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Obese and overweight individuals are at risk of developing type 2 diabetes (T2D), insulin resistance and certain types of cancer. In order to understand the genetic processes and mechanisms that underlie susceptibility to develop obesity and those that control deposition of adipose tissue at different body sites, we utilize genetic and genomic methodologies to establish the genetic and epigenetic components of these mechanisms.


1. Meta-analyses of genome-wide association studies (GWAS) and in type 2 diabetes, obesity and fat distribution:

As part of the GIANT consortium (Genetic Investigation of ANthropometric Traits) we are primarily responsible for the genome-wide association analyses on anthropometric traits (BMI, Height, Weight, Waist Circumference, Hip Circumference, and Waist-hip ratio). We have developed and implemented analyses for these traits in collaboration with other analyses groups in the GIANT collaboration.



GIANT consortium contributing cohorts

DGI/MIGEN: Guillaume Lettre, Liz Speliotes, Ben Voight, Leif Groop, Joel Hirschhorn, Sekar Kathiresan, FUSION: Anne Jackson, Heather Stringham, Cristen Willer, Gonçalo Abecasis, Michael Boehnke, Karen Mohlke, SardiNIA: Gonçalo Abecasis, Serena Sanna, Paul Scheet, Manuela Uda, David Schlessinger, NHS: Frank Hu, Lu Qi, David Hunter, CoLaus: Toby Johnson, Kijoung Song, Vincent Mooser, Dawn Waterworth, EPIC, Fenland: Shengxu Li, Jian'An Luan, Eleanor Wheeler, Jing Hua Zhao, Inés Barroso, Ruth Loos, Nick Wareham, KORA: Eva Albrecht, Christian Gieger, Iris Heid, Claudia Lamina, Thomas Winkler, Erich Wichmann, PLCO: Sonja Berndt, Stephen Chanock, Richard Hayes, WTCCC (UKBS, CAD, HT, T2D, B58C): David Evans, David Hadley, Alistair Hall, Hana Lango, Massimo Mangino, Inga Prokopenko, Joshua Randall, Chris Wallace, Michael Weedon, Ele Zeggini, Mark Caulfield, Tim Frayling, Reedik Magi, Teresa Ferreira, Mark McCarthy, Patricia Munroe, Willem Ouwehand, Nilesh Samani, David Strachan, Twins UK: Massimo Mangino, Nicole Soranzo, Panos Deloukas, Tim Spector, deCODE Consortium: Daniel Gudbjartsson, Valgerdur Steinthorsdottir, Gudmar Thorleifsson, Lambertus Kiemeney, Unnur Thorsteinsdottir, Kári Stefansson, CHARGE (AGES,  ARIC,  CHS, Framingham,  Rotterdam): Adrienne Cupples, Karol Estrada, Nicole Glazer, Talin Haritunians, Nancy Heard-Costa, Keri Monda, Fernando Rivadeneira, Albert Smith, Cornelia van Duijn, Carola Zillikens, Caroline Fox, Vilmundur Gudnason, Tamara Harris, Robert Kaplan, Kari North, André Uitterlinden, Amish, Jeff O'Connell, Family Heart Study, Ingrid Borecki, Mary Feitosa, Shamika Ketkar, Remaining ENGAGE (ERF,  EGP,  Finnish Twins,  GENMETS,  NESDA,  NFBC,  NTR), Yuri Aulchenko, Tonu Esko, Jouke-Jan Hottenga, Andres Metspalu, Mari Nelis, Samuli Ripatti, Cornelia van Duijn, Tim Zandbelt, Nicole Vogelzangs, Dorret Boomsma, Marjo-Riita Jarvelin, Mark McCarthy, Leena Peltonen-Palotie, Brenda Penninx, EUROSPAN (MICROS,  ORCADES,  VIS,  KORCULA,  N. Sweden), Asa Johansson, Harry Campbell, Ulf Gyllensten, Caroline Hayward, Igor Rudan, Andrew Hicks, Peter Pramstaller, Jim Wilson, Alan Wright, ADVANCE, Devin Absher, Tim Assimes, Joshua Knowles, Thomas Quertermous, CAPS,  CAHRES, Erik Ingelsson, GERMIFS, Michael Preuss, Jeanette Erdmann, PROCARDIS, John Peden, Anders Hamsten, Hugh Watkins, SEARCH, Jonathan Tyrer, Paul Pharoah, SHIP, Alexander Teumer, Henry Völzke, Henri Wallaschofski, CNRS/ICL, Nabila Boutia-Naji, Christian Dina, David Meyre, Philippe Froguel, Essen Obesity Study, Anke Hinney, Andre Scherag, Johannes Hebebrand.

2. Low-frequency variants (LFV) in overall and central adiposity

GWAs have hereto identified more than 20 common variants robustly associated to overall and central adiposity. However, these associations explain ~2% of the variance of adiposity. Available sample-sizes and traditional statistical association are likely to be underpowered to identify low frequency variants (LFV) with moderate effect sizes. We have developed a method for detecting association of quantitative phenotypes with accumulations of minor alleles at LFV within gene coding regions. This novel approach, models the phenotype in a regression framework as a function of the proportion of LFV at which an individual carries minor alleles. To date, we have screened samples from two cohorts and we test for association of accumulations of variants with a minor allele frequency <1% to overall (BMI) and central adiposity (waist-circumference [WC] and waist-hip ratio [WHR], both adjusted for BMI, age and gender). Analyses have been performed separately within each cohort, and then combined through meta-analysis. 


Dr Andrew Morris

Dr Reedik Magi

Prof. Leena Peltonen, Dr. Samuli Riipati



1. MicroRNA

MicroRNA (miRNA) are short non-coding RNA molecules involved in post-transcriptional control of gene expression, thought to be involved in numerous biological processes including tissue differentiation, development and glucose homeostasis.

A. miRNA expression in animal models:

Using microarrays, we have established differences in miRNA expression profiles between diabetic and non-diabetic rats, by measuring global expression of miRNAs in insulin target tissues (liver, skeletal muscle and adipose tissue) in a rat model for type-2-diabetes (Goto-Kyoto) and comparing these to global miRNA expression profiles in normal genetically-distant and genetically-related control rats (Brown Norway and Wistar-Kyoto). Though the use of algorithms that predict genes targeted by these miRNAs we are able to predict the identity of biological pathways that might be affected by the over/under expression of the miRNAs identified in these profile studies.


Prof. Dominic Gauguier, Drs. Pam McKaski, Massimiliano Ria

Dr. Helen Lockstone

Prof. Mark McCarthy

B. eQTL of microRNA and mRNA

We are identifying genetic drivers which determine miRNA and mRNA expression using the data generated as a part of the MolOBB study in the EU funded MolPAGE consortium (Molecular Phenotyping to Accelerate Genomic Epidemiology).

MolOBB is a case-control study relating to Metabolic Syndrome which contains human subjects from a range of different BMIs, We are currently investigating differential mRNA and micro-RNA expression between abdominal and gluteal fat tissues, as well as differential expression associated with other relevant phenotypes. mRNA- and micro-RNA eQTL analysis is pursued with the aim of identifying genetic drivers behind the expression patterns observed.


Members of Molecular Phenotyping to Accelerate Genomic Epidemiology,

but in particularly:

Dr Krina Zondervan

Dr Chris Holmes

Dr Fredrik Karpe

Prof. Mark McCarthy

Dr. Mattias Rantalainen

2. Methylation

DNA methylation is an epigenetic change involved in gene silencing, genomic imprinting and transcriptional regulation of tissue-specific genes during cellular differentiation. DNA methylation involves the modification of a cytosine base by a Dnmt at the C5 position. We have generated a genome-wide database of variation (SNPs), which have the potential to generate or destroy methylation sites and are working toward determining which of these variants are indeed methylated and how their methylation may influence the incidence of T2D in humans. We are also working to assess variation in DNA CpG methylation, which may be associated with the aetiology of T2D, using an array based analysis of DNA methylation using a NimbleGen custom tiling array.


Dr Charlotte Ling

Dr. Vardman Rakyan

Prof. Stephan Beck, Dr. Christopher Bell

Prof. Graham Hitman, Dr. Sarah Finer