Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes

By Andrew P. Morris, Benjamin F. Voight, T. Teslovich, T. Ferreira, A. Segrè, V. Steinthorsdottir, R. Strawbridge, H. Khan, H. Grallert, A. Mahajan and 194 more. Nature Genetics, 2012

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To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip, including 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two showing sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of additional common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signaling and cell cycle regulation, in diabetes pathogenesis.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/70de4f61311714f1ef71ee81ed0244d3bf44517b

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Citations in the corpus

Bias due to participant overlap in two‐sample Mendelian randomization (2016, methodology): “Examples include associations of genetic variants with lipid fractions from the Global Lipids Genetics Consortium (The Global Lipids Genetics Consortium, 2013) and with type 2 diabetes from the DIAGRAM consortium (Morris et al., 2012).”
Bayesian weighted Mendelian randomization for causal inference based on summary statistics (2018, background): “proinsulin 10,701 (Strawbridge et al., 2011) Metabolic Trait Glycated haemoglobin levels 123,665 (Wheeler et al., 2017) Metabolic Trait Gout 69,374 (Kottgen et al., 2013) Metabolic Trait T2D 69,033 (Morris et al., 2012) Metabolic Trait Urinary albumin to creatinine ratio 54,450 (Teumer et al., 2016)”
Estimating cross-population genetic correlations of causal effect sizes (2018, methodology): “The T2D data set included summary statistics from 69,033 EUR ancestry individuals (Morris et al., 2012) and summary statistics from 18,817 EAS ancestry individuals (Cho et al., 2011), each computed at 1,054,079 genotyped or imputed SNPs.”
Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors (2015, background): “Several consortia with large numbers of participants, such as CARDIoGRAMplusC4D for coronary artery disease [13] and DIAGRAM for type 2 diabetes [14], have published data on the association of catalogues of genetic variants with either risk factors or disease status (a list of consortia is given in ”
Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods (2015, methodology): “These data are increasingly becoming available from large consortia, such as the Global Lipids Genetics Consortium (GLGC) for lipid fractions [13] and DIAGRAM for type 2 diabetes [14].”