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SHARLEE CLIMER, ALAN R. TEMPLETON, AND WEIXIONG ZHANG ACM-BCB, NIAGARA FALLS AUGUST 2010 SplittingHeirs: Inferring Haplotypes by Optimizing Resultant Dense.

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Presentation on theme: "SHARLEE CLIMER, ALAN R. TEMPLETON, AND WEIXIONG ZHANG ACM-BCB, NIAGARA FALLS AUGUST 2010 SplittingHeirs: Inferring Haplotypes by Optimizing Resultant Dense."— Presentation transcript:

1 SHARLEE CLIMER, ALAN R. TEMPLETON, AND WEIXIONG ZHANG ACM-BCB, NIAGARA FALLS AUGUST 2010 SplittingHeirs: Inferring Haplotypes by Optimizing Resultant Dense Graphs

2 Overview Introduction Definition of haplotype inference problem Previous approaches SplittingHeirs Experimental results

3 Introduction Only 0.1% of human DNA has variation Most of this variation is due to Single Nucleotide Polymorphisms (SNPs) Most SNPs have only two variants, or alleles, within a population Broad definition of haplotype: A set of alleles for a given set of SNPs in relatively close proximity on a chromosome Image source:

4 Introduction DNA is transcribed to produce RNA RNA is translated, ultimately producing proteins Variation in non-coding regions might have an effect on regulation SNPs throughout the genome may be of interest Image source:

5 Humans are diploid  Pairs of chromosomes Common sequencing produces a meld of the two haplotypes, referred to as a genotype Computational methods used to infer a pair of haplotypes from a genotype  Phasing the genotype G C SNP1SNP2 T T G CT Introduction G T A C + C T A G C T A C + G T A G ?

6 Importance of accuracy when inferring haplotypes from genotypes  Frequently an early step in expensive and vitally important studies SNP1SNP2SNP1SNP2 C T CG CT

7 Introduction Possible to identify the separate haplotypes directly  Only feasible for very small studies Useful for testing accuracy of computational methods  Andres et al. [Genet. Epi. 2007] found computational methods had poor accuracy and confidence levels were error prone  PHASE [Stephens et al., AJHG 2001]  fastPhase [Scheet and Stephens, AJHG 2006]  HAP [Halperin and Eskin, Bioinformatics 2004]  GERBIL [Kimmel and Shamir, PNAS 2005] Errors in confidence levels suggest that the models might not fully capture biological properties

8 Problem Definition Let ‘ 0 ’ and ‘ 1 ’ represent the two possible alleles for a given SNP Haplotype represented by a string of binary values Genotype for a pair of haplotypes  ‘ 0 ’ if both alleles are ‘ 0 ’  ‘ 1 ’ if both alleles are ‘ 1 ’  ‘ 2 ’ if heterozygous G T A C C T A G

9 Problem Definition For k heterozygous sites, there are 2 k-1 feasible solutions Not apparent which solution is more likely than another Population-level characteristics  There tends to be relatively few unique haplotypes  There tends to be clusters of haplotypes that are similar to each other  Some haplotypes are relatively common

10 Problem Definition Given a set of genotypes drawn from a population: 1) Find the set of haplotypes that exist in the set 2) For each genotype, determine the pair of haplotypes that is mostly likely to exist in the given individual Image source:

11 Example g1: g2: g3: g4: g5: Example problem  5 individuals  8 SNP sites Display solutions as graphs  Each node represents a unique haplotype  Edge weight  Measure of difference between haplotypes  Set equal to the number of sites that differ between the haplotypes  Edges with smallest distances are shown

12 Example g1: g2: g3: g4: g5: Solution found by:  Clark’s Subtraction Method [Mol. Biol. And Evol. 1990]  Pure Parsimony [Gusfield, CPM’03]  EM [Excoffier and Slatkin, Mol. Biol.Evol. 1995] 5 unique haplotypes Haplotypes are not very similar to each other

13 Example g1: g2: g3: g4: g5: No Perfect Phylogeny solution Solution found by HAP 6 unique haplotypes Haplotypes are slightly more similar to each other

14 Example g1: g2: g3: g4: g5: Solution found by PHASE 9 unique haplotypes Haplotypes are more similar to each other

15 Example g1: g2: g3: g4: g5: PHASE favors pair- wise similarities Essentially evaluating a nearest-neighbor graph

16 SplittingHeirs SplittingHeirs favors cluster-wide similarities, as well as reduced cardinality Cast as a Mixed Integer Linear Program (MIP) Minimize: where d i = the weight of edge i h = the cardinality of the haplotype set u = a weighting factor

17 SplittingHeirs Enforce cluster-wide similarities by requiring a minimum density of edges in the graph Additional constraint: where e = number of edges  is a configurable parameter Can be decreased for highly diverse sample Can be increased for sample with low diversity

18 Example g1: g2: g3: g4: g5: Solution found by SplittingHeirs 8 unique haplotypes Haplotypes are quite similar to each other

19 Results Tested on 7 sets of haplotype data for which the true phase is known n is the number of individuals m is the number of sites # Ambiguous is the number of genotypes that have more than one feasible solution

20 Results

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22 Conclusions Introduced a biologically intuitive model that optimizes cluster-wide similarities and reduced cardinality Globally optimal solutions can be computed for small regions  Candidate locus studies Future work  Speed up computation  Use model to guide an approximation method Image source:

23 Acknowledgments Olin Fellowship NIH grants  P50-GM  R01-GM087194A2  U01-GM NSF grants  IIS  DBI Alzheimer’s Association grant Thanks to:  Taylor Maxwell  Gerold Jaeger


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