Coordinating Center Report. Paul Harris, Marylyn Ritchie, Josh Denny, & Brad Malin

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1 Coordinating Center Report Paul Harris, Marylyn Ritchie, Josh Denny, & Brad Malin

2 accelerate phenotype development & sharing expand genomic data integration enhance methods to determine re-identification & privacy risks Coordinating Center provide logistical support & project facilitation sites External Scientific Panel

3 Accelerate phenotype development & sharing

Phenotyping Efficiency and Transportability: PheKB Designed to enable a workflow and integrated tools and standards that guide the user in navigating from early stage development to final public

4 Phenotyping Efficiency and Transportability: PheKB Designed to enable a workflow and integrated tools and standards that guide the user in navigating from early stage development to final public sharing. emerge, PCORnet, NIH Collaboratory, PGRN, PGPop 414 active users, 52 institutions 30 publicly available phenotypes, 62 phenotypes in development 149 implementations (for 53 phenotypes) 3,086 views/month..increasing the efficiency and accuracy of phenotype definition

5 PheKB phenotype views Type II DM ADHD MidSouth CDRN Coronary Heart Disease Atrial fibrillation Demonstration Drug Induced Liver Injury Cardiac Conduction Dementia Severe Early Childhood Obesity Ace-inhibitor induced cough Type-2 Diabetes Demonstration Crohns Disease Demonstration Peripheral Arterial Disease Cataracts Diabetic Retinopathy Height Hypothyroidism Lipids Rheumatoid Arthritis Demonstration Multiple Sclerosis Demonstration Red blood cell indices High Density Lipoproteins White blood cell indices Unique Page Views

6 Phenotype Modalities used in 92 PheKB phenotypes Data modalities or methods Number of phenotypes utilizing these features Public (N=30) Non-Public* (N=62) Percent of Total ICD-9 Codes % Medications % Natural Language Processing % CPT Codes % Laboratory test results %

7 Sharing to other networks Key example - T2D algorithm reused in 40 non-emerge projects NIH Collaboratory, PCORnet sites using PheKB now 7 phenotypes in PheKB currently amongst these 25 non-emerge papers reference PheKB as a source

data dictionary Guarantees that shared files are consistently formatted to minimize

8 Data Sharing The Data Dictionary/Data Validation Tool (Luke Rasmussen) Verifies a data dictionary file adherence to emerge and dbgap standards Verifies a data set against a data dictionary Guarantees that shared files are consistently formatted to minimize time needed to clean/reformat data..increasing the efficiency and accuracy of data sharing

9 Results Dissemination and Re-use: PheWAS Catalog An interactive tool of posted results from a large PheWAS study. Currently disseminating the first of many emerge PheWAS study results. PheWAS results for >3000 SNPs identified in GWAS studies search SNPs, phenotypes, genes make/save graphs export data sets 2,300 users visit to the site

10 R PheWAS Package ex=generateexample() EHR Biobank ICD9 Codes Demographics R PheWAS Package PheWAS Code Translation Phenotypes Genotypes Association Testing Results Plotting

11 Some emerge PheWAS examples GWAS QRS duration (VU) GWAS Platelet size/volume (Mayo) GWAS Herpes Zoster (GHC/UW) Pediatric PheWAS (CCHMC and CHOP) Null variant studies (Network) CNV PheWAS (VU/GHC/UW/CHOP) Neanderthal PheWAS (VU, UW) PCSK9 (Mayo) FTO introns (Adult and pediatric) PheWAS on autism comorbidities HLA variants, Functional variants (Marshfield) Developing text and NLP-based PheWAS

12 Shameer et al. Hum Genet 2014 PheWAS of Platelet size (a) and volume (b)

Drag and Drop feature that utilized basic Boolean-logic Used to assess how well a phenotype might do in a

13 Hypothesis Generation and Testing: emerge Record Counter (erc) Web-based research tool for members of the emerge community with over 55,000 subjects with demographic, diagnosis, and procedure codes. Drag and Drop feature that utilized basic Boolean-logic Used to assess how well a phenotype might do in a genotyped population as well as for site-level assessment improving early stage feasibility to produce the highest rate of return on resources.

14 Medication extraction and curation Developed and distributed MedEx (emerge 1) and MedEx-UIMA (emerge 2) MedEx-UIMA maps to RxNorm codes for specific drug and generic ingredient Have used these and other tools to clean SPHINX medication data Data dictionaries for collection and validation of central data for emerge GWAS and emerge-pgx sets

15 Expand genomic data integration

16 Evaluation of quality control for merging genomic datasets Dataset IMPUTE2 Imputed Data # Genotyped Samples Merged emerge-i 1M 2,634 Merged emerge-i ,029 Geisinger 3,111 Group Health/ U Wash 731 Marshfield/Essentia/PSU 616 Mayo Clinic 3,121 Mount Sinai 6,290 Northwestern 2,951 Vanderbilt 7,616 CCHMC/BCH 5,346 CHOP 6,850 Total All IMPUTE2 Imputed Samples 55,292 Developed emerge imputation pipeline Round 1: BEAGLE Round 2: IMPUTE2 Merged imputed dataset released to dbgap Determined subtle batch effects which need to be corrected for by site, platform, sex, and PCs

18 Extraction of additional information from merged GWAS datasets CNV calling in emerge-i data All intensity files available for emerge-i CNV calling done by CC and by CHOP QC ed CNV dataset available from CC CNV calling requested in emerge-ii data CC solicited intensity files for emerge-ii Limited sets have been submitted thus far

19 Evaluation of genetic models for clinical relevance Null variant PheWAS project led by CC Selected 25 null variants Adult and pediatric PheWAS conducted Manuscript in progress

20 emerge-pgx CC developed variant calling pipeline for emerge-pgx in collaboration with genomics workgroup Performed variant calling for all emerge-pgx samples Concordance analyses across sites and platforms (manuscript in progress) Developed SPHINX for disseminating variant information to the community (manuscript submitted)

21 Average of 200 Unique users per month. 2,334 unique users since release and 7,447 page views since release

22 Impact & Leadership Led a special issue to Frontiers in Genetics

23 Enhance methods to determine re-identification & privacy risks

24 emerge Data Privacy Advancements Reviews of the Field Biobanking Malin. Identifiability in biobanks: models, measures, and mitigation strategies. Human Genetics Attacks & Protections Naveed. Privacy in the genomic era. ACM Computing Surveys. In Press. Evaluations Risk-Utility Frontiers Across emerge States W. Xia. R-U policy frontiers for health data de-identification PheWAS Anonymization Across emerge Sites R. Heatherly. Multi-site anonymization. JAMIA 2 nd round. R. Heatherly. Size matters. J Biomedical Informatics Technology Enhancements Natural Language De-identification Carrell. Hiding in plain sight: use of realistic surrogates to reduce exposure of protected health information in clinical text. JAMIA Li. De-identification of clinical narratives through writing complexity measures. IJMI Risk-Utility Frontiers for De-identification W. Xia. Efficient discovery of de-identification policies. ACM CODASPY Secure Multiparty Computation W. Xie, et al. SecureMA: Protecting participant privacy in genetic association meta-analysis. Bioinformatics Policy Assessments Diagnosis Re-identification Adversarial Knowledge R. Heatherly. Enabling genomic-phenomic association discovery without sacrificing anonymity. PLoS One Game Theory for Demographic Re-identification Z. Wan. A Game theoretic framework for analyzing re-identification risks. PLoS One W. Xia. Process-based data privacy. In review.

25 Policy: Stackelberg Games (Z. Wan, et al. PLoS One. 2015) Sharing Strategy 1 Utility 1 Risk B Publisher Benefit per record = $1200 Cost per violation = $300 ~35000 Census records 5-digit ZIP Age (Years) Race Sex No Attack Scenarios exist! Recipient s Best Strategy Attack Strategy A Utility A Risk A Attack Strategy B Utility B Risk B Attack Strategy C Utility C Risk C Recipient

26 Policy: The Homer Attack CONTROLS (e.g., No diabetes) Association Studies (e.g., Sphinx) SNP 1 SNP 2 SNP n Allele AA AT TT CC CG GG GG GT TT PRIVATE adversary Name SNP 1 SNP 2 SNP n Alice AA CG TT Bob AT GG GT Probability CASES (e.g., Diabetes) SNP 1 SNP 2 SNP n Allele AA AT TT CC CG GG GG GT TT Probability General Population (e.g., HapMAP) SNP 1 SNP 2 SNP n Allele AA AT TT CC CG GG GG GT TT Probability

27 Policy: Sphinx Genomic Inference Game Privacy risk: Likelihood statistic* Are you in the Sphinx pool or the population? More genomic regions we disclose, greater the risk Utility: Proportional to number of regions you disclose Can tune to account for associations with phenotype *Sankararaman S, Obozinski G, Jordan MI, Halperin E. Genomic privacy and limits of individual detection in a pool. Nature Genetics Sep;41(9):965-7.

28 Policy: Sphinx Analysis (Z. Wan, et al. In Development) Utility for emerge & Adversary: Each record worth $99 (based on 23andme) Each SNP has worth proportional to # instances in NHGRI GWAS catalog Probability of detection set to 1 Expected Penalty: is probability of detection * fine

29 Experiment: Sensitivity to Penalty Configuration All SNPs sorted according to MAF (ascending). Utility for each SNP is uniformly distributed Pre-processing keeps 322 SNPs The target set consists of 500 random samples from the pool and the reference respectively. The maximal benefit the publisher gain from publishing all these 322 SNPs is $17,720

30 Optimal # of Released SNPs

31 Major Finding Larger the expected penalty the more SNPs publishable When the expected penalty is equal to the benefit of a successful inference attack, the publisher can publish all the SNPs.

32 It s a Much Larger Analysis If only we had time. Larger publisher s utility more SNPs publishable Alternative: All SNPs sorted in descending MAF order Allows more SNPs to be published! The utility distribution for all 218 independent SNPs

33 Provide logistical support & project facilitation

34 Providing a National Home

35 Tools for project facilitation

36 Supporting and Streamlining Publications Policy, Process & Reporting Assess, Build, Report Meeting Support, Communication, and Project Management

37 Supporting and Streamlining Publications Policy, Process & Reporting Assess, Build, Report Meeting Support, Communication, and Project Management

38 Supporting and Streamlining Publications Policy, Process & Reporting Assess, Build, Report Meeting Support, Communication, and Project Management During emerge Phase II, the CC has provided support for: 700+ workgroup calls 16 Steering Committee/ESP Meetings 8 ESP Packets

39 emerge Coordinating Center Team Paul Harris Marylyn Ritchie Melissa Basford Bradley Malin Josh Denny Sarah Stallings Jacqueline Kirby Mollie Bodin Claar Adam Hardebeck Brianne Derveloy Peter Speltz Shefali Verma Anurag Verma John Wallace Yuki Bradford Sarah Pendergrass Alex Frase Scott Dudek Anna Okula Anastasia Lucas Jason Tan Lixen Chen Yi Jiang Lisa Bastarache Martha Shrubsole Raymond Heatherly Weiyi Xia Zhiyu Wan Jonathan Schildcrout Nathaniel Mercaldo Kyle Brothers

emerge-ii site report Vanderbilt

emerge-ii site report Vanderbilt 29 June 2015 Vanderbilt activities emerge II PGx implementation locally and emerge-pgx SCN5A/KCNH2 project provider attitudes Phenotype contributions Methods development