Kari Strause

Identifying genetic variants in noncoding DNA that impact gene expression and thereby contribute to disease risk remains a difficult but important challenge in genomic medicine. Modern reporter assays such as STARR-seq and MPRA provide an efficient and effective means of testing, in very high throughput, millions of variants captured directly from patient genomes. While these assays have previously been scaled to whole genomes and, separately, to populations, we report findings from the first whole-genome population-scale STARR-seq experiment performed on 100 individuals. In order to achieve that scale we devised a novel experimental design that partitions samples into pools so as to increase allele frequencies within pools and thereby reduce expected dropout and increase signal-to-noise ratio in experimental readouts. We show that this design produces more accurate estimates of variant effect sizes, and we provide a Bayesian model for robust estimation of those effect sizes that also reports full posterior distributions for assessment of confidence in estimates. Together, these methodological innovations facilitate the detection of functional regulatory variants, particularly rare variants, with much higher accuracy and at greater scale than previously possible. We demonstrate the utility of this approach on the task of functional annotation of quantitative trait loci such as eQTLs and caQTLs, and show concordance with patterns of constraint in transcription factor binding profiles.

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.