Genome-Wide Association Studies (GWAS)

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Genome-Wide Association Studies (GWAS)

• Epidemiology 243
• Molecular Epidemiology of Cancer
• Spring 2008

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Association Studies of Genetic Factors

• 1st generation
• Very small studies (lt100 cases)
• Usually not epidemiologic study design 1-2 SNPs
• 2nd generation
• Small studies (100-500 cases)
• More epi focus a few SNPs
• 3rd generation
• Large molecular epi studies (gt500 cases)
• Proper epi design pathways
• 4th generation
• Consortium-based pooled analyses (gt2000 cases)
• GxE analyses
• 5th generation
• Post-GWS studies

Boffeta, 2007
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International Lung Cancer Consortium (ILCCO)
Wichmann
Risch
McLaughlin
Schwarts
Wild
Boffetta
Kiyohara
Harris
Brennan
Goodman
Benhamou
Wiencke
Tajima
Christiani
Zhang
Landi
Hong
Stucker
Vineis
Yang
Chen
Berwick
Lan
Lazarus
Spitz
Thun
Le Marchand

3 cohort studies 17
population based case-control studies
13 hospital based case-control studies
2 studies with mixed controls
1 cross-sectional
study
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Issues in genetic association studies

• Many genes
• 25,000 genes, many can be candidates
• Many SNPs
• 12,000,000 SNPs, ability to predict functional
  SNPs is limited
• Methods to select SNPs
• Only functional SNPs in a candidate gene
• Systematic screen of SNPs in a candidate gene
• Systematic screen of SNPs in an entire pathway
• Genomewide screen
• Systematic screen for all coding changes

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Introduction

• A genome-wide association study is an approach
  that involves rapidly scanning markers across the
  complete sets of DNA, or genomes, of many people
  to find genetic variations associated with a
  particular disease.
• Once new genetic associations are identified,
  researchers can use the information to develop
  better strategies to detect, treat and prevent
  the disease. Such studies are particularly useful
  in finding genetic variations that contribute to
  common, complex diseases, such as asthma, cancer,
  diabetes, heart disease and mental illnesses.

http//www.genome.gov/20019523
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Definition of GWAS

• A genome-wide association study is defined as
  any study of genetic variation across the entire
  human genome that is designed to identify genetic
  associations with observable traits (such as
  blood pressure or weight), or the presence or
  absence of a disease (such as cancer) or
  condition.

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Potential of GWAS

• Whole genome information, when combined with
  epidemiological, clinical and other phenotype
  data, offers the potential for increased
  understanding of basic biological processes
  affecting human health, improvement in the
  prediction of disease and patient care, and
  ultimately the realization of the promise of
  personalized medicine.
• In addition, rapid advances in understanding the
  patterns of human genetic variation and maturing
  high-throughput, cost-effective methods for
  genotyping are providing powerful research tools
  for identifying genetic variants that contribute
  to health and disease.

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Potential of GWAS
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Selection of SNPs(Genome-wide association
studies)

• Molecular
• Higher requirements Affymetrix and Illumina
• Analytical
• Highest requirements Data management, automation
  
• Advantages
• No biological assumptions and can identify novel
  genes/pathways
• Excellent chance to identify risk alleles
• Utility in individual risk assessment
• Disadvantages
• High costs
• Concern of multiple tests

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SNP Selection
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SNP Selection
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Affymetrix Genome-Wide Human SNP Array

• The new Affymetrix Genome-Wide Human SNP Array
  6.0 features 1.8 million genetic markers,
  including more than 906,600 single nucleotide
  polymorphisms (SNPs) and more than 946,000 probes
  for the detection of copy number variation. The
  SNP Array 6.0 represents more genetic variation
  on a single array than any other product,
  providing maximum panel power and the highest
  physical coverage of the genome.

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The need for GWA

• Current understanding of disease etiology is
  limited
• Therefore, candidate genes or pathways are
  insufficient
• Current understanding of functional variants is
  limited
• Therefore, the focusing on nonsynonymous changes
  is not sufficient
• Results from linkage studies are often
  inconsistent and broad
• Therefore, the utility of identified linkage
  regions is limited
• GWA studies offer an effective and objective
  approach
• Better chance to identify disease associated
  variants
• Improve understanding of disease etiology
• Improve ability to test gene-gene interaction and
  predict disease risk

Xu JF, 2007
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GWA is promising

• Many diseases and traits are influenced by
  genetic factors
• i.e., they are caused by sequence variants in the
  genome
• Over 12 millions SNPs are known in the genome
• i.e., some SNPs will be directly or indirectly
  associated with causal variants
• The cost of SNP Genotyping is reduced
• i.e., it is affordable to genotype a large number
  of SNPs in the genome
• Large numbers of cases and controls are available
• i.e., there is statistical power to detect
  variants with modest effect
• When the above conditions are met
• associated SNPs will have different frequencies
  between cases

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GWA is challenging

• Many diseases and traits are influenced by
  genetic factors
• But probably due to multiple modest risk variants
• They confer a stronger risk when they interact
• True associated SNPs are not necessary highly
  significant
• Too many SNPs are evaluated
• False positives due to multiple tests
• Single studies tend to be underpowered
• False negatives
• Considerable heterogeneity among studies
• Phenotypic and genetic heterogeneity
• False positives due to population stratification

Xu, 2007
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Genome coverage

• Two major platforms for GWA
• Illumina HumanHap300, HumanHap550, and
  HumanHap1M
• Affymetrix GeneChip 100K, 500K, 1M, and 2.3M
• Genome-wide coverage
• The percentage of known SNPs in the genome that
  are in LD with the genotyped SNPs
• Calculated based on HapMap
• Calculated based on ENCODE

Xu, 2007
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Strategies for pre-association analysis

• Quality control
• Filter SNPs by genotype call rates
• Filter SNPs by minor allele frequencies
• Filter SNPs by testing for Hardy-Weinberg
  Equilibrium

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Data Analysis

• Single SNP analysis using pre-specified genetic
  models
• 2 x 3 table (2-df)
• Additive model (1-df), and test for additivity
• All possible genetic models (recessive, dominant)
  

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Data Analysis

• Haplotype analysis
• Gene-gene and gene-environment interactions
• Interaction with main effect
• Logistic regression
• Interaction without main effect data mining
• Classification and recursive tree (CART)
• Multifactor Dimensionality Reduction (MDR)

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Sample size needs as a function of genotype
prevalence and OR for main effects
Boffeta, 2007
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False Positives

• False positives too many dependent tests
• Adjust for number of tests
• Bonferroni correction
• Nominal significance level study-wide
  significance / number of tests
• Nominal significance level 0.05/500,000 10-7
• Effective number of tests
• Take LD into account
• Permutation procedure
• Permute case-control status
• Mimic the actual analyses
• Obtain empirical distribution of maximum test
  statistic under null hypothesis

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False Positives

• False discovery rate (FDR)
• Expected proportion of false discoveries among
  all discoveries
• Offers more power than Bonferroni
• Holds under weak dependence of the tests

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False Positives

• Bayesian approach
• Taking a priori into account, False-Positive
  Report Probability (FPRP)

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Confirmation in independent study populations

• The approach may limit the number of false
  positives
• Confirmation is needed to dissect true from false
  positives
• Replication, examine the results from the 2nd
  stage only
• Joint analysis, combining data from 1st stage
  with 2nd stage
• Multiple stages

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Issues of GWAS

• Population stratification
• Multiple Testing False Positives
• Gene-Environmental Interaction
• High Costs

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Kingsmore, 2008
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Kingsmore, 2008
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GWAS
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Proposed GWAS of Lung Cancer among Non-smokers
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Motives and Conceptual Framework For Study of
Genetic Susceptibility to Lung Cancer among
Non-smokers

• About 16 of the male smokers and 10 of female
  smokers will eventually develop lung cancer,
  which suggest exposures to other environmental
  carcinogens and individual genetic susceptibility
  may play an important role among non smoking lung
  cancer.
• It is suggested that 26 of lung cancer are
  associated with genetic susceptibility
  Lichtenstein P, et al. NEJM, 2000)
• We hypothesize that the variation of genetic
  susceptibility or single nucleotide polymorphisms
  (SNPs) of genes in inflammation, DNA repair, and
  cell cycle control pathways may be important on
  the development of lung cancer among non-smokers.

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DNA damage repaired
Defected DNA repair gene
If DNA damage not repaired
G0
If loose cell cycle control
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500K SNP Coverage Median intermarker distance
3.3 kb Mean intermarker distance
5.4 kb Average Heterozygosity
0.30 Average minor allele frequency
0.22 SNPs in genes 196,384 80 of genome within
10kb of a SNP
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Figure 1. The effects of SNPs on the Risk of Lung
Cancer among Smokers and Non-smokers
OR
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Hypothesis

• The overall hypothesis is that multiple sequence
  variants in the genome are associated with the
  risk of lung cancer among non-smokers.
  Specifically, we hypothesize that a number of
  common nonsmoking lung cancer risk-modifying SNPs
  are in strong LD with the SNPs arrayed on the
  500K GeneChip.

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Specific Aims

• Aim 1. To perform exploratory tests for
  association between 500K SNPs across the genome
  and lung cancer risk among 200 non-smoking lung
  cancer patients and 200 controls.
• Aim 2. To perform first stage of confirmatory
  association tests between lung cancer risk and
  more than 1,000 SNPs implicated in Aim 1 among an
  independent set of 600 pairs of cases and
  controls.

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Specific Aims

• Aim 3. To perform second stage of confirmatory
  association tests between lung cancer risk and
  more than 500 SNPs that were replicated in Aim 2
  among an additional 600 cases and 600 controls.
  Additional SNPs will also be added from our
  ongoing pathway specific analyses of DNA repair,
  cell cycle regulation, inflammation and metabolic
  pathways based on non-smokers in our lung cancer
  study.
• Aim 4. To perform fine mapping association
  studies in the flanking regions of each of the
  30-100 SNPs confirmed in Aim 3 among the entire
  1,400 cases and 1,400 controls. The large number
  of cases with non-smoking lung cancer in this
  study population also allows us to identify SNPs
  that are associated with risk of the disease
  among nonsmokers.

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Specific Aims

• Aim 5. To explore the generalizability of the
  SNPs identified in Specific Aims 1-4 within a
  Chinese population of 600 nonsmoking lung cancer
  cases and 600 nonsmoking controls. The relatively
  homogeneous Chinese population not only allows us
  to further confirm the associations, but also
  improves our ability to finely map the SNPs
  associated with lung cancer risk among
  non-smokers.

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Discussion Costs

• Affy 500 k SNP chip 1000/case
• 2000 x 10002m
• 1000 x 10001m
• 500 x 10000.5 M
• 500 x 3000 (SNP) x 0.15225, 000
• 500 x 30 (SNP) x 0.15 2,250

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