Tang Ji, Zhang Huanlin, Zhang Hai, Zhu Hao
Bioinformatics Section, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
Network Center, Southern Medical University, Guangzhou 510515, China.
Comput Struct Biotechnol J. 2023 Jun 10;21:3443-3451. doi: 10.1016/j.csbj.2023.06.008. eCollection 2023.
The influence of adaptive evolution on disease susceptibility has drawn attention; however, the extent of the influence, whether favored mutations also influence drug responses, and whether the associations between the three are population-specific remain unknown. Using a reported deep learning network to integrate seven statistical tests for detecting selection signals, we predicted favored mutations in the genomes of 17 human populations and integrated these favored mutations with reported GWAS sites and drug response-related variants into the database PopTradeOff (http://www.gaemons.net/PopFMIntro). The database also contains genome annotation information on the SNP, sequence, gene, and pathway levels. The preliminary data analyses suggest that substantial associations exist between adaptive evolution, disease susceptibility, and drug responses and that the associations are highly population-specific. The database may be valuable for disease studies, drug development, and personalized medicine.
适应性进化对疾病易感性的影响已引起关注；然而，其影响程度、有利突变是否也会影响药物反应，以及三者之间的关联是否具有人群特异性仍不清楚。我们使用一个已报道的深度学习网络整合七种用于检测选择信号的统计测试，预测了17个人类群体基因组中的有利突变，并将这些有利突变与已报道的全基因组关联研究（GWAS）位点及药物反应相关变异整合到数据库PopTradeOff（http://www.gaemons.net/PopFMIntro）中。该数据库还包含单核苷酸多态性（SNP）、序列、基因和通路水平的基因组注释信息。初步数据分析表明，适应性进化、疾病易感性和药物反应之间存在大量关联，且这些关联具有高度人群特异性。该数据库可能对疾病研究、药物开发和个性化医疗具有重要价值。
