Saturation mutagenesis of twenty disease-associated regulatory elements at single base-pair resolution

Martin Kircher,Max Schubach,Nadav Ahituv,Joseph F Costello,Fumitaka Inoue,Jay Shendure,Beth Martin,Chenling Xiong,Robert J A Bell

doi:10.1038/s41467-019-11526-w

Abstract

The majority of common variants associated with common diseases, as well as an unknown proportion of causal mutations for rare diseases, fall in noncoding regions of the genome. Although catalogs of noncoding regulatory elements are steadily improving, we have a limited understanding of the functional effects of mutations within them. Here, we perform saturation mutagenesis in conjunction with massively parallel reporter assays on 20 disease-associated gene promoters and enhancers, generating functional measurements for over 30,000 single nucleotide substitutions and deletions. We find that the density of putative transcription factor binding sites varies widely between regulatory elements, as does the extent to which evolutionary conservation or integrative scores predict functional effects. These data provide a powerful resource for interpreting the pathogenicity of clinically observed mutations in these disease-associated regulatory elements, and comprise a rich dataset for the further development of algorithms that aim to predict the regulatory effects of noncoding mutations.

Highlights

The majority of common variants associated with common diseases, as well as an unknown proportion of causal mutations for rare diseases, fall in noncoding regions of the genome
We selected the low-density lipoprotein receptor (LDLR) promoter, where mutations have been shown to cause familial hypercholesterolemia (FH), a disorder that results in accelerated atherosclerosis and increased risk for coronary heart disease[31,32,33]
Here we selected sequences with known regulatory potential—and sequences previously implicated in human disease—and sought to quantify the consequences of all possible single nucleotide variants (SNVs) on that potential

Summary

Introduction

The majority of common variants associated with common diseases, as well as an unknown proportion of causal mutations for rare diseases, fall in noncoding regions of the genome. We find that the density of putative transcription factor binding sites varies widely between regulatory elements, as does the extent to which evolutionary conservation or integrative scores predict functional effects. These data provide a powerful resource for interpreting the pathogenicity of clinically observed mutations in these disease-associated regulatory elements, and comprise a rich dataset for the further development of algorithms that aim to predict the regulatory effects of noncoding mutations. To advance our understanding of disease as well as the clinical utility of genetic information, it is clear that we need to develop scalable multiplex assays of variant effect (MAVEs)[8], methods for accurately assessing the functional consequences of noncoding variants

Methods

Results

Conclusion