IRS1 G972R polymorphism and type 2 diabetes: a paradigm for the difficult ascertainment of the contribution to disease susceptibility of 'low-frequency-low-risk' variants.
IRS1 G972R polymorphism and type 2 diabetes: a paradigm for the difficult ascertainment of the contribution to disease susceptibility of 'low-frequency-low-risk' variants.
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DOI:
10.1007/s00125-009-1426-4
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发表时间:
2009-09
期刊:
影响因子:
8.2
通讯作者:
Trischitta, V.
中科院分区:
文献类型:
--
作者:
Morini, E.;Prudente, S.;Succurro, E.;Chandalia, M.;Zhang, Y. -Y.;Mammarella, S.;Pellegrini, F.;Powers, C.;Proto, V.;Dallapiccola, B.;Cama, A.;Sesti, G.;Abate, N.;Doria, A.;Trischitta, V.
关键词:
Results on the association between the IRS-1 G972R polymorphism and type 2 diabetes have been conflicting. To obtain further insights onto this topic, we performed a meta-analysis of all available case-control studies. Meta-analysis of 32 studies (12,076 cases and 11,285 controls). The relatively infrequent R972 variant was not significantly associated with type 2 diabetes, (odds ratio [OR] =1.09, 95% confidence interval [CI] 0.96–1.23, p=0.184 under a dominant model). Some evidence of heterogeneity was observed across studies (p=0.1). In the 14 studies (9,713 individuals) in which the mean age at type 2 diabetes diagnosis was available, this variable explained 52% of heterogeneity (p=0.03). When these studies were subdivided into tertiles of mean age at diagnosis, the OR for diabetes was 1.48 (95% CI 1.17–1.87), 1.22 (95% CI 0.97–1.53), and 0.88 (95% CI 0.68–1.13) in the youngest, intermediate and oldest tertile, respectively (p for trend of ORs=0.0022). Our findings illustrate the difficulties of ascertaining the contribution of “low frequency-low risk” variants to type 2 diabetes susceptibility. In the specific context of the R972 variant, ~200,000 study subjects would be needed to have 80% power to identify a 9% increase in diabetes risk at genome-wide significance level. Under these circumstances, a strategy aimed at improving outcome definition and decreasing its heterogeneity may critically enhance our ability to detect genetic effects, thereby decreasing the required sample size. Our data suggest that focusing on early-onset diabetes, which is characterized by a stronger genetic background, may be part of such strategy.
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