Effect of algorithms on the identification of copy number variants in healthy Finnish individuals and individuals with autism spectrum disorder

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School of Science | Master's thesis
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Date
2012
Department
Tietojenkäsittelytieteen laitos
Major/Subject
Informaatiotekniikka
Mcode
T-61
Degree programme
Language
en
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Abstract
Copy Number Variations, a form of genomic structural variation, are known to alter the gene expression, thereby causing phenotypic variation and increasing the risk of disease susceptibility. In order to understand the role of CNVs in the genetic variation of human populations, it has become increasingly common to incorporate CNV maps in disease-association studies. Yet, there is neither an experimental method nor an algorithm that can accurately identify a CNV. In addition to this void, population-specific differences exist in CNV maps making it more complicated to get the big picture of genomic variations. To address some of these issues, this study makes an assessment of two most popular algorithms available for Illumina-based platforms alongside contributing to the global variation map with Finnish population-specific CNVs. Illumina HumanOmniExpress-12 v1 beadchip was used for genotyping 203 trios (father, mother, offspring) of healthy individuals and 90 other trios, in which the offspring is affected with autism spectrum disorder (ASD). CNVs were identified using PennCNV and QuantiSNP, two most popular algorithms for Illumina-based platforms, which uses Hidden Markov Models to detect CNVs. Comparisons were made to check the agreement in CNV-calls, differences in size statistics and size dis
Description
Supervisor
Lähdesmäki, Harri
Thesis advisor
Järvelä, Irma
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https://urn.fi/URN:NBN:fi:aalto-2020122858870
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[dipl] Perustieteiden korkeakoulu / SCI