Functional Variants Identified Efficiently through an Integrated Transcriptome and Epigenome Analysis
Abstract Although genome-wide association studies (GWAS) have identified numerous genetic loci associated with complex diseases, the underlying molecular mechanisms of how these loci contribute to disease pathogenesis remain largely unknown, due to the lack of an efficient strategy to identify these...
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2018
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/7d1d4c154ce1401395cde1f92e38ef20 |
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| Sumario: | Abstract Although genome-wide association studies (GWAS) have identified numerous genetic loci associated with complex diseases, the underlying molecular mechanisms of how these loci contribute to disease pathogenesis remain largely unknown, due to the lack of an efficient strategy to identify these risk variants. Here, we proposed a new strategy termed integrated transcriptome and epigenome analysis (iTEA) to identify functional genetic variants in non-coding elements. We considered type 2 diabetes mellitus as a model and identified a well-known diabetic risk variant rs35767 using iTEA. Furthermore, we discovered a new functional SNP, rs815815, involved in glucose metabolism. Our study provides an approach to directly and quickly identify functional genetic variants in type 2 diabetes mellitus, and this approach can be extended to study other complex diseases. |
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