Discovery of shared genomic loci using the conditional false discovery rate approach

  • Olav B. SmelandEmail author
  • Oleksandr Frei
  • Alexey Shadrin
  • Kevin O’Connell
  • Chun-Chieh Fan
  • Shahram Bahrami
  • Dominic Holland
  • Srdjan Djurovic
  • Wesley K. Thompson
  • Anders M. Dale
  • Ole A. AndreassenEmail author
Part of the following topical collections:
  1. Genetic epidemiology of complex diseases


In recent years, genome-wide association study (GWAS) sample sizes have become larger, the statistical power has improved and thousands of trait-associated variants have been uncovered, offering new insights into the genetic etiology of complex human traits and disorders. However, a large fraction of the polygenic architecture underlying most complex phenotypes still remains undetected. We here review the conditional false discovery rate (condFDR) method, a model-free strategy for analysis of GWAS summary data, which has improved yield of existing GWAS and provided novel findings of genetic overlap between a wide range of complex human phenotypes, including psychiatric, cardiovascular, and neurological disorders, as well as psychological and cognitive traits. The condFDR method was inspired by Empirical Bayes approaches and leverages auxiliary genetic information to improve statistical power for discovery of single-nucleotide polymorphisms (SNPs). The cross-trait condFDR strategy analyses separate GWAS data, and leverages overlapping SNP associations, i.e., cross-trait enrichment, to increase discovery of trait-associated SNPs. The extension of the condFDR approach to conjunctional FDR (conjFDR) identifies shared genomic loci between two phenotypes. The conjFDR approach allows for detection of shared genomic associations irrespective of the genetic correlation between the phenotypes, often revealing a mixture of antagonistic and agonistic directional effects among the shared loci. This review provides a methodological comparison between condFDR and other relevant cross-trait analytical tools and demonstrates how condFDR analysis may provide novel insights into the genetic relationship between complex phenotypes.



National Institutes of Health (NS057198; EB00790); National Institutes of Health NIDA/NCI: U24DA041123; the Research Council of Norway (229129; 213837; 248778; 223273; 249711); the South-East Norway Regional Health Authority (2017-112); KG Jebsen Stiftelsen (SKGJ-2011-36).

Compliance with ethical standards

Conflict of interest

OA.A. has received speaker’s honorarium from Lundbeck and is a consultant for Healthlytix. C.C.F. is under employment of Multimodal Imaging Service, dba Healthlytix, in addition to his research appointment at the University of California, San Diego. A.M.D. is a founder of and holds equity interest in CorTechs Labs and serves on its scientific advisory board. He is also a member of the Scientific Advisory Board of Healthlytix and receives research funding from General Electric Healthcare (GEHC). The terms of these arrangements have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. Remaining authors have no conflicts of interest to declare.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical MedicineUniversity of OsloOsloNorway
  2. 2.Department of Cognitive ScienceUniversity of California San DiegoSan DiegoUSA
  3. 3.Department of RadiologyUniversity of California of San DiegoSan DiegoUSA
  4. 4.Department of Family Medicine and Public HealthUniversity of California San DiegoSan DiegoUSA
  5. 5.Department of NeuroscienceUniversity of California San DiegoSan DiegoUSA
  6. 6.Center for Multimodal Imaging and GeneticsUniversity of California San DiegoSan DiegoUSA
  7. 7.Department of Medical GeneticsOslo University HospitalOsloNorway
  8. 8.NORMENT Centre, Department of Clinical ScienceUniversity of BergenBergenNorway

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