Statistical analysis of genetic interactions.
Statistical analysis of genetic interactions.
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DOI：
10.1017/s0016672310000595
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发表时间：
2010-12
影响因子：
1.5
通讯作者：
Yi, Nengjun
中科院分区：
文献类型：
--
作者：
Yi, Nengjun
许多常见的人类疾病和复杂的性状具有高度遗传性，并受到多种遗传和环境因素的影响。尽管全基因组关联研究（GWAS）已经成功地鉴定了许多疾病相关的变异，但这些遗传变异只能解释大多数复杂疾病的遗传性的一小部分。遗传互作（基因-基因和基因-环境）对复杂性状和疾病的产生有重要作用，可能是遗传力缺失的主要原因之一。本文综述了在动植物实验杂交和人类遗传关联研究中用于识别遗传相互作用的统计方法和相关计算机软件。主要的讨论福尔斯属于三个广泛的问题，在统计分析的遗传相互作用：定义，检测和解释的遗传相互作用。综述了近年来基于现代技术的高维数据处理方法，包括惩罚似然法和层次模型，并讨论了这些方法之间的关系。我通过强调未来研究的一些领域来结束这篇评论。
Many common human diseases and complex traits are highly heritable and influenced by multiple genetic and environmental factors. Although genome-wide association studies (GWAS) have successfully identified many disease-associated variants, these genetic variants explain only a small proportion of the heritability of most complex diseases. Genetic interactions (gene-gene and gene-environment) substantially contribute to complex traits and diseases and could be one of the main sources of the missing heritability. This paper provides an overview of the available statistical methods and related computer software for identifying genetic interactions in animal and plant experimental crosses and human genetic association studies. The main discussion falls under the three broad issues in statistical analysis of genetic interactions: the definition, detection and interpretation of genetic interactions. Recently developed methods based on modern techniques for high-dimensional data are reviewed, including penalized likelihood approaches and hierarchical models; the relationships among these methods are also discussed. I conclude this review by highlighting some areas of future research.