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Association statistics under the PPL...

Huang, Yungui.

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Association statistics under the PPL framework.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Association statistics under the PPL framework./
Author:	Huang, Yungui.
Description:	142 p.
Notes:	Source: Dissertation Abstracts International, Volume: 72-09, Section: B, page: 5378.
Contained By:	Dissertation Abstracts International72-09B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3461158
ISBN:	9781124742465

Association statistics under the PPL framework.
Huang, Yungui.

Association statistics under the PPL framework. - 142 p.

Source: Dissertation Abstracts International, Volume: 72-09, Section: B, page: 5378.

Thesis (Ph.D.)--The University of Iowa, 2011.

In this dissertation, the posterior probability of linkage (PPL) framework is extended to the analysis of case-control (CC) data and three new linkage disequilibrium (LD) statistics are introduced. These statistics measure the evidence for or against LD, rather than testing the null hypothesis of no LD, and they therefore avoid the need for multiple testing corrections. They are suitable not only for CC designs but also can be used in application to family data, ranging from trios to complex pedigrees, all under the same statistical framework, allowing for the unified analysis of these disparate data structures. They also provide the other core advantages of the PPL framework, including the use of sequential updating to accumulate LD evidence across potentially heterogeneous sets of subsets of data; parameterization in terms of a very general trait likelihood, which simultaneously considers dominant, recessive, and additive models; and a straightforward mechanism for modeling two-locus epistasis. Finally, being implemented within the PPL framework, the new statistics readily allow linkage information obtained from distinct data, to be incorporated into LD analyses in the form of a prior probability distribution. Performance of the proposed LD statistics is examined using simulated data. In addition, the effects of key modeling violations on performance are assessed. These statistics are also applied to a previously published type 1 diabetes (T1D) family dataset with a few candidate genes with previously reported weak associations, and another T1D CC dataset also previously published as a genome-wide association (GWA) study with some strong associations reported. The new LD statistics under the PPLD framework confirm most of the findings in the published work and also find some new SNPs suspected of being associated with T1D. Sequential updating between the family dataset and the CC dataset dramatically increased the association signal strength for a CTLA4 SNP genotyped in both studies. Linkage information gleaned from the family dataset is also combined into the LD analysis of the CC dataset to demonstrate the utility of this unique feature of the PPL framework, and specifically for the new LD statistics.

ISBN: 9781124742465Subjects--Topical Terms:

517247
Statistics.

Association statistics under the PPL framework.
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005 20141022133320.5
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020 $a 9781124742465
035 $a (MiAaPQ)AAI3461158
035 $a AAI3461158
040 $a MiAaPQ $c MiAaPQ
100 1 $a Huang, Yungui. $3 2101986
245 1 0 $a Association statistics under the PPL framework.
300 $a 142 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-09, Section: B, page: 5378.
500 $a Advisers: Veronica J. Vieland; Alberto M. Segre.
502 $a Thesis (Ph.D.)--The University of Iowa, 2011.
520 $a In this dissertation, the posterior probability of linkage (PPL) framework is extended to the analysis of case-control (CC) data and three new linkage disequilibrium (LD) statistics are introduced. These statistics measure the evidence for or against LD, rather than testing the null hypothesis of no LD, and they therefore avoid the need for multiple testing corrections. They are suitable not only for CC designs but also can be used in application to family data, ranging from trios to complex pedigrees, all under the same statistical framework, allowing for the unified analysis of these disparate data structures. They also provide the other core advantages of the PPL framework, including the use of sequential updating to accumulate LD evidence across potentially heterogeneous sets of subsets of data; parameterization in terms of a very general trait likelihood, which simultaneously considers dominant, recessive, and additive models; and a straightforward mechanism for modeling two-locus epistasis. Finally, being implemented within the PPL framework, the new statistics readily allow linkage information obtained from distinct data, to be incorporated into LD analyses in the form of a prior probability distribution. Performance of the proposed LD statistics is examined using simulated data. In addition, the effects of key modeling violations on performance are assessed. These statistics are also applied to a previously published type 1 diabetes (T1D) family dataset with a few candidate genes with previously reported weak associations, and another T1D CC dataset also previously published as a genome-wide association (GWA) study with some strong associations reported. The new LD statistics under the PPLD framework confirm most of the findings in the published work and also find some new SNPs suspected of being associated with T1D. Sequential updating between the family dataset and the CC dataset dramatically increased the association signal strength for a CTLA4 SNP genotyped in both studies. Linkage information gleaned from the family dataset is also combined into the LD analysis of the CC dataset to demonstrate the utility of this unique feature of the PPL framework, and specifically for the new LD statistics.
590 $a School code: 0096.
650 4 $a Statistics. $3 517247
650 4 $a Biology, Genetics. $3 1017730
650 4 $a Biology, Biostatistics. $3 1018416
690 $a 0463
690 $a 0369
690 $a 0308
710 2 $a The University of Iowa. $b Statistical Genetics. $3 2101987
773 0 $t Dissertation Abstracts International $g 72-09B.
790 $a 0096
791 $a Ph.D.
792 $a 2011
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3461158