1

Standards for SNPs Analysis with Decision Trees Tools.

Linda Fiaschi

Supervisors:

Jon GaribaldiNatalio Krasnogor

IMA Seminar 24/02/2009

2

Outline

• Genetic background and clinical objectives

• Disease : Pre-eclampsia

• Method of analysis

• My Methodology: ADTree, C4.5, ID3

• Results

• Conclusions

• Future Work

1

3

Genetics : SNPs

• The DNA of most people is 99.9 percent thesame.

• 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.

2

4

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

3

5

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.

4

6

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.

5

Comparison

Cases: Sick Controls: HealthyClassification

Rules

7

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.

6

8

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

8

9

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 ASv = 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.

9

10

ID3 Algorithm Example

Delivery week

Liver measures

< 35.5 >= 35.5

<94 >=94

1(15\4) 0(25\0)

Systolic Pressure

<152.5 >=152.5

Age1(9\1)

1(26\2) 0(31\0)

<36.3 >=36.3

10

11

From ID3 to C4.5 Algorithm

• Handling both continuous and discrete attributes

• Handling training data with missing attribute values

• Pruning trees after creation

11

12

Methodology

A progressive analysis: detection of significant results deepened and confirmed in the subsequent analysis.

Pre-processing of the Data

Data Analysis

12

13

Pre-processing

13

14

Data Analysis

Kappa Value: proportion of agreementcorrected for chance between two judgesassigning 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

14

A

A

Statistical Significance

15

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

15

16

Results: Pre-processing I

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.

Babies dataset (372X58)

1. Attributes: Gestation at birth (day and week), weight, disease status, live at birth

16

17

Data Analysis I

Kappa Analysis:

17

18

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

18

19

Results: Data Analysis III

C4.5 Results Analysis:

19

20

Results: Data Analysis IV

Cross Analysis: common attributes between ADTree and C4.5

20

21

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 > 28) (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 beunderweight (CBC < 10).

21

22

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”.

22

23

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

23

24

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

24