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Genome-wide algorithm for detecting ...

Xu, Yaji.

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Genome-wide algorithm for detecting CNV associations with diseases .

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Genome-wide algorithm for detecting CNV associations with diseases ./
作者:	Xu, Yaji.
面頁冊數:	95 p.
附註:	Source: Dissertation Abstracts International, Volume: 71-05, Section: B, page: .
Contained By:	Dissertation Abstracts International71-05B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3398980
ISBN:	9781109735697

Genome-wide algorithm for detecting CNV associations with diseases .
Xu, Yaji.

Genome-wide algorithm for detecting CNV associations with diseases . - 95 p.

Source: Dissertation Abstracts International, Volume: 71-05, Section: B, page: .

Thesis (Ph.D.)--The University of Texas School of Public Health, 2010.

SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). The quality of the inferences about copy number can be affected by many factors including batch effects, DNA sample preparation, signal processing, and analytical approach. Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP genotyping data. However, these algorithms lack specificity to detect small CNVs due to the high false positive rate when calling CNVs based on the intensity values. Association tests based on detected CNVs therefore lack power even if the CNVs affecting disease risk are common. In this research, by combining an existing Hidden Markov Model (HMM) and the logistic regression model, a new genome-wide logistic regression algorithm was developed to detect CNV associations with diseases. We showed that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than an existing popular algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV.

ISBN: 9781109735697Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Genome-wide algorithm for detecting CNV associations with diseases .
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500 $a Source: Dissertation Abstracts International, Volume: 71-05, Section: B, page: .
500 $a Advisers: Christopher I. Amos; Yunxin Fu.
502 $a Thesis (Ph.D.)--The University of Texas School of Public Health, 2010.
520 $a SNP genotyping arrays have been developed to characterize single-nucleotide polymorphisms (SNPs) and DNA copy number variations (CNVs). The quality of the inferences about copy number can be affected by many factors including batch effects, DNA sample preparation, signal processing, and analytical approach. Nonparametric and model-based statistical algorithms have been developed to detect CNVs from SNP genotyping data. However, these algorithms lack specificity to detect small CNVs due to the high false positive rate when calling CNVs based on the intensity values. Association tests based on detected CNVs therefore lack power even if the CNVs affecting disease risk are common. In this research, by combining an existing Hidden Markov Model (HMM) and the logistic regression model, a new genome-wide logistic regression algorithm was developed to detect CNV associations with diseases. We showed that the new algorithm is more sensitive and can be more powerful in detecting CNV associations with diseases than an existing popular algorithm, especially when the CNV association signal is weak and a limited number of SNPs are located in the CNV.
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