Integrative approaches to understand systemic lupus erythematosus etiology in trans-ancestry genetic studies

跨祖先遗传学研究中了解系统性红斑狼疮病因的综合方法

基本信息

批准号：
10708065
负责人：
Laura Carrel
金额：
$ 69.83万
依托单位：
PENNSYLVANIA STATE UNIV HERSHEY MED CTR
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-09-20 至 2027-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10708065
关键词：
Address Affect Antinuclear Antibodies Autoimmune Diseases Biological Assay Clinical Complement Computer software Data Data Set Diagnosis Disease Disease Progression Early Diagnosis Early treatment Enhancers Environmental Exposure Etiology European ancestry Exanthema Feedback Female Frequencies Gene Frequency Genetic Genetic Heterogeneity Genetic Models Genetic Risk Genetic study Genotype Heritability Heterogeneity Human Individual Linkage Disequilibrium Logistics Lupus Maps Meta-Analysis Methods Modeling Multiple Organ Failure Organ Phenotype Population Quality of life Reporter Research Signal Transduction Statistical Methods Symptoms Systemic Lupus Erythematosus Testing Validation Variant causal model causal variant cost effective data integration disorder risk empowerment experimental study genetic architecture genetic variant genome wide association study genome-wide improved interest method development multiple datasets prevent public health relevance risk prediction simulation statistics success trait

项目摘要

ABSTRACT Systemic lupus erythematosus (SLE) is a heritable autoimmune disease that primarily affects young females. SLE symptoms can be very heterogeneous, which posts great challenges in early diagnosis. In its advanced stages, SLE can lead to multiple organ failures and even fatality. Early diagnosis is critical for controlling and mitigating the symptoms and improving the quality of life. Genome-wide association studies of SLE to date have identified >150 loci. Yet, the causal variants remain elusive for most loci. There is great interest to integrate datasets from diverse human populations to further empower discovery, refine causal variants, and improve the risk prediction accuracy. In this proposal, we seek to aggregate available GWAS summary statistics from a myriad of autoimmune diseases. We will develop improved meta-analysis methods that effectively integrate data from multiple traits and ancestries. We will also develop better fine-mapping methods that can integrate statistical approaches and experimental validations. Finally, we will develop more accurate genetic risk scores using datasets from multiple ancestries and traits, and combine them with commonly used lab values for improved disease risk predictions. We will release useful software packages implementing these methods to benefit studies of other traits and maximize the impact of the proposed research.

抽象的全身性红斑狼疮（SLE）是一种可遗传的自身免疫性疾病，主要影响年轻女性。 SLE症状可能是非常异构的，这在早期诊断方面提出了巨大的挑战。在其高级阶段，SLE会导致多个器官失败甚至死亡。早期诊断对于控制和减轻症状并改善生活质量。迄今为止，SLE的全基因组关联研究已经确定了> 150个基因座。然而，对于大多数基因座，因果变体仍然难以捉摸。集成很感兴趣来自各种人群的数据集，以进一步增强发现，完善因果变体并改善风险预测准确性。在此提案中，我们试图汇总可用的GWAS摘要统计数据无数自身免疫性疾病。我们将开发有效整合的改进的荟萃分析方法来自多个特征和祖先的数据。我们还将开发更好的精细映射方法，可以集成统计方法和实验验证。最后，我们将建立更准确的遗传风险评分使用来自多个祖先和特征的数据集，并将它们与常用的实验室值结合改善疾病风险预测。我们将发布实施这些方法的有用软件包对其他特征的利益研究，并最大程度地提高了拟议研究的影响。