Methoden und Algorithmen der Kopplungsanalyse bei quantitativen Phänotypen

Motivation: Krankheiten beim Menschen werden zu einem großen Teil durch geneti- sche Varianten beeinflusst oder verursacht. Um den Krankheitsmechanismus zu ver- stehen und um Patienten ursächlich behandeln zu können, ist ein erster Schritt, die genetische Variante im menschlichen Genom zu lokali M...

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Bibliographic Details
Main Author: Künzel, Thomas
Contributors: Strauch, Konstantin (Prof. Dr.) (Thesis advisor)
Format: Dissertation
Published: Philipps-Universität Marburg 2012
Online Access:PDF Full Text
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Table of Contents: Objective: To a large degree human diseases are influenced or caused by genetic variants. In order to understand the mechanism of the disease and to treat patients in a causative way, a first step is to locate the genetic variants in the human genome. An important tool for this goal is linkage analysis. In this work, a parametric method for linkage analysis of quantitative phenotypes is presented. The method provides a test for linkage as well as an estimate of different parameters of the genotype-phenotype relation.We have implemented our new method in the program genehunter-qmod and performed simulations to compare its power and type I error to existing methods, i.e. variance components analysis (VCA) and Haseman-Elston regression. Methods: The phenotype is modeled as a normally distributed variable, with a separate distribution for each genotype. Estimates of the genotype-specific expectation values and standard deviations are obtained by maximizing the LOD score over these parameters with a gradient-based optimization called pgrad method. That way, genehunter-qmod can both locate the putative disease locus and provide specific information about the genotype-phenotype relation. Results: genehunter-qmod has lower power to detect linkage than VCA in ca- se of a normal distribution and with sib pairs. However, it outperforms VCA and Haseman-Elston regression for larger pedigrees, non-randomly ascertained data or non-normally distributed phenotypes. Here, the higher power even goes along with conservativeness, while VCA has an inflated type I error. Parameter estimation tends to underestimate residual variances, but performs better for expectation values of the genotype-specific phenotype distributions. Conclusion: With genehunter-qmod, a powerful new tool is provided to ex- plicitly model quantitative phenotypes in the context of linkage analysis. Because genehunter-qmod is based on the Lander-Green algorithm, it can simultaneously use many markers in the analysis, which makes it applicable to gene-mapping projects based on SNP arrays. The program is freely available at