将数学模型与基因组联系起来：联合估计模型参数和这些参数的全基因组标记效应。
Connecting mathematical models to genomes: joint estimation of model parameters and genome-wide marker effects on these parameters.
机构信息
Japan Institute of Crop Science, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-8518, Japan.
Research Center for Agricultural Information Technology, National Agriculture and Food Research Organization, Tsukuba, Ibaraki 305-0856, Japan.
出版信息
Bioinformatics. 2020 May 1;36(10):3169-3176. doi: 10.1093/bioinformatics/btaa129.
MOTIVATION
Parameters of mathematical models used in biology may be genotype-specific and regarded as new traits. Therefore, an accurate estimation of these parameters and the association mapping on the estimated parameters can lead to important findings regarding the genetic architecture of biological processes. In this study, a statistical framework for a joint analysis (JA) of model parameters and genome-wide marker effects on these parameters was proposed and evaluated.
RESULTS
In the simulation analyses based on different types of mathematical models, the JA inferred the model parameters and identified the responsible genomic regions more accurately than the independent analysis (IA). The JA of real plant data provided interesting insights into photosensitivity, which were uncovered by the IA.
AVAILABILITY AND IMPLEMENTATION
The statistical framework is provided by the R package GenomeBasedModel available at https://github.com/Onogi/GenomeBasedModel. All R and C++ scripts used in this study are also available at the site.
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
动机
生物学中数学模型的参数可能具有基因型特异性，并可视为新的特征。因此，准确估计这些参数并对估计参数进行关联映射，可能会对生物过程的遗传结构产生重要发现。本研究提出并评估了一种联合分析（JA）模型参数和全基因组标记对这些参数影响的统计框架。
结果
基于不同类型数学模型的模拟分析表明，JA 比独立分析（IA）更准确地推断模型参数并确定负责的基因组区域。对真实植物数据的 JA 分析提供了对光敏感性的有趣见解，而这些见解是通过 IA 揭示的。
可用性和实现
该统计框架由可在 https://github.com/Onogi/GenomeBasedModel 上获得的 R 包 GenomeBasedModel 提供。本研究中使用的所有 R 和 C++脚本也可在该网站上获得。
补充信息
补充数据可在 Bioinformatics 在线获得。
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