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Genetic research designs in the real world Vishwajit L Nimgaonkar MD, PhD University of Pittsburgh nimga@pitt.edu
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Complex disorders: models of causation Genetic factors: Several genes induce cumulative, small but discrete effects + Environmental factors: etiological role / increased variability -No Etiological Factor Necessary or Sufficient -Formal proof dependent on statistical analyses
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Factors influencing mapping efforts What is the phenotype? What polymorphisms are being used? What is the study design?
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Key phenotype issues Is the phenotype heritable? –Proportion of risk due to genetic factors? –Proportion of risk due to an individual gene (# genes?) Familial aggregation does not necessarily prove genetic etiology Can the phenotype be evaluated reliably?
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t Discrete (disease) Continuous (liability) 01 What is the phenotype? (L Almasy, PhD)
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Phenotypes Qualitative (diagnostic status) –Clinically relevant –Difficulties in delineating ‘genetic’ phenotype Quantitative (‘endophenotype’) –Heritable –Differences between cases and controls –Differences between unaffected relatives & controls –Plausible role in pathogenesis, proximate to Dx
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What polymorphisms? Single nucleotide polymorphisms: SNPs Repeat polymorphisms Insertions / deletions
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What is the study design?
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Gene mapping studies: concepts Examine correlation between genetic variation and trait of interest Significant correlation establishes genetic etiology
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Human genome: 3 billion base pairs (estimated variations = 8,000,000 – 10,000,000) Problems 1. All genetic variations unknown 2. All variants can not be evaluated
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Recombination
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Marker A2 Marker A1 Marker B1 Marker B2 *Mutation* Haplotype 1 Gene mapping concepts control case
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Recombination based gene mapping Generations: Transmission Of Disease Gene Ill Individual 1 2 3 n Transmission Of Normal Gene Healthy Individual
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Linkage / Association Linkage Association generations founder
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What is the study design? POSITIONAL CLONING Step 1: Identify large shared chromosomal segments among cases within families (LINKAGE) Step 2: Narrow the shared region using cases and controls (ASSOCIATION).
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Linkage: haplotype sharing
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Related issues Ascertainment and recruitment! Power: more is better! ‘much, much more’ preferred Design modification –Two stage design (accept lower lod cutoffs) –Covariate based analyses
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Linkage: affected sib-pairs (identity by descent) A,BA,C A,B C,D Alleles shared IBD: 01 Prevalence: 2 0.25 0.50 0.25
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ASP analysis Convenient design Concerns –Truncation of family size due to morbidity –‘True’ sibling recurrence risk –Uncertain paternity –Twinning Power: n = 400 ASPs; power > 80% for λs = 3.0 (LOD = 3)
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Quantitative trait mapping Quantitative trait analyses –Standard variance component analyses –Multipoint analyses –Sequential search strategies –Epistasis –Multivariate analyses –Bivariate analyses with diagnosis + trait
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Sample size required for 80% power to detect linkage to a QTL at a LOD of 3 (Almasy et al.)
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Associations at the population-level Generations: Transmission Of Disease Gene Ill Individual 1 2 3 n Transmission Of Normal Gene Healthy Individual
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Factors influencing associations Sample selection & size Population history (fitness, drift, migration) Features of mutations (no, age, frequency) Features of markers (informativeness, LD) Number of comparisons Ethnic admixture
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Family based associations (haplotype relative risk) A, CB, D A, BC, D Hypothetical control
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Transmission Disequilibrium Test (TDT) A1, A1 A1, A3 A1, A2A3, A4 A1, A4 A2, A1 A4, A3 A2, A2 A1, A2 AcceptRejectAccept
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Family based associations Recruitment expensive Ascertainment may be biased Easier than multiplex pedigrees Power: Issues –Uncertain paternity –Genotyping errors –Power diminishes for case-parent duos
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‘Novel’ designs Cytogenetic abnormalities Pooled DNA analyses
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Thank you!! Collaborators: –Laura Almasy, PhD –Bernie Devlin, PhD –Rodeny Go, PhD –Ruben Gur, PhD –Raquel Gur, MD, PhD
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