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Estimating and Reconstructing Recombination in Populations: Problems in Population Genomics

Estimating and Reconstructing Recombination in Populations: Problems in Population Genomics. Dan Gusfield UC Davis. Different parts of this work are joint with Satish Eddhu, Charles Langley, Dean Hickerson, Yun Song, Yufeng Wu, Z. Ding. University of Puerto Rico, Mayaguez, Feb. 22, 2007.

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Estimating and Reconstructing Recombination in Populations: Problems in Population Genomics

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  1. Estimating and Reconstructing Recombination in Populations: Problems in Population Genomics Dan Gusfield UC Davis Different parts of this work are joint with Satish Eddhu, Charles Langley, Dean Hickerson, Yun Song, Yufeng Wu, Z. Ding University of Puerto Rico, Mayaguez, Feb. 22, 2007

  2. What is population genomics? • The Human genome “sequence” is done. • Now we want to sequence many individuals in a population to correlate similarities and differences in their sequences with genetic traits (e.g. disease or disease susceptibility). • Presently, we can’t sequence large numbers of individuals, but we can sample the sequences at SNP sites.

  3. SNP Data • A SNP is a Single Nucleotide Polymorphism - a site in the genome where two different nucleotides appear with sufficient frequency in the population (say each with 5% frequency or more). Hence binary data. • SNP maps are being compiled with a density of about 1 site per 1000. • SNP data is what is mostly collected in populations - it is much cheaper to collect than full sequence data, and focuses on variation in the population, which is what is of interest.

  4. Haplotype Map Project: HAPMAP • NIH lead project ($100M) to find common SNP haplotypes (“SNP sequences”) in the Human population. • Association mapping: HAPMAP used to try to associate genetic-influenced diseases with specific SNP haplotypes, to either find causal haplotypes, or to find the region near causal mutations. • The key to the logic of Association mapping is historical recombination in populations. Nature has done the experiments, now we try to make sense of the results.

  5. Our work: Reconstructing the Evolution of SNP Sequences • I: Clean mathematical and algorithmic results: Galled-Trees, near-uniqueness, graph-theory lower bound, and the Decomposition theorem • II: Practical computation of Lower and Upper bounds on the number of recombinations needed. Construction of (optimal) phylogenetic networks; uniform sampling; haplotyping with ARGs • III: Extension to Gene Conversion • IV: Applications

  6. Perfect Phylogeny: Where it all starts

  7. The Evolution of SNP Sequencesby Point Mutations sites 12345 Ancestral sequence 00000 1 4 Site mutations on edges 3 00010 The tree derives the set M: 10100 10000 01011 01010 00010 2 10100 5 10000 01010 01011 Extant sequences at the leaves

  8. Sequence Recombination 01011 10100 S P 5 Single crossover recombination 10101 A recombination of P and S at recombination point 5. The first 4 sites come from P (Prefix) and the sites from 5 onward come from S (Suffix).

  9. A Phylogenetic Network or ARG 00000 4 00010 a:00010 3 1 10010 00100 5 00101 2 01100 S b:10010 P S 4 01101 c:00100 p g:00101 3 d:10100 f:01101 e:01100

  10. A Min ARG for Kreitman’s data ARG created by SHRUB

  11. An illustration of why we are interested in recombination:Association Mapping of Genetic Diseases Using ARGs

  12. Association Mapping • A major strategy being practiced to find genes influencing disease from haplotypes of a subset of SNPs. • Disease mutations: unobserved. • A simple example to explain association mapping and why ARGs are useful, assuming the true ARG is known. Disease mutation site 0 1 0 0 1 SNPs

  13. The single disease mutation occurs near sites 1 or 2! What part of 01100d, e, finherit? 1 2 3 4 5 d: e: f: ? ? Very Simplistic Mapping the Unobserved Mutation of Mendelian Diseases with ARGs 00000 Assumption (for now): A sequence is diseasediff it carries the single disease mutation 4 00010 a:00010 3 1 10010 00100 5 00101 2 b:10010 01100 S P S 4 c:00100 01101 P g:00101 2 d:10100 f:01101 Where is the disease mutation? e:01100 Diseased

  14. Mapping Disease Gene with Inferred ARGs • “..the best information that we could possibly get about association is to know the full coalescent genealogy…” – Zollner and Pritchard, 2005 • But we do not know the true ARG! • Goal: infer ARGs from SNP data for association mapping • Not easy and often approximation (e.g. Zollner and Pritchard) • Improved results to do Y. Wu (RECOMB 2007)

  15. Using the Network in Association Mapping • Given case-control data M, uniformly sample the minimum ARGs (in practice for small windows of fixed number of SNPs) • Build the ``marginal” tree for each interval between adjacent recombination points in the ARG • Look for non-random clustering of cases in the tree; accumulate statistics over the trees to find the best mutation explaining the partition into cases and controls.

  16. One Min ARG for the data sample Input Data 00101 10001 10011 11111 10000 00110 Seqs 0-2: cases Seqs 3-5: controls

  17. Tree Cases The marginal tree for the interval past both breakpoints Input Data 00101 10001 10011 11111 10000 00110 Seqs 0-2: cases Seqs 3-5: controls

  18. Back to Clean Algorithmic and Mathematical Results

  19. The Perfect Phylogeny Model forthe History of SNP sequences Only one mutation per site allowed. sites 12345 Ancestral sequence 00000 1 4 Site mutations on edges 3 00010 The tree derives the set M: 10100 10000 01011 01010 00010 2 10100 5 10000 01010 01011 Extant sequences at the leaves

  20. When can a set of sequences be derived on a perfect phylogeny? Classic NASC: Arrange the sequences in a matrix. Then (with no duplicate columns), the sequences can be generated on a unique perfect phylogeny if and only if no two columns (sites) contain all four pairs: 0,0 and 0,1 and 1,0 and 1,1 This is the 4-Gamete Test

  21. A richer model 10100 10000 01011 01010 00010 10101 added 12345 00000 1 4 M 3 00010 2 10100 5 Pair 4, 5 fails the four gamete-test. The sites 4, 5 ``conflict”. 10000 01010 01011 Real sequence histories often involve recombination.

  22. Network with Recombination 10100 10000 01011 01010 00010 10101 new 12345 00000 1 4 M 3 00010 2 10100 5 P 10000 01010 The previous tree with one recombination event now derives all the sequences. 01011 5 S 10101

  23. Minimizing recombinations in Phylogenetic networks Problem: given a set of sequences M, find a phylogenetic network generating M, minimizing the number of recombinations used to generate M. The minimization objective is a rough, but useful, reflection of the true number of ``observable” recombinations that have occurred in the derivation of M.

  24. Minimization is an NP-hard Problem There is no known efficient solution to this problem and there likely will never be one. What we can do: Solve special cases optimally with efficient algorithms (galled-trees); Solve small data-sets optimally with algorithms that are not provably efficient but work well in practice; Efficiently compute lower and upper bounds on the number of needed recombinations (HapBound, Shrub);

  25. Galled-Trees: an efficient special case

  26. Definition: Recombination Cycle • In a Phylogenetic Network, with a recombination node x, if we trace two paths backwards from x, then the paths will eventually meet. • The cycle specified by those two paths is called a ``recombination cycle”.

  27. Galled-Trees • A phylogenetic network where no recombination cycles share an edge is called a galled tree. • A cycle in a galled-tree is called a gall. • Problem: If Haplotype matrix M cannot be generated on a true tree, can it be generated on a galled-tree?

  28. Incompatibility Graph 4 4 3 1 3 2 5 1 s p a: 00010 2 c: 00100 b: 10010 d: 10100 2 5 s p 4 g: 00101 e: 01100 f: 01101

  29. Results about galled-trees • Theorem: Efficient (provably polynomial-time) algorithm to determine whether or not any haplotype set H can be derived on a galled-tree. • Theorem: A galled-tree (if one exists) produced by the algorithm minimizes the number of recombinations used over all possible phylogenetic-networks. • Theorem: If M can be derived on a galled tree, then the Galled-Tree is ``nearly unique”. This is important for biological conclusions derived from the galled-tree. Gusfield et al. papers from 2003-2005.

  30. Elaboration on Near Uniqueness Theorem: The number of arrangements (permutations) of the sites on any gall is at most three, and this happens only if the gall has two sites. If the gall has more than two sites, then the number of arrangements is at most two. If the gall has four or more sites, with at least two sites on each side of the recombination point (not the side of the gall) then the arrangement is forced and unique. Theorem: All other features of the galled-trees for M are invariant.

  31. Efficient Bounding Algorithms We cannot efficiently compute the exact minimum number of needed recombinations, in general, but we can efficiently compute close lower and upper bounds on the minimum number. The bounds and the computations to obtain them have many practical applications.

  32. The general composite lower bound method (S. Myers 2002) Given a set of intervals on the line, and for each interval I, a number N(I), which is a (local) lower bound on the number of recombinations needed in interval I, define Vmin as the minimum number vertical lines needed so that every interval I intersects at least N(I) of the vertical lines. Vmin is a valid lower bound on the total number of recombinations needed in the whole data. Vmin is a called a composite bound. Vmin is easy to compute by a left-to-right myopic algorithm.

  33. 8 2 2 1 2 1 3 2 The Composite Method (Myers & Griffiths 2003) 1. Given a set of intervals, and 2. for each intervalI,a numberN(I) Composite Problem: Find the minimum number of vertical lines so that everyI intersects atleast N(I) vertical lines. M

  34. Haplotype (local) Lower Bound (S. Myers) • Rh = Number of distinct sequences (rows) - Number of distinct sites (columns) -1 <= minimum number of recombinations needed (folklore) • Generally Rh is really bad bound, often negative, when used on large intervals, but Very Good when used as local bounds on small intervals with the Composite Method, and other methods.

  35. Composite Subset Method (Myers) • Let S be a subset of sites, and Rh(S) be the haplotype bound computed on the input sequences restricted to the sites in S. If the leftmost site in S is L and the rightmost site in S is R, then use Rh(S) as a local bound N(I) for interval I = [S,L]. • Compute Rh(S) on many subsets, and then solve the composite problem to find a valid composite bound.

  36. RecMin (S. Myers) • Computes local bounds using subsets of sites, but limits the size and the span of the subsets. Default parameters are s = 6, w = 15 (s = size, w = span). • Generally, impractical to set s and w large, so generally one doesn’t know if increasing the parameters would increase the composite bound. • Still, RecMin often gives a bound more than three times the HK bound. Example LPL data: HK gives 22, RecMin gives 75.

  37. Optimal RecMin Bound (ORB) • The Optimal RecMin Bound is the lower bound that RecMin would produce if both parameters were set to their maximum possible values. • In general, RecMin cannot compute the ORB in practical time. • We have developed a practical program, HAPBOUND, based on integer linear programming that guarantees to compute the ORB, and have incorporated ideas that lead to even higher lower bounds than the ORB.

  38. HapBound: The general approach For an interval of sites I, let H(I) be the largest haplotype lower bound obtained from any subset of sites in I. We have shown that we can efficiently compute H(I) by using integer linear programming. We set N(I) = H(I) in the composite method, and the resulting composite bound is the ORB.

  39. HapBound vs. RecMin on LPL from Clark et al. 2 Ghz PC

  40. Example where RecMin has difficulity in Finding the ORB on a 25 by 376 Data Matrix

  41. Constructing Optimal Phylogenetic Networks Optimal = minimum number of recombinations. Called Min ARG.

  42. Kreitman’s 1983 ADH Data • 11 sequences, 43 segregating sites • Both HapBound and SHRUB took only a fraction of a second to analyze this data. • Both produced 7 for the number of detected recombination events • Therefore, independently of all other methods, our lower and upper bound methods together imply that 7 is the minimum number of recombination events.

  43. A Min ARG for Kreitman’s data ARG created by SHRUB

  44. The Human LPL Data (Nickerson et al. 1998) (88 Sequences, 88 sites) Our new lower and upper bounds Optimal RecMin Bounds (We ignored insertion/deletion, unphased sites, and sites with missing data.)

  45. An illustration of why we are interested in recombination:Association Mapping of Complex Diseases Using ARGs

  46. Association Mapping • A major strategy being practiced to find genes influencing disease from haplotypes of a subset of SNPs. • Disease mutations: unobserved. • A simple example to explain association mapping and why ARGs are useful, assuming the true ARG is known. Disease mutation site 0 1 0 0 1 SNPs

  47. The single disease mutation occurs near sites 1 or 2! What part of 01100d, e, finherit? 1 2 3 4 5 d: e: f: ? ? Very Simplistic Mapping the Unobserved Mutation of Mendelian Diseases with ARGs 00000 Assumption (for now): A sequence is diseasediff it carries the single disease mutation 4 00010 a:00010 3 1 10010 00100 5 00101 2 b:10010 01100 S P S 4 c:00100 01101 P g:00101 2 d:10100 f:01101 Where is the disease mutation? e:01100 Diseased

  48. Mapping Disease Gene with Inferred ARGs • “..the best information that we could possibly get about association is to know the full coalescent genealogy…” – Zollner and Pritchard, 2005 • But we do not know the true ARG! • Goal: infer ARGs from SNP data for association mapping • Not easy and often approximation (e.g. Zollner and Pritchard) • Improved results to do Y. Wu (RECOMB 2007)

  49. Application: Association Mapping • Given case-control data M, uniformly sample the minimum ARGs (in practice for small windows of fixed number of SNPs) • Build the ``marginal” tree for each interval between adjacent recombination points in the ARG • Look for non-random clustering of cases in the tree; accumulate statistics over the trees to find the best mutation explaining the partition into cases and controls.

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