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Am J Hum Genet. 2015 Aug 6;97(2):260-71. doi: 10.1016/j.ajhg.2015.06.007. Epub 2015 Jul 16.

Leveraging Functional-Annotation Data in Trans-ethnic Fine-Mapping Studies.

Author information

1
Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA.
2
Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: pasaniuc@ucla.edu.

Erratum in

  • Am J Hum Genet. 2015 Aug 6;97(2):353.

Abstract

Localization of causal variants underlying known risk loci is one of the main research challenges following genome-wide association studies. Risk loci are typically dissected through fine-mapping experiments in trans-ethnic cohorts for leveraging the variability in the local genetic structure across populations. More recent works have shown that genomic functional annotations (i.e., localization of tissue-specific regulatory marks) can be integrated for increasing fine-mapping performance within single-population studies. Here, we introduce methods that integrate the strength of association between genotype and phenotype, the variability in the genetic backgrounds across populations, and the genomic map of tissue-specific functional elements to increase trans-ethnic fine-mapping accuracy. Through extensive simulations and empirical data, we have demonstrated that our approach increases fine-mapping resolution over existing methods. We analyzed empirical data from a large-scale trans-ethnic rheumatoid arthritis (RA) study and showed that the functional genetic architecture of RA is consistent across European and Asian ancestries. In these data, we used our proposed methods to reduce the average size of the 90% credible set from 29 variants per locus for standard non-integrative approaches to 22 variants.

PMID:
26189819
PMCID:
PMC4573268
DOI:
10.1016/j.ajhg.2015.06.007
[Indexed for MEDLINE]
Free PMC Article

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