Systems genetics for drug target discovery

被引:29
|
作者
Penrod, Nadia M. [1 ]
Cowper-Sal-Iari, Richard [1 ]
Moore, Jason H. [1 ,2 ]
机构
[1] Dartmouth Med Sch, Computat Genet Lab, Lebanon, NH 03756 USA
[2] Dartmouth Med Sch, Inst Quantitat Biomed Sci, Lebanon, NH 03756 USA
关键词
CHRONIC MYELOGENOUS LEUKEMIA; GENOME-WIDE ASSOCIATION; FACTOR-H POLYMORPHISM; TYROSINE KINASE; MISSING HERITABILITY; ABL PROTEIN; EXPRESSION; IDENTIFICATION; COMPLEMENT; CELLS;
D O I
10.1016/j.tips.2011.07.002
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
The collection and analysis of genomic data has the potential to reveal novel druggable targets by providing insight into the genetic basis of disease. However, the number of drugs targeting new molecular entities, approved by the US Food and Drug Administration has not increased in the years since the collection of genomic data has become commonplace. The paucity of translatable results can be partly attributed to conventional analysis methods that test one gene at a time in an effort to identify disease-associated factors as candidate drug targets. By disengaging genetic factors from their position within the genetic regulatory system, much of the information stored within the genomic data set is lost. Here we discuss how genomic data is used to identify disease-associated genes or genomic regions, how disease-associated regions are validated as functional targets, and the role network analysis can play in bridging the gap between data generation and effective drug target identification.
引用
收藏
页码:623 / 630
页数:8
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