GRPa-PRS:一种风险分层方法,用于识别多基因疾病中的基因调节途径
Xiaoyang Li1,2, Brisa S Fernandes1, Andi Liu1,3
1Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
medRxiv : the preprint server for health sciences
|July 10, 2023
概括
我们开发了一个新的框架来识别影响疾病风险和恢复力的基因调节途径 (GRPas),为复杂的多基因疾病提供了新的见解. 这种方法有助于发现保护因素和潜在的治疗点.
科学领域:
- 遗传学 是一个遗传学.
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
背景情况:
- 多基因风险评分 (PRS) 预测疾病易感性,但并不完全考虑那些发展或逃避疾病的人.
- 尽管有遗传倾向,但未知的抵制因素可能会影响疾病的结果.
- 了解这些因素对于推进疾病发病,预防和早期干预策略至关重要.
研究的目的:
- 开发一种新的计算框架,GRPa-PRS,用于识别基因调节通路 (GRPas).
- 根据PRS和临床诊断对个体进行分层,以探索差异性基因表达和途径.
- 调查疾病风险和弹性途径,并验证该框架在多基因疾病中的通用性.
主要方法:
- 开发了一个基于PRS的分层框架,并应用于阿尔茨海默病 (AD) 队列.
- 基因调节表达 (GReX) 被归因,并使用丰富和变异分析确定了差异性GRPas.
- 该框架在AD和精神分裂症 (SCZ) 数据集上进行了测试,有或没有APOE效应,并使用正交性测试进行了验证.
主要成果:
- 确定了已知的与AD相关的途径 (例如,粉样β清除,蛋白结合) 和新的弹性途径 (例如,信号传递).
- 发现了SCZ相关的途径,包括线粒体功能和肌肉发育.
- 与基于变异的PRS方法相比,GRPa-PRS方法在识别差异性途径方面表现出更大的一致性.
结论:
- GRPa-PRS框架系统地探索PRS层中的差异GReX和GRPas.
- 这种方法为与疾病风险和弹性相关的途径提供了新的见解.
- 该框架可用于研究其他多基因复杂疾病.
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