# Presentation: 673873

## Objectives of Symposium

- To identify common, critical issues that have been encountered in applying genomic technologies to population studies at NIH and creative approaches to`solving them;
- To develop approaches for prioritizing and conducting population studies using`genomic technologies for use by individual ICs as desired
- To identify new tools for genomics, categorization of phenotypes, and database standardization required for genome-wide association and sequence-based studies.

**Notes:**
don't have to spend millions to characterize
don't have to wait years for outcomes
often extensive characterization of environmental exposure permits identification of potential GxE

## Panel 1

- Beena Akolkar			NIDDK
- Stephen Chanock			NCI
- Luigi Ferrucci				NIA
- Daniela Gerhard			NCI
- Eric Green				NHGRI
- Jim Mullikin				NHGRI

## Design Field Study		$1,500,000
Conduct Field Study		$2,500,000
DNA Extraction Request	     $75,000
Genotyping WGS		$2,500,000		
Data Analysis			   $200,000
Follow-up Genotype		$1,400,000
Publication 			Priceless....(8.175M)

- Conduct Field Study		$2,500,000
- DNA Extraction Request	     $75,000
- Genotyping WGS		$2,500,000
- Data Analysis			   $200,000
- Follow-up Genotype		$1,400,000
- Publication 			*Priceless....(8.175M)*

## Genomics: Different Paths

**Wide sweep**
**Microarray**
- Looks at all transcripts in one assay
- Uses oligo-dT to capture ‘transcripts’
- Provides snap-shot of genes

**Focused analysis**
- Target each unique region
**Sequence** read (~500 bp)
**Genotype**  (1 key bp)
- Requires many assays
- Issues in design & analysis

## Function

**Whole Genome, or Partial Genome Scans **
**Are Designed to Identify Genetic Markers**

## What Tools Do We Have?

- Extensive data base of common SNPs (MAF>5%)
- Technologies for small to large (1 to 106 SNPs)
- Analytical programs for simple analyses
- Main effect
- Population structure
- Sequencing technology for ‘targeted regions’

- 2006

## What Tools Do We Need?

- Extensive data base of _un_common SNPs (MAF<5%)
*Flexible* Technologies for small to large (1 to 106 SNPs)
- Targeted to different populations
- Analytical programs for complex analyses
- Gene-gene interaction
- Environmental measurements
- Complete genome sequence technology

- Post 2006

## Progress in Genotyping Technology

**1**

**10**

**10****2**

**10****3**

**10****4**

**10****5**

**10****6**

- Nb of
- SNPs

- Cost per
- genotype
- Cents (USD)

**10**

**1**

**10****2**

- ABI
- TaqMan

- ABI
- SNPlex

- Illumina
- Golden Gate

- Illumina
- Infinium/Sentrix

- Affymetrix
- 100K/500K

- Perlegen

- Affymetrix
- MegAllele

- 2001

- 2006

- Affymetrix
- 10K

- Sequenom

## Genotype Opportunities

- Few SNPs                Many SNPs                  Extreme SNPs

-     1                     24-48 			1.5-20K	>100K

- #SNPs/
- assay

**Cost **/ genotype

**Inflexibility **/ SNP in an assay

## 2006 What is Available for Whole Genome SNP Scans

**Coverage analysis based on HapMap II Data**
**	****Build 20 MAF **_**>**_**5%, r****2**** **_**> **_**0.8 (pair-wise)**
**	****	****	****	****	****	****	****CEU****	****YRI****	****JPT/CHB**
**Illumina****	****	****HumanHap300****	****	****80%****	****35%****	****40%**
**Illumina****	****	****HumanHap500****	****	****91%****	****58%****	****88%**
**Affymetrix*****	****500k Mapping****	****	****63*%****	****41%****	****63%**
**Perlegen****	****	****“Custom Choice”****	****“Set by amount paid....”**
- 							***77% ****(with 50k MegA)**

## Quantums of Genotype Cost

- Scope				     Cost/SNP	Total
- Singleplex				$0.25		 $0.25
- Multiplex (6-48)			$0.10		 $5.00
- Maxiplex (1500)			$0.04		$60.00
- Super-plex (24,000)		$0.01		$250.00
- Extreme-plex (>105)		$0.0013	$750.00
*Central point: **	**Think cost per sample*

## 2-stage WGS strategy Power as a function of MAF and  sample sizes typed in the first stage

**Disease model**
- - Prevalence 1%
- - Single susceptibility SNP  with a linkage
-   disequilibrium  r2 = 0.8 with  1 genotyped SNP
- - Dominant transmission
- - Genotype relative risk : 1.5

**Study design**
- # Cases = # Controls
- # Cases in stage 1 : as indicated
- # SNPs in stage 1 : 500,000
- # Cases in stage 2 :  2,000
- # SNPs in stage 2 : 25,000
- Significance level 0.00002

-  Note: Significance level = 0.00002 => 10 false positives

**0.05**

- MAF

- Power
- %

**1200 cases**

**600 cases**

**300 cases**

- Skol Nat Genet 2006

## http://cgems.cancer.gov

**Follow-up Study #1 **
**~3000 cases/ 3000 controls**

**Follow-up Study #2**
**~2500 cases/ 2500 controls**

**Follow-up Study #3 **
**~2500 cases/ 2500 controls**

**Initial Study**
**1150 cases/1150 controls**

- ~24,000 SNPs

- ~1,500 SNPs

- 200+ New
- ht-SNPs

**>500,000 Tag SNPs**

**25-50 Loci**

- Finely mapped haplotypes

**Replication Strategy for Prostate Cancer in CGEMS**

**http://cgems.cancer.gov**

## Scan in 1200 cases and 1200 controls
Validation in 3 studies each 2000/2000

**Scan in 1200 cases and 1200 controls**
**Validation in 3 studies each 2000/2000**

**0**

**.1**

**.2**

**.3**

**.4**

**.5**

**MAF**

**0.8**

**0.6**

**0.4**

**0.2**

**0.0**

**1.0**

- Initial
- scan

- Replication studies

- Entire
- project

**Power**

**Dominant , ****odds ratio 1.5 ; r2 = 0.8**** with the functional SNP**

**CGEMS: Detection Probability for 3 Stage Model**

## Strategy for SNP Selection for Whole Genome Studies in Prostate Cancer

- To test all SNPs is presently too costly
- Utilize a strategy that capitalizes on linkage disequilibrium between SNPs

- Haplotype blocks defined by Gabriel et al
- Based on D’ values for linkage disequilibrium

## A quick note on ‘ideal’ power

- r2 represents the statistical correlation between two loci
- Suppose SNP1 is involved in disease susceptibility and we genotype cases and controls at a nearby site SNP2
- To achieve the same power to detect associations at SNP2 as we would have at SNP1, sample size must increase by a factor of approximately 1/r2

## Justification of CostBased on what you are looking for

**Size of Effect**
**Odds ratio 1.3 -> 2.5**
**Sufficiently high allele frequency**
**Population attributable risk**
**True Negative**
**	*****Alternatively, tells you to look no more...***

## Issues in Extreme Genotyping 

- Assay optimization
- Errors in mapping, design & primers
- Software calling algorithm ‘in silico faith’
- Reliance on programs
- Impossible to check 800,000,000 genotypes
- DNA Source (blood, buccal, other)
- Quantity
- Quality
- Whole genome amplified- (aka previously WGA)
- Results in LOH
- 97-98% Representation

## Issues with Pooling Studies 

- Accuracy
- DNA quantification- Haque BMC Biotech 2003, 3:20.
- Restriction of additional analyses
- Pools defined by case/control
- False negatives
- False positives
- ? Increase by what proportion
- Substantial cost savings

## Current Conundrums of WGS	

- Marker Selection
- Representation of variation across genome
- Blocks, bins and tags.....
- Effect of Copy Number Variation (CNV)
- Number of scans per disease
- Disease and Sub-type
- Distinct populations
- Survival
- Pharmacogenomics
- Population genetic issues
- Stratification
- Admixed populations

## What Do We Look For In New Technologies?

- Inflexion points: Cost shifts
- Flexibility of technology
- Cosmopolitan target set
- Tailor to study population (prior knowledge of structure)
- Efficient use of DNA
- Accurate software for data management and analysis

## Slide 22

## Central Issues: Panel 1

**Current standards for genotyping technology: data completeness and reproducibility, genomic coverage, comparability across platforms, turnaround time, cost **
**Current standards for sequencing technology: data completeness and reproducibility, comparability across platforms, turnaround time, cost**
**Adopting new technologies**
**Proposals for continued sharing of experience NIH-wide**
**IP Issues and their impact on scientific decisions**

## Value Added Analysis in CGEMS 

- Opportunity to investigate
- Gene:environment
- Covariates: BMI, smoking, serum levels
- Gene:gene interactions
- Explore pathways
- Follow-up in cohort studies in CGEMS

## Parallel Approaches To Identifying Genetic Determinants of Disease

**Parallel Approaches To Identifying Genetic Determinants of Disease**

**Validation in Clinical Study**
**And *****In Vitro***** Correlation**

**Human Genome**

**High Density, Genome**
**Wide Genetic Map**

******     ********               ********       ********         ********                 ******

**Genome Wide**
**Association Study**

- Odds
- Ratio

- Genetic Marker

******   ********     **** ****    ********    ********    ******

- ‡

- ‡**  ********  ******

**Map SNPs and Haplotypes**
** ****In Candidate Gene(s)**

**Informative SNP and **
**Candidate Gene Haplotype**

**Candidate Gene**

**Map SNPs**

****** ********  ******

**Candidate Gene**
**Association Study**

****** ********  ******‡

**Informative SNP and **
**Candidate Gene Haplotype**

- 1.0

## Whole Genome Scans:SNPs

- Illumina
- tagSNPs based on HapMapII
- 2 parts (317k + 240k)
- New 1 chip (540k)

- Affymetrix
- Designed pre-HapMapII
- Spaced 500k markers
- Genic enrichment
- Redundancy
- Useful
- ‘Enrich’ with Megallele
- 	3K *(90% Smith AJHG)*
- 	100k

## Sequence Analysis

- Germ-line
- Susceptibility/outcome
- Somatic analysis
- Cancer
- Comparative analysis
- Molecular evolution
- Insight into sequences of signficance

## Shift in Sequence Technology

- Target Amplicons
- Small to Large
- Diagnostic to Genome
- Assess:
- Unique Regions of
- Genome
- Annotate variation

- Highly Parallel
- “Whole Genome”
- Assess:
- Complete genome
- Assembly
- Computationally Challenging

## Issues in Sequence Analysis

**Rare Variants**
- Family Studies 			Are There Enough?
- Functional Analysis 		Very Slow!
- Annotation issues 			Database?
- Population-specific issues		Database?
**Comparison with altered tissue**
- 	Duplicate effort			Parallel analysis
**Copy Number Variation**
- 	Annotation issues		Database?

## Future Issues

- Proteomics
- Epigenomics
- Metabolomics

## Search for Genetic Contribution to Complex Diseases

**Well positioned for**
- Common SNPs (>5%)
- High throughput technology
**Not as well positioned for**
- Uncommon variants
- Structural variants (copy number variants)
- Populations not in the “BIG 3”
- CEU, Yoruba, East Asia

## Whole Genome Scans (WGS=WGA)

- Public Health Impact
- Specific Aim(s)
- Etiology
- Survival
- Pharmacogenomics
- Value-added Analyses
- Co-variates
- Biomarkers
- Gene-environment interactions

## Considerations in Whole Genome Scans

- Extent of Coverage of Genome
- Primary Scan
- Adequate Size
- Expected measured effect
- Ascertainment of Population Structure
- Study Design
- Single study vs combined (heterogeneity)
- Replication Strategy
- Power calculations for how many stages
- Joint vs consecutive analysis *(Skol Nat Genet 2006)*
- Design
- Prospective vs. Retrospective

- Trade-off

## (www.hapmap.org)

**Goal: To construct a haplotype map across the entire genome**
**270 individuals (Nigerians, Japanese, Chinese and whites)**
**Phase 1: completed 03/01/2005 **
**1,000,000 common SNPs (≥ 5%) genotyped: 1 per ~5 kb**
**Phase 2: completed 10/28/05**
**~4,000,000 common SNPs (>5%) genotyped: 1 per ~1.5 kb**
**A few hundred thousand SNPs will be needed to capture common variation across the entire genome (2005-2006)**
**A framework for comprehensive candidate gene and genome-wide association studies**
**Between 500,000 and 1.000,000**

## http://cgems.cancer.gov

**http://cgems.cancer.gov**

## Estimated number of SNPs in the human genome as a function of their minor allele frequency

**Estimated number of SNPs in the human genome as a function of their minor allele frequency**

**10****6**

**2.10****6**

**3.10****6**

**4.10****6**

**5.10****6**

**6.10****6**

**7.10****6**

**0**

- Minor Allele Frequency (MAF)

- Estimated
- number
- of SNPs

**>5%**

**>10%**

**>15%**

**>20%**

**>25%**

**>30%**

**>35%**

**>40%**

**>45%**

- Common SNP : a SNP with MAF > 0.05 ; frequency of heterozytotes >≈ 10%

## CGEMS

**Conduct whole genome SNP scans**
**	****Prostate**
**	****Breast **
**Rapid sequential replication studies**
**Aggressive time-line**
**Initial Scan in a Cohort Study**
**PLCO- Prostate Cancer**
**Nurses Health- Breast Cancer**

## Milestones for CGEMS Prostate Cancer Scan

**Milestones for CGEMS Prostate Cancer Scan**

- May

- Sept

- Jan

- May

- Sept

- Jan

- May

- Sept

- Jan

- May

- 2005

- 2006

- 2007

- 2008

- Assembly of
- Scientific Team

- SNP Selection Strategy
- and Analysis Plan

- Request for Proposal
- Choice of SNPs and
- Genotype Platform
- Selection

- Whole Genome Scan*

- Quality Control/
- Analysis of Scan

- Conduct Serial Replication Studies**

*Study 1*

*Study 2*

*Study 3*

*Study 4*

- Haplotype analysis of regions of interest

-  Note:   Breast cancer scan will begin approximately 4 months later and be completed within 36 months of the start of the prostate scan
-           * Whole genome scan of prostate will be performed in two parts
-          ** Timing and specific studies will depend upon technical throughput and cost- Executive summaries will be posted within 4 months of completion

- Preparation
- for caBIG

- Delivery
- to caBIG

## Whole Genome Scans

- Statistical Issues
- Primary scan
- Trade-off between size and detectable effect
- Replication plan
- Sufficiently powered to retain true positives
- Data availability
- Public access policy
- Public Tools
- Common Database Structure
- Consortial/Collaborative Efforts

## Comparison of HapMap 1 and HapMap 2for CEU MAF>5%

## Thinking about Copy Number Polymorphisms...

*C Lee 2005*

**Notes:**
a) Mutation path going from a unique sequence to a polymorphic multi-copy sequence. Arrows depict mutation (mut), fixation, duplication (dup) and gene conversion (g.c) events. Boxes show genotypes for each state. All events are reversible through loss of duplication and/or gene conversion (not shown).b) Patterns representative of DASH genotyping in normal DNA for the corresponding genotypes in a). Each line shows the negative derivative of the melting curve of a probe/target duplex for one sampled individual. The temperature on the X-axis ranges from 50-75 0C. Black arrows indicate melting points for the different alleles observed in each experiment. Pattern 3 shows the genotype for a normal SNP, with homozygous and heterozygous individuals. Patterns 4 – 7 are specific for SNPs in duplicated regions and are never observed for SNPs in unique DNA sequence. In pattern 5, all individuals are heterozygous for the interrogated sequence. Pattern 4 is similar to pattern 3, but shows an additional polymorphic component – allele copy number, evident in the shifting of peak heights. 

## 2-stage WGS strategy Power as a function of MAF and  sample sizes

**Disease model**
- - Prevalence 1%
- - Single susceptibility SNP  with a linkage
-   disequilibrium  r2 = 0.8 with 1 genotyped SNP
- - Dominant transmission
- - Genotype relative risk : 1.5

**Study design**
- # Cases = # Controls
- # Cases in stage 1 : **as indicated**
- # SNPs in stage 1 : 500,000
- # Cases in stage 2 :  **2 X # in stage 1**
- # SNPs in stage 2 : 25,000
- Significance level 0.00002

-  Note: For significance level = 0.00002 => 10 false positives

**0.05**

- MAF

- Power
- %

**1200 cases**

**600 cases**

**300 cases**

*Skol 2006*

## Challenges of Keeping Pace with Evolving Genotyping and Sequencing Technologies

**Stephen Chanock, M.D.**
- Senior Investigator, POB,CCR &
- Director, Core Genotyping Facility, NCI