Inferring the Molecular Mechanisms of Noncoding Alzheimer's Disease-Associated Genetic Variants

Abstract

Most of the loci identified by genome-wide association studies (GWAS) for late-onset Alzheimer's disease (LOAD) are in strong linkage disequilibrium (LD) with nearby variants all of which could be the actual functional variants, often in non-protein-coding regions and implicating underlying gene regulatory mechanisms. We set out to characterize the causal variants, regulatory mechanisms, tissue contexts, and target genes underlying these associations. We applied our INFERNO algorithm to the top 19 non-APOE loci from the IGAP GWAS study. INFERNO annotated all LD-expanded variants at each locus with tissue-specific regulatory activity. Bayesian co-localization analysis of summary statistics and eQTL data was performed to identify tissue-specific target genes. INFERNO identified enhancer dysregulation in all 19 tag regions analyzed, significant enrichments of enhancer overlaps in the immune-related blood category, and co-localized eQTL signals overlapping enhancers from the matching tissue class in ten regions (ABCA7, BIN1, CASS4, CD2AP, CD33, CELF1, CLU, EPHA1, FERMT2, ZCWPW1). In several cases, we identified dysregulation of long noncoding RNA (lncRNA) transcripts and applied the lncRNA target identification algorithm from INFERNO to characterize their downstream biological effects. We also validated the allele-specific effects of several variants on enhancer function using luciferase expression assays. By integrating functional genomics with GWAS signals, our analysis yielded insights into the regulatory mechanisms, tissue contexts, genes, and biological processes affected by noncoding genetic variation associated with LOAD risk.

Keywords: Alzheimer’s disease; bioinformatics; genetics; genomics; long noncoding RNA.

Figure 1:. LD expansion and functional annotation of top IGAP hits.

a) Flowchart of analysis approach. b) Genomic localization of all variants in P value- and LD-expanded set. c) Genomic partition proportions split by tag regions.

Figure 2:. Integrative analysis of annotations for IGAP top hits.

a) Integrative tissue context analysis of enhancer overlaps from FANTOM5 and Roadmap datasets. b) Results of LD-collapsed bootstrapping for enhancer annotation overlap enrichments c) Distributions of variant probability of underlying highly colocalized signals stratified by annotation overlap. d) Barplots of numbers of variant – eQTL comparisons across tag regions stratified by motif overlap, enhancer support, and concordant support in a relevant tissue class.

Figure 3:. Functional variant in EPHA1 region upregulates EPHA1-AS1 lncRNA which regulates the JAK2 signaling axis.

a) Genome browser view of the region around rs11765305 (in red box) including relevant FANTOM5 and Roadmap enhancer annotations. b) Luciferase assay results for rs11765305 in K562 cells. Luciferase expression is normalized against Renilla expression in the same well. Negative control is randomly sampled heterochromatin insert. A linear mixed model was applied to 5 biological replicates per condition, each with 4 technical replicates per experimental day. c) Scatterplot of Pearson and Spearman correlations between expression of EPHA1-AS1 and all other genes in the genome across all GTEx tissues.

Figure 4:. Luciferase and lncRNA analysis in the BIN1, CD33, and CD2AP regions.

a) Luciferase validation in the CD33 region. b) Luciferase validation in the BIN1 region. c) Luciferase validation in the CD2AP region. For all luciferase analyses, a linear mixed model was applied to 5 biological replicates per condition, each with 4 technical replicates per experimental day. d) Scatterplot of Pearson and Spearman correlations between expression of RP11–385F7.1 (CD2AP region) and all other genes in the genome across all GTEx tissues.