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How Accurate is Pure Parsimony Haplotype Inferencing

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Presentation on theme: "How Accurate is Pure Parsimony Haplotype Inferencing"— Presentation transcript:

1 How Accurate is Pure Parsimony Haplotype Inferencing
How Accurate is Pure Parsimony Haplotype Inferencing? Sharlee Climer Department of Computer Science and Engineering Department of Biology Washington University in Saint Louis Joint work with Weixiong Zhang and Gerold Jaeger RECOMB SNPs Workshop/Jan 28, 2007

2 Pure Parsimony Pure Parsimony Haplotype Inferencing (PPHI)
Find smallest set of unique haplotypes that can resolve a set of genotypes Suggested by Earl Hubbell in 2000 Cast as an Integer Linear Program (IP) by Dan Gusfield [CPM’03] Great research interest RECOMB SNPs Workshop/Jan 28, 2007

3 Overview Biological forces Haplotypes with low frequency
Define haplotype classes Data sets Characteristics of real data RECOMB SNPs Workshop/Jan 28, 2007

4 Biological forces RECOMB SNPs Workshop/Jan 28, 2007

5 Biological forces RECOMB SNPs Workshop/Jan 28, 2007

6 Biological forces RECOMB SNPs Workshop/Jan 28, 2007

7 Biological forces RECOMB SNPs Workshop/Jan 28, 2007

8 Biological forces RECOMB SNPs Workshop/Jan 28, 2007

9 Biological forces RECOMB SNPs Workshop/Jan 28, 2007

10 Biological forces RECOMB SNPs Workshop/Jan 28, 2007

11 Biological forces Relatively few unique haplotypes
Subset of haplotypes with low frequency Problems for PPHI Large number of optimal solutions True biological solution might not be parsimonious What are structural characteristics of optimal solutions? RECOMB SNPs Workshop/Jan 28, 2007

12 Classes of haplotypes Set of possible haplotypes is exponentially large Partition similar to Traveling Salesman Problem Backbone haplotypes Appear in every optimal solution Fat haplotypes Do not appear in any optimal solution Fluid haplotypes Appear in some, but not all, optimal solutions RECOMB SNPs Workshop/Jan 28, 2007

13 Backbone haplotypes Implicit backbones Explicit backbones
All haplotypes that resolve unambiguous genotypes Explicit backbones Can identify by solving at most one IP for each haplotype in solution that isn’t implicit backbone RECOMB SNPs Workshop/Jan 28, 2007

14 Backbone haplotypes h3 h7 h15 h27 h39 h50 h55 h79 h91 bb bb bb bb
RECOMB SNPs Workshop/Jan 28, 2007

15 Backbone graph RECOMB SNPs Workshop/Jan 28, 2007

16 Backbone graph RECOMB SNPs Workshop/Jan 28, 2007

17 An optimal solution RECOMB SNPs Workshop/Jan 28, 2007

18 Low frequency haplotype
RECOMB SNPs Workshop/Jan 28, 2007

19 Low frequency haplotype
RECOMB SNPs Workshop/Jan 28, 2007

20 Low frequency haplotype
RECOMB SNPs Workshop/Jan 28, 2007

21 Low frequency haplotype
RECOMB SNPs Workshop/Jan 28, 2007

22 Data sets 7 true haplotype data sets Orzack et al.[Genetics, 2003]
80 genotypes 9 sites ApoE Andres et al. [Genet. Epi., in press] 6 sets of complete data 39 genotypes 5 to 47 sites KLK13 and KLK14 RECOMB SNPs Workshop/Jan 28, 2007

23 Data sets HapMap data [Nature 2003, 2005] Phase unknown
Random instance generator 20 unique genotypes 20 sites Three populations CEU YRI JPT+CHB 22 chromosomes RECOMB SNPs Workshop/Jan 28, 2007

24 Size of haplotype backbone
RECOMB SNPs Workshop/Jan 28, 2007

25 Number of fluid haplotypes in each solution
RECOMB SNPs Workshop/Jan 28, 2007

26 Number of optimal solutions
RECOMB SNPs Workshop/Jan 28, 2007

27 Number of fluid haplotypes and solutions
RECOMB SNPs Workshop/Jan 28, 2007

28 Biological correctness
Data set # gen. # sites # BB hap. #fluid hap. # opt. sols. Avg. distance to real A 30 9 15 1 8 B 10 5 7 C 18 17 3 16 7.5 D 6 4 2.5 E 23 26 >1000 4.33 F 22 12 630 28.24 G 35 47 10.95 RECOMB SNPs Workshop/Jan 28, 2007

29 Biological correctness
Data set Parsimony # of haplotypes True # of haplotypes A 15 17 B 7 C 12 D E 16 F 18 G 28 32 RECOMB SNPs Workshop/Jan 28, 2007

30 Biological correctness
Accuracy of backbone haplotypes Two data sets (F and G) had errors One parsimony backbone haplotype not in real solution RECOMB SNPs Workshop/Jan 28, 2007

31 Number of solutions vs. number of genotypes
RECOMB SNPs Workshop/Jan 28, 2007

32 Conclusions Biological forces tend to minimize cardinality, but also create low frequency haplotypes Low frequency in unique genotypes might not be low frequency in full set Low frequency haplotypes Large number of optimal solutions True solution not necessarily parsimonious Combinatorial nature can lead to errors in backbones Parsimony combined with other biological clues RECOMB SNPs Workshop/Jan 28, 2007

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