1. Meredith L. Wilcox FIU, Department of Epidemiology/Biostatistics
SNP Data Analysis
Meredith L. Wilcox
FIU, Department of Epidemiology/Biostatistics

2. Presentation Outline Part I. - Preliminary Analysis
Introduction
Methods
Results
Discussion
Part II. - Ongoing Research
Purpose
Background
Software

3. Part I. Preliminary Analysis
- Introduction – Methods – Results – Discussion -

4. Part I. Introduction Purpose of the preliminary analysis:
To develop 2 initial prediction models for the leukemia SNP data…
- Logistic regression
- Naïve Bayes Classifier
To determine which of the two models is the better prediction model
Overall goal: to improve SNP data analysis

5. Part I. Introduction Data SNP data consisting of 220 binary variables
Output variable: CACO
Leukemia present/absence
Input variables:
Sex (F/M)
218 SNPs (dominant/not dominant)
Observations: 485 individuals
Data is complete; no missing values

6. Part I. Methods Models using R Logistic Regression
assumption: observations are independent
only simple logistic regression considered
(no interaction among input variables)
Naïve Bayes Classifier
assumption: input variables are independent given the outcome

7. Part I. Methods Variable Selection Goodness of Fit Measures
Selection based on log likelihood score
10 input variables per model
Goodness of Fit Measures
4-fold cross-validation
Area under ROC

8. Part I. Results Variables Selected
Same 10 SNPs were identified as input variables for both logistic regression and Naïve Bayes:
1. TFRC_rs TFRC_rs3326
2. TGFB1_rs HFE
3. HFE_rs RXRB_rs421446
4. HLA_DRB1_DQA1_rs ACP1_rs
5. LTF_rs DQA1_3UTR

9. Part I. Results Cross Validation
Average training/test error for each model
Logistic Regression
 lower average training/test error

10. Part I. Results- ROC curve
AUROC: LR=0.79; NB=0.70

11. Part I. Discussion Limitations of the preliminary analysis:
Better methods for variable selection available (stepwise procedures: forward selection, backward elimination).
Recessive, additive, & heterozygous properties of genes not included in analysis
Biology between disease & genes not considered in variable selection
Interaction not considered in logistic regression model

12. Part II. Ongoing Research
- Background – Purpose – Software -

13. Part II. Background Bayesian Networks
Probabilistic graphical model consisting of two components-
an acyclic directed graph (DAG)
a set of local probability distributions
Example of a DAG.
(nodes = random variables)
(arcs = conditional dependencies)

14. Part II. Background P(SNP1,L|SNP3) = P(SNP1|SNP3)*P(L|SNP3)
Markov Condition
P(SNP1,L|SNP3) = P(SNP1|SNP3)*P(L|SNP3)

15. Part II. Purpose To analyze SNP data using the following models:
Bayesian Networks
Multifactor Dimensionality Reduction
Support Vector Machine
To compare the prediction capability of the above models to other widely used models

16. Part II. Software Development of R code for Bayesian networks analysis
to search for “best” Bayesian Network
utilize search in variable ordering with MCMC method
Arc reversal

17. Thank you.