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Finding the Molecular Basis of Quantitative Genetic Variation

Finding the Molecular Basis of Quantitative Genetic Variation. Richard Mott Wellcome Trust Centre for Human Genetics Oxford UK. Genetic Traits. Quantitative (height, weight) Dichotomous (affected/unaffected) Factorial (blood group) Mendelian - controlled by single gene (cystic fibrosis)

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Finding the Molecular Basis of Quantitative Genetic Variation

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  1. Finding the Molecular Basis of Quantitative Genetic Variation Richard Mott Wellcome Trust Centre for Human Genetics Oxford UK

  2. Genetic Traits • Quantitative (height, weight) • Dichotomous (affected/unaffected) • Factorial (blood group) • Mendelian - controlled by single gene (cystic fibrosis) • Complex – controlled by multiple genes*environment (diabetes, asthma)

  3. Molecular Basis of Quantitative Traits QTL: Quantitative Trait Locus chromosome genes

  4. Molecular Basis ofQuantitative Traits QTL: Quantitative Trait Locus chromosome QTG: Quantitative Trait Gene

  5. Molecular Basis ofQuantitative Traits QTL: Quantitative Trait Locus chromosome SNP: Single Nucleotide Polymorphism QTG: Quantitative Trait Gene QTN: Quantitative Trait Nucleotide

  6. Association Studies • Compare unrelated individuals from a population • Phenotypes: • Cases vs Controls • Quantitative measure • Genotypes: state of genome at multiple variable locations (Single Nucleotide Polymorphism = SNP) in each individual • Seek correlation between genotype and phenotype

  7. Problems with Association Studies • Population stratification • Linkage Disequilibrium • Allele Frequencies • Multiple loci • Small Effect Sizes • Very few Successes

  8. Population Stratification • If the sampling population comprises genetically distinct sub-populations with different disease prevalences • Then - • Any variant that distinguishes the sub-populations is likely to show disease association

  9. Admixture Mapping • Population is homogeneous but each individual’s genome is a mosaic of segments from different populations • May be used to map disease loci • multiple sclerosis susceptibility • Reich et al 2005, Nature Genetics

  10. Linkage Disequilibrium Mouse

  11. Effects of Linkage Disequilibrium • Correlation between nearby SNPs • SNPs near to QTN will show association • Risk of false positive interpretation • But need only genotype “tagging” SNPs • ~ 1 million tagging SNPs will be in LD with ~50% of common variants in the human genome

  12. The Common-Disease Common-Variant Hypothesis • Says • disease-predisposing variants will exist at relatively high frequency (i.e. >1%) in the population. • are ancient alleles occurring on specific haplotypes. • detectable in an case-control study using tagging SNPs. • Alternative hypothesis says • disease-predisposing alleles are sporadic new mutations, perhaps around the same genes, on different haplotypes. • families with history of the same disease owe their condition to different mutations events. • Theoretically detectable with family-based strategies which do not assume a common origin for the disease alleles, but are harder to detect with case-control studies (Pritchard, 2001).

  13. Power Depends on • Disease-predisposing allele’s • Effect Size (Odds Ratio) • Allele frequency • Sample Size: #cases, #controls • Number of tagging SNPs • To detect an allele with odds ratio of 1.25 and with allele frequency > 1%, at 5% Bonferroni genome-wide significance and 80% power, we require • ~ 6000 cases, 6000 controls • ~ 0.5 million tagging SNPs, one of which must be in perfect LD with the causative variant • [Hirschorn and Daly 2005]

  14. WTCCCWellcome Trust Case-Control Consortium • 2000 cases from each of • Type I Diabetes • Type II Diabetes • rheumatoid arthritis, • susceptibility to TB • bipolar depression • …. and others … • 3000 common controls • 0.675 million SNPs • ~10 billion genotypes • Data expected mid 2006

  15. Mouse Models

  16. Disease studied directly Population and environment stratification Very many SNPs (1,000,000?) required Hard to detect trait loci – very large sample sizes required to detect loci of small effect (5,000-10,000) Potentially very high mapping resolution – single gene Very Expensive Animal Model required Population and environment controlled Fewer SNPs required (~100-10,000) Easy to detect QTL with ~500 animals Poorer mapping resolution – 1Mb (10 genes) Relatively inexpensive Map inHuman or Animal Models ?

  17. QTL Mapping in Mice using Inbred Line Crosses • Genetically Homozygous – genome is fixed, breed true. • Standard Inbred Strains available • Haplotype diversity is controlled far more than in human association studies • QTL detection is very easy • QTL fine mapping is hard

  18. Sizes of Mapped Behavioural QTL in rodents (% of total phenotypic variance)

  19. Physiological QTL

  20. Effect sizes of cloned genes

  21. QTL detection: F2 Intercross X A B

  22. QTL mapping: F2 Intercross X X A F1 B

  23. QTL mapping: F2 Intercross X X A F1 F2 B

  24. QTL mapping: F2 Intercross QTL +1 -1 0 0 0 +2 -2 F2 F1

  25. QTL mapping: F2 Intercross +1 -1 0 0 0 +2 -2 F2 F1

  26. QTL mapping: F2 Intercross Genotype a skeleton of markers across genome 20cM 0 0 +2 -2 F2

  27. QTL mapping: F2 Intercross AB AA AB BA AB BA AB BA AB BA BA BA BA BA BA AA BA BA BA AA 0 0 +2 -2 BB BB AB AA F2

  28. QTL mapping: F2 Intercross AB AA AB BA AB BA AB BA AB BA BA BA BA BA BA AA BA BA BA AA 0 0 +2 -2 BB BB AB AA F2

  29. Single Marker Association • Test of association between genotype and trait at each marker position. • ANOVA • F2 crosses are • good for detecting QTL • bad for fine-mapping • typical mapping resolution 1/3 chromosome – 20-30 cM

  30. Increasing mapping resolution • Increase number of recombinants: • more animals • more generations in cross

  31. Heterogeneous Stocks • cross 8 inbred strains for >10 generations

  32. Heterogeneous Stocks • cross 8 inbred strains for >10 generations

  33. Heterogeneous Stocks • cross 8 inbred strains for >10 generations 0.25 cM

  34. founders Mosaic Crosses G3 GN F20 inbreeding mixing chopping up HS, AI, outbreds F2, diallele RI (RIHS, CC)

  35. Analysis of mosaic crosses chromosome markers • Want to predict ancestral strain from genotype • We know the alleles in the founder strains • Single marker association lacks power, can’t distinguish all strains • Multipoint analysis – combine data from neighbouring markers alleles 1 1 2 1 2 1 1 1 2 2 1 2 2 1 1 1 1 2 1 1 2 1 1 1 1 1 2 2 1 2 1 2 1 1

  36. Analysis of mosaic crosses chromosome markers alleles 1 1 2 1 2 1 1 1 2 2 1 2 2 1 1 1 1 2 1 1 2 1 1 1 1 1 2 2 1 2 1 2 1 1 • Hidden Markov model HAPPY • Hidden states = ancestral strains • Observed states = genotypes • Unknown phase of genotypes • - analyse both chromosomes simultaneously • Output is probability that a locus is descended from a pair of strains • Mott et al 2000 PNAS

  37. Testing for a QTL • piL(s,t) = Prob( animal i is descended from strains s,t at locus L) • piL(s,t) calculated using • genotype data • founder strains’ alleles • Phenotype is modelled yi = Ss,tpiL(s,t)T(s,t) + Covariatesi + ei • Test for no QTL at locus L • H0: T(s,t) are all same • ANOVA • partial F test

  38. Example: Open Field Avtivity • Mouse Model for Anxiety

  39. OFA Tracking

  40. multipoint singlepoint significance threshold Talbot et al 1999, Mott et al 2000

  41. Relation Between Marker and Genetic Effect QTL Marker 2 Marker 1 No effect observable Observable effect

  42. How Much Mapping Resolution do we need?

  43. Mapping Resolution in Mouse QTL experiments • F2 • ~25-50 Mb [250-300 genes] • HS • 1-5 Mb [10-50 genes] • Need More Resolution

  44. Other Outbred Populations • Commercially available outbreds may contain more historical recombination • Potentially finer mapping resolution • How to exploit it ?

  45. MF1 Outbred Mice MF1

  46. Analysis of MF1

  47. Single Marker Analysis

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