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  • 1. Association Analysis University of Louisville Center for Genetics and Molecular Medicine January 11, 2008 Dana Crawford, PhD Vanderbilt University Center for Human Genetics Research
  • 2. Association Analysis Outline
    • Study Design
    • SNPs versus Haplotypes
    • Analysis Methods
    • Candidate Gene
    • Whole Genome Analysis
    • Replication and Function
  • 3. Study Design Does your trait or phenotype have a genetic component?
    • Segregation analysis
    • Recurrence risks
    • Heritability
    • Other sources of evidence for a genetic
      • component
  • 4. Classic Segregation Analysis
    • Determines if a major gene is involved
    • Compares data to Mendelian models, such as
    • Autosomal dominant
    • Autosomal recessive
    • X-linked
    • Results can be used as parameters for
    • linkage analysis (e.g. parametric LOD)
    • Subject to ascertainment bias
    Note: More complex methods needed for complex traits
  • 5. Recurrence Risks The chance that a disease present in the family will recur in that family “ Lightning striking twice” If recurrence risk is greater in the family compared with unrelated individuals, the disease has a “genetic” component Suggests familial aggregation
  • 6. Recurrence Risks Measured using the risk ratio ( λ ) Sibling risk ratio = λ s Cystic fibrosis λ s = (0.25/0.0004) = 500 Huntington disease λ s = (0.50/0.0001) = 5000 λ s = sibling recurrence risk population prevalence
  • 7. Recurrence Risks: Complex traits λ h ere is for first degree relative Merikangas and Risch (2003) Science 302:599-601.
  • 8. Heritability Think “twin studies” The proportion of phenotypic variation in a population attributable to genetic variation Quantitative traits Heritability measured as h 2 (Can also be family studies)
  • 9. Heritability and Quantitative Traits Determined by genes and environment Boys Girls Mexican Americans Blacks Whites Mexican Americans Blacks Whites Example: Height NHANES 1971-1974 versus NHANES 1999-2002 Freedman et al (2006) Obesity 14:301-308
  • 10. Heritability and Quantitative Traits Trait variation = genetic + environment Genetic variation = additive + dominant Environmental variation = familial/household + random/individual σ T 2 = σ G 2 + σ E 2 σ G 2 = σ a 2 + σ d 2 σ E 2 = σ f 2 + σ e 2 h B 2 = σ G 2 / σ T 2 Broad Sense heritability Narrow Sense heritability h N 2 = σ a 2 / σ T 2
  • 11. Heritability and Twins Studies h 2 = 2(r MZ – r DZ ), where r is the correlation coefficient Monozygotic = same genetic material = r ~ 100% Dizygotic = half genetic material = r ~ 50%
  • 12. Heritability and Twins Studies Trait r(MZ) r(DZ) Reference Cholesterol 0.76 0.39 Fenger et al SBP 0.60 0.32 Evans et al BMI 0.67 0.32 Schousboe et al Perceived pitch 0.67 0.44 Drayna et al
  • 13. Heritability: Is everything genetic? Trait r(MZ) r(DZ) Reference Vote choice 0.81 0.69 Hatemi et al Religiousness 0.62 0.42 Koenig et al
  • 14. Other Evidence For A Genetic Component Monogenic disorders Example: Phenotype of interest is sensitivity to warfarin dosing, but there are no heritability estimates Solution: Rare, familial disorder of warfarin resistance
  • 15. Other Evidence For A Genetic Component Case Reports Example: Phenotype of interest is susceptibility to Neisseria meningitidis (prevalence: 1/100,000) Solution: Case report of recurrent N. meningitidis in patient
  • 16. Other Evidence For A Genetic Component
    • Animal models
    • Biochemistry or biological pathways
    • Expression data
    • Previous genetic association studies
    Other good arguments…
  • 17. Study Design How well can you diagnose the disease or measure the trait?
    • Narrow definitions better than all-inclusive definitions
    • There are many paths that lead to the same
    • phenotype
    • Avoid misclassification and measurement error
    • Direct measurement versus recall/survey data
    • or indirect proxies
    • Be aware of age of onset
    • Can your control become a case over time?
    Arguably most important step in study design
  • 18. Target Phenotypes Disease or Quantitative trait? Carlson et al. (2004) Nature 429:446-452 MI Note: SNPs associated with quantitative traits may not be associated with clinical endpoint CRP LDL-C IL6 LDLR Acute Illness Diet
  • 19. Study Design How many cases and controls will you need to detect an association?
    • Statistical Power
    • Null hypothesis: all alleles are equal risk
    • Given that a risk allele exists, how likely is a study to reject the null?
    • Study sample size ideally determined before you begin to recruit and genotype
  • 20.
    • Statistical significance
      • Significance = p(false positive)
      • Traditional threshold 5%
    • Statistical power
      • Power = 1- p(false negative)
      • Traditional threshold 80%
    • Traditional thresholds balance confidence in results against reasonable sample size
    Study Design What are the thresholds/variables in a general power calculation? Note: Significance threshold for 1 SNP tested
  • 21. Study Design
    • Power Calculation Resources
    • Quanto (hydra.usc.edu/gxe/)
    • Supports quantitative, discrete traits (unrelated
    • and family based)
    • Genetic Power Calculator
    • (pngu.mgh.arvard.edu/~purcell/gpc/)
    • Supports discrete traits, variance components,
    • quantitative traits for linkage and
    • association studies
    • (List of other software: linkage.rockefeller.edu/soft/)
  • 22. Study Design How can you maximize power for your study?
    • Large sample size
    • Better estimate of variability or risk
    • Chance of misclassification / measurement error
    • Large genetic effect size
    • SNP risk allele with large odds ratio or explains a lot of trait variance
      • This is unknown at beginning of study
      • Risk SNP is common
          • This is unknown at beginning of study
          • Calculate power for a range of common MAFs (5-45%)
      • Genotype the risk SNP directly
      • Risk SNP is unknown at beginning of study
      • Remember tagSNPs are imperfect proxies
      • Adjust sample size by 1/r 2
  • 23. Study Design Power calculation example: Cases: Adverse reaction (wheezing) to flu vaccination Controls: Vaccinated children with no adverse reactions Calculated using Quanto 1.1.1 MAF
  • 24. Study Design Power calculation example: Immunogenicity to influenza A (H5N1) vaccine Calculated using Quanto 1.1.1
  • 25. Study Design Why are you considering an association study instead of linkage?
    • Linkage analysis is powerful for disorders with
      • Discernable pattern of inheritance
      • Rare alleles w/ large genetic effect sizes
      • High penetrance
    • Not powerful for disorders that
      • have complex pattern of inheritance
      • are common
      • many risk alleles with small effect sizes
      • have low penetrance
  • 26. Common variant/common disease hypothesis
    • Common genetic variants confer susceptibility
    • Risk-conferring alleles ancient; common across most
    • populations
    • Risk-conferring allele has small effect
    • Multiple risk alleles expected for common disease;
    • also environment
    Study Design
  • 27. Study Design Should you design a candidate gene or whole genome study?
    • Candidate gene association study
      • Interrogate specific genes or regions
      • Based on previous knowledge or
    • biological plausibility
      • Hypothesis testing
    • Whole genome association study
      • Interrogate the “entire” genome
      • No previous knowledge required
      • Hypothesis generation
  • 28. Candidate gene association studies
    • Choose gene based on previous knowledge
      • Gene function
      • Biological pathway
      • Previous linkage or association study
    • Choose DNA variations for genotyping
      • Direct association approach
      • Indirect association approach
  • 29. Direct Candidate Gene Association Study Genotype “functional” SNPs Example: Nonsynonymous SNPs Collins et al (1997) Science 278:1580-1581
  • 30. Direct Candidate Gene Association Study Problem: We don’t know what is functional and what is not functional Botstein and Risch (2003) Nat Genet 33 Suppl:228-37.
  • 31. Direct Candidate Gene Association Study What would we miss? Functional synonymous SNPs in MDR1 alter P-glycoprotein activity Komar (2007) Science 315:466-467
  • 32. Direct Candidate Gene Association Study What would we miss?
    • 99% human genome is non-coding
    • Non-coding SNPs or DNA variations in
      • Introns
      • Intergenic regulatory regions
  • 33. Indirect Candidate Gene Association Study
    • Genotype a fraction of all SNPs regardless of “function”
    • Rely on SNP-SNP correlations (linkage disequilibrium)
    • to capture information for SNPs not genotyped
    Kruglyak (2005) Nat Genet 37:1299-1300
  • 34. Indirect Candidate Gene Association Study Linkage disequilibrium (LD) r 2 = 0 SNPs are independent r 2 = 1 SNPs are perfectly correlated AND have the same minor allele frequency Measured by r 2 r 2 = [f(A 1 B 1 ) – f(A 1 )f(B 1 )] 2 f(A 1 )f(A 2 )f(B 1 )f(B 2 )
  • 35. Indirect Candidate Gene Association Study Using LD to pick “tagSNPs” CRP European-descent 10 SNPs >5% MAF CRP European-descent 4 tagSNPs r 2 >0.80
  • 36. Indirect Candidate Gene Association Study “ tagSNPs” are population specific CRP European-descent 4 tagSNPs CRP African-descent 10 tagSNPs
  • 37. Indirect Candidate Gene Association Study
    • “ tagSNPs” are
    • population specific
    • Merge sets for
    • “ cosmopolitan” set
    http://gvs.gs.washington.edu/GVS/
  • 38. Indirect Candidate Gene Association Study Multiple testing
    • Testing many SNPs for association with
    • disease status
    • No consensus on correcting p-value
      • Bonferroni
      • False Discovery Rate
    • Need to replicate findings in independent study
  • 39. Indirect Candidate Gene Association Study: Pros and Cons
    • Can interrogate all common SNPs in gene
    • SNPs must be known and genotypes available
    • to calculate LD and pick tagSNPs
    • Multiple testing within a gene
    • Limited to previous knowledge
  • 40. Whole Genome Association Study
    • Can now genotype 100K – 1 million SNPs
    • Coverage depends on platform and chip
      • tagSNPs capturing HapMap common SNPs
      • Genic SNPs overrepresented
      • Conserved non-coding SNPs represented
      • Evenly spaced across genome
    Illumina Infinium assay Affymetrix GeneChips
  • 41. Whole Genome Association Study
    • Same study design and challenges as
    • candidate gene
      • Mostly case-control (retrospective)
      • Multiple testing
    • Data storage and higher-order interaction
    • testing issues
    • Hypothesis generation tool (replication)
  • 42. Manolio et al. Nature Reviews Genetics 7 , 812 – 820 (October 2006) Case/Control Study Designs For either candidate gene or whole genome
  • 43. Study Pros Cons Case/Control Easier to collect Subject to bias Less expensive No risk estimates Case/Control Study Designs: Pros and Cons Prospective Risk estimates Harder to collect More expensive Subject to bias For rare outcomes, case/control design may be only option
  • 44. Case/Control Study Designs: Pros and Cons Types of bias
    • Bias in selection of cases
    • Those that are currently living
    • Miss fatal or short episodes of disease
    • Might miss mild diseases
    • Referral/admission bias
    • Non-response bias
    • Exposure suspicion bias
    • Family information bias
    • Recall bias
    Manolio et al. Nature Reviews Genetics 7 , 812–820 (October 2006) Often ignored in genetic association studies
  • 45. Analysis Methods Genotype QC
    • Test for departures of Hardy-Weinberg Equilibrium
    • Test for gender inconsistencies
    • Eliminate very rare SNPs (no power)
    • Eliminate SNPs with low genotyping efficiency
    • Eliminate samples with low genotyping efficiency
  • 46. Analysis Methods What statistical methods do you use to analyze your data?
    • SNP by SNP (borrowed from epidemiology)
    • Chi-square and Fisher’s exact
    • 2x2 table
    • 2x3 table
    • Logistic and linear regression
    • Covariates
    • Haplotypes
    • Haplo.stats and regression
    • Interactions
      • Traditional regression
      • MDR (Ritchie et al)
  • 47. Analysis Methods Odds ratio (OR) = ratio of odds of minor allele in Cases (A/C) and Controls (B/D) OR (A*D)/(B*C) The Case/Control Study Case Control Minor allele A B Major allele C D
  • 48. For genotypes, set homozygous for major allele (A) as “ referent” genotype, and calculate 2 odds ratios: Analysis Methods Case Control Aa A B AA C D Case Control aa A B AA C D
  • 49. Analysis Methods Case/control: Interpretation of Odds Ratio 1.0 – Referent >1.0 – Greater odds of disease compared with controls <1.0 – Lesser odds of disease compared with controls Confidence Intervals: probably contain true OR OR does not measure risk*
  • 50. Prospective cohort
    • Disease free at beginning of study
    • Followed over time for disease (“incident”)
    • Follow “exposed” and “unexposed” groups
    • Gold-standard study design
    Analysis Methods
  • 51. Analysis Methods Prospective cohort Risk Ratio (RR) = Incidence of disease in Exposed A/(A+B) or Unexposed C/(C+D) Case Control Total Exposed A B (A+B) Unexposed C D (C+D)
  • 52. Prospective Study: Interpretation of Risk Ratio 1.0 – Referent >1.0 – Risk for disease increases <1.0 – Risk for disease decreases Confidence Intervals: probably contain true RR *For rare diseases, OR ~ RR Analysis Methods
  • 53. Case/control: Matching Age Gender Race Warning: Can “over match” and miss describing an interesting factor Bad Example: Cases: Adults with heart disease Controls: Newborns without heart disease Analysis Methods
  • 54. Case/control: Stratifying Age Gender Race Warning: Need sufficient sample size to stratify or split the data into males and females Ex. Cases with heart disease Aged-matched controls without heart disease (Exposure: smoking status) Stratify for Gender Specific Risks Analysis Methods
  • 55. Problems in Case/Control genetic association studies –
    • “ Confounding” by race or
    • ancestry
    • AKA population stratification
    • Solutions:
    • Match
    • Stratify
    • Adjust (using genetic
    • markers)
    • “ Trios”
    Cardon and Palmer (2003) Lancet 361:598-604 Analysis Methods
  • 56.
    • Given
      • Height as “target” or “dependent” variable
      • Sex as “explanatory” or “independent” variable
    • Fit regression model
      • height =  *sex + 
    Analysis Methods Regression
  • 57. Analysis Methods
    • Given
      • Quantitative “target” or “dependent” variable y
      • Quantitative or binary “explanatory” or “independent” variables x i
    • Fit regression model
      • y =  1 x 1 +  2 x 2 + … +  i x i + 
    Regression
  • 58.
    • Works best for normal y and x
    • Can include covariates
    • Fit regression model
      • y =  1 x 1 +  2 x 2 + … +  i x i + 
    • Estimate errors on  ’s
    • Use t-statistic to evaluate significance of  ’s
    • Use F-statistic to evaluate model overall
    • Use R 2 to evaluate variance explained by
      • model
    Analysis Methods Regression
  • 59. Analysis Methods Coding Genotypes 0 0 0 GG 0 1 1 AG 1 2 1 AA Recessive Additive Dominant Genotype Genotype can be re-coded in any number of ways for regression analysis
  • 60.  
  • 61.  
  • 62. Example of gene-environment Interaction and traditional regression
  • 63. Analysis Methods Statistical Packages for Genetic Association Studies
    • Candidate gene association study
    • SAS/Genetics
    • STATA
    • SPSS
    • R
    • PLINK
    • Whole genome association study
    • R
    • PLINK
  • 64. Analysis Methods Whole genome in PLINK (pngu.mgh.harvard.edu/~purcell/plink/) Can adjust for population stratification Can add covariates P<5x10 -8 Genome-wide significance P=5x10 -8 Plenge et al 2007 NEJM MHC removed P<1x10 -100 P<2x10 -11
  • 65. SNPs versus Haplotypes
    • There is no right answer: explore both
    • The only thing that matters is the correlation between the assayed variable and the causal variable
    • Sometimes the best assayed variable is a SNP, sometimes a haplotype
  • 66. SNPs versus Haplotypes
    • Haplo.stats (haplotype regression)
    • Lake et al, Hum Hered. 2003;55(1):56-65 .
    • PHASE (case/control haplotype)
      • Stephens et al, Am J Hum Genet. 2005 Mar;76(3):449-62
    • Haplo.view (case/control SNP analysis)
      • Barrett et al, Bioinformatics. 2005 Jan 15;21(2):263-5.
    • SNPHAP (haplotype regression?)
      • Sham et al Behav Genet. 2004 Mar;34(2):207-14.
    Statistical Packages for Genetic Association Studies with haplotypes
  • 67. Analysis Methods
    • Multiple testing
    • Bonferroni correction
    • Too conservative b/c each SNP tested
    • may not be independent (LD)
    • How many independent tests did you do?
    • See Conneely and Boehnke AJHG (in press)
    • False Discovery Rate
    • Also has arbitrary threshold
    • Best bet is replication
  • 68. Statistical Replication CRP SNPs and CRP levels in NHANES III Crawford et al Circulation 2006; 114:2458-2465 Carlson et al. AJHG 2005 ; 77:64-77 Results Consistent with CARDIA
  • 69.
    • Statistical replication is not always possible
    • Association may imply mechanism
    • Test for mechanism at the bench
      • Is predicted effect in the right direction?
      • Dissect haplotype effects to define functional SNPs
    Functional Replication
  • 70. Functional Replication CRP Evolutionary Conservation
    • TATA box: 1697
    • Transcript start: 1741
    • CRP Promoter region (bp 1444-1650) >75% conserved in mouse
  • 71. Functional Replication Low CRP Levels Associated with H1-4
    • USF1 (Upstream Stimulating Factor)
      • Polymorphism at 1440 alters USF1 binding site
    • 1420 1430 1440
    • H1-4 gcagctacCACGTGcacccagatggcCACTCGtt
    • H7-8 gcagctacCACGTGcacccagatggcCACTAGtt
    • H5-6 gcagctacCACGTGcacccagatggcCACTTGtt
  • 72. High CRP Levels Associated with H6
    • USF1 (Upstream Stimulating Factor)
      • Polymorphism at 1421 alters another USF1 binding site
    • 1420 1430 1440
    • H1-4 gcagctacCACGTGcacccagatggcCACTCGtt
    • H7-8 gcagctacCACGTGcacccagatggcCACTAGtt
    • H5 gcagctacCACGTGcacccagatggcCACTTGtt
    • H6 gcagctacCACATGcacccagatggcCACTTGtt
    Functional Replication
  • 73. CRP Promoter Luciferase Assay Carlson et al, AJHG v77 p64 Functional Replication
  • 74. Association Analysis Outline
    • Study Design
    • SNPs versus Haplotypes
    • Analysis Methods
    • Candidate Gene
    • Whole Genome Analysis
    • Replication and Function