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Fuzzy clustering in linkage analysis...

Kaabi, Belhassen.

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Fuzzy clustering in linkage analysis of complex diseases.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fuzzy clustering in linkage analysis of complex diseases./
Author:	Kaabi, Belhassen.
Description:	191 p.
Notes:	Source: Dissertation Abstracts International, Volume: 64-04, Section: B, page: 1601.
Contained By:	Dissertation Abstracts International64-04B.
Subject:	Biology, Genetics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3088690

Fuzzy clustering in linkage analysis of complex diseases.
Kaabi, Belhassen.

Fuzzy clustering in linkage analysis of complex diseases. - 191 p.

Source: Dissertation Abstracts International, Volume: 64-04, Section: B, page: 1601.

Thesis (Ph.D.)--Case Western Reserve University (Health Sciences), 2003.

We propose two applications of fuzzy clustering to linkage analysis of complex diseases. The first application consists of a new model-free method of linkage analysis based on using grade of membership scores resulting from a fuzzy clustering procedure to define the dependent variable for the various Haseman-Elston approaches. For a single continuous trait with low heritability, the aim was to identify subgroups such that the grade of membership scores to these subgroups would provide more information for linkage than the original trait. For a multivariate trait, the goal was to provide a means for data reduction and data mining. Simulation studies using continuous traits with relatively low heritability (<italic>H</italic> = 0.1, 0.2 and 0.3) showed that the new approach does not enhance power for a single trait. However, for a multivariate continuous trait (with 3 components) it is uniformly more powerful than the test proposed by Mangin <italic>et al.</italic> [1998] when there is pleiotropy. The second application consists of new model-based methods for linkage analysis using grade of membership scores resulting from a fuzzy clustering procedure to define three fuzzy forms of the LOD-score. For a single continuous trait with low heritability, the aim was to identify sub-groups, when such that the grade of membership scores to these subgroups incorporated in the new expression of the likelihood, would provide more information for linkage than using cut-off points to define a binary trait that is used in classic LOD-score methodology. For a multivariate trait case, this test provides a means for data reduction and linkage analysis as well. Simulation studies using continuous traits with relatively low heritability (<italic> H</italic> = 0.1, 0.2, 0.3, and 0.4) showed that the new approach enhances power for a single trait as well as for the multivariate trait case (4 continuous components, each with different low heritability, acting pleiotropically).Subjects--Topical Terms:

1017730
Biology, Genetics.

Fuzzy clustering in linkage analysis of complex diseases.
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500 $a Source: Dissertation Abstracts International, Volume: 64-04, Section: B, page: 1601.
500 $a Adviser: Robert C. Elston.
502 $a Thesis (Ph.D.)--Case Western Reserve University (Health Sciences), 2003.
520 $a We propose two applications of fuzzy clustering to linkage analysis of complex diseases. The first application consists of a new model-free method of linkage analysis based on using grade of membership scores resulting from a fuzzy clustering procedure to define the dependent variable for the various Haseman-Elston approaches. For a single continuous trait with low heritability, the aim was to identify subgroups such that the grade of membership scores to these subgroups would provide more information for linkage than the original trait. For a multivariate trait, the goal was to provide a means for data reduction and data mining. Simulation studies using continuous traits with relatively low heritability (<italic>H</italic> = 0.1, 0.2 and 0.3) showed that the new approach does not enhance power for a single trait. However, for a multivariate continuous trait (with 3 components) it is uniformly more powerful than the test proposed by Mangin <italic>et al.</italic> [1998] when there is pleiotropy. The second application consists of new model-based methods for linkage analysis using grade of membership scores resulting from a fuzzy clustering procedure to define three fuzzy forms of the LOD-score. For a single continuous trait with low heritability, the aim was to identify sub-groups, when such that the grade of membership scores to these subgroups incorporated in the new expression of the likelihood, would provide more information for linkage than using cut-off points to define a binary trait that is used in classic LOD-score methodology. For a multivariate trait case, this test provides a means for data reduction and linkage analysis as well. Simulation studies using continuous traits with relatively low heritability (<italic> H</italic> = 0.1, 0.2, 0.3, and 0.4) showed that the new approach enhances power for a single trait as well as for the multivariate trait case (4 continuous components, each with different low heritability, acting pleiotropically).
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