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Post-GWAS analysis

Post-GWAS analyses are a set of computational and statistical approaches that use summary statistics from GWAS, which capture variant–trait associations, to move beyond association mapping and translate statistical signals into biological interpretation by identifying causal variants, genes, tissues, pathways, and underlying disease mechanisms.

Why Post-GWAS Analysis?

Although GWAS has been highly successful in identifying genomic loci associated with complex traits and diseases, GWAS alone is insufficient for biological interpretation and clinical translation.

  • GWAS signals typically represent associations rather than causation, as lead variants are often proxies for the true causal variants due to linkage disequilibrium.
  • Most GWAS-identified variants lie in non-coding regions, making it difficult to infer the affected genes, tissues, or regulatory mechanisms directly from association results.
  • The effect sizes of individual variants are generally small, and GWAS does not explain how multiple variants act together within biological pathways or networks.
  • GWAS provides limited insight into context-specific effects, such as cell-type specificity, environmental interactions, or downstream molecular consequences.

Post-GWAS analyses address these limitations by prioritizing likely causal variants and genes, integrating GWAS results with functional genomic and multi-omic data (e.g., eQTL, chromatin accessibility, and epigenomic annotations), and identifying relevant tissues, cell types, and biological pathways. By moving beyond association to mechanism, post-GWAS approaches enable a deeper understanding of disease biology and support translational applications, including drug target identification, functional validation, and improved genetic risk prediction.

Overview

After identifying genome-wide significant associations, post-GWAS analyses address key biological and translational questions by leveraging GWAS summary statistics and integrating them with functional and multi-omic data:

  • What proportion of trait variation is genetic? SNP heritability estimation
  • What are the causal variants? Fine-mapping and colocalization
  • Which genes and pathways are involved? Functional annotation and pathway enrichment
  • How do variants affect molecular function? Regulatory annotation and QTL integration
  • Where do genetic effects act? Tissue- and cell-type enrichment
  • Are traits genetically related? Genetic correlation and shared architecture
  • What are the causal relationships? Mendelian randomization
  • Can we predict disease risk? Polygenic risk scores
  • Are effects shared across traits? Pleiotropy and cross-trait analysis
  • How can findings be translated? Drug target identification and prioritization

Heritability estimation and genetic architecture

Biological questions answered:

  • How much phenotypic variance is explained by common genetic variants?
  • Is the trait highly polygenic or driven by fewer loci?
  • How is heritability distributed across genomic annotations?

Common approaches:

  • SNP heritability estimation (LD score regression, GREML)
  • Partitioned heritability by functional annotations
  • Stratified LD score regression
  • Polygenicity and effect-size distribution modeling

Functional annotation

Biological questions answered:

  • Which genes are most likely to mediate GWAS associations?
  • What functional elements are enriched among associated variants?
  • Do variants affect coding sequences, regulatory elements, or both?

Common approaches:

  • Variant-to-gene mapping (positional, eQTL, chromatin interaction–based)
  • Functional annotation (CADD, SIFT, PolyPhen, RegulomeDB)
  • Gene-level association methods (MAGMA, Pascal)
  • Gene set and pathway enrichment (GO, KEGG, Reactome, MSigDB)
  • Tissue-specific expression enrichment (GTEx, single-cell atlases)

Fine-mapping

Biological questions answered:

  • Which specific variants are most likely to be causal?
  • What is the minimal credible set explaining the association?
  • Are there multiple independent signals within a locus?

Common approaches:

  • Bayesian fine-mapping (FINEMAP, SuSiE, CAVIAR)
  • Conditional and joint analysis
  • Annotation-informed fine-mapping
  • Multi-trait fine-mapping

Colocalization analysis

Biological questions answered:

  • Do GWAS and molecular QTL signals share a causal variant?
  • Which genes or proteins mediate trait associations?
  • In which tissues or cell types do these effects occur?

Common approaches:

  • eQTL and sQTL colocalization (GTEx, eQTLGen)
  • pQTL and metabolite QTL colocalization
  • Chromatin interaction data (Hi-C, promoter capture Hi-C)
  • Statistical tests (COLOC, eCAVIAR, HyPrColoc)

Pathway and network analysis

Biological questions answered:

  • What biological processes and pathways are perturbed?
  • How do associated genes interact in molecular networks?
  • Are specific network modules enriched for associations?

Common approaches:

  • Gene set enrichment analysis (GSEA, MAGMA, PASCAL)
  • Pathway databases (KEGG, Reactome, WikiPathways)
  • Protein–protein interaction and regulatory networks
  • Network propagation and module detection

Tissue and cell-type enrichment

Biological questions answered:

  • In which tissues or cell types do genetic effects manifest?
  • Are effects cell-type–specific or context-dependent?
  • Do regulatory mechanisms differ across developmental stages?

Common approaches:

  • Tissue-specific eQTL enrichment
  • Single-cell RNA-seq and ATAC-seq integration
  • Cell-type–specific heritability partitioning
  • Chromatin accessibility and epigenomic enrichment

Genetic correlation and cross-trait architecture

Biological questions answered:

  • Do traits share a common genetic basis?
  • What proportion of genetic effects is shared between traits?
  • Are shared associations driven by pleiotropy or causality?

Common approaches:

  • Genetic correlation estimation (LD score regression)
  • Cross-trait meta-analysis
  • Multivariate GWAS
  • Shared heritability partitioning

Mendelian randomization

Biological questions answered:

  • Is the relationship between exposure and outcome causal?
  • What is the direction and magnitude of the effect?
  • Are observed associations confounded by pleiotropy?

Common approaches:

  • Two-sample MR using GWAS summary statistics
  • Multivariable MR
  • Robust methods (MR-Egger, weighted median)
  • Sensitivity and reverse causality analyses

Polygenic risk scores

Biological questions answered:

  • Can aggregate genetic risk predict disease susceptibility?
  • How do thousands of small-effect variants combine?
  • Can PRS inform stratification or prevention strategies?

Common approaches:

  • PRS construction (clumping + thresholding, LDpred, PRS-CS)
  • Cross-ancestry PRS evaluation
  • Independent cohort validation
  • PRS × environment interaction analysis

Pleiotropy and cross-trait analysis

Biological questions answered:

  • Do variants influence multiple traits?
  • Are effects mediated by shared biological pathways?
  • Can pleiotropy explain comorbidity patterns?

Common approaches:

  • Pleiotropy detection and modeling
  • Multivariate association tests
  • Cross-phenotype risk prediction
  • Trait clustering based on shared genetics

Drug target identification and translation

Biological questions answered:

  • Which genes represent actionable therapeutic targets?
  • Can existing drugs be repurposed?
  • How can genetic evidence guide drug development?

Common approaches:

  • Integration with drug–gene interaction databases
  • Target prioritization using genetic evidence
  • Pathway-based drug repurposing
  • Clinical trial and pharmacogenomic evidence integration