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

Citation:

van de Geijn B, Finucane H, Gazal S, Hormozdiari F, Amariuta T, Liu X, Gusev A, Loh P-R, Reshef Y, Kichaev G, Raychauduri S, Price AL. Annotations capturing cell-type-specific TF binding explain a large fraction of disease heritability. Hum Mol Genet 2019;

Date Published:

2019 Oct 09

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 LD score regression with the baseline-LD model (which is not cell-type-specific) plus the new annotations. We determined that 100bp 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 (11.6x vs 7.3x; P = 9 x 10-14 for difference) and a 20% increase in cell-type-specific signal conditional on annotations from the baseline-LD model (P = 8 x 10-11 for difference). Our results show that TF binding annotations explain substantial disease heritability and can help refine genome-wide association signals.

Last updated on 01/28/2020