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  "Description": "We presented the Genotype-imputed Gene Set Enrichment\nAnalysis (GIGSEA), a novel method that uses\nGWAS-and-eQTL-imputed trait-associated differential gene\nexpression to interrogate gene set enrichment for the\ntrait-associated SNPs. By incorporating eQTL from large gene\nexpression studies, e.g. GTEx, GIGSEA appropriately addresses\nsuch challenges for SNP enrichment as gene size, gene boundary,\nSNP distal regulation, and multiple-marker regulation. The\nweighted linear regression model, taking as weights both\nimputation accuracy and model completeness, was used to perform\nthe enrichment test, properly adjusting the bias due to\nredundancy in different gene sets. The permutation test,\nfurthermore, is used to evaluate the significance of\nenrichment, whose efficiency can be largely elevated by\nexpressing the computational intensive part in terms of large\nmatrix operation. We have shown the appropriate type I error\nrates for GIGSEA (<5%), and the preliminary results also\ndemonstrate its good performance to uncover the real signal.",
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      "title": "GIGSEA: Genotype Imputed Gene Set Enrichment Analysis",
      "author": "| Shijia Zhu | Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA",
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        "4.1 Discrete-valued gene sets:",
        "4.2 Continuous-valued gene sets:",
        "4.3 User self-defined gene set",
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