人类白细胞抗原（HLA）基因与药物和疾病关联分析数据集

本数据集以“真实、准确、完整、可溯源”为原则，基于PharmGKB数据库、PubMed数据库、FDA和国家卫健委官网数据源，通过人工筛选、录入、多人审核与校对等方法，分析约6000篇文献的数据，展示了HLA基因与药物疗效、不良反应之间的关联信息，以及HLA不同等位基因对自身免疫性疾病、病毒感染性疾病、肿瘤或癌症及移植等疾病的影响。
数据集包含7个内容：HLA基因-药物/疾病信息总览、HLA相关政策法规、国标/行标、HLA相关指南、HLA基因对药物的影响、HLA基因对疾病的影响、HLA基因突变对药物/疾病影响的证据等级信息。
（1）文献数据集：主要包括序号、文献识别码、文献类型、标题和摘要的原文与译文、发表年份、期刊、影响因子与分区等。
（2）HLA-药物数据集：主要包括序号、文献识别码、研究类型、药物、药物类别、研究对象的一般情况、分组详情、研究结果等。
（3）HLA-疾病数据集：主要包括序号、文献识别码、研究类型、疾病分类、研究对象的一般情况、分组详情、研究结果、基因对药物的影响等。
（4）指南数据集：主要包括序号、文献识别码、标题和摘要的原文与译文、发布日期、制定者、出处、疾病或药物、基因位点、详细解读等。
（5）标准数据集：主要包括HLA相关政策法规的PDF原文及重点结论性信息。
（6）变体注释数据集：包括序号、位点、频率、基因、药物相关信息（药物名称、基因对药物的影响、表型、证据来源、证据文件编码、拟定证据等级）、疾病相关信息（疾病名称、基因对疾病的影响、表型、证据来源、证据文件编码、拟定证据等级）等。
数据集主要用于：①加强立项决策支持：通过分析研究热点和趋势，为HLA相关新项目的调研和立项提供决策支持；②提升检测业务精准度：为检测结果提供数据解读，包括HLA分型与杂合性缺失意义，疾病易感基因及精准用药等信息；③推动产业布局创新：为开发新的HLA基因检测位点方法或试剂盒提供数据支持；④数据挖掘分析：利用该库原始数据进行深入的数据挖掘分析，如关联分析、通路富集分析等，以发现新靶点、新方向。

This dataset is developed under the principles of authenticity, accuracy, completeness, and traceability, based on data sources including the PharmGKB database, PubMed database, official websites of the U.S. Food and Drug Administration (FDA), and the National Health Commission of China. Through manual screening, data entry, multi-person review and proofreading, we analyzed data from approximately 6,000 academic articles, and demonstrated the associations between Human Leukocyte Antigen (HLA) genes and drug efficacy as well as adverse reactions, along with the impacts of different HLA alleles on diseases including autoimmune diseases, viral infectious diseases, tumors/cancers, and transplantation-related conditions.

This dataset covers 7 categories: 1) Overview of HLA gene-drug/disease information; 2) HLA-related policies and regulations; 3) National/industry standards; 4) HLA-related guidelines; 5) Impact of HLA genes on drugs; 6) Impact of HLA genes on diseases; 7) Evidence level information regarding the effects of HLA gene mutations on drugs or diseases.

1. Literature dataset: Mainly includes serial number, literature identifier, document type, original and translated versions of title and abstract, publication year, journal name, impact factor and journal partition.
2. HLA-drug dataset: Mainly includes serial number, literature identifier, research type, drug name, drug category, general characteristics of study subjects, grouping details, and research results.
3. HLA-disease dataset: Mainly includes serial number, literature identifier, research type, disease classification, general characteristics of study subjects, grouping details, research results, and the impact of genes on drugs.
4. Guideline dataset: Mainly includes serial number, literature identifier, original and translated versions of title and abstract, release date, developer, source, related diseases or drugs, gene loci, and detailed interpretations.
5. Standard dataset: Mainly includes the original PDF files of HLA-related policies and regulations and their key conclusive information.
6. Variant annotation dataset: Includes serial number, locus, frequency, gene, drug-related information (drug name, impact of gene on drugs, phenotype, evidence source, evidence document code, proposed evidence level), disease-related information (disease name, impact of gene on diseases, phenotype, evidence source, evidence document code, proposed evidence level), and other related content.

The dataset is primarily used for the following purposes:
1. Strengthening project approval decision support: By analyzing research hotspots and trends, providing decision support for the investigation and project initiation of new HLA-related projects;
2. Enhancing the accuracy of testing services: Providing data interpretation for test results, including the significance of HLA typing and loss of heterozygosity, disease susceptibility genes, precision medication and other relevant information;
3. Promoting industrial layout innovation: Providing data support for the development of novel HLA gene detection locus methods or kits;
4. Data mining and analysis: Conducting in-depth data mining and analysis using the raw data in this repository, such as association analysis, pathway enrichment analysis, etc., to discover new therapeutic targets and new research directions.