EMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variants

BMC Medical Genomics

Volumn 9, Issue , 2016, Pages

  Verma, Anurag ^a,b   Verma, Shefali S ^a,b   Pendergrass, Sarah A ^b   Crawford, Dana C ^d   Crosslin, David R ^e   Kuivaniemi, Helena ^c   Bush, William S ^d   Bradford, Yuki ^a   Kullo, Iftikhar ^h   Bielinski, Suzette J ^h   Li, Rongling ⁱ   Denny, Joshua C ^f   Peissig, Peggy ^g   Hebbring, Scott ^g   De Andrade, Mariza ^h   Ritchie, Marylyn D ^a,b   Tromp, Gerard ^c  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b GEISINGER HEALTH SYSTEM   (United States)

^c UNIVERSITY OF STELLENBOSCH   (South Africa)

^d CASE WESTERN RESERVE UNIVERSITY   (United States)

^e UNIVERSITY OF WASHINGTON SCHOOL OF MEDICINE   (United States)

^f VANDERBILT UNIVERSITY   (United States)

^g MARSHFIELD CLINIC   (United States)

^h MAYO CLINIC   (United States)

ⁱ NATIONAL HUMAN GENOME RESEARCH INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AFRICAN; ARTICLE; CONTROLLED STUDY; DATA BASE; ELECTRONIC MEDICAL RECORDS AND GENOMICS; EUROPEAN; GENE IDENTIFICATION; GENE REPLICATION; GENETIC ASSOCIATION; GENETIC ASSOCIATION STUDY; GENETIC CODE; GENETIC VARIABILITY; GENOTYPE; HYPERTRIGLYCERIDEMIA; PHENOME WIDE ASSOCIATION STUDY; PHENOTYPE; PLEIOTROPY; PRIORITY JOURNAL; SINGLE NUCLEOTIDE POLYMORPHISM; STOP CODON; STOP GAIN VARIANT; ADULT; DEMOGRAPHY; ELECTRONIC HEALTH RECORD; GENETICS; GENOME-WIDE ASSOCIATION STUDY; HUMAN;

ADULT; DEMOGRAPHY; ELECTRONIC HEALTH RECORDS; GENETIC PLEIOTROPY; GENOME-WIDE ASSOCIATION STUDY; HUMANS; PHENOTYPE; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 84982153592     PISSN: None     EISSN: 17558794     Source Type: Journal    
DOI: 10.1186/s12920-016-0191-8     Document Type: Article

References (53)

1. Grant RW, Wexler DJ. Loss-of-function CYP2C9 variants: finding the correct clinical role for Type 2 diabetes pharmacogenetic testing. Expert Rev Cardiovasc Ther. 2010;8:339-43.
2. Scott SA. Personalizing medicine with clinical pharmacogenetics. Genet Med. 2011;13:987-95.
3. Kervestin S, Jacobson A. NMD: a multifaceted response to premature translational termination. Nat Rev Mol Cell Biol. 2012;13:700-12.
4. Rausell A, et al. Analysis of Stop-Gain and Frameshift Variants in Human Innate Immunity Genes. PLoS Comput Biol 2014;10:e1003757. doi: 10.1371/journal.pcbi.1003757.
5. Chen R, Davydov EV, Sirota M, Butte AJ. Non-synonymous and synonymous coding SNPs show similar likelihood and effect size of human disease association. PLoS One. 2010;5:e13574.
6. Namjou B et al. Phenome-wide association study (PheWAS) in EMR-linked pediatric cohorts, genetically links PLCL1 to speech language development and IL5-IL13 to Eosinophilic Esophagitis. Front Genet. 2014;5:401.
7. Denny JC et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol. 2013;31:1102-10.
8. Denny JC et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics. 2010;26:1205-10.
9. Pendergrass SA et al. Phenome-wide association study (PheWAS) for detection of pleiotropy within the Population Architecture using Genomics and Epidemiology (PAGE) Network. PLoS Genet. 2013;9:e1003087.
10. Hebbring SJ. The challenges, advantages and future of phenome-wide association studies. Immunology. 2014;141:157-65.
11. Hall MA et al. Detection of pleiotropy through a Phenome-wide association study (PheWAS) of epidemiologic data as part of the Environmental Architecture for Genes Linked to Environment (EAGLE) study. PLoS Genet. 2014;10:e1004678.
12. Solovieff N, Cotsapas C, Lee PH, Purcell SM, Smoller JW. Pleiotropy in complex traits: challenges and strategies. Nat Rev Genet. 2013;14:483-95.
13. Gough SC, Simmonds M. The HLA Region and Autoimmune Disease: Associations and Mechanisms of Action. Curr Genomics. 2007;8:453-65.
14. Ye Z et al. Phenome-wide association studies (PheWASs) for functional variants. Eur J Hum Genet. 2015;23:523-9.
15. Gottesman O. et al. The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future. Genet Med 2013. doi: 10.1038/gim.2013.72
16. Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet. 2012;44:955-9.
17. Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5:e1000529.
18. McCarty CA et al. The eMERGE Network: A consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med Genomics. 2011;4:13.
19. Khoury MJ, Millikan R, Little J, Gwinn M. The emergence of epidemiology in the genomics age. Int J Epidemiol. 2004;33:936-44.
20. Verma SS et al. Imputation and quality control steps for combining multiple genome-wide datasets. Front Genet. 2014;5:370.
21. Zheng X, et al. A High-performance Computing Toolset for Relatedness and Principal Component Analysis of SNP Data. Bioinformatics bts606 2012. doi: 10.1093/bioinformatics/bts606
22. Price AL et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38:904-9.
23. Cingolani P et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 2012;6:80-92.
24. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucl Acids Res. 2010;38:e164.
25. Paila U, Chapman BA, Kirchner R & Quinlan AR. GEMINI: Integrative Exploration of Genetic Variation and Genome Annotations. PLoS Comput Biol 2013;9.
26. McLaren W et al. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics. 2010;26:2069-70.
27. Habegger L et al. VAT: a computational framework to functionally annotate variants in personal genomes within a cloud-computing environment. Bioinformatics. 2012;28:2267-9.
28. Ng SB et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature. 2009;461:272-6.
29. McCarthy DJ et al. Choice of transcripts and software has a large effect on variant annotation. Genome Medicine. 2014;6:26.
30. Tonner P, Srinivasasainagendra V, Zhang S, Zhi D. Detecting transcription of ribosomal protein pseudogenes in diverse human tissues from RNA-seq data. BMC Genomics. 2012;13:412.
31. Pendergrass SA, Dudek SM, Crawford DC, Ritchie MD. Synthesis-View: visualization and interpretation of SNP association results for multi-cohort, multi-phenotype data and meta-analysis. BioData Min. 2010;3:10.
32. Hazra A et al. Common variants of FUT2 are associated with plasma vitamin B12 levels. Nat Genet. 2008;40:1160-2.
33. Webster RJ et al. The association of common genetic variants in the APOA5, LPL and GCK genes with longitudinal changes in metabolic and cardiovascular traits. Diabetologia. 2009;52:106-14.
34. Welter D et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucl Acids Res. 2014;42:D1001-6.
35. Scott LJ et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science. 2007;316:1341-5.
36. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, Novartis Institutes of BioMedical Research, et al. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science. 2007;316:1331-6.
37. Timpson NJ et al. Adiposity-related heterogeneity in patterns of type 2 diabetes susceptibility observed in genome-wide association data. Diabetes. 2009;58:505-10.
38. Stenson PD et al. The Human Gene Mutation Database: 2008 update. Genome Medicine. 2009;1:13.
39. Consortium, T. 1000 G. P. A map of human genome variation from population-scale sequencing. Nature. 2010;467:1061-73.
40. Allison P. Convergence Faliure in logistic Regression. at < http://www2.sas.com/proceedings/forum2008/360-2008.pdf >
41. Firth D. Bias reduction of maximum likelihood estimates. Biometrika. 1993;80:27-38.
42. Galetta F et al. Changes in heart rate variability and QT dispersion in patients with overt hypothyroidism. Eur J Endocrinol. 2008;158:85-90.
43. Bakiner O et al. Subclinical hypothyroidism is characterized by increased QT interval dispersion among women. Med Princ Pract. 2008;17:390-4.
44. Bell GI, Polonsky KS. Diabetes mellitus and genetically programmed defects in beta-cell function. Nature. 2001;414:788-91.
45. Florez JC et al. Type 2 diabetes-associated missense polymorphisms KCNJ11 E23K and ABCC8 A1369S influence progression to diabetes and response to interventions in the Diabetes Prevention Program. Diabetes. 2007;56:531-6.
46. Liu N-J et al. An analysis of the association between a polymorphism of KCNJ11 and diabetic retinopathy in a Chinese Han population. Eur J Med Res. 2015;20:3.
47. Reiner AP et al. Genome-wide association study of white blood cell count in 16,388 African Americans: the continental origins and genetic epidemiology network (COGENT). PLoS Genet. 2011;7:e1002108.
48. Suminaga R, Matsuo M, Takeshima Y, Nakamura H, Wada H. Nonsense mutation of the alpha-actinin-3 gene is not associated with dystrophinopathy. Am J Med Genet. 2000;92:77-8.
49. Riedl I, Osler ME, Benziane B, Chibalin AV & Zierath JR. Association of the ACTN3 R577X polymorphism with glucose tolerance and gene expression of sarcomeric proteins in human skeletal muscle. Physiol Rep 2015;3(3). doi: 10.14814/phy2.12314.
50. Moyes DL, Naglik JR. The mycobiome: influencing IBD severity. Cell Host Microbe. 2012;11:551-2.
51. Ferwerda B et al. Human dectin-1 deficiency and mucocutaneous fungal infections. N Engl J Med. 2009;361:1760-7.
52. Carvalho A et al. Polymorphisms in toll-like receptor genes and susceptibility to pulmonary aspergillosis. J Infect Dis. 2008;197:618-21.
53. McGovern DPB et al. Fucosyltransferase 2 (FUT2) non-secretor status is associated with Crohn's disease. Hum Mol Genet. 2010;19:3468-76.