Leveraging functional annotation to identify genes associated with complex diseases

Abstract

To increase statistical power to identify genes associated with complex traits, a number of transcriptome-wide association study (TWAS) methods have been proposed using gene expression as a mediating trait linking genetic variations and diseases. These methods first predict expression levels based on inferred expression quantitative trait loci (eQTLs) and then identify expression-mediated genetic effects on diseases by associating phenotypes with predicted expression levels. The success of these methods critically depends on the identification of eQTLs, which may not be functional in the corresponding tissue, due to linkage disequilibrium (LD) and the correlation of gene expression between tissues. Here, we introduce a new method called T-GEN (Transcriptome-mediated identification of disease-associated Genes with Epigenetic aNnotation) to identify disease-associated genes leveraging epigenetic information. Through prioritizing SNPs with tissue-specific epigenetic annotation, T-GEN can better identify SNPs that are both statistically predictive and biologically functional. We found that a significantly higher percentage (an increase of 18.7% to 47.2%) of eQTLs identified by T-GEN are inferred to be functional by ChromHMM and more are deleterious based on their Combined Annotation Dependent Depletion (CADD) scores. Applying T-GEN to 207 complex traits, we were able to identify more trait-associated genes (ranging from 7.7% to 102%) than those from existing methods. Among the identified genes associated with these traits, T-GEN can better identify genes with high (>0.99) pLI scores compared to other methods. When T-GEN was applied to late-onset Alzheimer’s disease, we identified 96 genes located at 15 loci, including two novel loci not implicated in previous GWAS. We further replicated 50 genes in an independent GWAS, including one of the two novel loci.

Hippocampal transcriptome-wide association study and neurobiological pathway analysis for Alzheimer’s disease

“…This study extends AD-related genetic loci identified by prior AD-GWAS studies [ 4 – 6 , 8 – 11 ] by providing evidence that some loci ( APOE , TOMM40 , PVRL2 , EXOC3L2 , KAT8 and HLA-DRB5 ) may lead to AD by affecting the gene expression levels in the hippocampus. Among the 24 AD- and hippocampus-related genes identified in this study, previous studies only provide clues for the associations of hippocampal expression of PRSS36 , KAT8 , HLA-DRB5 [ 4 ], TOMM40 [ 20 ], CEACAM19 and PVRL2 [ 21 ] with AD. However, some indirect evidence may support other associations.…”

A Statistical Framework to Identify Cell Types Whose Genetically Regulated Proportions are Associated with Complex Diseases

“…5a ), we found marginal evidence of enriched genetic signals in the fibroblasts of lung tissue (p=7.5e-2). IPF-associated genes identified by T-GEN 37 did not show any significant enrichment in any cell type of lung. Therefore, though neither was significant after Bonferroni correction, both transcriptomic and gene-set based genetic analyses suggest the importance of myofibroblasts and fibroblasts consistent with cWAS.…”

Investigating the role of commoncis-regulatory variants in modifying penetrance of putatively damaging, inherited variants in severe neurodevelopmental disorders

“…Moreover, genes associated with Mendelian diseases are likely depleted for common cis-eQTLs 40,[51][52][53][54][55] . Future studies could potentially incorporate methods that use rare eQTLs 53 , trans-eQTLs, and epigenetic information 56 to predict gene expression. Alternatively, they could measure gene expression directly using RNA sequencing, to assess whether expression patterns (whatever their causes) are modifying penetrance of rare variants.…”