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Research Techniques Day Friday 16 th January, 2009

Genetic Association of Disease Richard Mott Wellcome Trust Centre for Human Genetics (with some slides by Lon Cardon ). Research Techniques Day Friday 16 th January, 2009. Association Studies. Simplest design possible Correlate phenotype with genotype Candidate genes for specific diseases

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Research Techniques Day Friday 16 th January, 2009

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  1. Genetic Association of DiseaseRichard MottWellcome Trust Centre for Human Genetics(with some slides by Lon Cardon) Research Techniques Day Friday 16th January, 2009

  2. Association Studies Simplest design possible Correlate phenotype with genotype Candidate genes for specific diseases common practice in medicine/genetics Pharmacogenetics genotyping clinically relevant samples (toxicity vs efficacy) Positional cloning recent popular design for human complex traits Genome-wide association with millions available SNPs, can search whole genome exhaustively

  3. SNPs trait variant chromosome Affection Trait1…Traitn haplotypes A 10.3 75.66 A 9.9 -99 U 15.8 101.22 genotypes alleles Definitions Population Data

  4. Allelic Association SNPs trait variant chromosome Genetic variation yields phenotypic variation More copies of ‘B’ allele More copies of ‘b’ allele

  5. 2a Genotype Genetic Value BB Bb bb a d -a Biometrical Model d bb midpoint Bb BB Va (QTL) = 2pqa2 (no dominance)

  6. Simplest Regression Model of Association Yi = a + bXi + ei where Yi = trait value for individual i Xi = 1 if allele individual i has allele ‘A’ 0 otherwise i.e., test of mean differences between ‘A’ and ‘not-A’ individuals 0 1

  7. Association Study Designs and Statistical Methods • Designs • Family-based • Trio (TDT), sib-pairs/extended families (QTDT) • Case-control • Collections of individuals with disease, matched with sample w/o disease • Some ‘case only’ designs • Statistical Methods • Wide range: from t-test to evolutionary model-based MCMC • Principle always same: correlate phenotypic and genotypic variability

  8. Biometrical basis Variance model (linkage) pijk = proportion of alleles shared ibd at marker s2a = additive genetic variance parameter s2g = polygenic (residual) variance parameter s2e = environmental (residual) variance parameter Linear model (association) Likelihood Linear Model of Association (Fulker et al, AJHG, 1999)

  9. 3/5 2/6 Allele coded by CA copies 2 = CACA 6 = CACACACACACA 3/2 5/2 4/3 Disease linked to ‘5’ allele in dominant inheritance 3/5 3/2 4/5 Linkage: Allelic association WITHIN FAMILIES affected unaffected

  10. Allelic Association: Extension of linkage to the population 3/5 2/6 3/5 2/6 3/6 3/2 5/6 5/2 Both families are ‘linked’ with the marker, but a different allele is involved

  11. 3/5 2/6 3/6 5/6 Allelic Association Extension of linkage to the population 3/6 2/4 4/6 2/6 3/2 6/2 6/6 6/6 All families are ‘linked’ with the marker Allele 6 is ‘associated’ with disease

  12. Allelic Association Controls Cases 6/6 6/2 3/5 3/4 3/6 5/6 2/4 3/2 3/6 6/6 4/6 2/6 2/6 5/2 Allele 6 is ‘associated’ with disease

  13. Power of Linkage vs Association • Association generally has greater power than linkage • Linkage based on variances/covariances • Association based on means

  14. Localization • Linkage analysis yields broad chromosome regions harbouring many genes • Resolution comes from recombination events (meioses) in families assessed • ‘Good’ in terms of needing few markers, ‘poor’ in terms of finding specific variants involved • Association analysis yields fine-scale resolution of genetic variants • Resolution comes from ancestral recombination events • ‘Good’ in terms of finding specific variants, ‘poor’ in terms of needing many markers

  15. First (unequivocal) positional cloning of a complex disease QTL

  16. Future • Mechanism • finds variants not genes • variant may be distant from gene on which it acts • Rare Variants • resequencing • Very large studies • Biobank

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