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Standards for SNPs Analysis with
Decision Trees Tools.
Linda Fiaschi
Supervisors:
Jon Garibaldi
Natalio Krasnogor
IMA Seminar 24/02/2009
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Outline
• Genetic background and clinical objectives
• Disease : Pre-eclampsia
• Method of analysis
• My Methodology: ADTree, C4.5, ID3
• Results
• Conclusions
• Future Work
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Genetics : SNPs
• The DNA of most people is 99.9 percent the
same.
• Single Nucleotide Polymorphisms (SNPs) are
DNA sequence variations that occur when a single
nucleotide (A,T,C,or G) is changed, which occur
approximately once every 100 to 300 bases
• The resulting different forms of the same gene
are called Alleles. People can have two identical
or two different alleles for a particular gene.
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Clinical objectives on SNPs
• The majority have no effect, others cause subtle differences in
countless characteristics, like appearance.
• Genetic factors may also confer susceptibility or resistance to a
disease and determine the severity or progression of disease
• Genetic factors also affect a person's response to drug therapy
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Disease: Pre-eclampsia
• It occurs during pregnancy and the postpartum
period and affects both the mother and the unborn baby.
• Affecting at least 5-8% of all pregnancies, it is a rapidly progressive
condition characterized by high blood pressure and the presence of
protein in the urine.
• Pre-eclampsia and other hypertensive disorders of pregnancy are
a responsible for 76,000 deaths globally each year.
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Case-Control Analysis
Case-control studies use patients who already have a disease or
other condition and look back to see if there are characteristics of
these patients that differ from those who don’t have the disease.
Comparison
Cases: Sick
Controls: Healthy
Classification
Rules
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Decision Tree Analysis
•
One of the most widely used and practical forms of machine
learning and data mining
•
It assigns a class to an input pattern through tests
• Test: has mutually exclusive and exhaustive outcomes
• Test: is either multivariate or univariate
• Attributes: is categorical or numeric
• Tree: 2 classes (Boolean) or more.
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ADTree Algorithm
•
They are a natural generalization of
decision trees
•
They are competitive with other
boosted decision tree algorithms
•
The rules are usually smaller in size
and easier to interpret
•
In addition to classification they give
a measure of confidence
• For each instance there is a multi-path:
the sum of all the prediction nodes gives
the classification
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ID3 Algorithm
Gain measures how well a given attribute separates training
examples into targeted classes.
Gain(S, A) = Entropy(S) – Σ((|Sv| / |S|) * Entropy(Sv) )
S is each value v of all possible values of attribute A
Sv = subset of S for which attribute A has value v
|Sv| = number of elements in Sv
|S| = number of elements in S
Entropy(S) = Σ((-p(I) log2 p(I))
- S is a collection of c outcomes
- Σ is over c.
- p(I) is the proportion of S belonging to class I.
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ID3 Algorithm Example
Delivery week
< 35.5
Liver measures
<94
1(15\4)
>=94
>= 35.5
Systolic Pressure
<152.5
>=152.5
0(25\0)
1(9\1)
Age
<36.3
1(26\2)
>=36.3
0(31\0)
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From ID3 to C4.5 Algorithm
•
Handling both continuous and discrete attributes
•
Handling training data with missing attribute values
•
Pruning trees after creation
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Methodology
A progressive analysis: detection of significant results deepened and
confirmed in the subsequent analysis.
Pre-processing of the Data
Data Analysis
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Pre-processing
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A
Data Analysis
Statistical Significance
Kappa Value:
proportion of
agreement
corrected for
chance between
two judges
assigning cases to
a set of categories
Kappa[8] Agreement
<0
No agreement
0.0-0.2
Slight
0.2-0.4
Fair
0.4-0.6
Moderate
0.6-0.8
Substantial
0.8-1.0
Almost perfect
A
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Experimental Dataset
4529 Patients
Genotype: 52 SNP attributes
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•
•
•
•
•
•
AGT gene: SNPs 1-8, alleles 1 and 2
AGTR1 gene: SNPs 9-12, alleles 1 and 2
TNF gene: SNPs 13-16, alleles 1 and 2
F5 gene: SNP 17, alleles 1 and 2
NOS3 gene: SNPs 18-22 and 24, alleles 1 and 2
MTHFR gene: SNPs 25, 26, alleles 1 and 2
AGTR2 gene: SNP 27
Phenotype: 53 clinical attributes
• 5 individual's identity data
• 34 maternal data: physical and physiological parameters,
pregnancy details and current treatments
• 6 fetal data: weight and gestational age at birth
• 8 medical history data of parents, partners or siblings
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Results: Pre-processing I
Babies dataset (372X58)
1. Attributes: Gestation at birth (day and
week), weight, disease status, live at birth
2. Class: CBC - birth-weight centile corrected for gestation at birth, baby
sex, ethnicity, mother's height and weight and number of pregnancies.
50 is normal weight, below 50 is underweight.
3. Missing Value: we retain missing values using the appropriate
codification for the chosen algorithm.
4. Data Balancing: case-control ratio depends on the chosen CBC
threshold to transform it from numeric to Boolean.
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Data
Analysis I
Kappa Analysis:
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Results: Data Analysis II
Balancing of the data:
CBC = 6: 147 cases (39.5%) and 225 controls
CBC = 10: 177 cases (47.6%) and 195 controls
CBC = 28: 243 cases (65.3%) and 129 controls
> 33%
ADTree results Analysis
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Results:
Data Analysis III
C4.5 Results Analysis:
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Results: Data Analysis IV
Cross Analysis: common attributes between ADTree and C4.5
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Results: Data Analysis V
Analysis with common attributes for CBC= 28
(ADTree Kappa = 0.41, C4.5 Kappa = 0.38) :
Male babies, born after the 35th week of gestation and with:
AGT SNP3 allele2 = 1
(CBC > 28)
AGT SNP3 allele2 = 2 &
AGTR1 SNP11 allele2 = 1
(CBC < 28)
Analysis with only Gestational week and CBC = 10
(Kappa value = 0.42 for both the ADTree and C4.5) :
Babies delivered before 35 or 35.5 week of gestation are likely to be
underweight (CBC < 10).
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Conclusions
• Guideline for data mining in the specific application of case-control
analysis for SNPs.
• Methodological point of view: attributes are rejected, instances
are decreased (screening stage).
• Clinical perspective: Significance of threshold CBC = 10 and
dependency of CBC on the “week of delivery”.
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Future Work
•
Genotype of the mothers rather that the babies.
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Recoding of the SNPs
•
Redundant interaction between attributes
•
Non linear interaction between attributes
•
Heritable trend can be detected across the two generations
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References
[1] J. Han and M. Kamber, Data Mining: Concept and Techniques.Morgan Kaufmann, 2006.
[2] N. M. Laird and C. Lange, “Family-based designs in the age of largescale gene-association
studies,” Nature Reviews Genetics, pp. 385–394, 2006.
[3] J. R. Quinlan, “Induction of decision trees,” Machine Learning, vol. 1, pp. 81–106, 1986.
[4] J. R. Quinlan, “C4.5: Programs for machine learning,” Machine Learning, vol. 16, no. 3, pp. 235–
240, 1994.
[5] Y. Freund and L. Mason, “The alternating decision tree learning algorithm,” Proceedings of the
Sixteenth International Conference on Machine Learning, pp. 124–133, 1999.
[6] J. Cohen, “A coefficient of agreement for nominal scales,” Educational and Psychological
Measurement, vol. 20, no. 1, pp. 37–46, 1960.
[7] D. G. Altman, Practical Statistics for Medical Research., Chapman and Hall, Eds. CRC Press,
1991.
[8] Landis, J. R. and Koch, The measurement of observer agreement for categorical data.
Biometrics. (1977) pp. 159--174
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