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Am J Hum Genet. 2015 Jul 2;97(1):139-52. doi: 10.1016/j.ajhg.2015.05.016.

Disentangling the Effects of Colocalizing Genomic Annotations to Functionally Prioritize Non-coding Variants within Complex-Trait Loci.

Author information

1
Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02446, USA; Partners Center for Personalized Genetic Medicine, Boston, MA 02446, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Cambridge CB10 1SA, UK.
2
Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02446, USA; Partners Center for Personalized Genetic Medicine, Boston, MA 02446, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
3
Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02446, USA; Partners Center for Personalized Genetic Medicine, Boston, MA 02446, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Bioinformatics and Integrative Genomics, Harvard University, Cambridge, MA 02138, USA.
4
Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02446, USA; Partners Center for Personalized Genetic Medicine, Boston, MA 02446, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Harvard-MIT Division of Health Sciences and Technology, Boston, MA 02115, USA.
5
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, Boston, MA 02215, USA.
6
Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA; Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL 60637, USA.
7
Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
8
Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02446, USA; Partners Center for Personalized Genetic Medicine, Boston, MA 02446, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Asan Institute for Life Sciences, Asan Medical Center, Seoul 138-736, Republic of Korea; Department of Medicine, University of Ulsan College of Medicine, Seoul 138-736, Republic of Korea.
9
Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02446, USA; Partners Center for Personalized Genetic Medicine, Boston, MA 02446, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Institute of Inflammation and Repair, University of Manchester, Manchester M13 9PT, UK. Electronic address: soumya@broadinstitute.org.

Abstract

Identifying genomic annotations that differentiate causal from trait-associated variants is essential to fine mapping disease loci. Although many studies have identified non-coding functional annotations that overlap disease-associated variants, these annotations often colocalize, complicating the ability to use these annotations for fine mapping causal variation. We developed a statistical approach (Genomic Annotation Shifter [GoShifter]) to assess whether enriched annotations are able to prioritize causal variation. GoShifter defines the null distribution of an annotation overlapping an allele by locally shifting annotations; this approach is less sensitive to biases arising from local genomic structure than commonly used enrichment methods that depend on SNP matching. Local shifting also allows GoShifter to identify independent causal effects from colocalizing annotations. Using GoShifter, we confirmed that variants in expression quantitative trail loci drive gene-expression changes though DNase-I hypersensitive sites (DHSs) near transcription start sites and independently through 3' UTR regulation. We also showed that (1) 15%-36% of trait-associated loci map to DHSs independently of other annotations; (2) loci associated with breast cancer and rheumatoid arthritis harbor potentially causal variants near the summits of histone marks rather than full peak bodies; (3) variants associated with height are highly enriched in embryonic stem cell DHSs; and (4) we can effectively prioritize causal variation at specific loci.

PMID:
26140449
PMCID:
PMC4572568
DOI:
10.1016/j.ajhg.2015.05.016
[Indexed for MEDLINE]
Free PMC Article

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