Risk prediction with genomic data

Genome wide association study (GWAS) is widely used with various machine learning algorithms to predict disease risk. This thesis investigates this widely used approach of GWAS using Single Nucleotide Polymorphism (SNP) genotype data and a novel approach of disease risk prediction with whole exome sequencing data, namely Whole Exome Wide Association Study (WEWAS). It further applies a discriminating machine learning algorithm, namely a Support Vector Machine (SVM) with different Kernel functions. For this study, only SNPs generated using genotyping technology, which focuses more on common variants, are used initially for disease prediction. Later, the whole exome data generated using Next Generation Sequencing (NSG) technology is used in the prediction. Another distinction between traditional GWAS and the new approach, WEWAS, presented in this thesis is the use of insertions and deletions in the genomic sequence (INDEL) together with SNPs as a feature for prediction. A substantial improvement in the prediction accuracy is achieved using the latter approach. The success of the approach of using NSG data shows that it contains valuable information which the SNP genotyping method is unable to capture.

Name thesis:degree_name

Master of Science in Bioinformatics - (M.S.)

Discipline thesis:degree_discipline

Computer Science

Year

2014

Author dc:creator

• Jadhav, Bharati

Contributors dc:contributor

• Usman W. Roshan
• Jason T. L. Wang
• Zhi Wei

• Genome wide association study (GWAS)
• Disease risk prediction
• Single Nucleotide Polymorphism (SNP) genotype data
• Whole Exome Wide Association Study (WEWAS)
• Bioinformatics
• Computer Sciences

Repository record dc:identifier

https://digitalcommons.njit.edu/theses/199

OAI identifier oai:identifier

oai:digitalcommons.njit.edu:theses-1198