Selection of Candidate Genes for Population Studies

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Selection of Candidate Genes for Population
Studies

• Zuo-Feng Zhang, MD, PhD
• Epidemiology 243 Molecular Epidemiology

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Gene Selection for Molecular Studies

• Selection of putative genetic factors is the
  central issue of the molecular epidemiological
  studies even thought the selection of the
  putative risk factors are equally important
  because of the focus of the molecular
  epidemiology is the assessment of
  gene-environment interaction

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Two Types of Genes

• High Risk Genes
• Low Risk Genes

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Familiar Disease Genes (High Risk Gene)

• -High penetrance
• -High AR/RR
• -Gene frequency low (lt1)
• -Study setting family
• -Study type Linkage
• -PAR low
• -Role of Environment Modest

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Example of High Risk Genes

• Mutations of TP53 gene
• BRCA1 and BRCA2
• RB gene mutations

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Susceptibility Genes (Low Risk Genes)

• -Low penetrance
• -Low AR/RR
• -Gene frequency high (gt1-90)
• -Study setting population
• -Study type association
• -PAR high
• -Role of Environment critical

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Approach for High Risk Genes

• Functional approach (forward genetics) from
  genotype to phenotype
• Positional approach (reversed genetics) from
  phenotype to genotype

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Functional Approach An Example

• From patients with DNA repair defects
• a cell line is created
• Add certain fragment of human chromosome
• Produce a repair component phenotype

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Positional Approach

• Linkage analysis
• Loss of heterozygosity (LOH)
• Chromosome abnormalities

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Linkage analysis

• It is method to identify the disease loci
• Family based, need sufficient sample size
• Germline DNA from affected and unaffected
  individuals
• A genetic mechanism (autosomal dominant/recessive)
  
• A set of markers

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Loss of Heterozygosity (LOH)

• Need both normal and tumor tissues
• The loss of signal in targeted tissue (tumor) in
  comparison with normal tissue
• If LOH consistently observed in a particular
  region, an indication of an important gene is
  indicated in the region.

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Chromosome Abnormalities

• Deletion
• Insertion
• Microsatellite instability

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In-depth Approaches to Identify Candidate Genes

• When above three methods indicate a region in
  chromosome, further work is needed to identify
  particular candidate genes
• -Mutation screening
• -restoration of normal phenotype by transfection
  of a normal allele
• -mouse model of disease by introducing defective
  mutations

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Approaches for Low Risk Genes

• Linkage analysis may not be feasible because it
  requires a relatively large sample size (If the
  OR2, 2500 family would be needed)

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Approaches for Low Risk Genes

• New techniques will be needed to identify the low
  risk susceptibility genes
• -Automated micro-array genechips
• -SNP identification

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Selection of Putative Genes (1)

• Inter-individual variation in the trait exist in
  the population
• -If there is very small variation of the
  phenotype in the population, the rationale to
  examine the genotype is weak.
• -If there is a very large variation of the
  phenotype, other potential factors need to be
  considered

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Selection of Putative Genes (2)

• The gene is involved in the process related to
  carcinogenesis
• -DNA repair
• -Chromosome stability
• -Activities of oncogenes/tumor suppressor genes
• -cell cycle control/signal tranduction

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Selection of Putative Genes (3)

• The trait exhibits an inheritance pattern
  consistent with Mendelian transmission
• Any phenotype should have a genetic basis

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Selection of Putative Genes (3)

• Certain phenotypes such as mutagen sensitivity
  has been reported to be associated with many
  smoking related cancers, however, the precise
  nature of this susceptibility factor remains
  incompletely understand because the genotype
  associated with mutagen sensitivity is still
  unclear.

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Selection of Putative Genes (4)

• Gene action exists in relevant organ.
• -CYP1A1 is largely absent from liver, but
  present in lung
• -CYP2D6 is expressed in brain
• -GSTM1 has some expression in lung
• -GSTP1 is expressed in lung

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Selection of Putative Genes (5)

• Gene location and characterization.
• -Similar gene structure may indicate similar
  function
• -Most of mutations occur in the coding sequence,
  but mutations in intragenic noncoding may occur
• -Specific point mutation may indicate specific
  exposures

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Selection of Putative Genes

• Polymorphisms and mutation
• Gene-Gene interactions
• Animal models
• Human studies
• Genotype-phenotype
• Relation to disease
• Ethnic variation

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Selection of Putative Genes

• Gene-Gene interaction (phase I and phase II).
• -CYP1A1 and GSTM1 and lung cancer risk, PAH
  (carcinogens)
• -CYP2A6 and CYP2D6, NNK

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2-1. BackgroundThe summary of characteristics
and significance of the genes of interest.
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2-1. Background Theoretical model of
gene-gene/environmental interaction pathway
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2-1. Background Theoretical model of
gene-gene/environmental interaction pathway
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2-1. Background Theoretical model of
gene-gene/environmental interaction pathway
GSTM1
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2-1. Background Theoretical model of
gene-gene/environmental interaction pathway
DNA damage repaired
Defected DNA repair gene
If DNA damage not repaired
If loose cell cycle control
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DNA damage repaired
Defected DNA repair gene
If DNA damage not repaired
If loose cell cycle control
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2-1. BackgroundThe summary of epidemiological
literature for the genes of interest
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UCLA Prostate Cancer SPORE Development
ProjectSingle Nucleotide Polymorphisms (SNPs) of
Genes in the DNA Double Strand Break Repair
(DSBR) Pathways and Risk of Prostate Cancer, A
Preliminary Study

• Zuo-Feng Zhang, MD, PhD
• Department of EpidemiologyUCLA School of Public
  Health

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Epidemiological Observations Involvement of DSBR
Pathway Genes in Prostate Cancer Risk

• The risk of prostate cancer is known to be
  elevated in carriers of germline mutations in
  BRCA2
• Increased risk of prostate cancer is also
  observed in carriers of BRCA1 and CHEK2
  mutations, and also associated with SNPs of the
  ATM genes
• Those observations indicate possible involvement
  of DNA DSBR pathway genes

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Non-homologous Recombination
homologous recombination
BRCA1
BRCA2
Damage recognition cell cycle delay response
(DRCCD )
ATM
CHEK2(RAD53
BRCA1
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Hypotheses

• Single Nucleotide Polymorphisms (SNPs) of genes
  in the DNA Double Strand Break Repair (DSBR)
  Pathways may be associated with the
  susceptibility to prostate cancer.
• We further hypothesize that the SNPs of the DSBR
  may interplay each other and may modify effects
  of environmental factors on the risk of prostate
  cancer.

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Specific Aim 1

• To assay Single Nucleotide Polymorphisms (SNPs)
  of genes in double strand break (DSB) repair
  pathway, including genes involved in Homologous
  Recombinational Repair (HRR) RAD51, RAD52,
  RAD54L, NBS1, XRCC2, XRCC3, BRCA1, and BRCA2
  LIG4, and XRCC4 in Non-homologous end-joining
  (NHEJ), ATM, BRCA1, CHEK1, CHEK2 (RAD53), P53,
  and HUS1 in damage recognition cell cycle delay
  response (DRCCD) pathway.

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Specific Aim 2

• To evaluate independent effect of SNPs of the DSB
  repair pathway when potential confounding
  factors, such as age, race, and education and to
  assess potential combined effects of SNPs
• To explore possible effect modifications on
  nutritional factors on the risk of prostate
  cancer.

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Proposed Experimental Approach

• This study is based on a case-control study with
  a total of 122 cases with prostate cancer and 135
  healthy controls. All cases and controls were
  interviewed by a research nurse using a standard
  epidemiological questionnaire at MSKCC from 1993
  to 1997.
• Blood samples and tumor tissue specimens were
  collected.
• The SNPs will be genotyped in individual DNA
  samples using the SNPlex platform by ABI. The
  UCLA Sequencing and Genotyping Core Facility has
  recently added Applied Biosystems
  high-throughput SNP genotyping assay SNPlex
  to the available services. This assay is
  flexible, robust and highly reproducible.

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JCCC Genotyping Core ABI SNPlex, a New High
Throughput Approach to Identify SNPs of
Susceptibility Genes
www.genetics.ucla.edu/genotyping
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Zhang Lab SNP GenotypingPilot Project

• 75 passed design process
• 48 SNPs chosen for first pool
• Whole Genome Amplification of DNA for 3080
  samples
• 122,496 SNPs since genotyped since January

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Preliminary Results

• 99.4 reproducibility by automated scoring.
• 99.7 reproducibility by manual scoring.
• 6 SNPs never worked
• 96 call rate of remaining markers
• Comparable to results reported by other labs

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Study Population
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Progress of the Study

• Specific Aim 1, we have assayed selected single
  nucleotide polymorphisms (SNPs) of genes in
  double strand break repair (DSBR) pathway,
  including genes BRCA1, NBS1, TP53, APEX1, CHEK1,
  CHEK2, and ATM in 68 cases with prostate cancer
  and 90 healthy male controls using ABI SNPlex
  platform.

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Progress of the Study

• Specific Aim 2, we explored independent effect of
  SNPs of the genes mentioned above in the DSBR
  pathway when potential confounding factors, such
  as age, race, and education were controlled.

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Results of Preliminary study

• The adjusted ORs are
• 4.6 (95CI 0.6-34.1) for BRCA1 (rs8176109) 5.0
  (95 CI 1.1-22.3) for NBS1 (rs9995)
• 3.1 (95CI 0.46-21.2) for TP53 (rs2909430)
• 2.0 (95CI 0.49-8.02) for APEX1 (rs3136820) 2.6
  (95CI 0.58-11.6) for CHEK1 (rs506504)
• 0.6 (95CI 0.17-2.3) for ATM (rs228591).

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Future Plan

• We will continue our proposed specific aims by
  assaying additional SNPs in the DSBR pathway
  genes as well as other pathways including other
  DNA repair pathways, metabolic, inflammatory, and
  cell cycle pathways among prostate cancer cases
  and controls. We will explore the independent
  effect of those SNPs on the risk of prostate
  cancer. We will also add the haplotype tagging
  SNPs of the DSBR pathways in order to identify
  haplotypes associated with prostate cancer risk.
  Those additional studies will have a greater
  impact on the translational research objectives
  of the SPORE.

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BRCA1 Haplotypes and Risk of Prostate Cancer
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Translational Potential of the Study

• Our results with relatively small samples size
  suggest potential involvement of SNPs of the DSBR
  pathway genes in the development of prostate
  cancer.
• If confirmed by studies with larger sample size,
  SNPs in DSBR pathway genes may be used in
  individual risk assessment, and identification of
  high risk population for intervention and
  chemoprevention

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The Selection Criteria of SNPs

• functional SNPs if possible
• amino-acid-changing SNPs
• SNPs in the functional region of the gene or SNPs
  without amino acid changes that were hypothesized
  to affect the transcription/ translation of the
  protein
• the rare allele frequency of SNPs must be equal
  to or higher than 5 in the general population

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Proposed Study 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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Issues in genetic association studies

• Many genes
• 25,000 genes, many can be candidates
• Many SNPs
• 10,000,000 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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Selection of SNPs(Genome-wide association
studies)

• Molecular
• Higher requirements Affymetrix and Perlegen
• 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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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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LIG SNP and Passive Smoking
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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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1040 controls, 601 head and neck cancer cases,
and 611 lung cancer cases
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GSTM1 and Lung Cancer among Non-Smokers
1.19 0.69-2.03
GSTM1 Normal
Null Smoking No
No
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GSTT1 and Lung Cancer among Non-Smokers
1.53 0.83-2.81
GSTT1 Normal Null
Smoking No
No
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p53 codon 72 and Lung Cancer among non-smokers
0.79 0.32-1.95
p53 A/A or A/P P/P Smoking No
No
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GSTP1 and Lung Cancer among Non-Smokers
0.69 0.39-1.24
GSTP1 Ile/Ile Any Val
Smoking No
No
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