東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Genome-wide gene-gene interaction an...

The University of Texas School of Public Health., Biostatistics.

FindBook

Google Book

Amazon

博客來

Genome-wide gene-gene interaction analysis for cardiovascular disease.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Genome-wide gene-gene interaction analysis for cardiovascular disease./
作者:	Liao, Yue.
面頁冊數:	48 p.
附註:	Adviser: Momiao Xiong.
Contained By:	Masters Abstracts International47-05.
標題:	Biology, Bioinformatics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1462683
ISBN:	9781109090451

Genome-wide gene-gene interaction analysis for cardiovascular disease.
Liao, Yue.

Genome-wide gene-gene interaction analysis for cardiovascular disease. - 48 p.

Adviser: Momiao Xiong.

Thesis (M.P.H.)--The University of Texas School of Public Health, 2009.

Numerous studies have been carried out to try to better understand the genetic predisposition for cardiovascular disease. Although it is widely believed that multifactorial diseases such as cardiovascular disease is the result from effects of many genes which working alone or interact with other genes, most genetic studies have been focused on identifying of cardiovascular disease susceptibility genes and usually ignore the effects of gene-gene interactions in the analysis. The current study applies a novel linkage disequilibrium based statistic for testing interactions between two linked loci using data from a genome-wide study of cardiovascular disease. A total of 53,394 single nucleotide polymorphisms (SNPs) are tested for pair-wise interactions, and 8,644 interactions are found to be significant with p-values less than 3.5x10-11. Results indicate that known cardiovascular disease susceptibility genes tend not to have many significantly interactions. One SNP in the CACNG1 (calcium channel, voltage-dependent, gamma subunit 1) gene and one SNP in the IL3RA (interleukin 3 receptor, alpha) gene are found to have the most significant pair-wise interactions. Findings from the current study should be replicated in other independent cohort to eliminate potential false positive results.

ISBN: 9781109090451Subjects--Topical Terms:

1018415
Biology, Bioinformatics.

Genome-wide gene-gene interaction analysis for cardiovascular disease.
LDR:02290nam 2200313 a 45 001 852642
005 20100630
008 100630s2009 ||||||||||||||||| ||eng d
020 $a 9781109090451
035 $a (UMI)AAI1462683
035 $a AAI1462683
040 $a UMI $c UMI
100 1 $a Liao, Yue. $3 1018616
245 1 0 $a Genome-wide gene-gene interaction analysis for cardiovascular disease.
300 $a 48 p.
500 $a Adviser: Momiao Xiong.
500 $a Source: Masters Abstracts International, Volume: 47-05, page: 2730.
502 $a Thesis (M.P.H.)--The University of Texas School of Public Health, 2009.
520 $a Numerous studies have been carried out to try to better understand the genetic predisposition for cardiovascular disease. Although it is widely believed that multifactorial diseases such as cardiovascular disease is the result from effects of many genes which working alone or interact with other genes, most genetic studies have been focused on identifying of cardiovascular disease susceptibility genes and usually ignore the effects of gene-gene interactions in the analysis. The current study applies a novel linkage disequilibrium based statistic for testing interactions between two linked loci using data from a genome-wide study of cardiovascular disease. A total of 53,394 single nucleotide polymorphisms (SNPs) are tested for pair-wise interactions, and 8,644 interactions are found to be significant with p-values less than 3.5x10-11. Results indicate that known cardiovascular disease susceptibility genes tend not to have many significantly interactions. One SNP in the CACNG1 (calcium channel, voltage-dependent, gamma subunit 1) gene and one SNP in the IL3RA (interleukin 3 receptor, alpha) gene are found to have the most significant pair-wise interactions. Findings from the current study should be replicated in other independent cohort to eliminate potential false positive results.
590 $a School code: 0219.
650 4 $a Biology, Bioinformatics. $3 1018415
650 4 $a Biology, Biostatistics. $3 1018416
650 4 $a Biology, Genetics. $3 1017730
690 $a 0308
690 $a 0369
690 $a 0715
710 2 $a The University of Texas School of Public Health. $b Biostatistics. $3 1018615
773 0 $t Masters Abstracts International $g 47-05.
790 $a 0219
790 1 0 $a Hixson, James $e committee member
790 1 0 $a Kapadia, Asha $e committee member
790 1 0 $a Xiong, Momiao, $e advisor
791 $a M.P.H.
792 $a 2009
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1462683