Dura, K. | bioRxiv Science

bioRxiv2025

The architecture of allele-specific regulatory variant effects across five human genomes

The overwhelming majority of genetic associations with complex traits and disease involve non-coding genetic variants. Those variants are substantially enriched in gene regulatory elements, indicating that allelic impacts on gene regulatory element activity is a major contributor to those associations. As a step towards fine mapping the mechanisms within those associations, several studies have identified variants associated with gene expression and chromatin accessibility within those phenotypic associations. However, connecting those associations with functional impacts on gene regulatory element activity remains a major challenge. Here, we functionally measured allele-specific regulatory element activity across five human genomes using the genome-wide reporter assay STARR-seq. We identified tens of thousands of gene regulatory elements and estimated allele effects at [~]200,000 genetic variants therein, including [~]10,000 indels in our study population. Allelic effects on regulatory element activity correspond closely with predicted impacts on transcription factor binding motifs. The measured variant effects also allow us to fine map potential causal variants within eQTLs and chromatin QTLs from the same population. Together, these results provide an initial atlas of genome-wide variant effects across the human genome and demonstrate the potential for such approaches to prioritize causal variants for future mechanistic investigation.

genomics↗

bioRxiv2024

A consensus variant-to-function score to functionally prioritize variants for disease

Identifying and functionally characterizing causal disease variants in genome-wide association studies remains a pressing challenge. Here, we construct a consensus variant-to-function (cV2F) score that assigns a single value to each common single-nucleotide variant in the genome, and helps to predict and characterize causal disease variants. The cV2F score leverages features reflecting variant-level experimentally and computationally predicted function (e.g. allelic imbalance and sequence-based deep learning models) and element-level function (e.g. predicted enhancers), and learns optimal combinations of features by training a gradient boosting model on GWAS fine-mapping results. The cV2F-annotated variants attained an AUPRC of 0.822 at identifying held-out fine-mapped variants. Variants with high cV2F scores are highly enriched for heritability (14.2x, s.e. 0.5) across 66 diseases/traits, are uniquely informative for disease heritability, and are highly predictive of variants implicated by reporter assays; cV2F substantially outperforms previous variant-to-function scores using all of these metrics. GWAS fine-mapping of 110 diseases/traits informed by cV2F identified 14.3% more confidently fine-mapped (PIP > 0.95) variants than non-functionally informed fine-mapping. We further constructed tissue/cell line-specific cV2F scores that prioritize variants based on regulatory potential in specific tissues/cell lines, attaining high heritability enrichment for tissue-related diseases/traits (15.6x, s.e. 2.3) while providing independent information (average correlation of 0.27 with the primary cV2F score). We highlight examples of GWAS loci for which cV2F pinpoints causal variants with high confidence and elucidates their functional role.

genetics↗