Widespread Site-Dependent Buffering of Human Regulatory Polymorphism

Widespread Site-Dependent Buffering of Human Regulatory Polymorphism:
by Matthew T. Maurano, Hao Wang, Tanya Kutyavin, John A. Stamatoyannopoulos

The average individual is expected to harbor thousands of variants within non-coding genomic regions involved in gene regulation. However, it is currently not possible to interpret reliably the functional consequences of genetic variation within any given transcription factor recognition sequence. To address this, we comprehensively analyzed heritable genome-wide binding patterns of a major sequence-specific regulator (CTCF) in relation to genetic variability in binding site sequences across a multi-generational pedigree. We localized and quantified CTCF occupancy by ChIP-seq in 12 related and unrelated individuals spanning three generations, followed by comprehensive targeted resequencing of the entire CTCF–binding landscape across all individuals. We identified hundreds of variants with reproducible quantitative effects on CTCF occupancy (both positive and negative). While these effects paralleled protein–DNA recognition energetics when averaged, they were extensively buffered by striking local context dependencies. In the significant majority of cases buffering was complete, resulting in silent variants spanning every position within the DNA recognition interface irrespective of level of binding energy or evolutionary constraint. The prevalence of complex partial or complete buffering effects severely constrained the ability to predict reliably the impact of variation within any given binding site instance. Surprisingly, 40% of variants that increased CTCF occupancy occurred at positions of human–chimp divergence, challenging the expectation that the vast majority of functional regulatory variants should be deleterious. Our results suggest that, even in the presence of “perfect” genetic information afforded by resequencing and parallel studies in multiple related individuals, genomic site-specific prediction of the consequences of individual variation in regulatory DNA will require systematic coupling with empirical functional genomic measurements.