Translating genome-wide association data from the WTCCC study into biological and clinical insights in type 2 diabetes
Lead Research Organisation:
University of Oxford
Department Name: RDM OCDEM
Abstract
Diabetes is a major and growing cause of disease and death in the UK and beyond. Most diabetes arising after early adulthood is due to ?type 2 diabetes?: almost 10 percent of the world?s population either has this condition or will develop it during their lifetime. Despite this global importance, understanding of the processes which lead to development of type 2 diabetes is far from complete, and much needs to be done to develop more effective approaches to prevention and treatment.
One of the most promising routes to a better understanding of diabetes comes from identifying the genes which influence an individual?s predisposition to develop the disease. In recent years, there have been some promising developments and several such genes have been identified. More recently, through advances in knowledge and technology that have followed the sequencing of the human genome, it has become possible to search for such genes in a systematic and ?genomewide? fashion. The applicants on this proposal are currently completing such a ?genomewide association? study (the largest yet conducted into the genetics of diabetes). Analysis of the experimental data will commence shortly and will provide initial clues to the locations of many novel diabetes genes. However, a great deal of work will still be required to separate real effects from spurious findings due to chance or error, and to establish beyond doubt the identities of the genes involved. This work, which involves studying a further 50,000 DNA samples, occupies the first part of our research proposal.
With such a set of confirmed diabetes-susceptibility signals in hand, we aim to translate these discoveries into an improved understanding of the biology of diabetes. We will do this in various ways: for instance, by studying how these genes interact with environmental factors (poor diet, lack of exercise) to influence risk of diabetes. Finally, we want to see whether it is practical to use this genetic information to improve the treatment and prevention of diabetes. We will ask whether the genetic differences we have identified will allow us to predict how likely it is that a given individual will develop diabetes and which treatments may be particularly beneficial.
The work will be performed by groups in Oxford, Exeter and Dundee who have spent the last decade working together to understand the causes of type 2 diabetes. The funding requested will enable them to make major strides towards this goal.
One of the most promising routes to a better understanding of diabetes comes from identifying the genes which influence an individual?s predisposition to develop the disease. In recent years, there have been some promising developments and several such genes have been identified. More recently, through advances in knowledge and technology that have followed the sequencing of the human genome, it has become possible to search for such genes in a systematic and ?genomewide? fashion. The applicants on this proposal are currently completing such a ?genomewide association? study (the largest yet conducted into the genetics of diabetes). Analysis of the experimental data will commence shortly and will provide initial clues to the locations of many novel diabetes genes. However, a great deal of work will still be required to separate real effects from spurious findings due to chance or error, and to establish beyond doubt the identities of the genes involved. This work, which involves studying a further 50,000 DNA samples, occupies the first part of our research proposal.
With such a set of confirmed diabetes-susceptibility signals in hand, we aim to translate these discoveries into an improved understanding of the biology of diabetes. We will do this in various ways: for instance, by studying how these genes interact with environmental factors (poor diet, lack of exercise) to influence risk of diabetes. Finally, we want to see whether it is practical to use this genetic information to improve the treatment and prevention of diabetes. We will ask whether the genetic differences we have identified will allow us to predict how likely it is that a given individual will develop diabetes and which treatments may be particularly beneficial.
The work will be performed by groups in Oxford, Exeter and Dundee who have spent the last decade working together to understand the causes of type 2 diabetes. The funding requested will enable them to make major strides towards this goal.
Technical Summary
Recent advances in the understanding of human genome sequence variation, allied to advances in genotyping technology and availability of large well-characterized clinical samples have reinvigorated efforts to identify the variants influencing susceptibility to type 2 diabetes (T2D). The Wellcome Trust Case Control Consortium is the largest genome-wide association study currently in progress worldwide and genotyping of 2000 T2D cases and 3000 UK controls on the 500k Affymetrix platform will be complete by October 2006. The current proposal outlines our plans to follow-up the findings of this unprecedented study of T2D genetics using the extensive clinical resources available to the applicants and their collaborators. We plan to:
(a) perform extensive validation and replication studies to ensure robust identification of the principal common T2D-susceptibility SNPs against the background of type 1 error and undetected bias. Key to this exercise are plans for combined analysis of all available dense-map T2D genome-wide association data (~5500 cases, 6500 controls from 4 groups) in early 2007 through the IGWANA consortium. We will take the 1500 variants with the most compelling association signals from this combined analysis through successive rounds of validation and replication involving up to 50,000 samples. As we show, this design provides excellent power to retrieve the majority of common SNP-based variants with modest or large T2D-susceptibility effects with negligibly low false positive report probabilities;
(b) identify the variants driving each association signal (through fine-mapping, resequencing, and assays of copy number variation);
(c) translate these findings into insights into the epidemiology, physiology, genetic architecture and biology of T2D, in studies of over 100,000 samples available to the applicants and their collaborators;
(d) evaluate the potential for the application of the findings to the clinical management of diabetes. A key resource will be the Wellcome-Trust-funded Dundee-based UK Type 2 Diabetes Genetics Consortium Case-Control Collection which provides substantial health-related outcome data on over 7000 diabetic cases within the UK?s most-advanced record linkage system.
The applicants have a strong track-record in the successful implementation of large-scale genetic studies. They have pioneered large-scale association studies in T2D, have responsibility for the T2D component of the WTCCC and are playing a leading role in several major international genetics consortia. They are uniquely-placed to deliver insights into the pathogenesis of T2D, and to initiate the task of translating those findings into advances in clinical care.
(a) perform extensive validation and replication studies to ensure robust identification of the principal common T2D-susceptibility SNPs against the background of type 1 error and undetected bias. Key to this exercise are plans for combined analysis of all available dense-map T2D genome-wide association data (~5500 cases, 6500 controls from 4 groups) in early 2007 through the IGWANA consortium. We will take the 1500 variants with the most compelling association signals from this combined analysis through successive rounds of validation and replication involving up to 50,000 samples. As we show, this design provides excellent power to retrieve the majority of common SNP-based variants with modest or large T2D-susceptibility effects with negligibly low false positive report probabilities;
(b) identify the variants driving each association signal (through fine-mapping, resequencing, and assays of copy number variation);
(c) translate these findings into insights into the epidemiology, physiology, genetic architecture and biology of T2D, in studies of over 100,000 samples available to the applicants and their collaborators;
(d) evaluate the potential for the application of the findings to the clinical management of diabetes. A key resource will be the Wellcome-Trust-funded Dundee-based UK Type 2 Diabetes Genetics Consortium Case-Control Collection which provides substantial health-related outcome data on over 7000 diabetic cases within the UK?s most-advanced record linkage system.
The applicants have a strong track-record in the successful implementation of large-scale genetic studies. They have pioneered large-scale association studies in T2D, have responsibility for the T2D component of the WTCCC and are playing a leading role in several major international genetics consortia. They are uniquely-placed to deliver insights into the pathogenesis of T2D, and to initiate the task of translating those findings into advances in clinical care.