Clinical implications of variant-to-function analysis at the coronary artery disease-associated LIPA locus

This commentary refers to ‘LIPA, a risk locus for coronary artery disease: decoding the variant-to-function relationship’, by F. Li et al., https://doi.org/10.1093/eurheartj/ehaf581 and the discussion piece ‘Limitations in experimental design for establishing the causal role of LIPA in atherogenesis’, by P. R. Benchimol-Barbosa https://doi.org/10.1093/eurheartj/ehag229. Coronary artery disease (CAD) has a genetic component and is highly polygenic. Genome-wide association studies (GWAS) have identified >300 loci, underscoring the contribution of common variation. Individual variants exert modest effects on gene expression and disease risk, but defining their causal roles and directionality is critical for elucidating mechanisms and informing therapeutic strategies. Rather than being direct therapeutic targets, many GWAS variants point to genes and pathways that can be pharmacologically manipulated for larger clinical impact. Connecting CAD-associated variants to function across loci will enable human genetics to reveal disease mechanisms and guide therapeutic development. In our recent study, we investigated the CAD-associated LIPA locus, showing that CAD risk alleles increase enhancer activity and enhancer-promoter interaction via enhanced PU.1 binding, leading to monocyte/macrophage-specific upregulation of LIPA.1 The Letter to the Editor commenting on our study2 noted that these alleles are common, with effect-allele frequency >30% in general populations, and therefore of considerable public health relevance. We note that GWAS signals reflect the cumulative contribution of many common variants across loci, whereas any single variant typically confers only a modest change in risk. Accordingly, risk prediction relies on aggregate genetic effects, and incorporating greater weight for variants with well-established causal mechanisms may further improve predictive performance.3 This is the context in which our findings could have clinical implications for risk prediction.