This application represents a response to RFA-DK-09-004 (Multiethnic Study of type 2 diabetes genes) from investigators experienced in the genetics of T2D and related complex traits, and in broader aspects of T2D pathophysiology. The applicants (collectively, the Global Diabetes Consortium [GDC]) will work with other members of the U01 Steering Committee to realize the objectives of the study through: contributing well-characterized, appropriately-consented samples for re-sequencing and genotyping. The samples within this proposal are of South Asian, East Asian, European, African and Arab origin (>100,000 samples in total), and representative of populations accounting for -70% of global T2D. Many of the samples have undergone high-density GWA scanning and/or have additional features which deliver added-value; contributing expertise in T2D genetics, large-scale genomics and T2D pathophysiology. The applicants have completed some of the first large-scale, high-density T2D GWA scans in European, S Asian, E Asian and Arab populations and bring links to relevant community and collaborative efforts including the 1000 Genomes Project and the recent T2D re-sequencing and fine-mapping experience of the WTCCC; working with the Steering Committee to develop plans for coordinated analysis. Our proposal describes plans to combine information from the 1000 Genomes project with de novo re-sequencing of contiguous regions and deep-re-sequencing of well-annotated sequence within GWA-proven T2D-association signals across a range of ethnic groups. Large-scale genotyping studies will be used to fine-map variants causal for the index association and those that represent independent causal events (particularly low-frequency, penetrate alleles). The plan anticipates a shift towards genome-wide discovery re-sequencing; building a strong global consortium of investigators. Oxford will contribute substantially to all four of these aims, contributing samples of European and S Asian origin;providing expertise in T2D genetics, large-scale genomics and statistical analysis;participating in the U0l steering committee;and taking responsibility for building a global T2D genetics consortium.

Public Health Relevance

The rising prevalence of T2D in societies worldwide constitutes a major challenge to global health, and improved therapeutic and preventative options are required. Recent genetic discoveries have provided important clues about the development of T2D, but further work is needed to define the causal mechanisms. The present proposal brings together a group of investigators with unequalled expertise and a wide range of clinical samples who will work with others in the U01 consortium to realize these important objectives.

Agency
National Institute of Health (NIH)
Institute
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)
Type
Research Project--Cooperative Agreements (U01)
Project #
4U01DK085545-05
Application #
8536731
Study Section
Special Emphasis Panel (ZDK1-GRB-G (O2))
Program Officer
Akolkar, Beena
Project Start
2009-09-20
Project End
2014-07-31
Budget Start
2013-08-01
Budget End
2014-07-31
Support Year
5
Fiscal Year
2013
Total Cost
$547,230
Indirect Cost
$16,760
Name
University of Oxford
Department
Type
DUNS #
226694883
City
Oxford
State
Country
United Kingdom
Zip Code
OX1 2-JD
Gallaugher, Michael; Canty, Angelo J; Paterson, Andrew D (2016) Factors associated with heterogeneity in microarray gene expression in peripheral blood mononuclear cells from large pedigrees. BMC Proc 10:91-95
Green, Alden; Cook, Kaitlyn; Grinde, Kelsey et al. (2016) A general method for combining different family-based rare-variant tests of association to improve power and robustness of a wide range of genetic architectures. BMC Proc 10:165-170
Friedrichs, Stefanie; Malzahn, Dörthe; Pugh, Elizabeth W et al. (2016) Filtering genetic variants and placing informative priors based on putative biological function. BMC Genet 17 Suppl 2:8
Datta, Ananda S; Biswas, Swati (2016) Comparison of haplotype-based statistical tests for disease association with rare and common variants. Brief Bioinform 17:657-71
Katsumata, Yuriko; Fardo, David W (2016) On combining family- and population-based sequencing data. BMC Proc 10:175-179
Held, Elizabeth; Cape, Joshua; Tintle, Nathan (2016) Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data. BMC Proc 10:141-145
Lent, Samantha; Deng, Xuan; Cupples, L Adrienne et al. (2016) Imputing rare variants in families using a two-stage approach. BMC Proc 10:209-214
Almeida, Marcio; Blondell, Lucy; Peralta, Juan M et al. (2016) Independent test assessment using the extreme value distribution theory. BMC Proc 10:245-249
Lek, Monkol; Karczewski, Konrad J; Minikel, Eric V et al. (2016) Analysis of protein-coding genetic variation in 60,706 humans. Nature 536:285-91
Valcarcel, Alessandra; Grinde, Kelsey; Cook, Kaitlyn et al. (2016) A multistep approach to single nucleotide polymorphism-set analysis: an evaluation of power and type I error of gene-based tests of association after pathway-based association tests. BMC Proc 10:349-355

Showing the most recent 10 out of 53 publications