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Topics in multipoint linkage and ass...

Xing, Chao.

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Topics in multipoint linkage and association analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Topics in multipoint linkage and association analysis./
作者:	Xing, Chao.
面頁冊數:	167 p.
附註:	Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 5530.
Contained By:	Dissertation Abstracts International67-10B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3237859
ISBN:	9780542928529

Topics in multipoint linkage and association analysis.
Xing, Chao.

Topics in multipoint linkage and association analysis. - 167 p.

Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 5530.

Thesis (Ph.D.)--Case Western Reserve University, 2007.

Genetic markers contain information on transmission patterns among members of a pedigree; therefore, adding genetic markers into segregation, linkage, and association analysis can improve the original approaches in terms of statistical efficiency and power. Multipoint algorithms, in particular the Lander-Green algorithm, greatly facilitate analysis using multiple markers. However there are also potential problems in a multipoint analysis. In this dissertation, we investigate several topics in linkage and association analysis that arise when using the multipoint algorithm. We study the null distribution of multipoint model-based lod scores by identifying the factors influencing their distribution both analytically and by simulation. In particular, we study by simulation the impact of pre-specified penetrance functions and patterns of affection status on the lod score distribution. We systemically investigate the impact of the degree of linkage disequilibrium, marker allele frequencies, and association type on estimating the probabilities of sharing alleles identical by descent among affected sib pairs when using multipoint algorithms, and on the validity of different model-free linkage statistics and designs under the assumption of linkage equilibrium. We prove that setting all the penetrance functions equal at a putative trait locus leads to independence between the trait and markers in the context of multipoint analysis. We then propose a general framework for joint segregation, linkage, and association analysis based on testing the equality of penetrance functions. Under this framework, we propose a generalized multipoint mixture model for family-based association studies and, in particular, we investigate by simulation the logistic mixture model for analyzing binary traits under the assumption of monogenic trait models. To allow for residual familial correlations, we further suggesting using a regressive multivariate logistic model in family-based association studies.

ISBN: 9780542928529Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Topics in multipoint linkage and association analysis.
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