2026-06-11 千葉大学
図：冠動脈疾患全ゲノム解析の機械学習フレームワーク
＜関連情報＞
- https://www.chiba-u.ac.jp/news/research-collab/post_686.html
- https://www.ahajournals.org/doi/10.1161/CIRCGEN.125.005341
機械学習により、希少遺伝子変異の寄与が明らかになり、日本人集団における冠動脈疾患のリスク予測が向上する Machine Learning Reveals the Contribution of Rare Genetic Variants and Enhances Risk Prediction for Coronary Artery Disease in the Japanese Population
Hirotaka Ieki, MD, PhD, Sai Zhang, PhD, Satoshi Koyama, MD, PhD, Martin Kjellberg, MS, Hiroki Yoshida, MD, PhD, Ryo Kurosawa, MD, PhD, Hiroshi Matsunaga, MD, PhD, … ,the Biobank Japan Project
Circulation: Genomic and Precision Medicine Published: 4 June 2026
DOI:https://doi.org/10.1161/CIRCGEN.125.005341
Abstract
BACKGROUND:
GWASs (genome-wide association studies) have advanced our understanding of coronary artery disease (CAD) genetics and enabled the development of polygenic risk scores (PRSs) for estimating genetic risk based on common variant burden. However, GWASs have limitations in analyzing rare variants due to insufficient statistical power, thereby constraining PRS performance.
METHODS:
We conducted whole-genome sequencing of 1752 Japanese patients with CAD and 3019 controls. A machine learning-based analytical framework was applied to identify and interpret rare genetic variants associated with CAD pathogenesis.
RESULTS:
This approach identified 59 CAD-related genes, including known causal genes such as LDLR and those not previously captured by GWASs. A rare variant-based risk score derived from the framework demonstrated distinct clinical characteristics compared with a conventional common variant-based PRS. The rare variant-based risk score significantly discriminated CAD cases and predicted cardiovascular mortality in an independent cohort. Furthermore, combining the rare variant-based risk score with the traditional PRS improved CAD prediction compared with the PRS alone (area under the curve, 0.66 versus 0.61; P=0.007).
CONCLUSIONS:
These findings underscore the distinct and complementary value of the rare variant-based risk score compared with the conventional PRS, highlighting the enhanced predictive power achieved through their integration. This comprehensive approach proposes broader genetic profiling, offering substantial potential for improved clinical risk stratification and personalized prevention strategies.
