Blood cell traits’ GWAS loci colocalization with variation in PU.1 genomic occupancy prioritizes causal noncoding regulatory variants

• Published in: Cell genomics 2023-07, Vol.3 (7), p.100327, Article 100327
• Main Authors: Jeong, Raehoon, Bulyk, Martha L.
• Format: Article
• Language: English
• Subjects: blood cell traits; ChIP-seq; colocalization; fine-mapping; genome-wide association study; noncoding variants; PU.1; TF bQTL; transcription factor binding quantitative trait locus; transcription factors; variant-to-function
• ISSN: 2666-979X; 2666-979X
• DOI: 10.1016/j.xgen.2023.100327

Genome-wide association studies (GWASs) have uncovered numerous trait-associated loci across the human genome, most of which are located in noncoding regions, making interpretation difficult. Moreover, causal variants are hard to statistically fine-map at many loci because of widespread linkage disequilibrium. To address this challenge, we present a strategy utilizing transcription factor (TF) binding quantitative trait loci (bQTLs) for colocalization analysis to identify trait associations likely mediated by TF occupancy variation and to pinpoint likely causal variants using motif scores. We applied this approach to PU.1 bQTLs in lymphoblastoid cell lines and blood cell trait GWAS data. Colocalization analysis revealed 69 blood cell trait GWAS loci putatively driven by PU.1 occupancy variation. We nominate PU.1 motif-altering variants as the likely shared causal variants at 51 loci. Such integration of TF bQTL data with other GWAS data may reveal transcriptional regulatory mechanisms and causal noncoding variants underlying additional complex traits. [Display omitted] •69 PU.1 binding QTLs colocalize with blood cell trait associations•PU.1 motif-altering variants are likely causal at 51 colocalized loci•Variants affect chromatin accessibility, histone marks, and gene expression levels•TF-centered strategy pinpoints likely causal variants and mechanisms at GWAS loci Identifying the causal variants and mechanisms of noncoding genomic loci associated with traits is challenging. Jeong and Bulyk present a computational strategy to utilize population-level data on transcription factor (TF) occupancy to pinpoint trait-associated loci that are likely driven by variants altering the TF’s binding site motif and binding levels.