Computational Identification of Recessive Mutations in Cancers using High Throughput SNP-arrays

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Title: Computational Identification of Recessive Mutations in Cancers using High Throughput SNP-arrays
Author: Laakso, Marko
Other contributor: Helsingin yliopisto, Matemaattis-luonnontieteellinen tiedekunta, Tietojenkäsittelytieteen laitos
University of Helsinki, Faculty of Science, Department of Computer Science
Helsingfors universitet, Matematisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap
Publisher: Helsingfors universitet
Date: 2007
Language: eng
Thesis level: master's thesis
Abstract: This thesis presents a highly sensitive genome wide search method for recessive mutations. The method is suitable for distantly related samples that are divided into phenotype positives and negatives. High throughput genotype arrays are used to identify and compare homozygous regions between the cohorts. The method is demonstrated by comparing colorectal cancer patients against unaffected references. The objective is to find homozygous regions and alleles that are more common in cancer patients. We have designed and implemented software tools to automate the data analysis from genotypes to lists of candidate genes and to their properties. The programs have been designed in respect to a pipeline architecture that allows their integration to other programs such as biological databases and copy number analysis tools. The integration of the tools is crucial as the genome wide analysis of the cohort differences produces many candidate regions not related to the studied phenotype. CohortComparator is a genotype comparison tool that detects homozygous regions and compares their loci and allele constitutions between two sets of samples. The data is visualised in chromosome specific graphs illustrating the homozygous regions and alleles of each sample. The genomic regions that may harbour recessive mutations are emphasised with different colours and a scoring scheme is given for these regions. The detection of homozygous regions, cohort comparisons and result annotations are all subjected to presumptions many of which have been parameterized in our programs. The effect of these parameters and the suitable scope of the methods have been evaluated. Samples with different resolutions can be balanced with the genotype estimates of their haplotypes and they can be used within the same study.
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