Widespread Allelic Heterogeneity in Complex Traits.
Farhad Hormozdiari, Anthony Zhu, Gleb Kichaev, Chelsea J-T Ju, Ayellet V Segrè, Jong Wha J Joo, Hyejung Won, Sriram Sankararaman, Bogdan Pasaniuc, Sagiv Shifman, Eleazar Eskin
Author Information
Farhad Hormozdiari: Department of Computer Science, University of California, Los Angeles, CA 90095, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA.
Anthony Zhu: Department of Computer Science, University of California, Los Angeles, CA 90095, USA.
Gleb Kichaev: Bioinformatics IDP, University of California, Los Angeles, CA 90095, USA.
Chelsea J-T Ju: Department of Computer Science, University of California, Los Angeles, CA 90095, USA.
Ayellet V Segrè: Cancer Program, The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142, USA.
Jong Wha J Joo: Department of Computer Science, University of California, Los Angeles, CA 90095, USA; Department of Computer Science Engineering, Dongguk University-Seoul, 04620 Seoul, South Korea.
Hyejung Won: Neurogenetics Program, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA.
Sriram Sankararaman: Department of Computer Science, University of California, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, CA 90095, USA.
Bogdan Pasaniuc: Department of Human Genetics, University of California, Los Angeles, CA 90095, USA; Department of Pathology and Laboratory Medicine, University of California, Los Angeles, CA 90095, USA.
Sagiv Shifman: Department of Genetics, The Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel. Electronic address: sagiv@vms.huji.ac.il.
Eleazar Eskin: Department of Computer Science, University of California, Los Angeles, CA 90095, USA; Department of Human Genetics, University of California, Los Angeles, CA 90095, USA. Electronic address: eeskin@cs.ucla.edu.
Recent successes in genome-wide association studies (GWASs) 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 GWASs and four expression quantitative trait loci (eQTL) datasets. We identified a total of 4,152 loci with strong evidence of AH. The proportion of all loci with identified AH is 4%-23% in eQTLs, 35% in GWASs of high-density lipoprotein (HDL), and 23% in GWASs of schizophrenia. For eQTLs, we observed a strong correlation between sample size and the proportion of loci with AH (R = 0.85, p = 2.2 × 10), 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.
