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Phenome-wide association study

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In genetics and genetic epidemiology, a phenome-wide association study, abbreviated PheWAS, is a study design in which the association between single-nucleotide polymorphisms or other types of DNA variants is tested across a large number of different phenotypes.[1] The aim of PheWAS studies (or PheWASs) is to examine the causal linkage between known sequence differences and any type of trait, including molecular, biochemical, cellular, and especially clinical diagnoses and outcomes.[2][3][4] It is a complementary approach to the genome-wide association study, or GWAS, methodology.[5] A fundamental difference between GWAS and PheWAS designs is the direction of inference: in a PheWAS it is from exposure (the DNA variant) to many possible outcomes, that is, from SNPs to differences in phenotypes and disease risk. In a GWAS, the polarity of analysis is from one or a few phenotypes to many possible DNA variants.[6] The approach has proven useful in rediscovering previously reported genotype-phenotype associations,[2][7] as well as in identifying new ones.[8]

The PheWAS approach was originally developed due to the widespread availability of both anonymized human clinical electronic health record (EHR) data and matched genotype data. At the same time massive genome and phenome data sets for model organisms were being assembled that also proved effective for PheWAS.[9] PheWASs have also been conducted using data from existing epidemiological studies.[6] In 2010, a proof-of-concept PheWAS study was published based on EHR billing codes from a single study site.[10] 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.[6] This paper also coined the abbreviation "PheWAS".[11] As of 2019, PheWAS in the EHR has been conducted using ICD-9-CM,[12] ICD-10, and ICD-10-CM[13] 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. ^ a b 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. ^ Wang, X.; Pandey, A.K.; Mulligan, M.K.; Williams, E.G.; Mozhui, K.; Li, Z.; Jovaisaite, V.; Quarles, L.D.; Xiao, Z.; Huang, J.; Capra, J.A.; Chen, Z.; Taylor, W.L.; Bastarache, L.; Niu, X.; Pollard, K.S.; Ciobanu, D.C.; Reznik, A.O.; Tishkov, A.V.; Zhulin, I.B.; Peng, J.; Nelson, S.F.; Denny, J.C.; Auwerx, J.; Williams, R.W. (2016-02-02). "Joint mouse-human phenome-wide association to test gene function and disease risk". Nature Communications. 7: 10464. doi:10.1038/ncomms10464. PMC 4740880. PMID 26833085.
  5. ^ 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.
  6. ^ a b c 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.
  7. ^ 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.
  8. ^ 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.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  9. ^ Li, H.; Wang, X.; Rukina, D.; Huang, Q.; Lin, T.; Sorrentino, V.; Zhang, H.; Sleiman, M.B.; Arends, D.; McDaid, A.; Luan, P.; Ziari, N.; Velazeuez-Villegas, L.A.; Gariani, K.; Kutalik, Z.; Schoonjans, K.; Radcliffe, R.A.; Prins, P.; Morgenthaler, S.; Williams, R.W.; Auwerx, J. (2018-02-24). "An integrated systems genetics and omics tookit to probe gene function". Cell Systems. 6: 90–012. doi:10.1016/j.cels.2017.10.016. PMID 29199021.
  10. ^ 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.
  11. ^ 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.
  12. ^ 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.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  13. ^ 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.{{cite journal}}: CS1 maint: unflagged free DOI (link)


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