title: Massively parallel quantification of the regulatory effects of noncoding genetic variation in a human cohort.
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.
authors.affiliation: 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;
authors.forename: Christopher M
authors.initials: CM
authors.lastname: Vockley
authors.affiliation: 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;
authors.forename: Cong
authors.initials: C
authors.lastname: Guo
authors.affiliation: 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;
authors.forename: William H
authors.initials: WH
authors.lastname: Majoros
authors.affiliation: Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA;
authors.forename: Michael
authors.initials: M
authors.lastname: Nodzenski
authors.affiliation: Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA;
authors.forename: Denise M
authors.initials: DM
authors.lastname: Scholtens
authors.affiliation: Division of Endocrinology, Metabolism and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA;
authors.forename: M Geoffrey
authors.initials: MG
authors.lastname: Hayes
authors.affiliation: Division of Endocrinology, Metabolism and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA;
authors.forename: William L
authors.initials: WL
authors.lastname: Lowe
authors.affiliation: 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.
authors.forename: Timothy E
authors.initials: TE
authors.lastname: Reddy
mesh_terms: D019295:Computational Biology / Q000379:methods; D014644:Genetic Variation; D015894:Genome, Human; D006239:Haplotypes; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D006801:Humans; D066230:Patient-Specific Modeling; D040641:Quantitative Trait Loci; D012045:Regulatory Sequences, Nucleic Acid