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Widespread allelic heterogeneity in complex traits

Farhad Hormozdiari, Anthony Zhu, Gleb Kichaev, Ayellet V. Segré, Chelsea J.-T. Ju, Jong Wha Joo, Hyejung Won, Sriram Sankararaman, Bogdan Pasaniuc, Sagiv Shiffman, Eleazar Eskin
doi: https://doi.org/10.1101/076984
Farhad Hormozdiari
Department of Computer Science, University of California, Los Angeles, CA
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Anthony Zhu
Department of Computer Science, University of California, Los Angeles, CA
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Gleb Kichaev
Bioinformatics IDP, University of California, Los Angeles, CA
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Ayellet V. Segré
Cancer Program, The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA
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Chelsea J.-T. Ju
Department of Computer Science, University of California, Los Angeles, CA
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Jong Wha Joo
Department of Computer Science, University of California, Los Angeles, CA
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Hyejung Won
Neurogenetics Program, Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, CA
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Sriram Sankararaman
Department of Computer Science, University of California, Los Angeles, CADepartment of Human Genetics, University of California, Los Angeles, CA
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Bogdan Pasaniuc
Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CADepartment of Human Genetics, University of California, Los Angeles, CA
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Sagiv Shiffman
Department of Genetics, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem
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  • For correspondence: sagiv@vms.huji.ac.il eeskin@cs.ucla.edu
Eleazar Eskin
Department of Computer Science, University of California, Los Angeles, CADepartment of Human Genetics, University of California, Los Angeles, CA
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  • For correspondence: sagiv@vms.huji.ac.il eeskin@cs.ucla.edu
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Abstract

Recent successes in genome-wide association studies (GWAS) make it possible to address important questions about the genetic architecture of complex traits, such as allele frequency and effect size. One lesser-known aspect of complex traits is the extent of allelic heterogeneity (AH) arising from multiple causal variants at a locus. We developed a computational method to infer the probability of AH and applied it to three GWAS and four expression quantitative trait loci (eQTL) datasets. We identified a total of 4152 loci with strong evidence of AH. The proportion of all loci with identified AH is 4-23% in eQTLs, 35% in GWAS of High-Density Lipoprotein (HDL), and 23% in schizophrenia. For eQTL, we observed a strong correlation between sample size and the proportion of loci with AH (R2=0.85, P = 2.2e-16), indicating that statistical power prevents identification of AH in other loci. Understanding the extent of AH may guide the development of new methods for fine mapping and association mapping of complex traits.

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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 September 22, 2016.
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Widespread allelic heterogeneity in complex traits
Farhad Hormozdiari, Anthony Zhu, Gleb Kichaev, Ayellet V. Segré, Chelsea J.-T. Ju, Jong Wha Joo, Hyejung Won, Sriram Sankararaman, Bogdan Pasaniuc, Sagiv Shiffman, Eleazar Eskin
bioRxiv 076984; doi: https://doi.org/10.1101/076984
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Widespread allelic heterogeneity in complex traits
Farhad Hormozdiari, Anthony Zhu, Gleb Kichaev, Ayellet V. Segré, Chelsea J.-T. Ju, Jong Wha Joo, Hyejung Won, Sriram Sankararaman, Bogdan Pasaniuc, Sagiv Shiffman, Eleazar Eskin
bioRxiv 076984; doi: https://doi.org/10.1101/076984

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