全基因组关联研究
遗传关联
数据科学
计算机科学
计算生物学
人工智能
生物
遗传学
单核苷酸多态性
基因
基因型
作者
Erping Long,Pin Wan,Qingyu Chen,Zhiyong Lu,Jiyeon Choi
出处
期刊:Cell genomics
[Elsevier]
日期:2023-06-01
卷期号:3 (6): 100320-100320
被引量:4
标识
DOI:10.1016/j.xgen.2023.100320
摘要
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.
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