Artificial intelligence-assisted identification of genetic factors predisposing high-risk individuals to asymptomatic heart failure

Ning I. Yang, Chi Hsiao Yeh, Tsung Hsien Tsai, Yi Ju Chou, Paul Wei Che Hsu, Chun Hsien Li, Yun Hsuan Chan, Li Tang Kuo, Chun Tai Mao, Yu Chiau Shyu, Ming Jui Hung, Chi Chun Lai*, Huey Kang Sytwu, Ting Fen Tsai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Heart failure (HF) is a global pandemic public health burden affecting one in five of the general population in their lifetime. For high-risk individuals, early detection and prediction of HF progression reduces hospitalizations, reduces mortality, improves the individual’s quality of life, and reduces associated medical costs. In using an artificial intelligence (AI)-assisted genome-wide association study of a single nucleotide polymorphism (SNP) database from 117 asymptomatic high-risk individuals, we identified a SNP signature composed of 13 SNPs. These were annotated and mapped into six protein-coding genes (GAD2, APP, RASGEF1C, MACROD2, DMD, and DOCK1), a pseu-dogene (PGAM1P5), and various non-coding RNA genes (LINC01968, LINC00687, LOC105372209, LOC101928047, LOC105372208, and LOC105371356). The SNP signature was found to have a good performance when predicting HF progression, namely with an accuracy rate of 0.857 and an area under the curve of 0.912. Intriguingly, analysis of the protein connectivity map revealed that DMD, RASGEF1C, MACROD2, DOCK1, and PGAM1P5 appear to form a protein interaction network in the heart. This suggests that, together, they may contribute to the pathogenesis of HF. Our findings demonstrate that a combination of AI-assisted identifications of SNP signatures and clinical parameters are able to effectively identify asymptomatic high-risk subjects that are predisposed to HF.

Original languageEnglish
Article number2430
JournalCells
Volume10
Issue number9
DOIs
StatePublished - Sep 2021

Keywords

  • Artificial intelligence
  • Genetic factors
  • Heart failure
  • Single nucleotide polymorphism

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