ArticleFrontiers in genetics2014
A set-based association test identifies sex-specific gene sets associated with type 2 diabetes.
Article in Frontiers in genetics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Transcriptomic Analysis of Type 2 Diabetes Mellitus Combined with Lower Extremity Atherosclerotic Occlusive Disease.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
- Article
- Influence of obesity, parental history of diabetes, and genes in type 2 diabetes: A case-control study.Scientific reports · 2019Article
- Genetic testing for exercise prescription and injury prevention: AIS-Athlome consortium-FIMS joint statement.BMC genomics · 2017Article
- Gene set based association analyses for the WSSV resistance of Pacific white shrimp Litopenaeus vannamei.Scientific reports · 2017Article
- Sex and Gender Differences in Risk, Pathophysiology and Complications of Type 2 Diabetes Mellitus.Endocrine reviews · 2016Review
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Authors and funding
3 authors.
Funding
Abstract
Single variant analysis in genome-wide association studies (GWAS) has been proven to be successful in identifying thousands of genetic variants associated with hundreds of complex diseases. However, these identified variants only explain a small fraction of inheritable variability in many diseases, suggesting that other resources, such as multilevel genetic variations, may contribute to disease susceptibility. In this work, we proposed to combine genetic variants that belong to a gene set, such as at gene- and pathway-level to form an integrated signal aimed to identify major players that function in a coordinated manner conferring disease risk. The integrated analysis provides novel insight into disease etiology while individual signals could be easily missed by single variant analysis. We applied our approach to a genome-wide association study of type 2 diabetes (T2D) with male and female data analyzed separately. Novel sex-specific genes and pathways were identified to increase the risk of T2D. We also demonstrated the performance of signal integration through simulation studies.
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