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Zia A, Wang X, Bhatti A, Demirci FY, Zhao W, Rasheed A, Samuel M, Kiani AK, Ismail M, Zafar J, John P, Saleheen D, Kamboh MI. A replication study of 49 Type 2 diabetes risk variants in a Punjabi Pakistani population. Diabet Med 2016; 33:1112-7. [PMID: 26499911 DOI: 10.1111/dme.13012] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Revised: 08/03/2015] [Accepted: 10/21/2015] [Indexed: 12/28/2022]
Abstract
AIM The burden of Type 2 diabetes is alarmingly high in South Asia, a region that has many genetically diverse ethnic populations. Genome-wide association studies (GWAS) conducted largely in European populations have identified a number of loci predisposing to Type 2 diabetes risk, however, the relevance of such genetic loci in many South Asian sub-ethnicities remains elusive. The aim of this study was to replicate 49 single nucleotide polymorphisms (SNPs) previously identified through GWAS in Punjabis living in Pakistan. METHODS We examined the association of 49 SNPs in 853 Type 2 diabetes cases and 1945 controls using additive logistic regression models after adjusting for age and gender. RESULTS Of the 49 SNPs investigated, eight showed a nominal association (P < 0.05) that also remained significant after controlling for the false discovery rate. The most significant association was found for rs7903146 at the TCF7L2 locus. For a per unit increase in the risk score comprising of all the 49 SNPs, the odds ratio in association with Type 2 diabetes risk was 1.16 (95% CI 1.13-1.19, P < 2.0E-16). CONCLUSION These results suggest that some Type 2 diabetes susceptibility loci are shared between Europeans and Punjabis living in Pakistan.
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Affiliation(s)
- A Zia
- Atta-Ur-Rahman School of Applied Biosciences (ASAB), National University of Science and Technology (NUST), Islamabad, Pakistan
| | - X Wang
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, USA
| | - A Bhatti
- Atta-Ur-Rahman School of Applied Biosciences (ASAB), National University of Science and Technology (NUST), Islamabad, Pakistan
| | - F Y Demirci
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, USA
| | - W Zhao
- Institute of Translational Medicine and Human Genetics, Department of Medicine, University of Pennsylvania, Philadelphia, USA
| | - A Rasheed
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - M Samuel
- Center for Non-Communicable Diseases, Karachi, Pakistan
| | - A K Kiani
- Atta-Ur-Rahman School of Applied Biosciences (ASAB), National University of Science and Technology (NUST), Islamabad, Pakistan
| | - M Ismail
- Institute of Biomedical and Genetic Engineering (IBGE), Islamabad, Pakistan
| | - J Zafar
- Pakistan Institute of Medical Sciences (PIMS), Islamabad, Pakistan
| | - P John
- Atta-Ur-Rahman School of Applied Biosciences (ASAB), National University of Science and Technology (NUST), Islamabad, Pakistan
| | - D Saleheen
- Institute of Translational Medicine and Human Genetics, Department of Medicine, University of Pennsylvania, Philadelphia, USA
- Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, USA
| | - M I Kamboh
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, USA
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Burgess S, Thompson SG, Burgess S, Thompson SG, Andrews G, Samani NJ, Hall A, Whincup P, Morris R, Lawlor DA, Davey Smith G, Timpson N, Ebrahim S, Ben-Shlomo Y, Davey Smith G, Timpson N, Brown M, Ricketts S, Sandhu M, Reiner A, Psaty B, Lange L, Cushman M, Hung J, Thompson P, Beilby J, Warrington N, Palmer LJ, Nordestgaard BG, Tybjaerg-Hansen A, Zacho J, Wu C, Lowe G, Tzoulaki I, Kumari M, Sandhu M, Yamamoto JF, Chiodini B, Franzosi M, Hankey GJ, Jamrozik K, Palmer L, Rimm E, Pai J, Psaty B, Heckbert S, Bis J, Anand S, Engert J, Collins R, Clarke R, Melander O, Berglund G, Ladenvall P, Johansson L, Jansson JH, Hallmans G, Hingorani A, Humphries S, Rimm E, Manson J, Pai J, Watkins H, Clarke R, Hopewell J, Saleheen D, Frossard R, Danesh J, Sattar N, Robertson M, Shepherd J, Schaefer E, Hofman A, Witteman JCM, Kardys I, Ben-Shlomo Y, Davey Smith G, Timpson N, de Faire U, Bennet A, Sattar N, Ford I, Packard C, Kumari M, Manson J, Lawlor DA, Davey Smith G, Anand S, Collins R, Casas JP, Danesh J, Davey Smith G, Franzosi M, Hingorani A, Lawlor DA, Manson J, Nordestgaard BG, Samani NJ, Sandhu M, Smeeth L, Wensley F, Anand S, Bowden J, Burgess S, Casas JP, Di Angelantonio E, Engert J, Gao P, Shah T, Smeeth L, Thompson SG, Verzilli C, Walker M, Whittaker J, Hingorani A, Danesh J. Bayesian methods for meta-analysis of causal relationships estimated using genetic instrumental variables. Stat Med 2010; 29:1298-311. [PMID: 20209660 DOI: 10.1002/sim.3843] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Genetic markers can be used as instrumental variables, in an analogous way to randomization in a clinical trial, to estimate the causal relationship between a phenotype and an outcome variable. Our purpose is to extend the existing methods for such Mendelian randomization studies to the context of multiple genetic markers measured in multiple studies, based on the analysis of individual participant data. First, for a single genetic marker in one study, we show that the usual ratio of coefficients approach can be reformulated as a regression with heterogeneous error in the explanatory variable. This can be implemented using a Bayesian approach, which is next extended to include multiple genetic markers. We then propose a hierarchical model for undertaking a meta-analysis of multiple studies, in which it is not necessary that the same genetic markers are measured in each study. This provides an overall estimate of the causal relationship between the phenotype and the outcome, and an assessment of its heterogeneity across studies. As an example, we estimate the causal relationship of blood concentrations of C-reactive protein on fibrinogen levels using data from 11 studies. These methods provide a flexible framework for efficient estimation of causal relationships derived from multiple studies. Issues discussed include weak instrument bias, analysis of binary outcome data such as disease risk, missing genetic data, and the use of haplotypes.
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