Additional file 2 of Integrative multi-omics analysis identifies genetically supported druggable targets and immune cell specificity for myasthenia gravis

Additional file 2: Table S1. Basic Characteristics of eQTL Databases, pQTL Studies, and GWAS Datasets in the study. Table S2. An overview of druggable proteins and the coverage of genes/proteins in eQTLGen and pQTL studies. Table S3. Genome-wide significant loci in MG GWAS from Chia et al. Table S4....

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Hauptverfasser: Li, Jiao, Wang, Fei, Li, Zhen, Feng, Jingjing, Men, Yi, Han, Jinming, Xia, Jiangwei, Zhang, Chen, Han, Yilai, Chen, Teng, Zhao, Yinan, Zhou, Sirui, Da, Yuwei, Chai, Guoliang, Hao, Junwei
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Sprache:eng
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Zusammenfassung:Additional file 2: Table S1. Basic Characteristics of eQTL Databases, pQTL Studies, and GWAS Datasets in the study. Table S2. An overview of druggable proteins and the coverage of genes/proteins in eQTLGen and pQTL studies. Table S3. Genome-wide significant loci in MG GWAS from Chia et al. Table S4. The cis-eQTLs instruments used for drug target gene expression on MG risk in the primary analysis. The outcome is MG GWAS from Chia et al. (1,873 patients and 36,370 controls). Table S5. MR full results using cis-eQTLs on MG risk across different datasets. Table S6. Colocalization results using cis-eQTLs on MG across different datasets. Table S7. The cis-pQTLs instruments used for protein expression on MG risk in the primary analysis. The outcome is MG GWAS from Chia et al. (1,873 patients and 36,370 controls). Table S8. MR full results of proteins using sentinel cis-pQTLs from six large proteomic studies on MG risk across different datasets. Table S9. Colocalization results using cis-pQTLs on MG risk across different datasets. Table S10. Colocalization results for genes/proteins using eQTL from the DICE database.
DOI:10.6084/m9.figshare.26697080