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Improved methods for multi-trait fine mapping of pleiotropic risk loci

Gleb Kichaev, Megan Roytman, Ruth Johnson, Eleazar Eskin, Sara Lindstroem, Peter Kraft, Bogdan Pasaniuc
doi: https://doi.org/10.1101/054684
Gleb Kichaev
1Bioinformatics Inter-departamental Program, University of California Los Angeles, Los Angeles, CA., USA
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  • For correspondence: gkichaev@ucla.edu
Megan Roytman
1Bioinformatics Inter-departamental Program, University of California Los Angeles, Los Angeles, CA., USA
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Ruth Johnson
2Dept of Mathematics, University of California Los Angeles, Los Angeles, CA., USA
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Eleazar Eskin
1Bioinformatics Inter-departamental Program, University of California Los Angeles, Los Angeles, CA., USA
3Dept of Computer Science, University of California Los Angeles, Los Angeles, CA., USA
4Dept of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA., USA
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Sara Lindstroem
5Dept of Epidemiology, University of Washington, Seattle, WA., USA,
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Peter Kraft
6Program in Genetic Epidemiology and Statistical Genetics, Harvard School of Public Health, Boston, MA., USA.
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Bogdan Pasaniuc
1Bioinformatics Inter-departamental Program, University of California Los Angeles, Los Angeles, CA., USA
4Dept of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA., USA
7Dept of Pathology and Laboratory Medicine, David Geffen School of Medicine University of California Los Angeles, Los Angeles, CA., USA
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Abstract

Genome-wide association studies (GWAS) have identified thousands of regions in the genome that contain genetic variants that increase risk for complex traits and diseases. However, the variants uncovered in GWAS are typically not biologicaly causal, but rather, correlated to the true causal variant through linkage disequilibrium (LD). To discern the true causal variant(s), a variety of statistical fine-mapping methods have been proposed to prioritize variants for functional validation. In this work we introduce a new approach, fastPAINTOR, that leverages evidence across correlated traits, as well as functional annotation data, to improve fine-mapping accuracy at pleiotropic risk loci. To improve computational efficiency, we describe an new importance sampling scheme to perform model inference. First, we demonstrate in simulations that by leveraging functional annotation data, fastPAINTOR increases fine-mapping resolution relative to existing methods. Next, we show that jointly modeling pleiotropic risk regions improves fine-mapping resolution relative to standard single trait and pleiotropic fine mapping strategies. We report a reduction in the number of SNPs required for follow-up in order to capture 90% of the causal variants from 23 SNPs per locus using a single trait to 12 SNPs when fine-mapping two traits simultaneously. Finally, we analyze summary association data from a large-scale GWAS of lipids and show that these improvements are largely sustained in real data.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 21, 2016.
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Improved methods for multi-trait fine mapping of pleiotropic risk loci
Gleb Kichaev, Megan Roytman, Ruth Johnson, Eleazar Eskin, Sara Lindstroem, Peter Kraft, Bogdan Pasaniuc
bioRxiv 054684; doi: https://doi.org/10.1101/054684
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Improved methods for multi-trait fine mapping of pleiotropic risk loci
Gleb Kichaev, Megan Roytman, Ruth Johnson, Eleazar Eskin, Sara Lindstroem, Peter Kraft, Bogdan Pasaniuc
bioRxiv 054684; doi: https://doi.org/10.1101/054684

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