“Phenome-wide association study”的意思、由来-开放百科全书

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] It is a complementary approach to the genome-wide association study, or GWAS, methodology.^[3] 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.^[4] The approach has proven useful in rediscovering previously reported genotype-phenotype associations,^[2]^[5] as well as in identifying new ones.^[6]

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.^[4] In 2010, a proof-of-concept PheWAS study was published based on EHR billing codes from a single study site.^[7] 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.^[4] This paper also coined the abbreviation "PheWAS".^[8]

References

1. ^{{Cite journal |last=Pendergrass |first=S.A. |last2=Brown-Gentry |first2=K. |last3=Dudek |first3=S.M. |last4=Torstenson |first4=E.S. |last5=Ambite |first5=J.L. |last6=Avery |first6=C.L. |last7=Buyske |first7=S. |last8=Cai |first8=C. |last9=Fesinmeyer |first9=M.D. |date=2011-05-18 |title=The use of phenome-wide association studies (PheWAS) for exploration of novel genotype-phenotype relationships and pleiotropy discovery |journal=Genetic Epidemiology |language=en |volume=35 |issue=5 |pages=410–422 |doi=10.1002/gepi.20589 |issn=0741-0395 |pmc=3116446 |pmid=21594894}}
2. ^¹{{Cite journal |last=Denny |first=Joshua C. |last2=Bastarache |first2=Lisa |last3=Roden |first3=Dan M. |date=2016-08-31 |title=Phenome-Wide Association Studies as a Tool to Advance Precision Medicine |journal=Annual Review of Genomics and Human Genetics |volume=17 |pages=353–373 |doi=10.1146/annurev-genom-090314-024956 |issn=1545-293X |pmc=5480096 |pmid=27147087}}
3. ^{{Cite journal |last=Hebbring |first=Scott J. |date=February 2014 |title=The challenges, advantages and future of phenome-wide association studies |journal=Immunology |volume=141 |issue=2 |pages=157–165 |doi=10.1111/imm.12195 |issn=1365-2567 |pmc=3904236 |pmid=24147732}}
4. ^¹²{{Cite journal |last=Bush |first=William S. |last2=Oetjens |first2=Matthew T. |last3=Crawford |first3=Dana C. |date=2016-02-15 |title=Unravelling the human genome–phenome relationship using phenome-wide association studies |url=http://www.nature.com/articles/nrg.2015.36 |journal=Nature Reviews Genetics |language=En |volume=17 |issue=3 |pages=129–145 |doi=10.1038/nrg.2015.36 |pmid=26875678 |issn=1471-0056}}
5. ^{{Cite journal |last=Hebbring |first=Scott J. |date=2014-01-09 |title=The challenges, advantages and future of phenome-wide association studies |journal=Immunology |language=en |volume=141 |issue=2 |pages=157–165 |doi=10.1111/imm.12195 |issn=0019-2805 |pmc=3904236 |pmid=24147732}}
6. ^{{Cite journal |last=Cronin |first=Robert M. |last2=Field |first2=Julie R. |last3=Bradford |first3=Yuki |last4=Shaffer |first4=Christian M. |last5=Carroll |first5=Robert J. |last6=Mosley |first6=Jonathan D. |last7=Bastarache |first7=Lisa |last8=Edwards |first8=Todd L. |last9=Hebbring |first9=Scott J. |date=2014 |title=Phenome-wide association studies demonstrating pleiotropy of genetic variants within FTO with and without adjustment for body mass index |journal=Frontiers in Genetics |language=English |volume=5 |pages=250 |doi=10.3389/fgene.2014.00250 |issn=1664-8021 |pmc=4134007 |pmid=25177340}}
7. ^{{Cite journal |last=Denny |first=Joshua C. |last2=Ritchie |first2=Marylyn D. |last3=Basford |first3=Melissa A. |last4=Pulley |first4=Jill M. |last5=Bastarache |first5=Lisa |last6=Brown-Gentry |first6=Kristin |last7=Wang |first7=Deede |last8=Masys |first8=Dan R. |last9=Roden |first9=Dan M. |date=2010-05-01 |title=PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations |journal=Bioinformatics |volume=26 |issue=9 |pages=1205–1210 |doi=10.1093/bioinformatics/btq126 |issn=1367-4811 |pmc=2859132 |pmid=20335276}}
8. ^{{Cite journal |last=Roden |first=Dan M. |date=2017-03-26 |title=Phenome-wide association studies: a new method for functional genomics in humans |journal=The Journal of Physiology |language=en |volume=595 |issue=12 |pages=4109–4115 |doi=10.1113/jp273122 |issn=0022-3751 |pmc=5471509 |pmid=28229460}}

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