Annotations capturing cell type-specific TF binding explain a large fraction of disease heritability.

Area of Science:

• Genetics
• Genomics
• Computational Biology

Background:

• Regulatory variation significantly influences complex diseases.
• Transcription factor (TF) binding is crucial for gene regulation and is often cell type-specific.
• Current TF binding annotations lack cell type specificity and direct measurement for many pairs, hindering disease heritability studies.

Purpose of the Study:

• To develop and evaluate novel approaches for annotating TF binding sites.
• To assess the contribution of genetic variation in TF binding sites to complex disease heritability.
• To improve the cell type specificity and accuracy of TF binding annotations.

Main Methods:

• Investigated TF binding annotation strategies using directly measured chromatin data and sequence-based predictions.
• Employed stratified linkage disequilibrium (LD) score regression to partition heritability for 49 diseases and complex traits.
• Integrated sequence-based TF binding predictions (MotifMap) with cell type-specific chromatin marks (ChromImpute).

Main Results:

• TF binding annotations created by intersecting sequence-based predictions with cell type-specific chromatin data explained a substantial fraction of disease heritability.
• The optimal method involved 100 bp windows around MotifMap predictions intersected with a union of six cell type-specific chromatin marks.
• This approach yielded a 58% increase in heritability enrichment compared to chromatin marks alone (11.6× vs. 7.3×) and improved cell type-specific signal.

Conclusions:

• TF binding annotations are critical for understanding complex disease heritability.
• Combining sequence-based predictions with cell type-specific chromatin data provides a powerful strategy to overcome limitations in current annotation methods.
• These refined annotations can help pinpoint genetic variants associated with disease risk and refine genome-wide association signals.