philippe Amouyel, Wei Zhao, Andres Metspalu, Yao Hu, Olle Melander, Kari Kuulasmaa, Jason Flannick, Torben Jørgensen, Stephen S. Rich, Nicole Soranzo, Bruce M. Psaty, Rohit Varma, Epidemiology, and Internal Medicine

Subjects
0301 basic medicine, Male, Inference, Genome-wide association study, Whole Exome Sequencing, 0302 clinical medicine, type 2 diabetes, coding variant associations signals, mechanistic inference, fine mapping, Coding region, Chromosome Mapping/statistics & numerical data, European Continental Ancestry Group/genetics, CONFERS SUSCEPTIBILITY, Exome sequencing, 11 Medical and Health Sciences, Genetics, Genetics & Heredity, 0303 health sciences, MAGIC Consortium, Chromosome Mapping, Whole Exome Sequencing/statistics & numerical data, Identification (information), RARE VARIANTS, LOW-FREQUENCY, Female, ExomeBP Consortium, Life Sciences & Biomedicine, SUSCEPTIBILITY LOCI, Posterior probability, European Continental Ancestry Group, 030209 endocrinology & metabolism, Context (language use), Computational biology, Biology, GENOTYPE IMPUTATION, Article, White People, GENETIC ARCHITECTURE, 03 medical and health sciences, SDG 3 - Good Health and Well-being, QUALITY-CONTROL, Exome Sequencing, Genome-Wide Association Study/statistics & numerical data, Journal Article, GIANT Consortium, Humans, Genetic Predisposition to Disease, GENOME-WIDE ASSOCIATION, Alleles, Genetic association, 030304 developmental biology, FATTY LIVER-DISEASE, Science & Technology, Genetic Variation, 06 Biological Sciences, Genetic architecture, Minor allele frequency, BODY-MASS INDEX, 030104 developmental biology, Diabetes Mellitus, Type 2, Diabetes Mellitus, Type 2/classification, 030217 neurology & neurosurgery, Coding (social sciences), Genome-Wide Association Study, Developmental Biology
Abstract

Identification of coding variant associations for complex diseases offers a direct route to biological insight, but is dependent on appropriate inference concerning the causal impact of those variants on disease risk. We aggregated coding variant data for 81,412 type 2 diabetes (T2D) cases and 370,832 controls of diverse ancestry, identifying 40 distinct coding variant association signals (at 38 loci) reaching significance (p−7). Of these, 16 represent novel associations mapping outside known genome-wide association study (GWAS) signals. We make two important observations. First, despite a threefold increase in sample size over previous efforts, only five of the 40 signals are driven by variants with minor allele frequency 1.29. Second, we used GWAS data from 50,160 T2D cases and 465,272 controls of European ancestry to fine-map these associated coding variants in their regional context, with and without additional weighting to account for the global enrichment of complex trait association signals in coding exons. At the 37 signals for which we attempted fine-mapping, we demonstrate convincing support (posterior probability >80% under the “annotation-weighted” model) that coding variants are causal for the association at 16 (including novel signals involving POC5 p.His36Arg, ANKH p.Arg187Gln, WSCD2 p.Thr113Ile, PLCB3 p.Ser778Leu, and PNPLA3 p.Ile148Met). However, at 13 of the 37 loci, the associated coding variants represent “false leads” and naïve analysis could have led to an erroneous inference regarding the effector transcript mediating the signal. Accurate identification of validated targets is dependent on correct specification of the contribution of coding and non-coding mediated mechanisms at associated loci.

Published
2018
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19. NanoLINEN: Nanotoxicology Link Between India and European Nations

Author

Marie Carrière, Bensu Karahalil, Pieter van Broekhuizen, Heinz-Jorn Moriske, Erdem Coskun, Dietmar Fuchs, Heidi Becker, Juan Fernandez Tajes, Blanca Laffon, Gert van der Laan, Rishi Shanker, Nathalie Herlin-Boime, Alok Dhawan, Ayse Basak Engin, and João Paulo Teixeira

Subjects
Validation of Test Methods, Engineering, Engineered Nanomaterials, Ar e Saúde Ocupacional, business.industry, International Cooperation, Engineered nanomaterials, Biomedical Engineering, India, Pharmaceutical Science, Medicine (miscellaneous), Bioengineering, Nanotechnology, Toxicology, Nanotoxicology, Models, Organizational, General Materials Science, Engineering ethics, European Union, business
Abstract

Nanotoxicology link between India and European Nations (NanoLINEN) is a consortium of 7 European laboratories and Indian Institute of Toxicology Research (CSIR Laboratory) from India to strengthen the research ties in the area of Nanomaterial Toxicology. The goal of this project is to develop robust risk assessment methodologies that will be useful for the community manufacturing and using nano-products.

Published
2011
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20. Developing a network view of type 2 diabetes risk pathways through integration of genetic, genomic and functional data

Author

Juan Fernández-Tajes, Kyle J. Gaulton, Martijn van de Bunt, Jason Torres, Matthias Thurner, Anubha Mahajan, Anna L. Gloyn, Kasper Lage, and Mark I. McCarthy

Subjects
Medicine, Genetics, QH426-470
Abstract

Abstract Background Genome-wide association studies (GWAS) have identified several hundred susceptibility loci for type 2 diabetes (T2D). One critical, but unresolved, issue concerns the extent to which the mechanisms through which these diverse signals influencing T2D predisposition converge on a limited set of biological processes. However, the causal variants identified by GWAS mostly fall into a non-coding sequence, complicating the task of defining the effector transcripts through which they operate. Methods Here, we describe implementation of an analytical pipeline to address this question. First, we integrate multiple sources of genetic, genomic and biological data to assign positional candidacy scores to the genes that map to T2D GWAS signals. Second, we introduce genes with high scores as seeds within a network optimization algorithm (the asymmetric prize-collecting Steiner tree approach) which uses external, experimentally confirmed protein-protein interaction (PPI) data to generate high-confidence sub-networks. Third, we use GWAS data to test the T2D association enrichment of the “non-seed” proteins introduced into the network, as a measure of the overall functional connectivity of the network. Results We find (a) non-seed proteins in the T2D protein-interaction network so generated (comprising 705 nodes) are enriched for association to T2D (p = 0.0014) but not control traits, (b) stronger T2D-enrichment for islets than other tissues when we use RNA expression data to generate tissue-specific PPI networks and (c) enhanced enrichment (p = 3.9 × 10− 5) when we combine the analysis of the islet-specific PPI network with a focus on the subset of T2D GWAS loci which act through defective insulin secretion. Conclusions These analyses reveal a pattern of non-random functional connectivity between candidate causal genes at T2D GWAS loci and highlight the products of genes including YWHAG, SMAD4 or CDK2 as potential contributors to T2D-relevant islet dysfunction. The approach we describe can be applied to other complex genetic and genomic datasets, facilitating integration of diverse data types into disease-associated networks.

Published
2019
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21. Discovery of circulating proteins associated to knee radiographic osteoarthritis

Author

Lucía Lourido, Burcu Ayoglu, Juan Fernández-Tajes, Natividad Oreiro, Frauke Henjes, Cecilia Hellström, Jochen M. Schwenk, Cristina Ruiz-Romero, Peter Nilsson, and Francisco J. Blanco

Subjects
Medicine, Science
Abstract

Abstract Currently there are no sufficiently sensitive biomarkers able to reflect changes in joint remodelling during osteoarthritis (OA). In this work, we took an affinity proteomic approach to profile serum samples for proteins that could serve as indicators for the diagnosis of radiographic knee OA. Antibody suspension bead arrays were applied to analyze serum samples from patients with OA (n = 273), control subjects (n = 76) and patients with rheumatoid arthritis (RA, n = 244). For verification, a focused bead array was built and applied to an independent set of serum samples from patients with OA (n = 188), control individuals (n = 83) and RA (n = 168) patients. A linear regression analysis adjusting for sex, age and body mass index (BMI) revealed that three proteins were significantly elevated (P

Published
2017
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22. Bivalve Omics: State of the Art and Potential Applications for the Biomonitoring of Harmful Marine Compounds

Author

Paola Venier, Marco Gerdol, Juan Fernández-Tajes, Chiara Manfrin, Victoria Suárez-Ulloa, and José M. Eirín-López

Subjects
marine invertebrates, omics, bioinformatics, pollution, biomonitoring, biotoxins, heavy metals, PAHs, Biology (General), QH301-705.5
Abstract

The extraordinary progress experienced by sequencing technologies and bioinformatics has made the development of omic studies virtually ubiquitous in all fields of life sciences nowadays. However, scientific attention has been quite unevenly distributed throughout the different branches of the tree of life, leaving molluscs, one of the most diverse animal groups, relatively unexplored and without representation within the narrow collection of well established model organisms. Within this Phylum, bivalve molluscs play a fundamental role in the functioning of the marine ecosystem, constitute very valuable commercial resources in aquaculture, and have been widely used as sentinel organisms in the biomonitoring of marine pollution. Yet, it has only been very recently that this complex group of organisms became a preferential subject for omic studies, posing new challenges for their integrative characterization. The present contribution aims to give a detailed insight into the state of the art of the omic studies and functional information analysis of bivalve molluscs, providing a timely perspective on the available data resources and on the current and prospective applications for the biomonitoring of harmful marine compounds.

Published
2013
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23. The CHROMEVALOA Database: A Resource for the Evaluation of Okadaic Acid Contamination in the Marine Environment Based on the Chromatin-Associated Transcriptome of the Mussel Mytilus galloprovincialis

Author

José M. Eirín-López, Josefina Méndez, Julián Dorado, Juan Ausio, Ciro Rivera-Casas, Rodrigo González-Romero, Vanessa Aguiar-Pulido, Juan Fernández-Tajes, and Victoria Suárez-Ulloa

Subjects
okadaic acid, Harmful Algae Blooms, mussels, chromatin, database, Mytilus galloprovincialis, Biology (General), QH301-705.5
Abstract

Okadaic Acid (OA) constitutes the main active principle in Diarrhetic Shellfish Poisoning (DSP) toxins produced during Harmful Algal Blooms (HABs), representing a serious threat for human consumers of edible shellfish. Furthermore, OA conveys critical deleterious effects for marine organisms due to its genotoxic potential. Many efforts have been dedicated to OA biomonitoring during the last three decades. However, it is only now with the current availability of detailed molecular information on DNA organization and the mechanisms involved in the maintenance of genome integrity, that a new arena starts opening up for the study of OA contamination. In the present work we address the links between OA genotoxicity and chromatin by combining Next Generation Sequencing (NGS) technologies and bioinformatics. To this end, we introduce CHROMEVALOAdb, a public database containing the chromatin-associated transcriptome of the mussel Mytilus galloprovincialis (a sentinel model organism) in response to OA exposure. This resource constitutes a leap forward for the development of chromatin-based biomarkers, paving the road towards the generation of powerful and sensitive tests for the detection and evaluation of the genotoxic effects of OA in coastal areas.

Published
2013
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24. A Federated Database for Obesity Research: An IMI-SOPHIA Study

Author

Carl Delfin, Iulian Dragan, Dmitry Kuznetsov, Juan Fernandez Tajes, Femke Smit, Daniel E. Coral, Ali Farzaneh, André Haugg, Andreas Hungele, Anne Niknejad, Christopher Hall, Daan Jacobs, Diana Marek, Diane P. Fraser, Dorothee Thuillier, Fariba Ahmadizar, Florence Mehl, Francois Pattou, Frederic Burdet, Gareth Hawkes, Ilja C. W. Arts, Jordi Blanch, Johan Van Soest, José-Manuel Fernández-Real, Juergen Boehl, Katharina Fink, Marleen M. J. van Greevenbroek, Maryam Kavousi, Michiel Minten, Nicole Prinz, Niels Ipsen, Paul W. Franks, Rafael Ramos, Reinhard W. Holl, Scott Horban, Talita Duarte-Salles, Van Du T. Tran, Violeta Raverdy, Yenny Leal, Adam Lenart, Ewan Pearson, Thomas Sparsø, Giuseppe N. Giordano, Vassilios Ioannidis, Keng Soh, Timothy M. Frayling, Carel W. Le Roux, and Mark Ibberson

Subjects
federated database system, obesity, risk prediction, remote statistical analysis, bioinformatics, Science
Abstract

Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.

Published
2024
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25. Whole blood co-expression modules associate with metabolic traits and type 2 diabetes: an IMI-DIRECT study

Author

Valborg Gudmundsdottir, Helle Krogh Pedersen, Gianluca Mazzoni, Kristine H. Allin, Anna Artati, Joline W. Beulens, Karina Banasik, Caroline Brorsson, Henna Cederberg, Elizaveta Chabanova, Federico De Masi, Petra J. Elders, Ian Forgie, Giuseppe N. Giordano, Harald Grallert, Ramneek Gupta, Mark Haid, Torben Hansen, Tue H. Hansen, Andrew T. Hattersley, Alison Heggie, Mun-Gwan Hong, Angus G. Jones, Robert Koivula, Tarja Kokkola, Markku Laakso, Peter Løngreen, Anubha Mahajan, Andrea Mari, Timothy J. McDonald, Donna McEvoy, Petra B. Musholt, Imre Pavo, Cornelia Prehn, Hartmut Ruetten, Martin Ridderstråle, Femke Rutters, Sapna Sharma, Roderick C. Slieker, Ali Syed, Juan Fernandez Tajes, Cecilia Engel Thomas, Henrik S. Thomsen, Jagadish Vangipurapu, Henrik Vestergaard, Ana Viñuela, Agata Wesolowska-Andersen, Mark Walker, Jerzy Adamski, Jochen M. Schwenk, Mark I. McCarthy, Ewan Pearson, Emmanouil Dermitzakis, Paul W. Franks, Oluf Pedersen, and Søren Brunak

Subjects
Type 2 diabetes, Transcriptomics, Co-expression modules, Omics data integration, Medicine, Genetics, QH426-470
Abstract

Abstract Background The rising prevalence of type 2 diabetes (T2D) poses a major global challenge. It remains unresolved to what extent transcriptomic signatures of metabolic dysregulation and T2D can be observed in easily accessible tissues such as blood. Additionally, large-scale human studies are required to further our understanding of the putative inflammatory component of insulin resistance and T2D. Here we used transcriptomics data from individuals with (n = 789) and without (n = 2127) T2D from the IMI-DIRECT cohorts to describe the co-expression structure of whole blood that mainly reflects processes and cell types of the immune system, and how it relates to metabolically relevant clinical traits and T2D. Methods Clusters of co-expressed genes were identified in the non-diabetic IMI-DIRECT cohort and evaluated with regard to stability, as well as preservation and rewiring in the cohort of individuals with T2D. We performed functional and immune cell signature enrichment analyses, and a genome-wide association study to describe the genetic regulation of the modules. Phenotypic and trans-omics associations of the transcriptomic modules were investigated across both IMI-DIRECT cohorts. Results We identified 55 whole blood co-expression modules, some of which clustered in larger super-modules. We identified a large number of associations between these transcriptomic modules and measures of insulin action and glucose tolerance. Some of the metabolically linked modules reflect neutrophil-lymphocyte ratio in blood while others are independent of white blood cell estimates, including a module of genes encoding neutrophil granule proteins with antibacterial properties for which the strongest associations with clinical traits and T2D status were observed. Through the integration of genetic and multi-omics data, we provide a holistic view of the regulation and molecular context of whole blood transcriptomic modules. We furthermore identified an overlap between genetic signals for T2D and co-expression modules involved in type II interferon signaling. Conclusions Our results offer a large-scale map of whole blood transcriptomic modules in the context of metabolic disease and point to novel biological candidates for future studies related to T2D.

Published
2020
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