A machine learning model for disease risk prediction by integrating genetic and non-genetic factors

孟德尔随机化 生命银行 计算机科学 机器学习 疾病 人口 全基因组关联研究 遗传关联 预测建模 人工智能 单核苷酸多态性 生物信息学 医学 遗传变异 基因型 生物 遗传学 内科学 环境卫生 基因
作者
Yu Xu,Chonghao Wang,Zeming Li,Yunpeng Cai,Ouzhou Young,Aiping Lyu,Lu Zhang
标识
DOI:10.1109/bibm55620.2022.9994925
摘要

Polygenic risk score (PRS) has been widely used to identify the high-risk individuals from the general population, which would be helpful for disease prevention and early treatment. Many methods have been developed to calculate PRS by weighting and aggregating the phenotype-associated risk alleles from genome-wide association studies. However, only considering genetic effects may not be sufficient for risk prediction because the disease risk is not only related to genetic factors but also non-genetic factors, e.g., diet, physical exercise et al. But it is still a challenge to integrate these genetic and non-genetic factors into a unified machine learning framework for disease risk prediction. In this paper, we proposed PRSIMD (PRS Integrating Multi-source Data), a machine learning model that applies posterior regularization to integrate genetic and non-genetic factors to improve disease risk prediction. Also, we applied Mendelian Randomization analysis to identify the causal non-genetic risk factors for the selected diseases. We applied PRSIMD to predict type 2 diabetes and coronary artery disease from UK Biobank and observed that PRSIMD was significantly better than the existing methods to calculate PRS. In addition, we observed that PRSIMD achieved the better predictive power than the composite risk score. The codes of PRSIMD are available at: https://github.con ericcombiolab/PRSIMD
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
淑文发布了新的文献求助10
1秒前
单薄的夜南应助嘻哈采纳,获得10
1秒前
丘比特应助嘻哈采纳,获得10
1秒前
2秒前
酷酷的涵蕾完成签到 ,获得积分10
2秒前
redking发布了新的文献求助10
2秒前
4秒前
5秒前
6秒前
6秒前
HuiJN完成签到 ,获得积分10
6秒前
锤子完成签到,获得积分10
7秒前
在水一方应助lizibelle采纳,获得10
8秒前
hoyden完成签到,获得积分10
8秒前
Stardust发布了新的文献求助10
9秒前
11秒前
momo发布了新的文献求助10
12秒前
12秒前
乐乐应助Mo采纳,获得10
13秒前
13秒前
Liufgui应助Z6kjoA采纳,获得20
13秒前
爆米花应助科研通管家采纳,获得10
15秒前
NexusExplorer应助科研通管家采纳,获得10
15秒前
cangy发布了新的文献求助10
15秒前
英姑应助科研通管家采纳,获得10
15秒前
地表飞猪应助科研通管家采纳,获得10
15秒前
桐桐应助科研通管家采纳,获得10
15秒前
16秒前
Akim应助科研通管家采纳,获得10
16秒前
Orange应助科研通管家采纳,获得10
16秒前
地表飞猪应助科研通管家采纳,获得10
16秒前
科研通AI2S应助科研通管家采纳,获得10
16秒前
酷波er应助科研通管家采纳,获得10
16秒前
地表飞猪应助科研通管家采纳,获得10
16秒前
16秒前
YamDaamCaa应助科研通管家采纳,获得30
16秒前
luo关闭了luo文献求助
17秒前
Jogging完成签到,获得积分10
18秒前
Villanellel发布了新的文献求助30
18秒前
李健应助ttt采纳,获得10
18秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3989334
求助须知:如何正确求助?哪些是违规求助? 3531428
关于积分的说明 11253936
捐赠科研通 3270119
什么是DOI,文献DOI怎么找? 1804887
邀请新用户注册赠送积分活动 882087
科研通“疑难数据库(出版商)”最低求助积分说明 809173