Common Inherited Variation in Mitochondrial Genes Is Not Enriched for Associations with Type 2 Diabetes or Related Glycemic Traits
Common Inherited Variation in Mitochondrial Genes Is Not Enriched for Associations with Type 2 Diabetes or Related Glycemic Traits
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DOI：
10.1371/journal.pgen.1001058
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发表时间：
2010-08-01
期刊：
影响因子：
4.5
通讯作者：
Altshuler, David
中科院分区：
文献类型：
--
作者：
Segre, Ayellet V.;Groop, Leif;Altshuler, David
在糖尿病患者和胰岛素抵抗个体的骨骼肌中观察到线粒体功能障碍。此外，线粒体DNA中的遗传性突变可能导致一种罕见的糖尿病。然而，目前尚不清楚线粒体功能障碍是否是糖尿病常见形式的主要原因。到目前为止，与2型糖尿病（T2 D）密切相关的常见遗传变异并不影响线粒体功能。一种可能性是，多个线粒体基因包含共同影响T2 D风险的适度遗传效应。为了检验这一假设，我们开发了一种名为Meta-Analysis Gene-set Enrichment of variaNT Associations（洋红; http：//www.broadinstitute.org/mpg/magenta）的方法。洋红类似于基因集富集分析，测试功能相关基因集是否富集与多基因疾病或性状的关联。洋红专门设计用于利用大型全基因组关联（GWA）研究荟萃分析的统计功效，这些荟萃分析的个体基因型不可用。这是通过将变异关联p值结合到基因得分中，然后校正混杂因素（如基因大小、变异数量和连锁不平衡特性）来实现的。通过模拟，我们确定了洋红可以检测到可能被单标记分析遗漏的关联的参数范围。我们通过识别脂质和脂蛋白GWA荟萃分析中已知的相关途径，验证了洋红在经验数据上的表现。然后，我们通过将洋红应用于三个基因组来验证我们的线粒体假设：线粒体基因的核调节基因、氧化磷酸化基因和1,000个核编码的线粒体基因。该分析是使用最新的T2 D GWA荟萃分析47，117人和荟萃分析7糖尿病相关的血糖性状（多达46，186非糖尿病个体）进行的。这项有力的分析发现，在所测试的任何基因集中，与T2 D或任何血糖特征的关联均没有显着富集。这些结果表明，影响核编码线粒体基因的常见变异对T2 D易感性的遗传贡献最多很小。
Mitochondrial dysfunction has been observed in skeletal muscle of people with diabetes and insulin-resistant individuals. Furthermore, inherited mutations in mitochondrial DNA can cause a rare form of diabetes. However, it is unclear whether mitochondrial dysfunction is a primary cause of the common form of diabetes. To date, common genetic variants robustly associated with type 2 diabetes (T2D) are not known to affect mitochondrial function. One possibility is that multiple mitochondrial genes contain modest genetic effects that collectively influence T2D risk. To test this hypothesis we developed a method named Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA; http://www.broadinstitute.org/mpg/magenta). MAGENTA, in analogy to Gene Set Enrichment Analysis, tests whether sets of functionally related genes are enriched for associations with a polygenic disease or trait. MAGENTA was specifically designed to exploit the statistical power of large genome-wide association (GWA) study meta-analyses whose individual genotypes are not available. This is achieved by combining variant association p-values into gene scores and then correcting for confounders, such as gene size, variant number, and linkage disequilibrium properties. Using simulations, we determined the range of parameters for which MAGENTA can detect associations likely missed by single-marker analysis. We verified MAGENTA's performance on empirical data by identifying known relevant pathways in lipid and lipoprotein GWA meta-analyses. We then tested our mitochondrial hypothesis by applying MAGENTA to three gene sets: nuclear regulators of mitochondrial genes, oxidative phosphorylation genes, and,1,000 nuclear-encoded mitochondrial genes. The analysis was performed using the most recent T2D GWA meta-analysis of 47,117 people and meta-analyses of seven diabetes-related glycemic traits (up to 46,186 non-diabetic individuals). This well-powered analysis found no significant enrichment of associations to T2D or any of the glycemic traits in any of the gene sets tested. These results suggest that common variants affecting nuclear-encoded mitochondrial genes have at most a small genetic contribution to T2D susceptibility.