Phenome-wide association study

In genetic epidemiology, a phenome-wide association study, abbreviated PheWAS, is a study design in which the association between single-nucleotide polymorphisms and a large number of different phenotypes is statistically estimated.[1] The aim of PheWAS studies (or PheWASs) is to examine many different phenotypes to see which, if any, are associated with a given genetic variant.[2][3] It is a complementary approach to the genome-wide association study, or GWAS, methodology.[4] A fundamental difference between the GWAS and PheWAS designs is in the direction of inference: in PheWASs, it is from exposure to outcome, the reverse of that used in GWASs.[5] The approach has proven useful in rediscovering previously reported genotype-phenotype associations,[2][6] as well as in identifying new ones.[7]

The PheWAS approach was originally developed partly due to the widespread availability of anonymized electronic health record (EHR) data. However, PheWASs have also been conducted using data from existing epidemiological studies.[5] In 2010, a proof-of-concept PheWAS study was published based on EHR billing codes from a single study site.[8] Though this study was generally underpowered, its results suggested the potential existence of new associations between multiple phenotypes, possibly due to a common underlying cause. As of 2016, this study is the oldest PheWAS in the EHR-linked eMERGE database.[5] This paper also coined the abbreviation "PheWAS".[9] As of 2019, PheWAS in the EHR has been conducted using ICD-9-CM,[10] ICD-10, and ICD-10-CM[11] diagnosis codes.

References
  1. Pendergrass, S.A.; Brown-Gentry, K.; Dudek, S.M.; Torstenson, E.S.; Ambite, J.L.; Avery, C.L.; Buyske, S.; Cai, C.; Fesinmeyer, M.D. (2011-05-18). "The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery". Genetic Epidemiology. 35 (5): 410–422. doi:10.1002/gepi.20589. ISSN 0741-0395. PMC 3116446. PMID 21594894.
  2. Denny, Joshua C.; Bastarache, Lisa; Roden, Dan M. (2016-08-31). "Phenome-Wide Association Studies as a Tool to Advance Precision Medicine". Annual Review of Genomics and Human Genetics. 17: 353–373. doi:10.1146/annurev-genom-090314-024956. ISSN 1545-293X. PMC 5480096. PMID 27147087.
  3. Bush, William S.; Oetjens, Matthew T.; Crawford, Dana C. (March 2016). "Unravelling the human genome-phenome relationship using phenome-wide association studies". Nature Reviews. Genetics. 17 (3): 129–145. doi:10.1038/nrg.2015.36. ISSN 1471-0064. PMID 26875678.
  4. Hebbring, Scott J. (February 2014). "The challenges, advantages and future of phenome-wide association studies". Immunology. 141 (2): 157–165. doi:10.1111/imm.12195. ISSN 1365-2567. PMC 3904236. PMID 24147732.
  5. Bush, William S.; Oetjens, Matthew T.; Crawford, Dana C. (2016-02-15). "Unravelling the human genome–phenome relationship using phenome-wide association studies". Nature Reviews Genetics. 17 (3): 129–145. doi:10.1038/nrg.2015.36. ISSN 1471-0056. PMID 26875678.
  6. Hebbring, Scott J. (2014-01-09). "The challenges, advantages and future of phenome-wide association studies". Immunology. 141 (2): 157–165. doi:10.1111/imm.12195. ISSN 0019-2805. PMC 3904236. PMID 24147732.
  7. Cronin, Robert M.; Field, Julie R.; Bradford, Yuki; Shaffer, Christian M.; Carroll, Robert J.; Mosley, Jonathan D.; Bastarache, Lisa; Edwards, Todd L.; Hebbring, Scott J. (2014). "Phenome-wide association studies demonstrating pleiotropy of genetic variants within FTO with and without adjustment for body mass index". Frontiers in Genetics. 5: 250. doi:10.3389/fgene.2014.00250. ISSN 1664-8021. PMC 4134007. PMID 25177340.
  8. Denny, Joshua C.; Ritchie, Marylyn D.; Basford, Melissa A.; Pulley, Jill M.; Bastarache, Lisa; Brown-Gentry, Kristin; Wang, Deede; Masys, Dan R.; Roden, Dan M. (2010-05-01). "PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations". Bioinformatics. 26 (9): 1205–1210. doi:10.1093/bioinformatics/btq126. ISSN 1367-4811. PMC 2859132. PMID 20335276.
  9. Roden, Dan M. (2017-03-26). "Phenome-wide association studies: a new method for functional genomics in humans". The Journal of Physiology. 595 (12): 4109–4115. doi:10.1113/jp273122. ISSN 0022-3751. PMC 5471509. PMID 28229460.
  10. Wei, Wei-Qi; Bastarache, Lisa A.; Carroll, Robert J.; Marlo, Joy E.; Osterman, Travis J.; Gamazon, Eric R.; Cox, Nancy J.; Roden, Dan M.; Denny, Joshua C. (2017). "Evaluating phecodes, clinical classification software, and ICD-9-CM codes for phenome-wide association studies in the electronic health record". PLOS One. 12 (7): e0175508. doi:10.1371/journal.pone.0175508. ISSN 1932-6203. PMC 5501393. PMID 28686612.
  11. Wu, Patrick; Gifford, Aliya; Meng, Xiangrui; Li, Xue; Campbell, Harry; Varley, Tim; Zhao, Juan; Carroll, Robert; Bastarache, Lisa; Denny, Joshua C.; Theodoratou, Evropi (2019-11-29). "Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation". JMIR Medical Informatics. 7 (4): e14325. doi:10.2196/14325. ISSN 2291-9694. PMC 6911227. PMID 31553307.


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