Document Type
Thesis
Date of Award
Spring 5-31-2013
Degree Name
Master of Science in Bioinformatics - (M.S.)
Department
Computer Science
First Advisor
Usman W. Roshan
Second Advisor
Zhi Wei
Third Advisor
Alexandros V. Gerbessiotis
Abstract
With the recent advances in the next generation sequencing technologies, short read sequences of human genome are made more accessible. Paired end sequencing of short reads is currently the most sensitive method for detecting somatic mutations that arise during tumor development. In this study, a novel approach to optimize the detection of structural variants using a new short read alignment program is presented.
Pairwise interaction effects of the Single Nucleotide Polymorphisms (SNPs) have proven to uncover the underlying complex disease traits. Computing the disease risk based on the interaction effects of SNPs on a case - control study is a difficult problem. As another part of the thesis, a fast GPU program that can calculate the chi-square statistics of SNP-SNP interactions and output the significant interacting SNPs is presented. The algorithm is applied to the datasets of seven common diseases obtained from Wellcome Trust Case Control Consortium (WTCCC). The algorithm computed the significant SNP pairs much faster than the existing algorithms and also identifies 3 significant pairs associated with genes IL23R and C11orf30 which are associated with pathogenesis in the Crohns disease dataset.
Recommended Citation
Ramakrishnan, Srividya, "A GPU program to compute SNP-SNP interactions in genome-wide association studies" (2013). Theses. 289.
https://digitalcommons.njit.edu/theses/289
