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Nonparametric functional mapping of ...

Yang, Jie.

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Nonparametric functional mapping of quantitative trait loci.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Nonparametric functional mapping of quantitative trait loci./
Author:	Yang, Jie.
Description:	145 p.
Notes:	Adviser: George Casella.
Contained By:	Dissertation Abstracts International67-08B.
Subject:	Biology, Bioinformatics. -
Online resource:	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.
LDR:02595nam 2200313 a 45 001 967218
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020 $a 9780542808296
035 $a (UnM)AAI3228873
035 $a AAI3228873
040 $a UnM $c UnM
100 1 $a Yang, Jie. $3 965506
245 1 0 $a Nonparametric functional mapping of quantitative trait loci.
300 $a 145 p.
500 $a Adviser: George Casella.
500 $a Source: Dissertation Abstracts International, Volume: 67-08, Section: B, page: 4509.
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.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Biology, Genetics. $3 1017730
650 4 $a Statistics. $3 517247
690 $a 0369
690 $a 0463
690 $a 0715
710 2 0 $a University of Florida. $3 718949
773 0 $t Dissertation Abstracts International $g 67-08B.
790 $a 0070
790 1 0 $a Casella, George, $e advisor
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3228873