Massively parallel functional dissection of mammalian enhancers in vivo

By Rupali Patwardhan, Joseph B Hiatt, Daniela M. Witten, Mee J. Kim, Robin P. Smith, Dalit May, Choli Lee, Jennifer M Andrie, Su-In Lee, Gregory M. Cooper and 3 more. 2012

552 citations recorded by the source corpus.

The functional consequences of genetic variation in mammalian regulatory elements are poorly understood. We report the in vivo dissection of three mammalian enhancers at single-nucleotide resolution through a massively parallel reporter assay. For each enhancer, we synthesized a library of >100,000 mutant haplotypes with 2–3% divergence from the wild-type sequence. Each haplotype was linked to a unique sequence tag embedded within a transcriptional cassette. We introduced each enhancer library into mouse liver and measured the relative activities of individual haplotypes en masse by sequencing the transcribed tags. Linear regression analysis yielded highly reproducible estimates of the effect of every possible single-nucleotide change on enhancer activity. The functional consequence of most mutations was modest, with ∼22% affecting activity by >1.2-fold and ∼3% by >2-fold. Several, but not all, positions with higher effects showed evidence for purifying selection, or co-localized with known liver-associated transcription factor binding sites, demonstrating the value of empirical high-resolution functional analysis.

Subfields: Molecular Biology

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Citations in the corpus

A general framework for estimating the relative pathogenicity of human genetic variants (2014, methodology): “Further, we obtained the HBB promoter data of Patwardhan et al. 29 .”
Learning the sequence determinants of alternative splicing from millions of random sequences. (2015, background): “…assays (MPRAs) that combine next-generation sequencing with extensive variation have been applied to study transcription (Melnikov et al., 2012; Patwardhan et al., 2012; Patwardhan et al., 2009; Sharon et al., 2012; Smith et al., 2013; White et al., 2013), translation (Noderer et al., 2014),…”
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A method to predict the impact of regulatory variants from DNA sequence (2015, methodology): “Therefore, we turned to a data set in which all possible variants within a 259-bp liver-specific enhancer of the ALDOB gene were tested using a massively parallel reporter assay in vivo in mouse liver 22 .”
The Post-GWAS Era: From Association to Function. (2018, result): “722 The American Journal of Human Genetics 102, 717–730, May 3, porter gene levels by less than 2-fold, consistent with eQTL effect sizes predicted by previous studies.(30,31,33) These results underline the importance of investigating the cellular or organismal effects of modest changes in target ge”
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Detection of ultra-rare mutations by next-generation sequencing (2012, methodology): “In addition to their application for high sensitivity detection of rare DNA variants, the degenerate tags in our Duplex Sequencing adapters can also be used for single-molecule counting to precisely determine absolute DNAorRNA copy numbers (25, 29).”
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Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks (2015, methodology): “8 procedure of synthesizing massive pools of oligonucleotides and measuring their activity in a parallel reporter assay (Melnikov et al. 2012; Patwardhan et al. 2012).”
Genome-Wide Quantitative Enhancer Activity Maps Identified by STARR-seq (2013, unknown): “(C) The genome-‐sequence was binned according to GC-‐content (non-‐ overlapping 100bp windows) into 10 equally populated bins ranging from low (1) to high (10) GC-‐content.”