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Genome-wide association studies (GWAS) have revolutionized our understanding of genetic associations with complex traits and diseases, but interpreting the functional consequences of identified loci, particularly those in noncoding regions, remains a challenge. Fine-mapping causal variants within GWAS loci is crucial for guiding subsequent experimental validation and therapeutic development. However, the presence of widespread linkage disequilibrium (LD) hinders accurate statistical fine-mapping, especially for common variants.
To address this challenge, researchers propose a strategy that combines transcription factor (TF) binding quantitative trait loci (bQTLs) and colocalization analysis. TF bQTLs are genomic loci where TF occupancy, as measured by ChIP-seq, is significantly associated with a genetic variant. Colocalization analysis tests the hypothesis that a genetic signal is shared between TF binding and GWAS traits, facilitating the identification of regulatory variants mediating GWAS associations.
The team focused on the TF PU.1, a master regulator in hematopoiesis, and analyzed PU.1 bQTLs in lymphoblastoid cell lines (LCLs) using publicly available ChIP-seq data from 49 individuals. They identified 1,497 PU.1 bQTLs, and through colocalization analysis, they discovered 69 blood cell trait GWAS loci potentially influenced by PU.1 occupancy variation. Importantly, they identified 51 loci where motif-altering variants in the PU.1 binding site were likely shared causal variants for both TF occupancy and blood cell traits.
The integration of TF bQTL data with GWAS information provides valuable insights into the transcriptional regulatory mechanisms and causal noncoding variants underlying complex traits. By utilizing motif models, such as gapped k-mer support vector machines (gkm-SVMs), the researchers could predict variants that altered the affinity of PU.1 binding motifs, even in the presence of LD. This approach allowed them to pinpoint likely causal variants within GWAS loci that affect in vivo TF binding, shedding light on the direct molecular consequences of trait-associated genetic variations.
The study's findings demonstrate the potential of TF bQTL colocalization analysis to refine the understanding of regulatory variants and their effects on TF binding and downstream traits. Additionally, by incorporating transcriptome data, the researchers identified candidate causal genes associated with blood cell traits. This integrative approach not only helps unravel the genetic architecture of complex traits but also offers valuable insights into gene dysregulation for traits of biomedical importance.
Read the original publication in Cell Genomics.
To address this challenge, researchers propose a strategy that combines transcription factor (TF) binding quantitative trait loci (bQTLs) and colocalization analysis. TF bQTLs are genomic loci where TF occupancy, as measured by ChIP-seq, is significantly associated with a genetic variant. Colocalization analysis tests the hypothesis that a genetic signal is shared between TF binding and GWAS traits, facilitating the identification of regulatory variants mediating GWAS associations.
The team focused on the TF PU.1, a master regulator in hematopoiesis, and analyzed PU.1 bQTLs in lymphoblastoid cell lines (LCLs) using publicly available ChIP-seq data from 49 individuals. They identified 1,497 PU.1 bQTLs, and through colocalization analysis, they discovered 69 blood cell trait GWAS loci potentially influenced by PU.1 occupancy variation. Importantly, they identified 51 loci where motif-altering variants in the PU.1 binding site were likely shared causal variants for both TF occupancy and blood cell traits.
The integration of TF bQTL data with GWAS information provides valuable insights into the transcriptional regulatory mechanisms and causal noncoding variants underlying complex traits. By utilizing motif models, such as gapped k-mer support vector machines (gkm-SVMs), the researchers could predict variants that altered the affinity of PU.1 binding motifs, even in the presence of LD. This approach allowed them to pinpoint likely causal variants within GWAS loci that affect in vivo TF binding, shedding light on the direct molecular consequences of trait-associated genetic variations.
The study's findings demonstrate the potential of TF bQTL colocalization analysis to refine the understanding of regulatory variants and their effects on TF binding and downstream traits. Additionally, by incorporating transcriptome data, the researchers identified candidate causal genes associated with blood cell traits. This integrative approach not only helps unravel the genetic architecture of complex traits but also offers valuable insights into gene dysregulation for traits of biomedical importance.
Read the original publication in Cell Genomics.