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Nonparametric functional mapping of ...

Yang, Jie.

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Nonparametric functional mapping of quantitative trait loci.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Nonparametric functional mapping of quantitative trait loci./
作者:	Yang, Jie.
面頁冊數:	145 p.
附註:	Adviser: George Casella.
Contained By:	Dissertation Abstracts International67-08B.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3228873
ISBN:	9780542808296

Nonparametric functional mapping of quantitative trait loci.
Yang, Jie.

Nonparametric functional mapping of quantitative trait loci. - 145 p.

Adviser: George Casella.

Thesis (Ph.D.)--University of Florida, 2006.

Functional mapping is a tool for detecting major genes responsible for different phenotypic curves, developed by Ma, Casella and Wu. The methodology uses a parametric functional form, usually derived from a biological law, to drive a maximum-likelihood-based test for a significant QTL (quantitative trait loci). However, in many situations there is no obvious functional form and, in such cases, this strategy will not be optimal.

ISBN: 9780542808296Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Nonparametric functional mapping of quantitative trait loci.
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502 $a Thesis (Ph.D.)--University of Florida, 2006.
520 $a Functional mapping is a tool for detecting major genes responsible for different phenotypic curves, developed by Ma, Casella and Wu. The methodology uses a parametric functional form, usually derived from a biological law, to drive a maximum-likelihood-based test for a significant QTL (quantitative trait loci). However, in many situations there is no obvious functional form and, in such cases, this strategy will not be optimal.
520 $a In this dissertation we propose to use nonparametric function estimation, typically implemented with B-splines, to estimate the underlying functional form of phenotypic trajectories, and then construct a nonparametric test to find evidence of existing quantitative trait loci. Using the representation of a nonparametric regression as a mixed model, we can easily derive a likelihood ratio test statistic. Two situations are considered: one is based on the dense-map assumption that QTL (quantitative trait loci) is on some marker and the other is the situation where the actual genes responsible for different underlying phenotypic trajectories might not just be on a marker locus, which is more realistic. For the dense-map case, after obtaining the joint distribution of all test statistics at every putative locus (each marker), we can then calculate the p-value directly. Simulation studies show that our method is both powerful and quick. We also provide an application to a real data set.
520 $a Nowadays with the cutting-edge biotechnology, more and more marker information can be obtained. Along with this exciting human achievement, the dataset has more possibility to have missing cells. In this dissertation we also take into account this case.
590 $a School code: 0070.
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650 4 $a Biology, Genetics. $3 1017730
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