The Questions

1. The Questions • Why study haplotypes? • How can haplotypes be inferred? • What are haplotype blocks? • How can haplotype information be used to test associations with disease phenotypes? • How shall we select a subset of informative SNPs for large-scale typing? • How can haplotype information be visualized

2. Methods for inferring haplotype blocks and informative SNP selectionDetecting haplotype blocks on Chromosomes 6,21,22

3. Hypothesis – Haplotype Blocks? • The genome consists largely of blocks of common SNPs with relatively little recombination shuffling in the blocks • Patil et. al, Science, 2001; Jeffreys et al. Nature Genetics; Daly et al. Nature Genetics, 2001 • Compare block detection methods. • How well we can detect haplotype blocks? • Are the detection methods consistent?

4. Block detection methods • Four gamete test, Hudson and Kaplan,Genetics, 1985, 111, 147-164. • A segment of SNPs is a block if between every pair (aA and bB) of SNPs at most 3 gametes (ab, aB, Ab, AB) are observed. • P-Value test • A segment of SNPs is a block if for 95% of the pairs of SNPs we can reject the hypothesis (with P-value 0.05 or 0.001) that they are in linkage equilibrium. • LD-based, Gabriel et al. Science,2002,296:2225-9 • Next slide

5. Gabriel et al. method • For every pair of SNPs we calculate an upper and lower confidence bound on D’ (Call these D’u, D’l) • We then split the pairs of SNPs into 3 classes: • Class I: Two SNPs are in ‘Strong LD’ if D’u > .98 and D’l > .7. • Class II: Two SNPs show ‘Strong evidence for recombination’ if D’u < .9. • Class III: The remaining SNP pairs, these are “uninformative”. • A contiguous set of SNPs is a block if • (Class II)/(Class I + ClassII) < 5%. • Special rules to determine if 2, 3 or 4 SNPs are a block. • Furthermore there are distance requirements on the chromosome to determine if the SNPs are a block.

6. Block View

7. Block comparison

8. Conclusions • Clear evidence of “blocky” structure in Chromosomes • Different block detection methods are highly concordant. • However, boundaries defined by these methods are not sharp and we believe there is no single “true” block partition.

9. Block free SNP selection

10. What does it mean to tag SNPs? • SNP = Single Nucleotide Polymorphism • Caused by a mutation at a single position in human genome, passed along through heredity • Characterizes much of the genetic differences between humans • Most SNPs are bi-allelic • Estimated several million common SNPs (minor allele frequency >10% • To tag = select a subset of SNPs to work with

11. Why do we tag SNPs? • Disease Association Studies • Goal: Find genetic factors correlated with disease • Look for discrepancies in haplotype structure • Statistical Power: Determined by sample size • Cost: Determined by overall number of SNPs typed • This means, to keep cost down, reduce the number of SNPs typed • Choose a subset of SNPs, [tag SNPs] that can predict other SNPs in the region with small probability of error • Remove redundant information

12. What do we know? • SNPs physically close to one another tend to be inherited together • This means that long stretches of the genome (sans mutational events) should be perfectly correlated if not for… • Recombination breaks apart haplotypes and slowly erodes correlation between neighboring alleles • Tends to blur the boundaries of LD blocks • Since SNPs are bi-allelic, each SNP defines a partition on the population sample. • If you are able to reconstruct this partition by using other SNPs, there would be no need to type this SNP • For any single SNP, this reconstruction is not difficult…

13. Complications: • But the Global solution to the minimum number of tag SNPs necessary is NP-hard • The predictions made will not be perfect • Correlation between neighboring tag SNPs not as strong as correlation between neighboring (not necessarily tagged) SNPs • Haplotype information is usually not available for technical reasons • Need for Phasing

14. Tagging SNPs can be partitioned into the following three steps: • Determining neighborhoods of LD: which SNPs can infer each other • Tagging quality assessment: Defining a quality measure that specifies how well a set of tag SNPs captures the variance observed • Optimization: Minimizing the number of tag SNPs

15. Optimal Haplotype Block-Free Selection of Tagging SNPs for Genome-Wide Association Studies Halldorsson et al (2004)

16. The Definition of Perfect Prediction ofa SNP from a set of SNPs

17. A G T A A C A C Hap1 Hap2 Site # or SNP # 1 2 3 4 Predicts SNP 3 Nothing to Predict “Predict a SNP” (cont) Predicts Predicts Each of SNPs 2 and 4 Predicts each of SNPs 2 and 3 Predicts SNP 4

18. A G T A A C A C A graphical notation “ The Blue box Predicts the Green SNP”

19. Three SNPs Predicting Each Other Only one of the three needs to be typed G T A C A C Either one will do

20. A Pair of SNPs Predicting Another SNP SNPs 1 and 3 together Predict SNP 4 G T A G C T A T G G T T 1 3 2 4 No single SNP (different than SNP 4) can predict SNP 4

21. Tagging SNPs can be partitioned into the following three steps: • Determining neighborhoods of LD: which SNPs can infer each other • Tagging quality assessment: Defining a quality measure that specifies how well a set of tag SNPs captures the variance observed • Optimization: Minimizing the number of tag SNPs

22. Finding Neighborhoods: • Goal is to select SNPs in the sample that characterize regions of common recent ancestry that will contain conserved haplotypes • Recent common ancestry means that there has been little time for recombination to break apart haplotypes • Constructing fixed size neighborhoods in which to look for SNPs is not desirable because of the variability of recombination rates and historical LD across the genome • In fact, the size of informative neighborhoods is highly variable precisely because of variable recombination rates and SNP density • Authors avoid block-building by recursively creating neighborhood with help of ‘informativeness’ measure

23. Defning Informativeness: • A measure of tagging quality assessment • Assume all SNPs are bi-allelic • Notation: • I(s,t) = Informativeness of a SNP s with respect to a SNP t • i, j are two haplotypes drawn at random from the uniform distribution on the set of distinct haplotype pairs. • Note: I(s,t) =1 implies complete predictability, I(s,t)=0 when t is monomorphic in the population. • I(s,t) easily estimated through the use of bipartite clique that defines each SNP • We can write I(s,t) in terms of an edge set • Definition of I easily extended to a set of SNPs S by taking the union of edge sets • Assumes the availability of haplotype phases • New measure avoids some of the difficulties traditional LD measures have experienced when applied to tagging SNP selection • The concept of pairwise LD fails to reliably capture the higher-order dependencies implied by haplotype structure

24. Bounded-Width Algorithm: k Most Informative SNPs (k-MIS) • Input: A set of n SNPs S • Output: subset of SNPs S’ such that I(S’,S) is maximal • In its most general form, k-MIS is NP-hard by reduction of the set cover problem to MIS • Algorithm optimizes informativeness, although easily adapted for other measures • Define distance between two SNPs as the number of SNPs in between them • k-MIS can be solved as long as distance between adjacent tag SNPs not too large

25. Define • Assignment As[i] • S(As) • Recursion function Iw(s,l, S(A)) = score of the most informative subset of l SNPs chosen from SNPs 1 through s such that As described the assignment for SNP s. • Pseudocode • Complexity: O(nk2w) in time and O(k2w) in space, assuming maximal window w

26. Evaluation • Algorithm evaluated by Leave-One-Out Cross-Validation • accumulated accuracy over all haplotypes gives a global measure of the accuracy for the given data set. • SNPs not typed were predicted by a majority vote among all haplotypes in the training set that were identical to the one being inferred • If no such haplotypes existed, the majority vote is taken among all training haplotypes that have the same allele call on all but one of the typed SNPs • etc. • When compared to block-based method of Zhang: • Presumably, the advantage is due to the cost imposed by artificially restricting the range of influence of the few SNPs chosen by block boundaries • ‘Informativeness’ was shown to be a “good” measure • aligned well with the leave-one-out cross validation results • extremely close to the results of optimizing for haplotype r2

27. Premise:Informative SNP selection • Select SNPs to use in an association study • Would like to associate single nucleotide polymorphisms (SNPs) with disease. • Very large number of SNPs • Chromosome wide studies, whole genome-scans. • For cost effectiveness, select only a subset. • Closely spaced SNPs are highly correlated • It is less likely that there has been a recombination between two SNPs if they are close to each other.

28. SNP selection within blocks • Zhang et al. PNAS, 2002. • Partition chromosome into haplotype blocks. • Zhang et al. RECOMB, 2003 • H. I. Avi-Itzhak,X. Su, F. M. De La Vega, PSB, 2003 • Sebastiani et al. PNAS 2003 • Patil et al., PNAS 2002. • Within blocks one can select the SNPs that maximize entropy or diversity. • Zhang et al. AJHG 2003. • Select a minimal number of SNPs with limited resources.

29. Block free SNP selection • For each SNP define a neighborhood of predictive SNPs. • Define a measure of informativeness, how well a set of SNPs predicts a target SNP. • Maximize informativeness over all SNPs.

30. LD Graph Theory The Definition of Perfect Prediction of a SNP from a set of SNPs Combinatorial interpretations of intermediate values of D’ and r2

31. G T A A G T A C G T A A G T A C A C G G A C A T A C G G A C A T Distinguishing SNPs SNPs distinguishing every pair of haplotypes G A G A A G A A G A G C A G A T

32. G T T C G A C A A C A T A C G T A T C T A T T A G T T C G A CT A T T A A C G C G A C A A T T A Perfect Distinguishibility

33. G T A A G T T C G T A A G T T C A C G G A C A T A C G G A C A T Predictive SNPs Set of SNPs Predicts SNP s s s G T G T A C A C GA A GT C AG G A A T

34. G T T C G A C A A C A T A C G T A T C T A T T A G T T C G A CT A T T A A C G C G A C A A T T A Perfect Prediction

35. The Informativeness Duality Lemma Let M be the SNPs/Haps matrix. S be the set of SNPs (columns). H be the set of Haplotypes (rows) T a subset of S. The following are equivalent: (1) Tperfectly predicts every SNP in S (2) Tperfectly distinguishes every pair of distinct haplotypes in H

36. A G T A A C A C Hap1 Hap2 Site # or SNP # 1 2 3 4 Predicts SNP 3 Nothing to Predict “Predict a SNP” (cont) Predicts Predicts Each of SNPs 2 and 4 Predicts each of SNPs 2 and 3 Predicts SNP 4

37. s 1 0 0 1 1 1 2 3 4 5 Informativeness • Each SNP defines a partition on the set of chromosomes • Infer the value each SNP in the population. • Our goal is to infer partitions defined by each one of the SNPs. • Inferring the partition of every SNP allows us to infer any possible haplotype. 1 GGGAT 2 GCTGA 3 ACGAT 4 ACGAT 5 ACTGA

38. t s 0 1 1 1 1 0 0 1 1 1 I(s,t) Informativeness • For a SNPs, and haplotypes I, J Ds(I,J)is the event that SNP s has different alleles for haplotypes I, J • Define I(s,t) = Pr(Ds(I,J) | Dt(I,J)) • I(s,t) can be estimated from a population sample • For each SNP s, define a bipartite graph on the haplotypes • Let E(s) denote the edge set

39. The Minimum Informative SNPs problem • Given a set S of SNPs, compute • The problem is NP-complete in general • Reduction from set cover • Tractable in practice • When only nearby SNPs are used as candidates

40. Bounded Width MIS • Only neighboring SNPs inform meaningfully • SNP i can only be used to infer SNP j if there is little evidence of recombination between i and j • I(w,S,t) = Informativeness of S w.r.t t when restricted to SNPs in S that are within w/2-neighborhood of t. • (k,w)-MIS problem: • Given a set T, compute the k most informative SNPs S that minimize I(w,S,T) • (k,w)-MIS can be computed in time O(nk2w), and space O(k2w)

41. Correct imputationBlock vs. block free # correct imputations Block Free Zhang et al. #SNPs typed Perlegen dataset

42. Correlation of informativeness with imputation in leave one out studies Informativeness Leave one out Block free #SNPs Perlegen dataset

43. Haplotype blocks

44. Haplotype Blocks

45. Union of possible haplotype blocks

46. Block free – SNPs selected

47. Haplotype block tagging SNPs

48. Haplotype block tagging SNPs

49. The Definition of Perfect Prediction ofa SNP from a set of SNPs

50. A G T A A C A C Hap1 Hap2 Site # or SNP # 1 2 3 4 Predicts SNP 3 Nothing to Predict “Predict a SNP” (cont) Predicts Predicts Each of SNPs 2 and 4 Predicts each of SNPs 2 and 3 Predicts SNP 4