# Presentation: 781283

## caBig Vocabularies and Common Data Elements Workshop

**February 19-20, 2004**

## caBig collaborators

- Carl Wilhelm Vogel, M.D., Ph.D.
- Center Director, CRCH
- Lynne R. Wilkens, Dr.P.H.
- Biostatistics, CRCH
- Leo Wang-Kit Cheung, Ph.D.
- Biostatistics, CRCH
- Kerry Kakazu, Ph.D.
- Assistant Director for Operations, CRCH
- Tak Sugimura, Ph.D.
- Interim Associate Director, University of Hawaii Center for Genomics, Proteomics and Bioinformatics Research (CGPBR), MHPCC

## CRCH

- Institute of University of Hawai`i
- Three major programs:
- Etiology / Epidemiology
- Prevention and Control
- Natural Products
- The state of Hawai`i has no majority race
- Five major ethnic groups: Japanese, Native Hawaiian, white, Filipino, Chinese
- Major mission of CRCH is to identify reasons for and solutions to differing cancer incidence and mortality rates among ethnic groups

## Multiethnic Cohort Study

- The Multiethnic Cohort (MEC) was created to study diet and lifestyle and cancer in different ethnic groups (Kolonel, PI)
- Joint project of UH and USC
- Measurement of gene-environment interactions with susceptibility genes
- Targeted groups: whites, Japanese, Native Hawaiians, African Americans and Latinos
- Main Sampling frame: Driver’s License Files, Voter’s registration, HFCA
- Ages 45-65 in 1993

## Multiethnic Cohort Study

- Over 215,000 participants completed a 26-page mailed scannable questionnaire.
- Questionnaire sections: demography, smoking history, reproductive history, personal and family medical history, physical activity, 20-page food frequency questionnaire, supplement use
- Followed over time for cancer incidence and mortality by passive and active means
- Follow-up questionnaires
- Substudies: calibration
- Prospective biospecimen collection
- Genotyping databank

## Sex and ethnic distribution

|  | Males | Females |
| --- | --- | --- |
| African American | 12,851 | 22,256 |
| Hawaiian | 6,120 | 7,851 |
| Japanese | 26,964 | 29,957 |
| Latino | 22,818 | 24,620 |
| White | 22,857 | 26,502 |
| Other | 5,200 | 7,255 |

## Bioinformatics Issues

- Consolidation of multiple static and dynamic data bases over time
- units
- Data sharing
- UH and USC
- Cohort consortium
- CHALLENGES:
- Version control (dynamic data sets, addition and correction to “static” data sets)
- Data dictionaries
- Dietary variables
- Genetic variables

## Data Management

- UH and USC manage the following
- data bases for their respective cohort members.

## Creation of Analysis Data Sets

- Food and
- Supplement
- Composition
-  Data

- Tumor
- Info

- Death
- Files

- Tracking System
- (Dynamic)

- Calibration
- Files

- ANALYSIS DATA SET PER
- CANCER SITE

- QX1

- ....

- QXn

- Lab1

- ...

- Labn

## Tasks

- Automate some data management tasks
- Better integrate data dictionaries with data
- Creation of all common variables
- Web site for checking out and logging in data bases for version control
- Work with MHPCC on meta data system using middleware

## Multivariate Analysis of Molecular Data in Epidemiology

**Leo Wang-Kit Cheung, Ph.D.**

## Different Approaches

** ****Traditional statistical approaches for epidemiology, e.g.:**
** ****Logistic regression model for estimating relative risks of those exposed compared to those unexposed**
** ****Linear regression / ANOVA model for explaining quantitative trait variation**
** ****Contemporary statistical and computational approaches, e.g.:**
** ****Artificial intelligence techniques, e.g. Artificial neural networks (ANNs)**
** ****Probabilistic modeling, e.g. Hidden Markov models (HMMs)**
** ****Simulation studies**
** ****Integrative multivariate analysis incorporates various data**

## Issues & Challenges

** ****In the post-genomic era, we would like to take advantage of various kinds of molecular data for epidemiologic research **
** ****Genomic sequence variations/polymorphisms, e.g. SNPs**
** ****Gene products, e.g. protein products/expression levels**
** ****Structure and function, e.g. protein folding**
** ****Pathways and physiology, e.g. protein-protein interactions**
** ****Determine which polymorphisms and which environmental factors are associated with complex diseases, such as cancer ...**
** ****Effect of a single genetic variation will likely be dependent on other genetic variations (gene-gene interactions) and environmental factors (gene-environment interactions)**

## Issues & Challenges (cont’d)

** ****Detecting and characterizing gene-gene and/or gene-environment interactions in cancer are both statistical and computational challenges today**
** ****Highly multi-dimensional problems ******** ****KEY DIFFICULTY**
** ****Reduction of dimensionality and multivariate analysis**
** ****Current specific interest 1: Investigation of cancer susceptibility genes in our Multiethnic Cohort (MEC) genetic study **
** ****Using SNP-based approach to look at genetic variations**
** ****Interested in making use of reduced haplotype diversity within the candidate genes ******** selecting haplotype-tagging SNPs based on maximizing the prediction of haplotype dosage**

## Issues & Challenges (cont’d)

** ****Investigation on CYP19 gene (Stram et al., Hum Hered 2003): 74 informative SNPs appear to fall into 4 regions of reduced haplotype diversity (Gabriel et al., Science 2002), i.e. discrete regions of high linkage disequilibrium or haplotype blocks. One of these blocks includes 19 of the 74 SNPs. The best set of 4 haplotype-tagging SNPs (htSNPs) captures at least 94% of variability in these 19 SNPs of the common haplotypes ******** reduce the number of SNPs dramatically!**
** ****Extensions on the method for selecting htSNPs**
** ****So far, studying one candidate gene at a time ..., what about considering interactions among multiple genetic and environmental factors **

## Issues & Challenges (cont’d)

** ****Specific interest 2: Effects of cancer-preventive foods on genome-wide gene expression**
** ****Experimental studies increasingly point to the likelihood that several dietary constituents may have a role in the etiology of cancer. E.g. Fruit and vegetable consumption has been consistently associated in epidemiologic studies with a decreased risk for a number of cancers**
** ****Interested in investigation on genome-wide changes in gene expression after consumption of specific fruit and vegetable rich diets. We are developing multivariate pattern recognition tools for profiling gene expression patterns under multiple biological conditions over time**

## Maui High Performance Computing CenterUniversity of Hawai`i (2001)

## MHPCC COMPUTING RESOURCES

- IBM SP SYSTEMS
- Tempest (USM)
-    P3 NH2 760 Proc
*   ***P4 Regatta  304 Proc**
- P3 NH2 Typhoon
-    16 Proc
- P3 WH2
-    320 Proc
- Squall
-    P3 NH2 32 Proc
**Huinalu / Linux**
**   ****520 Proc  **
- HP N4000 16 Proc
- Sun  16/8 Proc
- SGI ONYX 3400 8 Proc
**COMPUTING CAPACITY**
**   ****2,060 Processors**
**   ****3.5 TFLOPS**
**MEMORY**
**  ****1.1 TB**
**Storage**
**  ****18 TB Disk**
**  ****100 TB On-line Tape**

**MHPCC COMPUTING RESOURCES**

## UH/ MHPCC Bioinformatics Center

**Technology/ Training**

**Research/ Education**

**Developing Algorithms**
**Data Structure & Mining**
**Protein Folding**
**Extremophiles & Marine Microbes**
**IBM Life Sciences  Project            ****	****SUR Grant  $1M**
**Cancer Research Center**
** ****Infectious Disease**
**Bioterrorism**

**Industry Support**

**Partnerships**
**IBM Life Sciences Institute for Innovation**
**Hawaii Biotech**
**Technology Transfer**

**MHPCC Staffing**

**Staffing with joint MHPCC/UH appointments with Faculty, Post Docs, **

**Data Handling: acquire, store, mine, disseminate**
**Database Building & Maintenance**
**Data Fusion & Integration**
**Application Development ,Maintenance & Tools **
**Visualization**
**User Services**
**Network Support**
**Website**

## 31 Internet2 GigaPoPs as of March 2003