The flashfm approach for fine-mapping multiple quantitative trait

Figure 1. The flashfm approach for fine-mapping multiple quantitative traits.

Joint fine-mapping that leverages information between quantitative traits could improve accuracy and resolution over single-trait fine-mapping. Using summary statistics, flashfm (flexible and shared information fine-mapping) fine-maps signals for multiple traits, allowing for missing trait measurements and use of related individuals. In a Bayesian framework, prior model probabilities are formulated to favour model combinations that share causal variants to capitalise on information between traits. Simulation studies demonstrate that both approaches produce broadly equivalent results when traits have no shared causal variants. When traits share at least one causal variant, flashfm reduces the number of potential causal variants by 30% compared with single-trait fine-mapping. In a Ugandan cohort with 33 cardiometabolic traits, flashfm gave a 20% reduction in the total number of potential causal variants from single-trait fine-mapping.

Source from:

Hernández, N., Soenksen, J., Newcombe, P. et al. The flashfm approach for fine-mapping multiple quantitative traits. Nat Commun 12, 6147 (2021). https://doi.org/10.1038/s41467-021-26364-y