TY - JOUR
T1 - DeepRisk
T2 - A deep learning approach for genome-wide assessment of common disease risk
AU - Peng, Jiajie
AU - Bao, Zhijie
AU - Li, Jingyi
AU - Han, Ruijiang
AU - Wang, Yuxian
AU - Han, Lu
AU - Peng, Jinghao
AU - Wang, Tao
AU - Hao, Jianye
AU - Wei, Zhongyu
AU - Shang, Xuequn
N1 - Publisher Copyright:
© 2024
PY - 2024/7
Y1 - 2024/7
N2 - The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant interest. Although widely applied, traditional polygenic risk scoring methods fall short, as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms (SNPs). This presents a limitation, as genetic diseases often arise from complex interactions between multiple SNPs. To address this challenge, we developed DeepRisk, a biological knowledge-driven deep learning method for modeling these complex, nonlinear associations among SNPs, to provide a more effective method for scoring the risk of common diseases with genome-wide genotype data. Evaluations demonstrated that DeepRisk outperforms existing PRS-based methods in identifying individuals at high risk for four common diseases: Alzheimer's disease, inflammatory bowel disease, type 2 diabetes, and breast cancer.
AB - The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant interest. Although widely applied, traditional polygenic risk scoring methods fall short, as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms (SNPs). This presents a limitation, as genetic diseases often arise from complex interactions between multiple SNPs. To address this challenge, we developed DeepRisk, a biological knowledge-driven deep learning method for modeling these complex, nonlinear associations among SNPs, to provide a more effective method for scoring the risk of common diseases with genome-wide genotype data. Evaluations demonstrated that DeepRisk outperforms existing PRS-based methods in identifying individuals at high risk for four common diseases: Alzheimer's disease, inflammatory bowel disease, type 2 diabetes, and breast cancer.
KW - Common disease risk
KW - Deep learning
KW - Disease prevention
KW - Disease risk prediction
KW - Polygenic risk score
UR - http://www.scopus.com/inward/record.url?scp=85193708943&partnerID=8YFLogxK
U2 - 10.1016/j.fmre.2024.02.015
DO - 10.1016/j.fmre.2024.02.015
M3 - 文章
AN - SCOPUS:85193708943
SN - 2667-3258
VL - 4
SP - 752
EP - 760
JO - Fundamental Research
JF - Fundamental Research
IS - 4
ER -