From function to translation: Decoding genetic susceptibility to human diseases via artificial intelligence

Erping Long; Peixing Wan; Qingyu Chen; Zhiyong Lu; Jiyeon Choi

doi:10.1016/j.xgen.2023.100320

From function to translation: Decoding genetic susceptibility to human diseases via artificial intelligence

Cell Genom. 2023 May 4;3(6):100320. doi: 10.1016/j.xgen.2023.100320. eCollection 2023 Jun 14.

Authors

Erping Long^{1

2}, Peixing Wan³, Qingyu Chen⁴, Zhiyong Lu⁴, Jiyeon Choi²

Affiliations

¹ Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
² Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
³ Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
⁴ National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA.

Abstract

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.

Keywords: artificial intelligence; functional genomics; genome-wide association studies; translational genomics.

Publication types

Review