eDESE

eDESE applies a conditional gene-based association framework using an improved effective chi-squared statistic to map genetic variants to genes via physical distance and gene- or isoform-level cis-eQTLs for identifying susceptibility genes in complex diseases such as schizophrenia.

Key Features:

• Conditional Gene-Based Framework: Employs a conditional gene-based association analysis with an improved effective chi-squared statistic to control type I error and remove redundant associations in linkage disequilibrium.
• Integration of Isoform-Level eQTL Data: Incorporates isoform-level eQTLs to map variants to isoforms and genes, which has demonstrated greater statistical power than gene-level eQTLs in association analyses.
• Multi-Strategy Mapping: Maps variants to genes initially by physical distance and additionally by gene- and isoform-level variant–gene cis-eQTL associations.
• Application in Schizophrenia Research: Identified candidate susceptibility genes enriched for neuronal or synaptic signaling terms and antipsychotics–gene interaction terms, including seven genes that are targets of multiple antipsychotic drugs listed in DrugBank.
• Comparative Advantage: The isoform-level eQTL-based strategy identifies candidate susceptibility genes beyond those detected by MAGMA, S-PrediXcan, physical distance mapping, or gene-level eQTL mapping.
• Implementation in KGGSEE Platform: Implemented within the integrative platform KGGSEE for predicting candidate susceptibility genes and isoforms across multiple tissues.

Scientific Applications:

• Susceptibility Gene and Isoform Identification: Identification and prioritization of candidate susceptibility genes and isoforms in complex diseases with contributions from non-coding regions, exemplified by schizophrenia.
• Functional Enrichment and Drug-Target Prioritization: Prioritization of genes enriched for neuronal or synaptic signaling and for interactions with antipsychotics, supporting drug-target discovery and interpretation using DrugBank annotations.
• Cross-Tissue Variant–Gene Mapping: Mapping variant–gene relationships across multiple tissues using gene- and isoform-level cis-eQTL associations.

Methodology:

Uses an improved effective chi-squared statistic within a conditional gene-based association framework and maps variants to genes by physical distance and by gene- and isoform-level variant–gene cis-eQTL associations.