Annotations capturing cell type-specific TF binding explain a large fraction of disease heritability. (9th October 2019)

• Record Type: Journal Article
• Authors: van de Geijn, Bryce; Finucane, Hilary; Gazal, Steven; Hormozdiari, Farhad; Amariuta, Tiffany; Liu, Xuanyao; Gusev, Alexander; Loh, Po-Ru; Reshef, Yakir; Kichaev, Gleb; Raychauduri, Soumya; Price, Alkes L
• Is Part Of: Human molecular genetics. Volume 29:Number 7(2020)
• Page Start: 1057
• Page End: 1067
• Publication Date: 2019-10-09
• Subjects: Human molecular genetics -- Periodicals; Human chromosome abnormalities -- Periodicals; 572.8
• DOI: 10.1093/hmg/ddz226
• Languages: English
• ISSNs: 0964-6906

Abstract

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.