Common inherited variation in mitochondrial genes is not enriched for associations with type 2 diabetes or related glycemic traits

• Published in: PLoS genetics 2010-08, Vol.6 (8), p.e1001058
• Main Authors: Segrè, Ayellet V, Groop, Leif, Mootha, Vamsi K, Daly, Mark J, Altshuler, David
• Format: Article
• Language: English
• Subjects: Case-Control Studies; Clinical Medicine; Cohort Studies; Computational Biology/Genomics; Diabetes; Diabetes and Endocrinology/Type 2 Diabetes; Diabetes Mellitus, Type 2 - genetics; Diabetes Mellitus, Type 2 - metabolism; Endocrinology and Diabetes; Endokrinologi och diabetes; Gene mutations; Genes; Genetic aspects; Genetic Variation; Genetics; Genetics and Genomics/Complex Traits; Genetics and Genomics/Genetics of Disease; Genome-Wide Association Study; Glucose - metabolism; Insulin resistance; Klinisk medicin; Medical and Health Sciences; Medicin och hälsovetenskap; Mitochondrial DNA; Mitochondrial Proteins - genetics; Muscles; Physiological aspects; Polymorphism, Single Nucleotide; Quantitative Trait, Heritable; Studies; Type 2 diabetes
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1001058

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 approximately 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.