系统遗传学:基因表达的附加值。

Hfsp Journal Pub Date : 2010-02-01 Epub Date: 2010-01-29 DOI:10.2976/1.3292182
Peter M Visscher, Michael E Goddard
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引用次数: 4

摘要

了解基因型和表型之间的因果关系是遗传学的长期目标。除了允许测量许多DNA变异的高通量技术之外,还可以使用阵列技术测量特定组织中的基因表达。“系统遗传学”是一门新兴学科,它结合了基因型、基因表达和结果表型的密集数据,以回答从基因型到表型的因果途径的基本问题。Chen等人最近发表的一篇论文[Mol. system]。《生物学报》5,310(2009)]以酵母的耐药性为模型,探讨了mRNA表达的相对水平是否有助于阐明从基因型到表型的因果路径。这组作者表明,在无药物环境中测量的遗传标记和基因表达数据可以结合起来预测存在药物的酵母菌株的生长。他们认为,他们的预测可以用来确定因果途径,对于用于预测的基因子集,作者证明,这些基因通过删除基因或过度表达基因或在酵母菌株之间交换等位基因而对药物敏感性产生影响。这种方法也可以应用于包括人类在内的其他物种,并可能成为个性化医疗研究的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Systems genetics: the added value of gene expression.

Understanding causal relationships between genotypes and phenotypes is a long-standing aim in genetics. In addition to high-throughput technologies that allow the measurement of many DNA variants it is possible to measure gene expression in specific tissues using array technology. "Systems genetics" is an emerging discipline that combines dense data on genotypes, gene expression, and outcome phenotypes to answer fundamental questions about causal pathways from genotype to phenotype. A recent paper by Chen et al. [Mol. Syst. Biol. 5, 310 (2009)] addressed the question of whether relative levels of mRNA expression help to elucidate causal paths from genotype to phenotype, using drug resistance in yeast as a model. The authors show that data on genetic markers and on gene expression, measured in a drug-free environment, can be combined to predict the growth of a yeast strain in the presence of a drug. They argue that their prediction can be used to identify causal pathways and for a subset of the genes used in prediction, the authors demonstrate that these genes cause an effect on drug sensitivity by deleting the gene or overexpressing it or swapping alleles between strains of yeast. This approach can also be applied to other species, including humans, and may become a tool in the study of personalized medicine.

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Hfsp Journal
Hfsp Journal 综合性期刊-综合性期刊
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Frontiers in life science. Inherited adaptation of genome-rewired cells in response to a challenging environment. Network reconstruction reveals new links between aging and calorie restriction in yeast. Molecular motors as an auto-oscillator. Robustness versus evolvability: a paradigm revisited.
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