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Advanced Colocalization Scenarios with ColocBoost

This vignette uses representative simulation studies to illustrate two advanced colocalization scenarios addressed by ColocBoost:

1. Multiple causal variants within a genomic region

To reduce the combinatorial hypothesis space, Bayesian multi-trait colocalization methods commonly assume that each trait has at most one causal variant within a genomic region (one-causal-variant-per-trait assumption). This assumption becomes increasingly restrictive as the number of phenotypes increases and more distinct signals and trait-sharing patterns must be resolved. Collapsing these signals into a single-signal representation can obscure event-specific sharing patterns, leading to missed or incorrectly localized colocalization events. This concern has also been emphasized and evaluated for pairwise colocalization using COLOC (V5) (Wallace, 2021, PLOS Genetics).

Scenario 1: Heterogeneous effects across traits

A common multi-signal scenario arises when multiple causal variants are shared across traits but have heterogeneous effects. Consider two traits influenced by two causal variants. Under the one-causal-variant-per-trait assumption, each trait is represented only by its strongest signal (Figure 2b(i)):

Trait 1 is represented by causal variant 1, which has the strongest association with Trait 1.
Trait 2 is represented by causal variant 2, which has the strongest association with Trait 2.

The resulting single-signal representations appear as two distinct trait-specific signals, leading to a false conclusion of no colocalization even though both causal variants are shared across the two traits. ColocBoost instead resolves the two shared signals as distinct colocalization events.

Heterogeneous effects of two causal variants across traits.

Scenario 2: Non-causal strongest marginal effect

Another multi-signal scenario occurs when a non-causal variant tags multiple causal variants through LD and consequently has the strongest marginal association. Distinguishing marginal association from causal attribution motivates multi-effect fine-mapping methods such as SuSiE (Wang et al., 2020, JRSS B).

Consider two traits sharing the same two causal variants. Under the one-causal-variant-per-trait assumption (Figure 2b(ii)):

Trait 1 and Trait 2 are represented by the non-causal marginal lead (green dot), which has a stronger marginal association than either true causal variant (red dots).

The resulting single-signal representation incorrectly localizes the colocalized signal to a non-causal variant, whereas ColocBoost resolves the two shared causal signals as distinct colocalization events.

A non-causal variant has the strongest marginal association.

2. Colocalization with weaker effects in GWAS

In practice, it is often of interest to colocalize a disease GWAS with multiple molecular QTL traits to elucidate the functional basis of disease associations. An important technical aspect of GWAS-xQTL colocalization is that GWAS traits often have lower per-variant contributions to heritability than molecular xQTL traits.

Scenario 3: Weaker effects in disease GWAS

Consider a disease GWAS and an xQTL sharing the same two causal variants (Figure 2b(iii)):

xQTL shows strong association evidence for both causal variants.
Disease GWAS shows strong evidence for causal variant 1 but weaker evidence for causal variant 2.

COLOC (V5) identifies the event supported by the stronger GWAS signal but misses the second event with weaker GWAS evidence. As a two-stage approach that performs fine-mapping before colocalization, it may have reduced sensitivity to weaker signals with limited support in the initial single-trait analysis. ColocBoost identifies both shared signals using its disease-prioritized colocalization approach.

Colocalization with a weaker causal effect in the disease GWAS.

See Mixed Data-type and Disease Prioritized Colocalization for practical guidance on GWAS-xQTL analysis with the ColocBoost disease-prioritized mode.

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