L2g
The “locus-to-gene” (L2G) model derives features to prioritize likely causal genes at each GWAS locus based on genetic and functional genomics features. The main categories of predictive features are:
- Distance: (from credible set variants to gene)
- Molecular QTL Colocalization
- Chromatin Interaction: (e.g., promoter-capture Hi-C)
- Variant Pathogenicity: (from VEP)
The L2G model is distinct from the variant-to-gene (V2G) pipeline in that it:
- Uses a machine-learning model to learn the weights of each evidence source based on a gold standard of previously identified causal genes.
- Relies upon fine-mapping and colocalization data.
Some of the predictive features weight variant-to-gene (or genomic region-to-gene) evidence based on the posterior probability that the variant is causal, determined through fine-mapping of the GWAS association.
Details of the L2G model are provided in our Nature Genetics publication (ref - Nature Genetics Publication):
- Title: An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci.
- Authors: Mountjoy, E., Schmidt, E.M., Carmona, M. et al.
- Journal: Nat Genet 53, 1527–1533 (2021).
- DOI: 10.1038/s41588-021-00945-5