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Genome Res. 2015 Aug;25(8):1206-14. doi: 10.1101/gr.190090.115. Epub 2015 Jun 17.

Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort.

Author information

1
Department of Cell Biology, Duke University Medical School, Durham, North Carolina 27710, USA; Center for Genomic and Computational Biology, Duke University Medical School, Durham, North Carolina 27710, USA;
2
Center for Genomic and Computational Biology, Duke University Medical School, Durham, North Carolina 27710, USA; University Program in Genetics and Genomics, Duke University, Durham, North Carolina 27710, USA;
3
Center for Genomic and Computational Biology, Duke University Medical School, Durham, North Carolina 27710, USA; Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina 27710, USA;
4
Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA;
5
Division of Endocrinology, Metabolism and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA;
6
Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina 27710, USA; Department of Biostatistics and Bioinformatics, Duke University Medical School, Durham, North Carolina 27710, USA.

Abstract

We report a novel high-throughput method to empirically quantify individual-specific regulatory element activity at the population scale. The approach combines targeted DNA capture with a high-throughput reporter gene expression assay. As demonstration, we measured the activity of more than 100 putative regulatory elements from 95 individuals in a single experiment. In agreement with previous reports, we found that most genetic variants have weak effects on distal regulatory element activity. Because haplotypes are typically maintained within but not between assayed regulatory elements, the approach can be used to identify causal regulatory haplotypes that likely contribute to human phenotypes. Finally, we demonstrate the utility of the method to functionally fine map causal regulatory variants in regions of high linkage disequilibrium identified by expression quantitative trait loci (eQTL) analyses.

PMID:
26084464
PMCID:
PMC4510004
DOI:
10.1101/gr.190090.115
[Indexed for MEDLINE]
Free PMC Article

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