Unraveling the causal variants from genome wide association studies (GWASs) is pivotal for understanding genetic underpinnings of complex traits and diseases. Despite continuous efforts, tools to refine and prioritize GWAS signals need enhancement to address the direct causal implications of genetic variations. To overcome challenges related to statistical fine-mapping in identifying causal variants, CAUSALdb has been updated with novel features and comprehensive datasets, morphing into CAUSALdb2. This expanded repository integrates 15 057 updated GWAS summary statistics across 10 839 unique traits and implements both LD-based and LD-free fine-mapping approaches, including innovative applications of approximate Bayes Factor and SuSiE. Additionally, by incorporating larger LD reference panels such as TOPMED and UK Biobank, and integrating functional annotations via PolyFun, CAUSALdb2 enhances the accuracy and context of fine-mapping results. The database now supports interrogation of additional causal signals and offers sophisticated visualizations to aid researchers in deciphering complex genetic architectures. By facilitating a deeper and more precise characterisation of causal variants, CAUSALdb2 serves as a crucial tool for advancing the genetic analysis of complex diseases. Available freely, CAUSALdb2 continues to set benchmarks in the post-GWAS era, fostering the development of targeted diagnostics and therapeutics derived from responsible genetic research. Explore these advancements at http://mulinlab.org/causaldb.

中文翻译：

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CAUSALdb2：复杂特征的因果变体的更新数据库


从全基因组关联研究 （GWAS） 中解开因果变异对于理解复杂性状和疾病的遗传基础至关重要。尽管不断努力，但需要增强改进和优先考虑 GWAS 信号的工具，以解决遗传变异的直接因果影响。为了克服在识别因果变异时与统计精细映射相关的挑战，CAUSALdb 已更新为新的特征和全面的数据集，演变为 CAUSALdb2。这个扩展的存储库集成了 10839 个独特性状的 15057 个更新的 GWAS 摘要统计数据，并实施了基于 LD 和无 LD 的精细映射方法，包括近似贝叶斯因子和 SuSiE 的创新应用。此外，通过整合更大的 LD 参考面板（如 TOPMED 和 UK Biobank），并通过 PolyFun 集成功能注释，CAUSALdb2 提高了精细映射结果的准确性和上下文。该数据库现在支持询问其他因果信号，并提供复杂的可视化功能，以帮助研究人员破译复杂的遗传结构。通过促进对因果变异的更深入和更精确的表征，CAUSALdb2 成为推进复杂疾病遗传分析的重要工具。CAUSALdb2 免费提供，继续在后 GWAS 时代树立标杆，促进源自负责任的基因研究的靶向诊断和疗法的发展。在 http://mulinlab.org/causaldb 探索这些进步。