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Bioinformatics challenges for “genome-wide association” studies

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Presentation on theme: "Bioinformatics challenges for “genome-wide association” studies"— Presentation transcript:

1 Bioinformatics challenges for “genome-wide association” studies
Jason H. Moore, Folkert W. Asselbergs, Scott M. Williams (2010) Presented by: Md Rakibul Islam

2 Genome Wide Association Studies
A genetic study design that attempts to identify commonly occurring genetic variants that contribute to disease risk Single Nucleotide Polymorphism (SNP) are associated with traits (phenotype) Started from 2005 and still going …… Photo-Credit: Genome.GOV

3

4 Terminologies Manhattan Plot SNP/Allele Photo-Credit: Wikipedia

5 Terminologies Genotype-Phenotype

6 How GWAS Performed? Case and Control Others Gender Height
Blood pressure Body mass index (BMI) Insulin levels Photo-Credit: Genome Research Limited

7 Personalized Medicine
Why it’s Important? Disease Development Diagnose Treat Till now Identified Type 2 diabetes Alzheimer’s disease Parkinson’s disease Crohn’s disease ……. Personalized Medicine

8 What are the Challenges?
Discovered “Small effect” size SNP Modeling of complex genotype–phenotype relationships Photo-Credit: WikiPedia

9 This review discusses What’s wrong with them? Ignores known knowledge
Linear Modeling Framework (one SNP at a time), missing Context This review discusses Computational Methods (Data Mining, Machine Learning) Bio-Statistics Methods

10 Benefits of Data Mining and Machine Learning
Can model higher order non-liner interactions Feature Selection algorithms will reduce the exhaustive search Better interpretation of the non-linear genetic models

11 Computational Modeling using Random Forest
Advantages Noisy data False Positive data Good Performance

12 Computational Modeling Multifactor DR
Advantages Another way of detecting non-linear patterns Complementary of RF’s, NN, and Logistic Regression

13 Attribute Selection Filter Algorithm (Relief, ReliefF)

14 Attribute Selection Wrapper Algorithm (Genetic Programming)

15 Other Challenges Knowledge Database Construction Software Challenges

16 Final Combined Approach

17 Thank You

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