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A Data Compression Problem. The Minimum Informative Subset. Informativeness -based Tagging SNPs Algorithm. Outline:. Brief background to SNP selection A block-free tag SNP selection algorithm that maximizes ‘ informativeness ’ Halldorsson et al 2004. What does it mean to tag SNPs?.

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a data compression problem
A Data Compression Problem

The Minimum Informative Subset

outline
Outline:
  • Brief background to SNP selection
  • A block-free tag SNP selection algorithm that maximizes ‘informativeness’
    • Halldorsson et al 2004
what does it mean to tag snps
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
why do we tag snps
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
what do we know
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…
complications
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
slide8
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
haplotype based tagging snps htsnps
Haplotype-based tagging SNPs: htSNPs
  • Block-Based:
    • Define blocks as as set of SNPs that are in strong LD with each other, but not with neighboring blocks
    • Requires inference on exact location of haplotype blocks
      • Recombination between the blocks but not within the blocks
    • Within each block, choose a subset of SNPs sufficiently rich to be able to reconstruct diversity of the block
    • Many algorithms exist for creating blocks… few select the same boundaries!
how do we create haplotype blocks
How do we create Haplotype Blocks?
  • Recombination-based block building algorithm:
    • Infinite sites assumption [each site mutates at most once]
    • Assume no recombination within a block
    • Implies each block should follow the four-gamete condition for any pair of sites (See Hudson and Kaplan)
  • Diversity-based test: A region is a block if at least 80% of the sequences occur in more than one chromosome.
    • Test does not scale well to large sample sizes. (See Patil et al (2001))
    • To generalize this notion, one could look for sequences within a region accounting for 80% of the sampled population that each occur in at least 10% of the sample.
  • LD-based test:
    • D’ value of every pair of SNPs within the block shows significant LD given the individual SNP frequencies with a P-value of 0.001
  • Two SNPs are considered to have a useful level of correlation if they occur in the same haplotype block [i.e. they are physically close with little evidence of recombination]. The set of SNPs that can be used to predict SNP s can be found by taking the union of all putative haplotype blocks that contain SNP s.
    • It is possible that many overlapping block decompositions will meet the rules defined by a rule-based algorithm for finding haplotype blocks
hypothesis haplotype blocks
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?
block detection methods
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
gabriel et al method

Gabriel et al. method

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.
gabriel et al method1
Gabriel et al. method

Gabriel et al. method

    • 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.
one definition of block
One definition of block

Based on the Four Gamete test.

Intuition: when between two SNPs there are all four gametes, there is a recombination point somewhere inbetween the two sites

four gamete block test
Four Gamete Block Test
  • Hudson and Kaplan 1985

A segment of SNPs is a block if between every pair of SNPs at most 3 out of the 4 gametes (00, 01,10,11) are observed.

0 0 1

0 1 1

1 1 0

1 1 1

0 0 1

0 1 1

1 1 0

1 0 1

BLOCK

VIOLATES THE BLOCK DEFINITION

finding recombination hotspots many possible partitions into blocks
Finding Recombination Hotspots:Many Possible Partitions into Blocks

A C T A G A T A G C C T

G T T C G A C A A C A T

A C T C T A T G A T C G

G T T A T A C G A C A T

A C T C T A T A G T A T

A C T A G C T G G C A T

All four gametes are present:

slide19

The final result is a minimum-size set of sites crossing all constraints.

A C T A G A T A G C C T

G T T C G A C A A C A T

Find the left-most right endpoint of any constraint and mark the site before it a recombination site.

A C T C T A T G A T C G

Eliminate any constraints crossing that site.

Repeat until all constraints are gone.

G T T A T A C G A C A T

A C T C T A T A G T A T

A C T A G C T G G C A T

tagging snps
Tagging SNPs

Only 4 SNPs are needed to tag

all the different haplotypes

A------A---TG--

G------G---CG--

A------G---TC--

A------G---CC--

G------A---TG--

ACGATCGATCATGAT

GGTGATTGCATCGAT

ACGATCGGGCTTCCG

ACGATCGGCATCCCG

GGTGATTATCATGAT

An example of real data set

and its haplotype block

structure. Colors refer to the

founding population, one

color for each founding

haplotype

optimal haplotype block free selection of tagging snps for genome wide association studies

Optimal Haplotype Block-Free Selection of Tagging SNPs for Genome-Wide Association Studies

Halldorsson, Bafna, Lippert, Schwartz, Clark, Istrail (2004)

slide22
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
finding neighborhoods
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
defning informativeness
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
bounded width algorithm k most informative snps k mis
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
slide26
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
evaluation
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
a data compression problem1
A Data Compression Problem
  • Select SNPs to use in an association study
    • Would like to associate single nucleotide polymorphisms (SNPs) with disease.
  • Very large number of candidate 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.
association studies

Control

Non-responder

Disease

Responder

Allele 0

Allele 1

Marker A:

Allele 0 =

Allele 1 =

Marker A is associated with Phenotype

Association studies
association studies2

T

T

T

C

C

T

G

G

A

G

A

G

G

A

G

G

A

A

A

A

C

A

A

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Association studies
snp selection axiom hypothesis free associations
SNP-Selection Axiom:Hypothesis-free associations
  • Due to the many unknowns regarding the nature of common or complex disease, we should aim at SNP selection that confers maximal resolution power, i.e., genome-wide SNP scans with the hope of performing hypothesis-free disease associations studies, as opposed to hypothesis-driven candidate gene or region studies.
a new measure
A New Measure

Informativeness

snp selection axiom multi allelic measure
SNP-Selection Axiom:Multi-allelic measure
  • The tagging quality of the selected SNPs should by described by multi-allelic measure; sets of SNPs have combined information about predicting other SNPs
snp selection axioms ld consistency and block freeness
SNP-Selection Axioms:LD consistency and Block-freeness

The highly concordant results of the block detection methods make the interior of LD blocks adequate for sparse SNP selection. However, block boundaries defined by these methods are not sharp, with no single “true” block partition. SNP selection should avoid dependence of particular definitions of “ haplotype block.”

a new snp selection measure informativeness
A New SNP Selection Measure: Informativeness

It satisfies the following six Axioms:

  • Multi-allelic measure
  • LD consistency: compares well with measures of LD
  • Block-freeness:independence on any particular block definition
  • Hypothesis-free associations:optimization achieves maximum haplotype resolution
  • Algorithmically sound:practical for genome-wide computations
  • Statistically sound:passes overfitting and imputation tests
slide37

0

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Informativeness

s

h1

h2

s 1 s 2 s 3 s 4 s 5

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Informativeness

s1 s2 s3 s4 s5

I(s1,s2) = 2/4 = 1/2

s 1 s 2 s 3 s 4 s 51

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Informativeness

s1 s2 s3 s4 s5

I({s1,s2}, s4) = 3/4

s 1 s 2 s 3 s 4 s 52

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Informativeness

s1 s2 s3 s4 s5

I({s3,s4},{s1,s2,s5}) = 3

S={s3,s4} is a Minimal Informative Subset

informativeness

0

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s1

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Informativeness

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Graph theory insight

Minimum Set Cover= Minimum Informative Subset

e4

s4

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Edges

SNPs

informativeness1

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Graph theory insight

Minimum Set Cover {s3, s4}= Minimum Informative Subset

e4

s4

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SNPs

Edges

validation tests on publicly accessible data
ValidationTests on Publicly-Accessible Data
  • We performed tests using two publicly available datasets:

LPL dataset of Nickerson et al. (2000):

142 chromosomes typed at 88 SNPs

Chromosome 21 dataset of Patil et al. (2001):

20 chromosomes typed at 24,047 SNPs

  • We also performed tests on an AB dataset

Most of Chromosome 22

45 chromosomes typed at 4102 SNPs

slide48

A region of Chr. 2245 Caucasian samples

Two different runs of the Gabriel el al Block Detection method +

Zhang et al SNP selection algorithm

Our block-free algorithm

block free tagging minimum informative snps
Block free taggingMinimum informative SNPs

Perlegen Data Set Chromosome 21:

20 individuals, 24047 SNPs

Block Free method

Block Method

Informativeness

Number of SNPs

block free tagging minimum informative snps1

Block-free 21

Block-free 15

With blocks

Information

fraction

#SNPS

Block free taggingMinimum informative SNPs

Lipoprotein Lipase Gene, 71 individuals, 88 SNPs

correct imputation block vs block free
Correct imputationblock vs. block free

# correct

imputations

Block Free

Zhang et al.

#SNPs typed

Perlegen dataset

slide52

Correlations of informativeness with imputation in leave one out studies

Leave one out

Informativeness

Block free

#SNPs

Perlege dataset

conclusions
Conclusions
  • Existing LD based measures are not adequate for SNP subset selection, and do not extend easily to multiple SNPs
  • The Informativeness measure for SNPs is Block-free, and extends easily to multiple SNPs.
  • Practically feasible algorithms for genome-wide studies to compute minimum informative SNP subsets
  • We are able to show that by typing only 20-30% of the SNPs, we are able to retain 90% of the informativeness.
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