Mapping Alzheimer's Disease Variants to Their Target Genes Using Computational Analysis of Chromatin Configuration
January 9th, 2020
We present a protocol to identify functional implications of non-coding variants identified by genome-wide association studies (GWAS) using three-dimensional chromatin interactions.
While GWAS have successfully identified genomic regions associated with human traits and diseases, the biological impact of these risk variants is unclear. Here we outline a protocol to computationally predict putative target genes of GWAS risk variants using chromatin interaction profiles. Often times identification of risk genes is a first step to understand disease mechanisms, and allow for normal therapeutic approaches.
We hope that the results of this work could eventually lead to final strategies to diagnose and treat Alzheimer's disease. The main advantage of this technique is that by using 3D chromoton contact frequencies we can identify the genes affected by Alzheimer's disease risk variance even if they are thousands or even millions of base pairs away. When attempting this protocol, familiarity with R or an X pair system is critical because the user is expected to conduct the entire protocol with the system.
To perform this computational protocol, refer to the code in the text manuscript or onscreen. Begin, by setting up in R, to generate a G ranges object for credible, single nucleatide polymorphisms or SNPS. For positional mapping, setup in R then load the promoter and exonic region and generate a G range object.
Overlap the credible SNPS with the exonic regions and with the promoter regions. To link SNPS to their putative target genes using Chromaton interactions, load the Hi C dataset and generate a G range object. Overlap the credible SNPS with the Hi C G range object.
And compile AD candidate genes, defined by positional mapping and chromoton interaction profiles. Next, explore developmental trajectories. Setup in R and process the expression metadata.
Specify developmental stages and select cortical regions. Extract the developmental expression profiles of AD risk genes and compare prenatal versus postnatal expression levels. Investigate cell-type expression profiles by setting up in R and extracting cellular expression profiles of AD risk.
Finally, perform gene annotation enrichment analysis of AD risk genes. Download and configure Homer. Then run Homer and plot the enriched terms with R Studio.
A set of 800 credible SNPs was investigated using this process. Positional mapping revealed that 103 SNPs overlapped with promoters and 42 SNPs overlapped with Exons, while 84%of the SNPs remained unannotated. Using Hi-C datasets in the adult brain, an additional 208 SNPs were linked to 64 genes based on physical proximity.
In total, 284 AD credible SNPs were mapped to 112 AD risk genes. AD risk genes were associated with amyloid precursor proteins, amyloid Beta formation and immune response, which reflects the known biology of the disease. Developmental expression profiles of AD risk genes showed marked postnatal enrichment indicative of the age-associated elevated risk of the disease.
Finally, the genes were highly expressed in microglia the primary immune cells in the brain which supports the recurrent findings that AD has a strong immune basis. Here we use Hi-C data from the brain tissue to analyze a biological impact of Alzheimer's Disease risk variance. However, to apply this method to another GWAS study the level of the new Hi-C data in the relevant tissue is critical.
These results can be further studied and validated using crisper-based technologies, enhancer reporter assays, or by intersecting with other functional genomic datasets such as EQTLs. Here we identify dozens of Alzheimer's disease risk genes and we expect that the identification of these genes can help us understand their previously unknown role in Alzheimer's disease.
Overview
This study presents a protocol for identifying the functional implications of non-coding variants linked to Alzheimer's disease through genome-wide association studies (GWAS). By utilizing three-dimensional chromatin interactions, the protocol aims to predict target genes affected by these risk variants.
Key Study Components
Area of Science
- Neuroscience
- Genomics
- Bioinformatics
Background
- GWAS have identified genomic regions associated with human traits and diseases.
- The biological impact of risk variants remains unclear.
- Understanding risk genes is crucial for elucidating disease mechanisms.
- This research focuses on Alzheimer's disease and its genetic risk factors.
Purpose of Study
- To develop a computational protocol for predicting target genes of GWAS risk variants.
- To link SNPs to genes using chromatin interaction profiles.
- To explore the developmental expression profiles of Alzheimer's risk genes.
Methods Used
- Setting up in R to generate G ranges objects for SNPs.
- Positional mapping of SNPs with promoter and exonic regions.
- Using Hi-C datasets to link SNPs to target genes based on proximity.
- Performing gene annotation enrichment analysis with Homer.
Main Results
- 284 AD credible SNPs were mapped to 112 AD risk genes.
- Identified genes were associated with amyloid precursor proteins and immune response.
- Developmental expression profiles showed postnatal enrichment of AD risk genes.
- High expression in microglia supports the immune basis of Alzheimer's disease.
Conclusions
- The protocol effectively identifies genes linked to Alzheimer's disease risk variants.
- Results can inform future therapeutic strategies and diagnostics.
- Further validation can be achieved using CRISPR-based technologies.
Frequently Asked Questions
Executive Industry Relevance
Mapping non-coding GWAS variants to target genes enables early de-risking of Alzheimer's disease hypotheses by linking statistical associations to biological mechanisms. This computational approach supports target validation by prioritizing genes with developmental and cell-type expression patterns consistent with disease pathology. It informs portfolio decisions by identifying mechanistically plausible targets for downstream therapeutic exploration.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Scientific Value: Links non-coding variants to putative target genes using chromatin interaction data, enabling hypothesis generation for AD risk mechanisms.
- Operational Value: Uses fine-mapping and Hi-C overlap to prioritize genes for functional follow-up, reducing false positives in target selection.
- Scientific Value: Identifies genes involved in amyloid processing and immune response, aligning with established AD biology to increase confidence in target relevance.
Screening & Assay Development
- Scientific Value: Generates a curated gene set (112 AD risk genes) for use in developing phenotypic or biomarker assays relevant to AD pathways.
- Operational Value: Provides reproducible genomic coordinates and gene lists that can be standardized across teams for target engagement screening.
- Scientific Value: Enables screening readiness by defining genetically supported targets with postnatal and microglial expression profiles.
Translational & Preclinical Research
- Scientific Value: Connects genetic risk to developmental expression patterns, showing postnatal enrichment that mirrors age-dependent AD onset.
- Operational Value: Supports translational continuity by linking GWAS hits to genes expressed in microglia, a key cell type in neuroinflammation.
- Scientific Value: Facilitates mechanistic de-risking by highlighting immune-related genes, informing biomarker strategies and target selection for preclinical models.
Pipeline & Workflow Integration
The method fits within the discovery continuum from genetic association to target identification, enabling progression from GWAS hits to biologically grounded candidates for lead identification.
- Discovery Biology: Supports hypothesis testing by mapping non-coding SNPs to genes via 3D chromatin contacts, clarifying regulatory mechanisms.
- Screening: Delivers a standardized gene set with expression validation, enabling reproducible assay design for compound screening.
- Analytics: Produces quantitative outputs (SNP-gene links, enrichment terms) that allow cross-condition comparison and target prioritization.
- Translational Research: Connects genetic findings to microglial expression and developmental timing, supporting biomarker alignment and preclinical validity.
- Enterprise Reuse: Protocol is adaptable to any GWAS and Hi-C dataset, making it a scalable platform for target gene discovery across diseases.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence by linking statistical genetics to 3D genomics and expression data, reducing mechanistic ambiguity.
- Operational Value: Standardizes variant-to-gene mapping using R-based workflows and public datasets, improving reproducibility across sites.
- Strategic Value: Improves go/no-go decisions by prioritizing targets with convergent evidence from genetics, chromatin, and expression.
- Portfolio Impact: Enables risk-adjusted advancement by highlighting genes with biological plausibility in AD pathways.
Implementation Considerations
- Requires expertise in R, genomic range manipulation, and chromatin interaction data interpretation.
- Dependent on access to high-resolution Hi-C data from disease-relevant tissues (e.g., adult human brain).
- Needs standardization of SNP filtering and promoter/exon definitions across teams for consistent outputs.
- Must account for tissue and developmental specificity when applying to other diseases or models.
- Limited by the resolution and availability of chromatin datasets; validation with orthogonal methods (e.g., CRISPR, eQTLs) is recommended.
Biopharma FAQs
Chapters in this video
0:05
Title
1:00
Generation of a Granges Object for Credible SNPs and Positional Mapping
1:53
Developmental Expression Trajectories
2:18
Cell-type Expression Profiles and Gene Annotation Enrichment Analysis
2:46
Results: Determination and Characterization of Putative Target Genes of AD GWS Loci
3:52
Conclusion
