Long Erping, Wan Peixing, Chen Qingyu, Lu Zhiyong, Choi Jiyeon
Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Cell Genom. 2023 May 4;3(6):100320. doi: 10.1016/j.xgen.2023.100320. eCollection 2023 Jun 14.
While genome-wide association studies (GWAS) have discovered thousands of disease-associated loci, molecular mechanisms for a considerable fraction of the loci remain to be explored. The logical next steps for post-GWAS are interpreting these genetic associations to understand disease etiology (GWAS functional studies) and translating this knowledge into clinical benefits for the patients (GWAS translational studies). Although various datasets and approaches using functional genomics have been developed to facilitate these studies, significant challenges remain due to data heterogeneity, multiplicity, and high dimensionality. To address these challenges, artificial intelligence (AI) technology has demonstrated considerable promise in decoding complex functional datasets and providing novel biological insights into GWAS findings. This perspective first describes the landmark progress driven by AI in interpreting and translating GWAS findings and then outlines specific challenges followed by actionable recommendations related to data availability, model optimization, and interpretation, as well as ethical concerns.
虽然全基因组关联研究（GWAS）已经发现了数千个与疾病相关的基因座，但相当一部分基因座的分子机制仍有待探索。GWAS之后的合理下一步是解读这些基因关联以了解疾病病因（GWAS功能研究），并将这些知识转化为对患者的临床益处（GWAS转化研究）。尽管已经开发了各种使用功能基因组学的数据集和方法来促进这些研究，但由于数据的异质性、多样性和高维度，仍然存在重大挑战。为了应对这些挑战，人工智能（AI）技术在解码复杂的功能数据集以及为GWAS研究结果提供新的生物学见解方面展现出了巨大的潜力。本文首先描述了人工智能在解读和转化GWAS研究结果方面所推动的里程碑式进展，然后概述了具体挑战，接着提出了与数据可用性、模型优化与解读以及伦理问题相关的可行建议。
