Artificial Intelligence-Assisted Identification of Genetic Factors Predisposing High-Risk Individuals to Asymptomatic Heart Failure.
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 pseudogene (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.
['Yang|Ning-I|NI|', 'Yeh|Chi-Hsiao|CH|0000-0002-8850-4144', 'Tsai|Tsung-Hsien|TH|', 'Chou|Yi-Ju|YJ|', 'Hsu|Paul Wei-Che|PW|', 'Li|Chun-Hsien|CH|', 'Chan|Yun-Hsuan|YH|', 'Kuo|Li-Tang|LT|', 'Mao|Chun-Tai|CT|0000-0003-1138-5471', 'Shyu|Yu-Chiau|YC|0000-0003-1747-2226', 'Hung|Ming-Jui|MJ|', 'Lai|Chi-Chun|CC|', 'Sytwu|Huey-Kang|HK|', 'Tsai|Ting-Fen|TF|']
D000368:Aged D001185:Artificial Intelligence D005260:Female D020022:Genetic Predisposition to Disease D055106:Genome-Wide Association Study D000082742:Heart Disease Risk Factors D006333:Heart Failure D006801:Humans D008297:Male D008875:Middle Aged D020641:Polymorphism, Single Nucleotide
genetic factors heart failure artificial intelligence single nucleotide polymorphism