通过一种新的途径集群和途径子网络方法,阐明2型糖尿病的分子机制
Burcu Bakir-Gungor1,2, Miray Ünlü Yazici2, Gökhan Göy1
1Department of Computer Engineering, Abdullah Gül University, Kayseri, Turkey.
Turkish journal of biology = Turk biyoloji dergisi
|August 2, 2023
概括
将遗传数据与途径和网络信息相结合的新方法有助于发现2型糖尿病 (T2D) 中涉及的分子途径. 这种方法有助于发现与T2D风险相关的隐藏遗传变异.
科学领域:
- 遗传学 遗传学 是一个
- 生物信息学是一种生物信息学.
- 系统生物学 系统生物学
背景情况:
- 2型糖尿病 (T2D) 是一种复杂的多因素疾病,占糖尿病病例的90%.
- 全基因组关联研究 (GWAS) 确定了与T2D风险相关的遗传变异,但大部分遗传性仍然无法解释.
- 需要GWAS后的策略来分析变体的集体影响,并发现新的多基因关联.
研究的目的:
- 开发和应用一种新的后GWAS方法来识别T2D中与疾病相关的途径.
- 将遗传关联数据与生化途径,蛋白质-蛋白质相互作用 (PPI) 网络和SNP功能信息集成.
- 在蛋白质子网络,途径和途径子网络层面全面分析T2D背后的分子机制.
主要方法:
- 使用开发的方法重新分析了三个T2D GWAS元分析数据集.
- 结合了名义上显著的遗传关联证据与已知的生物化学途径,PPI网络和SNP功能数据.
- 集成的自上而下和自下而上的in silico方法用于全面的分子机制分析.
- 使用基于共享基因的相互信息,比较已识别的蛋白质子网络和受影响的途径.
主要成果:
- 开发的方法成功地确定了T2D中与疾病相关的途径.
- 大多数已识别的途径与已知的T2D病理生理学保持一致.
- 将SNP功能性质和PPI网络纳入GWAS分析有效地剖析关键的分子通路.
结论:
- 新的GWAS后战略提供了对传统缩方法的改进.
- 整合功能性SNP数据和PPI网络可以更好地发现T2D所涉及的分子途径.
- 这种方法可以更全面地了解T2D的遗传结构和分子机制.
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