The NIDDK Repositories – Adding value to shared resources

Rebekah Rasooly NIDDK, NIH

May, 2013

Central repository components

Contract funded since 2003.

Biosample repository (Fisher): archival storage of biological specimens Database repository (RTI): maintain archival datasets, respond to queries about data and stored samples Genetics repository (Rutgers Univ.): create immortalized cell lines, DNA extraction

Samples and data stored from >50 major multi-site clinical studies in diabetes, digestive, kidney, liver, and urologic diseases

Each study collects according to its own protocols

43 datasets available for sharing

23 GWAS datasets available for sharing through dbGAP

DNA and/or biosamples available for sharing from 28 studies

The NIDDK Central Repositories’ holdings:

Biosample Repository Genetics Repository Affiliated Repositories

Total samples 7,384,858 113,057 560,191

Types of studies Diabetes and Obesity Studies DCCT/EDIC (The Type 1 Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications) DPP (Diabetes Prevention Program) DPPOS (The Diabetes Prevention Program Outcome Study) LookAHEAD (Action for Health in Diabetes) HEALTHY (Middle-School Based Primary Prevention Trial of Type 2 Diabetes) TrialNet - TN01 (NATURAL HISTORY STUDY OF THE DEVELOPMENT OF TYPE 1 DIABETES) TEDDY (The Environmental Determinants of Diabetes in the Young)

Kidney Studies AASK Trial (The African American Study of Kidney Disease and Hypertension Study) CRIC (Chronic Renal Insufficiency Cohort Study) MDRD (The Modification of Diet in Renal Disease)

Liver Disease Studies A2ALL (The Adult-to-Adult Living Donor Liver Transplantation Cohort Study) HALT-C (The Hepatitis C Antiviral Long-term Treatment against Cirrhosis) VIRAHEP-C (The Study of Viral Resistance to Antiviral Therapy of Chronic Hepatitis C)

Urology Studies MTOPS (The Medical Therapy of Prostatic Symptoms) SISTEr (The Stress Incontinence Surgical Treatment Efficacy Trial)

NIDDK has custodianship of all samples and data transferred to the Repositories and no IP protections are attached

The Steering Committee of each study or study group has control of the samples and data during a “proprietary period” (2 years after the end of the study or study increment)

Expensive resources have to be useful: the Repositories’ sharing policies

Remove all identifiers, except some elements of dates (“Limited data set”)

Collect all forms, MOPs, key papers and analytic datasets from those papers

Reconcile sample list with phenotypic data

Carry out Dataset Integrity Check (DSIC) process

Curating studies

Curating studies – collect all forms, etc.

www.niddkrepository.org

Curating studies – perform Dataset Integrity Check

• verify that published results from the study can be reproduced using the archived datasets

• perform a small number of analyses to duplicate published results • intent is to provide confidence that the dataset distributed by the

NIDDK repository is a true copy of the study data • does not attempt to resolve minor or inconsequential discrepancies

with published results

www.niddkrepository.org

using the CaBIG Common Biorepository Model (CBM) of 30

variables an additional 140 variables that are domain- or study-specific

Curation for common variables

Index Variable_Name Description CBM (curated for

all studies)

Diabetes

Domain

Kidney

Domain

Liver

Domain

Study_Specific* Other_Common**

1 Ethnicity Ethnicity x x x x

2 Gender Gender x x x x

3 Race Race x x x x

4 ace_arb Use of antihypertensives (ACE inhibitors, ARBs) x x x

5 acr Albumin to creatinine ratio x

6 add_dx prior addisons disease x

7 aer Albumin Excretion Rate x

8 age Age x x x x

9 age_transplant Age at transplant x x

10 ageatonset Age at IDDM onset x

11 agegroup Age group x

12 aki Acute kidney injury (aka ARF) x x

13 alcohol Frequent alcohol use x x

14 assign Treatment group x

15 beckqaire Severe anxiety(BECK) x

'What's in the NIDDK CDR?'--public query tools for the NIDDK central data repository. Pan H, Ardini MA, Bakalov V, et al., 2013, Database (Oxford).

Studies are searchable

www.niddkrepository.org

No analytic dataset – impossible to recreate results in papers

No data dictionary for variables used in analysis – impossible to recreate results in paper

Errors in calculation

Poor or incomplete linkage of sample lists to phenotypic data

Sample labeling issues, including:

Labels applied incorrectly cannot be read by barcode scanner

Duplicate ids

Empty or nearly empty vials

Incorrectly preserved samples

Curation issues

Using the Repository – requests for data and samples

Requests for Repository materials

year

requests for

biosamples

requests for

genetic samples

total number of unique

samples data requests

2004 0 7 1936 0

2005 15 7 3658 4

2006 47 12 9391 5

2007 49 6 6979 15

2008 50 24 29271 16

2009 64 45 48561 33

2010 98 34 64195 29

2011 149 14 44110 58

2012 109 16 73113 94

2013 55 6 10638 14

Using the data and samples

91 publications by researchers who gained access to data and samples through the NIDDK Repository, including:

Papers based on the GWAS data sets in dbGAP

A paper that re-examined the data from a study of dialysis intensity (HEMO) and suggested a re-interpretation the major study conclusion (Argyropoulos, C et al., 2009, J. Am. Soc. Nephrol., 20, 2034-2043).

A paper that re-analyzed the IBD Genetics GWAS data to identify additional loci (Elding, H et al., 2011, Am J Hum Genet. 2011 Dec 9;89(6):798-805)

Publications on novel analytic methods or markers in NIDDK Repository-supplied samples

dbGAP – the NIDDK Repository: two different curatorial approaches

NIDDK Repository dbGAP

Manual Curation Automated curation

Elements of dates accepted No elements of dates accepted

DSIC No DSIC

Linkage to samples No linkage to samples

Expensive - ~$1M/year for the

Data Repository

Minimal costs – the NLM is

bearing the costs of acquiring

studies

Low volume High volume

dbgap NIDDK Repository

Year approved #

downloaded pct

downloaded approved pct

downloaded

2010 54 28 52% 29 100%

2011 91 52 57% 58 100%

2012 137 83 61% 94 100%

2013 53 17 32% 14 100%

dbGAP – the NIDDK Repository: two different curatorial approaches

• Curation is expensive • More familiarity = more sophisticated use of data • If investigators are not obliged to share their data,

they can get by with poor documentation and processing/storage errors

Lessons learned and cautionary notes

Project Officers Beena Akolkar Paul Eggers Bob Karp Contracting Specialist Rich Bailey Repository Specialists Sharon Kay Mobley Kris Moen

NIDDK Repository Staff

RTI – Data Repository Phil Cooley, PI Helen Pan, Sylvia Tan ThermoFisher, Biosample Repository Heather Higgins, PI Rutgers Univ., Genetics Repository Jay Tischfield, PI

www.niddkrepository.org