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Combinatorial Search CS for DiseaseAssociation:
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For tick-borne encephalitis virus-induced disease, a multi-SNP combination ... [4] Tick-borne encephalitis : 75 genotypes with 41 SNPs containing gene TLR3, PKR, ... – PowerPoint PPT presentation
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Title: Combinatorial Search CS for DiseaseAssociation:
1
Our contributions
SNP and Disease
MSC
x x 1 x x 2 x x x
0 1 1 0 1 2 1 0 2 sick
- A novel combinatorial method for finding disease-
associated multi-SNP combinations was
developed. - Multi-SNP combinations significantly associating
with diseases were found. - For Crohn's disease data (Daly, et al., 2001), a
few associated multi-SNP combinations with
multiple-testing-adjusted to p lt 0.05 were found,
while no single SNP or pair of SNPs showed
significant association. - For a dataset for an autoimmune disorder (Ueda,
et al., 2003), a few previously unknown
associated multi-SNP combinations were found. - For tick-borne encephalitis virus-induced
disease, a multi-SNP combination within a group
of genes showing a high degree of linkage
disequilibrium significantly associated with the
severity of the disease was found. - A model-fitting disease susceptibility prediction
methods based on the developed search methods
were proposed.
- SNP - single nucleotide polymorphism where two
or more different nucleotides occur in a large
percentage of population - 0 willde type/major (frequency) allele
- 1 mutation/minor (frequency) allele
- 2 heterozygous allele
- Searching for genetic risk factors for diseases
- Monogenic diseases
- A mutated gene is entirely responsible for the
disease - Complex diseases
- Affected by the interaction of multiple genes
- Significance of risk factor is usually measured
by Risk Rate or _ _ _Odds Ratio - We measure significance by the p-value of the
set of genotypes _defined by risk factor
0 1 1 1 0 2 0 0 1 sick
4 sick 1 healthy
0 0 1 0 0 0 0 2 1 sick
0 1 1 1 1 2 0 0 1 sick
check significance
0 0 1 0 1 2 1 0 2 sick
0 1 0 0 1 1 0 0 2 healthy
0 1 1 0 1 2 0 0 2 healthy
Statistical significance
- Multi-SNP combination (MSC) define a set of case
and control individuals - MSC is considered statistically significant if
the frequency of cases and controls distribution
has p-value lt 0.05 - A lot of reported findings are frequently not
reproducible on different populations. It is
believed that this happens because the p-values
are unadjusted to multiple testing
Disease-Associated Multi-SNP Combinations Search
Disease association analysis
- Given a population of n genotypes (or
haplotypes) each containing values of m SNPs from
0,1,2 and disease status (case or control) - Find all multi-SNP combinations with multiple
testing adjusted p-value of the frequency
distribution below 0.05
- Analysis of variation in suspected genes in case
and controls individuals is aimed at identifying
SNPs with considerably higher frequencies among
the case individuals than among the control
individuals - Most searches are done on a SNP-by-SNP basis
- Recently two-SNP analysis shows promising results
(Marchini et al, 2005) - Multi-SNP analyses are expected to find even
stronger disease associations - Common diseases can be caused by combinations of
several unlinked gene (SNPs) variations - We address the computational challenge of
searching for such multi-gene causal combinations
- The number of multi-SNP combinations is
infeasible high (3100 for 100 SNPs). - How to find associated multi-SNP combinations
without total checking? - Disease association analysis searches for a SNPs
or multi-SNP combinations with frequency among
cases considerably higher than among controls.
- If the reported SNP is found among 100 SNPs then
the probability that the SNP is associated with a
disease by - mere chance becomes 100 times larger
(Bonferroni). - Bonferroni is too crude (e.g., 3-SNP
combinations among 100 SNPs, p lt 0.0510-6) - We adjust resulted p-values via randomization
- Unadjusted p-value Probability of case/control
distribution in a set defined by MSC, computed by
binomial distribution - Multiple-testing adjusted p-value
randomization - Randomly permute the disease status of the
population to generate 10000 instances. - Apply searching methods on each instance to get
MSCs. - Compute the probability of MSCs that have a
higher unadjusted p-value than the observed - p-value.
- In our search we report only MSC with adjusted
p-value lt 0.05
- Combinatorial Search (CS) for Disease-Association
- checks all one-SNP, two-SNP, ..., m-SNP
case-closed MSCs - Case-closure of a MSC C is an MSC C, with
maximum number of SNPs, which consists of the
same set of cases and minimum number of controls.
- Case-closure allow finding of the statistically
significant MSC on the earlier stage of
searching. - Trivial MSCs and MSCs which coincide after
case-closure are avoided. That significantly
speedups the searching. - Faster than exhaustive search
- Finds more significant association on the early
stage of searching - Still slow for wide-genome studies
- Clustering-based Model-Fitting Algorithm for
Disease Susceptibility Prediction - For the given training dataset and tested
genotype consider two cases - tested genotype is added to the training dataset
as a sick - tested genotype is added to the training dataset
as a healthy - For the both cases obtain clustering by applying
CGS to find - the most disease-associated MSC (defines a set of
sick genotypes) - the most disease-resistant MSC (defines a set of
healthy genotypes) - Remove from the original dataset one which is
larger - Repeat this procedure until all genotypes are
removed - Predict susceptibility of the tested genotype
according to the case which has lower entropy of
clustering.
Results for Disease Susceptibility Prediction
Maximum Case(Control)-Free Cluster Problem
- Quality measure
- Find a maximum size cluster C containing only
cases or controls - Complimentary Greedy Search (CGS)
- 1. Find SNP with allele value removing a set
of genotypes with highest ratio of controls over
cases. - 2. Add the SNP to resulted MSC
- 3. Repeat 1-2 until all controls are removed.
Resultant MSC defines a subset of sick genotypes.
- 4. Adjust to multiple testing the p-value of
the resultant MSC.
- Leave-one-out cross validation results
Data Sets
- 3 Crohn's disease 387 genotypes with 103 SNPs
derived from the 616 KB region of human
Chromosome 5q31, 144 disease genotypes and 243
nondisease genotypes. (Daly et al., 2001). - 10 Autoimmune disorder 1024 genotypes with
108 SNPs containing gene CD28, CTLA4 and ICONS,
378 disease genotypes and 646 nondisease
genotypes. (Ueda et al., 2003). - 4 Tick-borne encephalitis 75 genotypes with
41 SNPs containing gene TLR3, PKR, OAS1, OAS2,
and OAS3, 21 disease genotypes and 54 nondisease
genotypes. (Barkash et al., 2006).
Disease Susceptibility Prediction Problem
- Given a sample population S (a training set) and
one more individual t?S with the known SNPs but
unknown disease status (testing individual), find
(predict) the unknown disease status - Disease Clustering Problem
- Given a population sample S, find a partition P
of S into clusters S S1?..?Sk , with disease
status 0 or 1 assigned to each cluster Si ,
minimizing entropy(P)
- Comparison of 5 prediction methods on 4 data on
all SNPs. Area under the CSPs ROC curve is 0.87
vs 0.52 under the SVMs curve
Results/comparison of searching methods
- Comparison of three methods for searching the
disease-associated and disease-resistant
multi-SNPs combinations with the largest PPV.
- Combinatorial search is able to find
statistically significant multi-gene
interactions, for data where no significant
association was detected before - Complimentary greedy search can be used in
susceptibility prediction - Optimization approach to prediction
- New susceptibility prediction is by 8 higher
than the best previously known - MLR-tagging efficiently reduces the datasets
allowing to find associated multi-SNP
combinations and predict susceptibility
for a given bound on the number of
individuals who are assigned incorrect status in
clusters of the partition P, error(P)lt ?P.
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