Future of clinical research in the EMR era: Phenome- Wide Associa:on Studies (PheWAS)

Transcription

1 Future of clinical research in the EMR era: Phenome- Wide Associa:on Studies (PheWAS) Josh Denny, MD, MS Associate Professor, Biomedical Informa:cs and Medicine Vanderbilt University, Nashville, Tennessee, USA CLINICAL RESEARCH FORUM ANNUAL MEETING 4/10/2014

2 Disclaimers I receive funding from: NIH: NLM, NHGRI, NIGMS, NCI, NCATS Reynolds Founda:on (Geriatrics Educa:on) Na:onal Board of Medical Examiners

3 People have different disease risk (and variable drug responses) Healthy Can we use EHR data to find the genomic basis of disease and drug response? Disease risk, severe Disease risk, self-limited Atypical or complicated Disease

4 Nat Biotech Nov 24;31(12):

5 Genome- wide associa:on studies (GWAS) Systema:c ways of surveying 500k- 5 million SNPs across the genome for a phenotype FOXE1 emerge GWAS for hypothyroidism discovers novel associa:on with FOXE1 (AJHG. 2011)

6 Currently, >1200 Published GWAS Published Genome-Wide Associations through 07/2012 with associa:ons for traits Published for > trait categories -8 NHGRI GWA Catalog

7 2010: 500th genome- wide associa:on study 2005: First genome- wide associa:on study BIOINFO-26(9)Cover.qxd 4/14/10 1:47 PM Page 1 VOLUME 26 NUMBER 9 MAY DISCOVERY NOTE Structural bioinformatics ProBiS algorithm for detection of structurally similar protein binding sites by local structural alignment J.Konc and D.Janez ic A machine learning approach to predicting protein ligand binding affinity with applications to molecular docking P.J.Ballester and J.B.O.Mitchell 2004: Publica:on of finished human genome sequence Alignment-free local structural search by writhe decomposition D.Zhi, M.Shatsky and S.E.Brenner Gene expression Reducing the algorithmic variability in transcriptome-based inference S.Tuna and M.Niranjan Integrative mixture of experts to combine clinical factors and gene markers K.-A.Lê Cao, E.Meugnier and G.J.McLachlan A signal noise model for significance analysis of ChIP-seq with negative control H.Xu, L.Handoko, X.Wei, C.Ye, J.Sheng, C.-L.Wei, F.Lin and W.-K.Sung Genome-wide inferring gene phenotype relationship by walking on the heterogeneous network Y.Li and J.C.Patra PathWave: discovering patterns of differentially regulated enzymes in metabolic pathways G.Schramm, S.Wiesberg, N.Diessl, A.-L.Kranz, V.Sagulenko, M.Oswald, G.Reinelt, F.Westermann, R.Eils and R.König Bioinformatics VOLUME 26 NUMBER 9 MAY PAGES Sequence analysis Localized motif discovery in gene regulatory sequences V.Narang, A.Mittal and W.-K.Sung Systems biology Pathway discovery in metabolic networks by subgraph extraction K.Faust, P.Dupont, J.Callut and J.van Helden issn (print) issn (online) 2010 Genome analysis Genome-wide synteny through highly sensitive sequence alignment: Satsuma M.G.Grabherr, P.Russell, M.Meyer, E.Mauceli, J.Alföldi, F.Di Palma and K.Lindblad-Toh MAY 1 ORIGINAL PAPERS NUMBER 9 LOTUS, a new domain associated with small RNA pathways in the germline I.Callebaut and J.-P.Mornon Genetics and population analysis PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene disease associations J.C.Denny, M.D.Ritchie, M.A.Basford, J.M.Pulley, L.Bastarache, K.Brown-Gentry, D.Wang, D.R.Masys, D.M.Roden and D.C.Crawford VOLUME 26 Sequence analysis Mutual information is critically dependent on prior assumptions: would the correct estimate of mutual information please identify itself? A.D.Fernandes and G.B.Gloor Bioinformatics Bioinformatics Going from where to why interpretable prediction of protein subcellular localization S.Briesemeister, J.Rahnenführer and O.Kohlbacher Identification of genetic network dynamics with unate structure R.Porreca, E.Cinquemani, J.Lygeros and G.Ferrari-Trecate Data and text mining Building a high-quality sense inventory for improved abbreviation disambiguation N.Okazaki, S.Ananiadou and J.Tsujii APPLICATIONS NOTE Genome analysis EuGène-maize: a web site for maize gene prediction P.Montalent and J.Joets Phylogentetics PanCGHweb: a web tool for genotype calling in pangenome CGH data J.R.Bayjanov, R.J.Siezen and S.A.F.T.van Hijum continued inside back cover oxford 2010: First EMR- based gene:c studies Green et al. Nature. 2011

8 Vanderbilt BioVU: an Opt- Out DNA Biobank Extrac:ng DNA from le\ over blood samples

9 John Doe

10 John Doe One way hash A7CCF99DE ~2 million records The Synthetic Derivative: can be updated

11 John Doe John Doe eligible? One way hash A7CCF99DE5732. A7CCF99DE Extract DNA A7CCF99DE ~2 million records The Synthetic Derivative: can be updated

12 Scrubbed Medical Record MR# is removed Replaced SSN and phone # Substituted names Shifted Dates

13 Resources for rapid, efficient EMR- based research at VUMC The Synthe:c Deriva:ve A de- iden:fied and con:nuously- updated image of the EMR (2 M records) BioVU DNA samples available: ~180,000 samples Plasma trial underway Redeposited genotypes Subjects with GWAS data: >18,000 Subjects with any genotyping: >70,000

14 emerge goals To perform GWAS using EMR-derived phenotypes To initiate implementation of actionable variants into the EMR Coordinating Center : pediatric sites

15 Typical emerge phenotyping process Iden:fy phenotype of interest Case & control algorithm development and refinement PPV<95% Manual review; assess precision PPV 95% Deploy at site 1 Validate at other sites Gene:c associa:on tests; replicate Example: Hypothyroidism GWAS algorithm No thyroid-altering medications (e.g., Phenytoin, Lithium) ICD$9s'for' Hypothyroidism Abnormal TSH/FT4 Thyroid replacement medication 2+ non-acute visits No ICD-9s for Hypothyroidism Normal TSH No thyroid replace. meds Demographics ICD/CPT Codes Medica:ons Labs NLP No secondary causes (e.g., pregnancy, ablation) Case' No hx of myasthenia gravis Control' Case PPV = 92.4% Control PPV = 98.5%

16 PheWAS Phenome- wide associa:on study Goal: Rapidly assess many phenotypes at once Genotypes of interest (e.g., SCN10A rs ) PheWAS Electronic Medical Record Phenotype mapping ~1,600 Clinical phenotypes (& controls) Compare with gene:c loci VanderbiltBioVU Denny et al. Bioinforma4cs. 2010

17 PheWAS phenotypes PheWAS Code PheWAS Phenotype 2 ICD9 codes to be a Case Controls can t be any of these: 250 Diabetes 250.* Multiple sclerosis T1D, T2D, secondary DM, abnormal glucose codes -log(p) Type 1 Diabetes Type 2 Diabetes Myeloid leukemia Diabetes mellitus Colon cancer Candidiasis Rheumatoid and other inflammatory arthri:s Malignant neoplasm of rectum Benign neoplasm of respiratory tract Acute renal failure Erythematous conditions Paroxysmal tachycardia Hemolytic anemias Polymyalgia rheumatica Rheumatoid arthri:s diseases ICD9 Code Group Felty s syndrome Contact dermatitis 714.* RA, psoria:c arthri:s, Lupus, etc Denny et al. Bioinforma4cs (~740 codes) Denny et al. Nat Biotech (~1650 codes)

18 PheWAS Popula:on 13,835 European- Ancestry individuals from 5 emerge sites with available GWAS data 2,080,550 unique dates of interac:on with the EMR Mean follow- up of 15.7 ± 10.3 years

19 PheWAS opublished f all Genome-Wide NHGRI G WAS Catalog SNPs Associations through 07/ Published for 18 trait categories 3,144 SNPs with prior GWAS- discovered associa:ons 674 SNPs with 86 phenotypes 751 SNP- phenotype associa:ons Test for replica:on of 751 associa:ons using PheWAS Replica:on Arm 3,144 SNPs PheWAS for each SNP to discovery pleiotropy Replica:on of novel associa:ons Discovery Arm NHGRI GWA Catalog Denny et al, Nat Biotech

20 Replica:ons of NHGRI GWAS associa:ons Binary traits via PheWAS P-value for replication: All - 210/751: 2x10-98 Powered - 51/77: 3x10-47 Denny et al, Nat Biotech 2013 Con:nuous traits

21 Q- Q plot ROC analysis observed p values True positive rate AUC=0.83 T2D vs. T1D errors Several FPs actually known outside GWAS Catalog expected p values False positive rate Denny et al, Nat Biotech 2013

22 Replica:on rate of known findings Associa:on Count Not Replicated Replicated % Replicated 100% 75% 50% 25% % Replicated Power to replicate in PheWAS 0% Denny et al, Nat Biotech 2013

23 PheWAS replica:ons compared to validated emerge algorithms Phenotype emerge Phenotype for GWAS cases/controls Type 2 Diabetes cases=2526; controls=5276 replicated SNPs cases/controls 15/23 cases=3122; controls=8106 PheWAS replicated SNPs 15/23 Demen:a cases=1599; controls=1866 6/18 cases=737; controls=9469 4/18 Hypothyroidism cases=1317; controls=5053 discovered FOXE1 (OR=0.74) cases=2051; controls=10106 replicated FOXE1 (OR=0.76) Denny et al, Nat Biotech 2013

24 Factors associated with replica:on Number of prior publica:ons Exactness of phenotype match SNP loca:on/func:onal status NOT associated Associa:on Count % 68% Not Replicated Replicated 80% 60% 100% Number of publica:ons repor:ng associa:on Denny et al, Nat Biotech 2013

25 Comparing Published ORs to PheWAS ORs Odds Ra:o catalog PheWAS

26 Using PheWAS to refine understanding of GWAS: normal cardiac conduc:on SCN5A/SCN10A n=5,272 Ritchie et al., Circula:on 2013

27 Phenome- wide associa:on study of rs (SCN10A) N=13617 subjects with EHR data cardiac arrhythmias atrial fibrillation disease codes Ritchie et al., Circula:on 2013

28 What happens in the heart healthy popula:on? Examined 5272 heart healthy people Followed for development of atrial fibrilla:on based on genotype Atrial fibrillation-free Atrial fibrillation free survival survival AA AG GG HR=1.49 per G allele p=0.001 AA AG GG Ritchie et al., Circula:on Years ECG Years since normal ECG (and no heart disease)

29 4 5 6 scale: n=21 Each dot=one phenotype TERT scale: n=12 Chromosome 3 PheWAS of all GWAS hits 6p25.3 GWA catalog association only GWA catalog association replicated by PheWAS New association found by PheWAS PheWAS'associa-ons'for'TERT$ Known:'glioma' 6p p q

30 4 5 6 scale: n=21 Each dot=one phenotype GWA catalog association only GWA catalog association replicated by PheWAS New association found by PheWAS PheWAS associa:ons for IRF4 TERT scale: n=12 Chromosome 3 PheWAS of all GWAS hits Known: hair, skin, eye color IRF4 6p25.3 6p p q

31 All PheWAS associa:ons - log(p)à Phenotype groups

32 Replica:ng select novel PheWAS associa:ons Synthe:c Deriva:ve (de- iden:fied EMR) Created (and validated) natural language processing algorithms to find biopsy records with ac:nic keratosis, seborrheic keratosis ~1-2 months Associa:on tests with exis:ng genotype data ~5 minutes PheWAS discovery Replica:on Phenotype SNP Nearest Gene Cases Odds ra:o p- value Cases Odds ra:o p- value Ac:nic keratosis rs IRF E E- 04 rs HERC2 2, E rs CDK E rs CDK5RAP E Seborrheic keratosis rs TERT 2, E Denny et al, Nat Biotech 2013

33 (OR = 1.26, P = ), acute myocardial infarction (OR = 1.28, P = ) and abdominal aortic aneurysm (OR = 1.29, P = 0.001), consistent with prior publications 3, but also with other near-significant vascular phenotypes such as unstable angina, carotid stenosis and hemorrhoids. Associations with hemorrhoids, abdominal aortic aneurysms and carotid stenosis all persisted when the regression model was adjusted for coronary atherosclerosis or myocardial infarction as a comorbidity. Our study replicated the association between rheumatoid arthrit and rs near HLA-DRB1 (Fig. 3d; OR = 1.56, P = This SNP was also strongly associated with type 1 diabetes (OR 1.44, P = ) and potentially associated with inflammato arthritides (OR = 1.64, P = ), a parent phenotype of gia cell arteritis (OR = 1.94, P = ). Both of these association persisted when adjusting for rheumatoid arthritis (P = for type 1 diabetes and P = for inflammatory arthritides Pleiotropy in Skin Diseases CDK10 rs EXOC2 rs IRF4 rs MC1R rs TERT rs TYR rs Actinic keratosis 2.1e e e e e-05 Seborrheic keratosis 1.7e e e e-03 Photodermatitis and sunburn e Nonmelanoma skin cancer 5.9e e e e e-12 Melanoma e e e e-04 Benign neoplasm of eye e Protect Risk 1.5 Protect Risk 1.5 Protect Risk Protect Risk 1.5 Protect Risk 1.5 Protect Risk Figure 4 Risk variants for skin phenotypes have different pleiotropy patterns. Association odds ratios are graphed on the x axis and P-values (numbers next to the bars) are from the PheWAS analysis for that SNP. All SNPs use the minor allele as the coded allele, except rs (TERT). Darker colored bars represent significant associations, calculated as P = 0.05 divided by the number of associations displayed, or 0.05/(6 phenotypes* SNPs) = Tests for heterogeneity revealed significant heterogeneity among the six phenotypes (I 2 = 59 94%, all P < 0.05) and among the six SNPs (I 2 = 23 83%, all P < 0.05). Bars oriented leftward toward protect represent SNPs in which the coded allele favors decreased prevalence of disease, and bars oriented rightward toward risk represent coded alleles favoring increased prevalence of disease. 6 ADVANCE ONLINE PUBLICATION NATURE BIOTECHNOLOG Denny et al, Nat Biotech 2013

34 Pleiotropy in Thyroid Diseases 34

35 phewascatalog.org PheWAS results for >3000 SNPs identified in GWAS studies 35

36 phewascatalog.org PheWAS results for >3000 SNPs identified in GWAS studies search SNPs, phenotypes, genes make/save graphs export data sets 36

37 The PheWAS x GWAS search space All observable human diseases (currently about 8,000) A high dimensional sparse matrix of at least 320 billion possible pair-wise correlations All measurable genetic variants (currently est. about 40 million loci)

38 EHR research will likely only grow EHR- linked biobanks (DNA or other samples) allow for rapid interroga:on of biology Can also be used for non- gene:c research: drug repurposing, adverse event detec:on, disease subtypes, disease correla:ons, etc. It will get beuer Meaningful Use, beuer EHRs, tools such as natural language processing, libraries of validated phenotype algorithms Work is needed to make EHRs research- compa:ble and to share data between sites

39 Informa:cs Lisa Bastarache Hua Xu Josh Peterson Brad Malin Dan Masys Robert Carroll Wei- Qi Wei BioVU/SD Melissa Basford Jill Pulley Erica Bowton Jay Cowan Sunny Wang Jenny Madison Sue Bradeen The Teams Medicine Dan Roden Ellen Clayton Jessica Delaney Sara Van Driest Jonathan Mosley Andrea Ramirez Peter Weeke Gene:cs Dana Crawford Marylyn Ritchie Todd Edwards Biosta:s:cs Jonathan Schildcrout Yaping Shi emerge Network Children s hospital of Philadelphia Boston Children s/cincinna: Children s Hospitals Northwestern Marshfield Clinic Mayo Clinic Group Health/UW Mount Sinai Geisinger Funding VICTR/NCATS NHGRI NLM NIGMS NCI 39