Com ompari rison on of of F Five C Commonly Used Ge Gene-Gene - - PowerPoint PPT Presentation

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Com ompari rison on of of F Five C Commonly Used Ge Gene-Gene I Interaction Detecting Metho hods in S n Schi hizophr hrenia Chung-Keng Hsieh and Guan-Hua Huang Institute of Statistics National Chiao Tung University Outline

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Com

mpari

rison

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f F

Five C Commonly Used Ge Gene-Gene I Interaction Detecting Metho hods in S n Schi hizophr hrenia

Chung-Keng Hsieh and Guan-Hua Huang Institute of Statistics National Chiao Tung University

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Outline

INTRODUCTION METHODOLOGY

Study population Preliminary analyses Study design Methods Cross validation

RESULTS CONCLUSION

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Outline

METHODOLOGY Study population Preliminary analyses Study design Methods Cross validation RESULTS CONCLUSION

INTRODUCTION

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INTRODUCTION

Single-locus methods Gene-gene interaction Genotype data Haplotype data

SNP NP

Hap aplo lotype B e Block

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INTRODUCTION

In the present study:

Assessed the importance of gene-gene interactions on schizophrenia risk

Data:

65 SNPs from 5 candidate genes 514 cases and 376 controls

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INTRODUCTION

Five commonly used gene-gene interaction detecting methods Cross validation

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Outline

INTRODUCTION RESULTS CONCLUSION

METHODOLOGY

Study population Preliminary analyses Study design Methods Cross validation

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Study population

Schizophrenia dataset

Data collection was based on TSLS program

Genotyping of markers on 5 candidate genes:

DISC1, NRG1, DAO, G72 and CACNG2

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Study population

514 schizophrenia cases and 376 controls Total 65 SNPs in five candidate genes

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Outline

INTRODUCTION RESULTS CONCLUSION

METHODOLOGY

Study population Preliminary analyses Study design Methods Cross validation

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Preliminary analyses

Data quality control:

exclude SNP if

HWE p value < 0.001 missing genotypes > 25% (SNP call rate < 75%) MAF is less than 1%

exclude individuals if

percentage of missing SNPs > 50%

After filtering data

55 SNPs 889 individuals (513 cases / 376 controls).

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Preliminary analyses

Missing data imputation:

Imputation: replacing missing genotypes with predicted values that are based on the

bserved genotypes at neighboring SNPs.

We implement data imputation by using the MDR Data Tool software

It will perform a simple frequency-based imputation.

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Outline

INTRODUCTION RESULTS CONCLUSION

METHODOLOGY

Study population Preliminary analyses Study design Methods Cross validation

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Study design

The data was analyzed by two strategies:

use the original genotype-based data

55 SNPs

use the haplotype-based data

10 Haplotype block + 29 SNPs

In haplotype-based study, we use the Haploview software to define haplotype block and use the PHASE software to estimate individual’s haplotype

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Outline

INTRODUCTION RESULTS CONCLUSION

METHODOLOGY

Study population Preliminary analyses Study design Methods Cross validation

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Methods

Chi-square test Logistic regression model (LRM) Bayesian epistasis association mapping (BEAM) algorithm Classification and regression trees (CART) Multifactor dimensionality reduction (MDR) method

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Outline

INTRODUCTION RESULTS CONCLUSION

METHODOLOGY

Study population Preliminary analyses Study design Methods Cross validation

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Cross Validation

We want to compare the abilities of prediction in these five methods

We randomly divided our genotype-based data into training set and testing set.

The sample size of training set doubles that of testing set.

We repeat this procedure 100 times to create 100 dataset

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Cross Validation

For each CV, we apply the five methods to the training set and get the best model for one-way, two-way, and three-way interaction. We use the training set to build a prediction rule for the best model

Like MDR, we compute the case-control ratio for each genotype combination While the prediction rule is built, we can calculate the prediction error

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Outline

INTRODUCTION METHODOLOGY

Study population Preliminary analyses Study design Methods Cross validation

CONCLUSION

RESULTS

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RESULTS

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RESULTS

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RESULTS

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RESULTS

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RESULTS

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RESULTS

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ne-way interaction

Box-plot of prediction error

two-way interaction three-way interaction

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Outline

INTRODUCTION METHODOLOGY

Study population Preliminary analyses Study design Methods Cross validation

RESULTS

CONCLUSION

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CONCLUSION

Our aim of this study is to propose a methodological issue in detecting gene- gene interaction We chose five commonly used methods and apply them to a schizophrenia data

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CONCLUSION

we find that SNPs rsDAO_13 and rsDAO_7 have strong main effect SNPs rsDAO_6, rsDAO_7, and rsG72_16 have strong gene-gene interaction effects LRM shows the best predictive ability in

ur data

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THANK YOU!

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