评估个体对代谢综合征的遗传易感性：可解释的机器学习方法
包含17个SNP的基因组数据，用于利用XGBoost等机器学习方法预测代谢综合征的遗传易感性，并提供可解释性分析。
基本信息
资源简介
评估个体对代谢综合征的遗传易感性：可解释的机器学习方法
【一句话概览】
本数据集是 figshare 公开的组学数据集，收录 4 个文件、合计 9.48 MB，主题涵盖 Metabolic syndrome、machine learning、genetic risk score、prediction model、SHAP，可用于生物标志物发现与分子分型研究，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method》。配套论文《Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method》发表于 Annals of Medicine（2025），DOI：10.1080/07853890.2025.2519679。项目资助方：The study was supported by grants from the National Natural Science Foundation of China (Grant Nos. 81873234 and 82474392), General Project of Natural Science Foundation of Fujian Province (Grant No. 2021J01891), Fujian Province Health Education Joint Research Program Project (Grant No. 2019-WJ-39), Research Initiation Funding Project for High-Level Talent at Fujian University of Traditional Chinese Medicine (X2024007-Talent), National Innovation and Entrepreneurship Training Program for College Students (Grant Nos. 202310393003 and S202310393001).。源站首次发布：2025-06-22。
【数据内容】
源站原始描述：Genome-wide association studies have provided profound insights into the genetic aetiology of metabolic syndrome (MetS). However, there is a lack of machine-learning (ML)-based predictive models to assess individual genetic susceptibility to MetS. This study utilized single-nucleotide polymorphisms (SNPs) as variables and employed ML-based genetic risk score (GRS) models to predict the occurrence of MetS, bringing it closer to clinical application. Feature selection was performed using Least Abs…
中文译文（机器翻译，仅供快速理解，请以原文为准）：全基因组关联研究为代谢综合征(MetS)的遗传病因学提供了深刻的见解。然而，缺乏基于机器学习(ML)的预测模型来评估个体对MetS的遗传易感性。本研究利用单核苷酸多态性(SNPs)作为变量，并采用基于ML的遗传风险评分(GRS)模型来预测MetS的发生，使其更接近临床应用。 使用最小绝对收缩和选择算子进行特征选择。采用六种ML算法构建GRS模型。利用五折交叉验证辅助模型的内部验证。使用受试者工作特征(ROC)曲线选择性能较好的GRS模型。然后应用SHapley加法解释(SHAP)来解释模型。提取GRS后，对BMI、年龄和性别进行分层分析。最后，通过多因素逻辑回归将这些传统风险因素和GRS整合，建立联合模型。 共选择17个SNPs进行分析。在GRS模型中，极端梯度提升(XGBoost)模型表现出优越的区分性能(AUC = 0.837)。XGBoost的最佳稳健性也通过五折交叉验证得到验证(平均ROC-AUC = 0.706)。基于XGBoost的SHAP算法不仅阐明了17个SNPs在所有样本中的全局效应，还描述了SNPs之间的相互作用，直观地展示了SNPs如何影响个体MetS的预测。GRS与MetS风险之间存在强相关性，特别是在年轻个体、男性和超重个体中观察到。此外，结合传统风险因素和GRS的模型表现出优异的区分性能(AUC = 0.962)和出色的稳健性(平均ROC-AUC = 0.959)。 本研究建立了一个可靠的基于XGBoost的GRS模型和一个GRS预测平台(https://metabolicsyndromeapps.shinyapps.io/geneticriskscore/)，以评估个体对MetS的遗传易感性。该模型具有高度可解释性，可为确定MetS一级预防措施的必要性提供个性化参考。此外，传统风险因素与GRS之间可能存在相互作用，将两者整合在一个综合模型中有…
【数据结构与技术规格】
文件数 4 个，合计 9.48 MB。iann_a_2519679_sm8546.tif：image/tiff，2.90 MB。iann_a_2519679_sm4772.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，10.63 KB。数据集 DOI：10.6084/m9.figshare.29378173.v1。
【主题与分类】
源站学科分类：Genetics、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Mathematical Sciences not elsewhere classified。主题标签：Metabolic syndrome、machine learning、genetic risk score、prediction model、SHAP。
【适用方向】
组学数据适用于生物标志物发现与分子分型相关建模研究。
【获取与许可】
源站页面：https://tandf.figshare.com/articles/dataset/Assessing_individual_genetic_susceptibility_to_metabolic_syndrome_interpretable_machine_learning_method/29378173
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method. Taylor & Francis. Huang, Tao; Li, Yuanyuan; Wang, Simin; Qiao, Shijie; Zheng, Xiujuan; Xiong, Wenhui; et al
【使用注意】
· 源站最后修改于 2026-01-21，引用时请注意版本时效。
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使用方式
数据集获取（Figshare）
- 源站标题：Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method
- DOI：10.6084/m9.figshare.29378173.v1
- 发布日期：2025-06-22
- 文件数：4 个
- 文件总体积：9.48 MB
- 源站页面：https://tandf.figshare.com/articles/dataset/Assessing_individual_genetic_susceptibility_to_metabolic_syndrome_interpretable_machine_learning_method/29378173
命令行下载
curl -L -o "iann_a_2519679_sm8600.tif" "https://ndownloader.figshare.com/files/55553641"
curl -L -o "iann_a_2519679_sm8546.tif" "https://ndownloader.figshare.com/files/55553644"
curl -L -o "iann_a_2519679_sm4772.docx" "https://ndownloader.figshare.com/files/55553647"
curl -L -o "iann_a_2519679_sm4771.docx" "https://ndownloader.figshare.com/files/55553650"
文件清单
iann_a_2519679_sm8600.tif（5.99 MB）iann_a_2519679_sm8546.tif（2.90 MB）iann_a_2519679_sm4772.docx（10.63 KB）iann_a_2519679_sm4771.docx（576.57 KB）
文件总体积 9.48 MB（源站 API 实测）。
数据说明
Genome-wide association studies have provided profound insights into the genetic aetiology of metabolic syndrome (MetS). However, there is a lack of machine-learning (ML)-based predictive models to assess individual genetic susceptibility to MetS. This study utilized single-nucleotide polymorphisms (SNPs) as variables and employed ML-based genetic risk score (GRS) models to predict the occurrence of MetS, bringing it closer to clinical application.
Feature selection was performed using Least Absolute Shrinkage and Selection Operator. Six ML algorithms were employed to construct GRS models. A fivefold cross-validation was utilized to aid in the internal validation of models. The receiver operating characteristic (ROC) curve was used to select the better-performing GRS model. The SHapley Additive exPlanations (SHAP) was then applied to interpret the model. After extracting GRS, stratified analysis of BMI, age and gender was performed. Finally, these conventional risk factors and GRS were integrated through multivariate logistic regression to establish a combined model.
A total of 17 SNPs were selected for analysis. Among the GRS models, the extreme gradient boosting (XGBoost) model demonstrated superior discriminative performance (AUC = 0.837). The XGBoost’s optimal robustness was also validated through five-fold cross-validation (mean ROC-AUC = 0.706). The XGBoost-based SHAP algorithm not only elucidated the global effects of 17 SNPs across all samples, but also described the interaction between SNPs, providing a visual representation of how SNPs impact the prediction of MetS in an individual. There was a strong correlation between GRS and MetS risk, particularly observed among young individuals, males and overweight individuals. Furthermore, the model combining conventional risk factors and GRS exhibited excellent discriminative performance (AUC = 0.962) and outstanding robustness (mean ROC-AUC = 0.959).
This study established a reliable XGBoost-based GRS model and a GRS prediction platform (https://metabolicsyndromeapps.shinyapps.io/geneticriskscore/) to assess individual genetic susceptibility to MetS. This model has high interpretability and can provide pe
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.29378173.v1（source: https://tandf.figshare.com/articles/dataset/Assessing_individual_genetic_susceptibility_to_metabolic_syndrome_interpretable_machine_learning_method/29378173）。
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