Open Access
Fine-Scale Genetic Mapping using
Zaher Dawy,Michel Sarkis,Joachim Hagenauer,Jakob C. Mueller +3 more
- 01 Jan 2007
1
TL;DR: This work model the biological system that relates DNA polymorphisms with complex traits as a linear mixing process and proposes a new fine-scale genetic mapping method based on independent component analysis that outputs both independent associated groups of SNPs in addition to specific associated SNPs with the phenotype.
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Abstract: The aim of genetic mapping is to locate the loci responsible for specific traits such as complex diseases. These traits are normally caused by mutations at multiple loci of unknown locations and interactions. In this work, we model the biological system that relates DNA polymorphisms with complex traits as a linear mixing process. Given this model, we propose a new fine-scale genetic mapping method based on independent component analysis. The proposed method outputs both independent associated groups of SNPs in addition to specific associated SNPs with the phenotype. It is applied to a clinical data set for the Schizophrenia disease with 368 individuals and 42 SNPs. It is also applied to a simulation study to investigate in more depth its performance. The obtained results demonstrate the novel characteristics of the proposed method compared to other genetic mapping methods. Finally, we study the robustness of the proposed method with missing genotype values and limited sample sizes.
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Citations
•Journal Article
Empirical threshold values for quantitative trait mapping.
TL;DR: In this paper, an empirical method based on the concept of permutation test is proposed for estimating threshold values that are tailored to the experimental data at hand, which is demonstrated using two real data sets derived from F(2) and recombinant inbred plant populations.
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Independent component analysis, a new concept?
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•Book
Independent Component Analysis
Aapo Hyvärinen,Juha Karhunen,Erkki Oja +2 more
- 18 May 2001
TL;DR: Independent component analysis as mentioned in this paper is a statistical generative model based on sparse coding, which is basically a proper probabilistic formulation of the ideas underpinning sparse coding and can be interpreted as providing a Bayesian prior.
The varimax criterion for analytic rotation in factor analysis
TL;DR: In this article, an analytic criterion for rotation is defined and the scientific advantage of analytic criteria over subjective (graphical) rotational procedures is discussed, and a computational outline for the orthogonal normal varimax is appended.
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Fast and robust fixed-point algorithms for independent component analysis
TL;DR: Using maximum entropy approximations of differential entropy, a family of new contrast (objective) functions for ICA enable both the estimation of the whole decomposition by minimizing mutual information, and estimation of individual independent components as projection pursuit directions.
Empirical threshold values for quantitative trait mapping.
TL;DR: An empirical method is described, based on the concept of a permutation test, for estimating threshold values that are tailored to the experimental data at hand, and is demonstrated using two real data sets derived from F(2) and recombinant inbred plant populations.
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