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Title:
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Gene Set Analysis for improving genetic association studies
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Author:
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Vilor Tejedor, Natàlia
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Other authors:
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Universitat de Vic. Escola Politècnica Superior; Universitat de Vic. Màster Universitari en Anàlisi de Dades Òmiques |
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Notes:
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Curs 2012-2013
Introduction. Genetic epidemiology is focused on the study of the genetic causes
that determine health and diseases in populations. To achieve this goal a common
strategy is to explore differences in genetic variability between diseased and nondiseased
individuals. Usual markers of genetic variability are single nucleotide
polymorphisms (SNPs) which are changes in just one base in the genome. The
usual statistical approach in genetic epidemiology study is a marginal analysis,
where each SNP is analyzed separately for association with the phenotype.
Motivation. It has been observed, that for common diseases the single-SNP
analysis is not very powerful for detecting genetic causing variants. In this work,
we consider Gene Set Analysis (GSA) as an alternative to standard marginal
association approaches. GSA aims to assess the overall association of a set of
genetic variants with a phenotype and has the potential to detect subtle effects of
variants in a gene or a pathway that might be missed when assessed individually.
Objective. We present a new optimized implementation of a pair of gene set
analysis methodologies for analyze the individual evidence of SNPs in biological
pathways. We perform a simulation study for exploring the power of the proposed
methodologies in a set of scenarios with different number of causal SNPs under
different effect sizes. In addition, we compare the results with the usual single-SNP
analysis method. Moreover, we show the advantage of using the proposed gene set
approaches in the context of an Alzheimer disease case-control study where we
explore the Reelin signal pathway.
Director/a: M. Luz Calle |
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Subject(s):
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-Alzheimer, Malaltia d'
-Genètica |
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Rights:
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Aquest document està subjecte a aquesta llicència Creative Commons
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
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Document type:
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Research/Master Thesis |
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