Additional file 1 of Incorporating genetic similarity of auxiliary samples into eGene identification under the transfer learning framework

Additional file 1. Table S1. Number of genes determined by whether the P-values of regression coefficients and TLegene-oScore are less than 0.05. Table S2. Number of genes determined by whether the P-values of regression coefficients and TLegene-fScore are less than 0.05.  Table S3. Number of genes determined by whether the P-values of regression coefficients and TLegene-aScore are less than 0.05. Table S4. Number of genes determined by whether the P-values of regression coefficients and TLegene-HMP are less than 0.05. Table S5. eGenes which identified by TLegene in the all ten TCGA cancers. Figure S1–S9. Comparison of power for the five test methods under the alternative scenarios. Figure S10. Upset plot represents the number of shared eGenes across the ten TCGA cancers. Figure S11. Bar plot represents the percentage of replicated eGenes by the traditional method using linear regression and PancanQTL, respectively. Figure S12. R2 distribution of SNP effects of eGenes identified in TCGA cancers. Figure S13. Result of the KEGG enrichment analysis of eGenes for COAD, LUAD and PAAD. Figure S14. Result of the GO enrichment analysis of eGenes for LUSC, LUAD, BRCA, COAD and PAAD. Figure S15. Enrichment of differentially expressed ones of all identified eGenes in terms of expression level across 54 GTEx tissues in BRCA and STAD. Figure S16. Enrichment of differentially expressed ones of all identified eGenes in terms of expression level across 54 GTEx tissues in COAD, LUAD, LUSC and PAAD. Figure S17. Result of the GO enrichment analysis of eGenes identified in the Geuvadis project. Figure S18. Enrichment of differentially expressed ones of all identified eGenes in terms of expression level across 54 GTEx tissues in Geuvadis

Additional file 1 of Exploring the association between birthweight and breast cancer using summary statistics from a perspective of genetic correlation, mediation, and causality

Additional file 1. Supplementary files

Additional file 1 of Detecting associated genes for complex traits shared across East Asian and European populations under the framework of composite null hypothesis testing

Additional file 1: Table S1. Complex traits available from the European and East Asian analyzed in the present study. Figure S1. Estimated false discovery rate under the simulation settings: (A) λ00=0.40, λ10=0.20, λ01=0.20, and λ11=0.2; (B) λ00=0.80, λ10=0.05, λ01=0.05, and λ11=0.10, and (C) λ00=0.90, λ10=0.01, λ01=0.01, and λ11=0.08. Here, the number of genes was set to 10000, and the false discovery rate was calculated as the proportion of non-overlapped associated genes among all identified ones. Figure S2. Estimated statistical power under the simulation settings: (A) λ00=0.40, λ10=0.20, λ01=0.20, and λ11=0.2; (B) λ00=0.80, λ10=0.05, λ01=0.05, and λ11=0.10, and (C) λ00=0.90, λ10=0.01, λ01=0.01, and λ11=0.08. Here, the number of genes was set to 10000, and the power was calculated as the proportion of truly overlapped associated genes among all identified ones. Figure S3. Estimated false discovery rate under the simulation settings: (A) λ00=0.40, λ10=0.20, λ01=0.20, and λ11=0.2; (B) λ00=0.80, λ10=0.05, λ01=0.05, and λ11=0.10, and (C) λ00=0.90, λ10=0.01, λ01=0.01, and λ11=0.08. Here, the number of genes was set to 20000, and the false discovery rate was calculated as the proportion of non-overlapped associated genes among all identified ones. Figure S4. Estimated statistical power under the simulation settings: (A) λ00=0.40, λ10=0.20, λ01=0.20, and λ11=0.2; (B) λ00=0.80, λ10=0.05, λ01=0.05, and λ11=0.10, and (C) λ00=0.90, λ10=0.01, λ01=0.01, and λ11=0.08. Here, the number of genes was set to 20000, and the power was calculated as the proportion of truly overlapped associated genes among all identified ones

Additional file 5 of A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

Additional file 5: Table S4. Unique genes with drug-gene interaction of directional effects on 14 psychiatric disorders

Additional file 1 of A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

Additional file 1: Table S1. A selective overview of previous pleiotropy studies on psychiatric disorders

Additional file 2 of A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

Additional file 2: Figure S1-S5. Figure S1. Graphical framework of the Mendelian randomization method using SNPs as instrumental variables for an exposure. A valid Mendelian randomization requires each of used SNP instruments satisfies three key model assumptions: (1) the relevance assumption, (2) the independence assumption, and (3) the exclusion restriction assumption. In the plot, solid or dotted arrow denotes the presence or absence of directional association. Figure S2. (A) Association of horizontal pleiotropy for causal genes, which can be examined by the composite-null based MAIUP method; (B) Association of mediated (or vertical) pleiotropy, which is also known as causality and can be examined by the Mendelian randomization method. Figure S3. (A) Number of associated genes (FDR < 0.05) discovered by the maximum P-value method between 14 psychiatric disorders; (B) Number of associated genes (FDR < 0.05) discovered by the direct FDR method between 14 psychiatric disorders. Figure S4. Result of the LRT method for examining the overall pleiotropy for each pair of the 14 psychiatric disorders. The P value is shown in the scale of -log10. Significant pleiotropy is marked with an asterisk after Bonferroni correction. Figure S5. (A) Proportion of heterogeneity in SNP genetic effects for pleiotropic genes across all significant pairs of the 14 psychiatric disorders. (B) Correlation of mean proportions of genetic effect heterogeneity of SNPs for pleiotropic genes in each pair of the 14 psychiatric disorders and their cross-trait genetic correlations. The estimated correlation coefficient and P value are shown on the top right

Additional file 4 of A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

Additional file 4: Table S3. List of all the unique pleiotropic genes and the number of psychiatric disorders which were affected by these genes

Additional file 6 of A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

Additional file 6: Table S5. Results of sensitivity analyses for significantly causal associations between psychiatric disorders

Additional file 3 of A comprehensive gene-centric pleiotropic association analysis for 14 psychiatric disorders with GWAS summary statistics

Additional file 3: Table S2. Pleiotropic genes for all pairs of 14 psychiatric disorders identified by PLACO (FDR < 0.05)

Additional file 1 of Integrative eQTL-weighted hierarchical Cox models for SNP-set based time-to-event association studies

Additional file 1