Standards for SNPs Analysis with Decision Trees Tools'

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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 dont 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) S((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) S((-p(I) log2 p(I))
• - S is a collection of c outcomes
• - S is over c.
• p(I) is the proportion of S belonging to class
  I.

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ID3 Algorithm Example

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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.
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Pre-processing
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Data Analysis
Statistical Significance
Kappa Value proportion of agreement corrected
for chance between two judges assigning cases to
a set of categories
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Experimental Dataset

• 4529 Patients
• Genotype 52 SNP attributes
• 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)
• Attributes Gestation at birth (day and week),
  weight, disease status, live at birth

• 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.
• Missing Value we retain missing values using the
  appropriate codification for the chosen
  algorithm.
• 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
gt 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
AGT SNP3 allele2 2

AGTR1 SNP11 allele2 1
(CBC gt 28)
(CBC lt 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 lt
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.
• 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. 385394, 2006. 3
J. R. Quinlan, Induction of decision trees,
Machine Learning, vol. 1, pp. 81106, 1986. 4
J. R. Quinlan, C4.5 Programs for machine
learning, Machine Learning, vol. 16, no. 3, pp.
235240, 1994. 5 Y. Freund and L. Mason, The
alternating decision tree learning algorithm,
Proceedings of the Sixteenth International
Conference on Machine Learning, pp. 124133,
1999. 6 J. Cohen, A coefficient of agreement
for nominal scales, Educational and
Psychological Measurement, vol. 20, no. 1, pp.
3746, 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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Acknowledgments

• Dr. Jonathan M Garibaldi
• Dr. Linda Morgan and Dr. Kevin Morgan from
  Clinical Chemistry
• Division, Institute of Genetics at the
  Queen's Medical Center,
• Nottingham
• This study was supported by the BIOPTRAIN FP6
  Marie-Curie EST
• Fellowship (MEST-CT-2004-007597).