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FINE SCALE MAPPING

FINE SCALE MAPPING. ANDREW MORRIS Wellcome Trust Centre for Human Genetics March 7, 2003. Outline. Introduction: fine scale mapping using high-density SNP haplotype data. Bayesian framework. Gene trees and the coalescent process. Genetic heterogeneity and shattered gene trees.

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FINE SCALE MAPPING

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  1. FINE SCALE MAPPING ANDREW MORRIS Wellcome Trust Centre for Human Genetics March 7, 2003

  2. Outline • Introduction: fine scale mapping using high-density SNP haplotype data. • Bayesian framework. • Gene trees and the coalescent process. • Genetic heterogeneity and shattered gene trees. • Markov chain Monte Carlo (MCMC) algorithm. • SNP genotype data. • Example: cystic fibrosis.

  3. Introduction • Candidate region of the order of 1Mb in length. • Refine location of putative disease locus within region. • Make use of high-density maps of single nucleotide polymorphisms (SNPs). • Type sample of affected cases and unaffected controls.

  4. Once upon a time… • Disease predisposition determined by single locus in candidate region. • Each case chromosome carries a copy of a disease allele, resulting from a single recent mutation event at disease locus. • Each control chromosome carries a copy of the ancient normal allele at the disease locus.

  5. In an ideal world… • Excess sharing of SNP haplotypes in the vicinity of the disease locus, among cases and not among controls. • Decreased probability of sharing as distance from disease locus increases. • Approximate location of disease locus inferred.

  6. Problems… • Gene tree and ancestral haplotypes are unknown. • Marker mutations lead to mismatch of alleles within preserved regions. • Multiple disease genes, multiple mutations, and dominance.

  7. Example: Cystic fibrosis (CF) • Fully penetrant recessive disorder, incidence ~1/2500 live births in white populations, less common in other populations. • Preliminary linkage analysis suggested 1.8Mb candidate region for a single CF gene on chromosome 7q31. • More recently, a 3bp deletion, ΔF508, has been identified in the CFTR gene at ~0.88Mb into the candidate region. • Now known that ΔF508 accounts for ~66% of all chromosomal mutations in individuals with CF. • Remainder of CF chromosomes carry copies of many other rare mutations in the same gene. • 23 RFLPs used to identify haplotypes in 92 control chromosomes and 94 case chromosomes, 62 of which have been confirmed to carry ΔF508.

  8. Challenges… • The ΔF508 locus does not lie at the centre of the region of high LD. • Non-ΔF508 case chromosomes are not expected to share the same founder marker haplotype. • Useful test-data set for fine-scale mapping methods…

  9. Challenges… • The ΔF508 locus does not lie at the centre of the region of high LD. • Non-ΔF508 case chromosomes are not expected to share the same founder marker haplotype. • Useful test-data set for fine-scale mapping methods…

  10. Published methods…

  11. Bayesian framework (1) • Assume disease locus exists in candidate region: aim is then to estimate its location. • Approximate the posteriordistribution of location. • Allows assignment of probabilities that disease locus lies in any particular area of the candidate region.

  12. Bayesian framework (2) • Aim is to approximate the posterior density of location of the disease locus, given SNP haplotypes in cases A and controls U, denoted f(x|A,U). • Depends on other model parameters M, including gene tree, population haplotype frequencies, etc… • Recover marginal posterior density by integration over these nuisance parameters, f(x|A,U) = ∫f(x,M|A,U)dM

  13. Bayesian framework (3) • By Bayes’ Theorem… f(x,M|A,U) = C f(A,U|x,M) f(x,M) • Normalising constant. • Likelihood of haplotype data given model parameters M and location x. • Prior density of M and x.

  14. Bayesian framework (3) • By Bayes’ Theorem… f(x,M|A,U) = C f(A,U|x,M) f(x,M) • Normalising constant. • Likelihood of haplotype data given model parameters M and location x. • Prior density of M and x.

  15. Bayesian framework (3) • By Bayes’ Theorem… f(x,M|A,U) = C f(A,U|x,M) f(x,M) • Normalising constant. • Likelihood of haplotype data given model parameters M and location x. • Prior density of M and x.

  16. Bayesian framework (3) • By Bayes’ Theorem… f(x,M|A,U) = C f(A,U|x,M) f(x,M) • Normalising constant. • Likelihood of haplotype data given model parameters M and location x. • Prior density of M and x.

  17. Control chromosomes • Assumed to carry an ancient normal allele at the disease locus. • Effects of recent shared ancestry of less importance, so simple model assumed: f(A,U|x,M) = f(A|x,M) f(U|h) • The likelihood, f(U|h), depends only on population SNP haplotype frequencies, h. • For many SNPs, the number of possible haplotypes is large, so frequencies are parameterised in terms of allele frequencies and first-order LD between pairs of adjacent loci.

  18. Gene trees • Representation of the recent shared ancestry of case chromosomes at the disease locus. • Star shaped tree: each case chromosome descends independently from founder. Assumes there is too much information in sample about ancestral recombination and mutation events. • Bifurcating tree: shared ancestral recombination and mutation events between chromosomes appear only once in their shared ancestry.

  19. Gene trees • Representation of the recent shared ancestry of case chromosomes at the disease locus. • Star shaped tree: each case chromosome descends independently from founder. Assumes there is too much information in sample about ancestral recombination and mutation events. • Bifurcating tree: shared ancestral recombination and mutation events between chromosomes appear only once in their shared ancestry.

  20. Tree specification • Topology T: the branching pattern of the tree. • Branch lengths, τ, determined by the waiting times, w, between merging events in the gene tree. • Scaled in units of 2N generations, where N is effective population size. Root Leaf nodes

  21. Prior probability model • Uniform prior probability model for population haplotype frequencies, the location of disease locus, and the effective population size. • Each gene tree topology has equal prior probability. • Prior probability model reduces to: f(x,M) = C f(w) • Need prior probability model for waiting times between merging events.

  22. The coalescent process (1) • Time between merging event from k to k-1 lineages. • Scaled in units of 2N generations. • Exponential distribution with rate k(k-1)/2.

  23. The coalescent process (1) • Time between merging event from k to k-1 lineages. • Scaled in units of 2N generations. • Exponential distribution with rate k(k-1)/2. Exponential: rate 8x7/2 = 28 Expected time: 0.0357

  24. The coalescent process (1) • Time between merging event from k to k-1 lineages. • Scaled in units of 2N generations. • Exponential distribution with rate k(k-1)/2. Exponential: rate 7x6/2=21 Expected time: 0.0476

  25. The coalescent process (1) • Time between merging event from k to k-1 lineages. • Scaled in units of 2N generations. • Exponential distribution with rate k(k-1)/2. Exponential: rate 2x1/2=1 Expected time: 1

  26. The coalescent process (2) • Assumes constant effective population size, N. • Flexible: can allow for exponential population growth and population sub-structure. • Assumes sample is ascertained at random from the population. Problem: case chromosomes ascertained because they carry a copy of the disease mutation. • Assumes sample has single common ancestor. Problem: genetic heterogeneity.

  27. The shattered coalescent model • Generalisation of the coalescent process to allow branches of the gene tree to be removed. • Introduce indicator variable, zb, for each node, b, taking the value 1 if b has a parent in the gene tree and 0 otherwise. • Allows for singleton leaf nodes, corresponding to sporadic case chromosomes, and disconnected sub-trees, corresponding to independent mutation events at the same disease locus. • Assume number of branches of gene tree not removed in the shattered coalescent process given by binomial distribution, with shattering parameterρ.

  28. Ancestral haplotypes • Haplotypes, I, carried by internal nodes of the gene tree are unknown. • To calculate posterior probability, need to integrate over distribution of possible ancestral haplotypes, which depends on gene tree and other model parameters. • Treated as augmented data in Bayesian framework: enters posterior probability through likelihood… f(x|A,U) = ∫ ∫ f(x,M,I|A,U)dMdI and… f(x,M,I|A,U) = C f(A,U,I|x,M) f(x,M)

  29. Likelihood calculations • If node has no parent in shattered gene tree, treat as a random chromosome from the population (sporadic or founder for mutation). • If node has parent in genealogy, depends on marker haplotype carried by the parental node, and the occurrence of recombination and mutation events along the connecting branch.

  30. Likelihood calculations • If node has no parent in shattered gene tree, treat as a random chromosome from the population (sporadic or founder for mutation). • If node has parent in genealogy, depends on marker haplotype carried by the parental node, and the occurrence of recombination and mutation events along the connecting branch.

  31. MCMC algorithm (1) • Need to calculate joint posterior distribution f(x,h,T,w,z,N,ρ,I|A,U). • Parameter space extremely complex, so cannot be calculated analytically. • Markov chain Monte Carlo (MCMC) algorithm approximates the posterior distribution by sampling from f(x,h,T,w,z,N,ρ,I|A,U). • Computationally intensive, but becoming more practical with improvements in computing power. • Can handle missing SNP data: treat as augmented data in the same way as ancestral haplotypes.

  32. MCMC algorithm (2) • Let S denote current set of model parameters {x,h,T,w,z,N,ρ,I}. • Propose “small” change to model parameters, S*. • Accept S* in place of S with probability f(S*|A,U)/f(S|A,U). • If S* is not accepted, the current parameter S is retained. • Initial burn-in to allow convergence of f(S|A,U) from random starting parameter set. • Subsequent sampling period, parameter set recorded every rth step of the algorithm: each recorded output represents a random draw from f(S|A,U).

  33. MCMC algorithm (3) Tree height Location ρ N 101 0.47374 2557.62766 4.24189612 10849.19083 0.78104 -1769.51173 102 0.40629 2112.19993 4.16846454 8804.63049 0.79777 -1788.66623 103 0.46534 1679.71719 4.30423786 7229.90233 0.75364 -1854.19049 104 0.48211 2229.24788 4.33740414 9669.14899 0.78009 -1763.70173 105 0.43808 2402.10599 4.29011844 10305.31919 0.82178 -1760.56671 106 0.44607 2275.33453 4.03331587 9177.14285 0.82601 -1775.90300 107 0.41822 3016.70273 4.39000994 13243.35496 0.77768 -1844.20629 108 0.40934 2534.50113 4.07270615 10322.27832 0.81590 -1861.97411 109 0.41032 3122.91416 4.25386813 13284.46504 0.82479 -1814.27448 110 0.45020 3209.14218 4.34316471 13937.83307 0.78422 -1801.44160 Log posterior probability

  34. MCMC algorithm (3) Tree height Location ρ N 101 0.47374 2557.62766 4.24189612 10849.19083 0.78104 -1769.51173 102 0.40629 2112.19993 4.16846454 8804.63049 0.79777 -1788.66623 103 0.46534 1679.71719 4.30423786 7229.90233 0.75364 -1854.19049 104 0.48211 2229.24788 4.33740414 9669.14899 0.78009 -1763.70173 105 0.43808 2402.10599 4.29011844 10305.31919 0.82178 -1760.56671 106 0.44607 2275.33453 4.03331587 9177.14285 0.82601 -1775.90300 107 0.41822 3016.70273 4.39000994 13243.35496 0.77768 -1844.20629 108 0.40934 2534.50113 4.07270615 10322.27832 0.81590 -1861.97411 109 0.41032 3122.91416 4.25386813 13284.46504 0.82479 -1814.27448 110 0.45020 3209.14218 4.34316471 13937.83307 0.78422 -1801.44160 Log posterior probability

  35. Cystic fibrosis: revisited • Assume a fixed recombination rate of 0.5cM per Mb and a marker mutation rate of 2.5 x 10-5 per locus, per generation. • Each run of MCMC algorithm begins with 20,000 step burn-in period: thrown away. • Subsequent 200,000 step sampling period, output recorded every 50th step of the algorithm: 4000 outputs. • Two analyses of CF data performed: control chromosomes (92) and (i) ΔF508 case chromosomes (62) only; (ii) all case chromosomes (94).

  36. Cystic fibrosis: summary statistics

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