| Title: | Annotations capturing cell type-specific TF binding explain a large fraction of disease heritability |
| Journal: | Human Molecular Genetics |
| Published: | 9 Oct 2019 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/31595288/ |
| DOI: | https://doi.org/10.1093/hmg/ddz226 |
| URL: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7206853 |
| Citations: | 19 (7 in last 2 years) as of 8 Aug 2024 |
Publication 12909
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Abstract
Regulatory variation plays a major role in complex disease and that cell type-specific binding of transcription factors (TF) is critical to gene regulation. However, assessing the contribution of genetic variation in TF-binding sites to disease heritability is challenging, as binding is often cell type-specific and annotations from directly measured TF binding are not currently available for most cell type-TF pairs. We investigate approaches to annotate TF binding, including directly measured chromatin data and sequence-based predictions. We find that TF-binding annotations constructed by intersecting sequence-based TF-binding predictions with cell type-specific chromatin data explain a large fraction of heritability across a broad set of diseases and corresponding cell types; this strategy of constructing annotations addresses both the limitation that identical sequences may be bound or unbound depending on surrounding chromatin context and the limitation that sequence-based predictions are generally not cell type-specific. We partitioned the heritability of 49 diseases and complex traits using stratified linkage disequilibrium (LD) score regression with the baseline-LD model (which is not cell type-specific) plus the new annotations. We determined that 100 bp windows around MotifMap sequenced-based TF-binding predictions intersected with a union of six cell type-specific chromatin marks (imputed using ChromImpute) performed best, with an 58% increase in heritability enrichment compared to the chromatin marks alone (11.6× vs. 7.3×, P = 9 × 10-14 for difference) and a 20% increase in cell type-specific signal conditional on annotations from the baseline-LD model (P = 8 × 10-11 for difference). Our results show that TF-binding annotations explain substantial disease heritability and can help refine genome-wide association signals.</p>
12 Keywords
- Binding Sites
- Chromatin
- Computational Biology
- Gene Expression Regulation
- Genetic Diseases, Inborn
- Humans
- Linkage Disequilibrium
- Molecular Sequence Annotation
- Multifactorial Inheritance
- Polymorphism, Single Nucleotide
- Protein Binding
- Transcription Factors
12 Authors
- Bryce van de Geijn
- Hilary Finucane
- Steven Gazal
- Farhad Hormozdiari
- Tiffany Amariuta
- Xuanyao Liu
- Alexander Gusev
- Po-Ru Loh
- Yakir Reshef
- Gleb Kichaev
- Soumya Raychauduri
- Alkes L Price
1 Application
| Application ID | Title |
|---|---|
| 16549 | Components of heritability in a UK Biobank cohort |
Enabling scientific discoveries that improve human health