A Systems-Genetics Approach and Data Mining Tool to Assist in the Discovery of Genes Underlying Complex Traits in Oryza sativa

Many traits of biological and agronomic significance in plants are controlled in a complex manner where multiple genes and environmental signals affect the expression of the phenotype. In Oryza sativa (rice), thousands of quantitative genetic signals have been mapped to the rice genome. In parallel, thousands of gene expression profiles have been generated across many experimental conditions. Through the discovery of networks with real gene co-expression relationships, it is possible to identify co-localized genetic and gene expression signals that implicate complex genotype-phenotype relationships. In this work, we used a knowledge-independent, systems genetics approach, to discover a high-quality set of co-expression networks, termed Gene Interaction Layers (GILs). Twenty-two GILs were constructed from 1,306 Affymetrix microarray rice expression profiles that were pre-clustered to allow for improved capture of gene co-expression relationships. Functional genomic and genetic data, including over 8,000 QTLs and 766 phenotype-tagged SNPs (p-value

植物诸多兼具生物学与农艺学重要价值的性状，其调控机制极为复杂——多种基因与环境信号共同影响性状表型的表达。在水稻（Oryza sativa）中，已有数千个数量遗传信号被锚定至水稻基因组。与此同时，针对多种实验条件已生成数千份基因表达谱数据。通过挖掘真实存在的基因共表达关联网络，可识别出共定位的遗传信号与基因表达信号，进而阐释复杂的基因型-表型关联机制。本研究采用无需先验知识的系统遗传学方法，挖掘得到一套高质量共表达网络，将其命名为基因互作层（Gene Interaction Layers，简称GILs）。研究人员从1306份经预聚类处理的Affymetrix水稻表达谱芯片数据中构建了22个GILs，该预聚类步骤可提升基因共表达关联的捕获效率。功能基因组学与遗传学数据包含超过8000个数量性状位点（Quantitative Trait Locus，QTL）以及766个表型标记单核苷酸多态性（Single Nucleotide Polymorphism，SNPs），其p值