摘要
在生物系统中,遗传相互作用指的是两个基因同时突变的表型异于它们分别突变表型叠加效果的现象.近年来,随着高通量技术的发展,遗传相互作用的高通量筛选得以实现,产生了大量遗传相互作用数据.通路基因指的是一组在同一条生物通路上相互协作的基因,它们共同完成某一项生命过程.发掘遗传相互作用数据中的通路基因可以研究基因之间如何进行相互作用共同影响某种表型,是理解生物学通路的结构和功能,生物系统进化规律,和研究复杂疾病的重要途径.然而,由于基因多效性以及高维数据处理困难等问题,如何发现遗传相互作用数据中的通路基因面临着很大挑战.本文中,我们通过计算基因间的条件独立性,即删除掉通路中的已知基因的影响后,研究基因间的相关性.而这些通路中的已知基因将在模型中作为种子基因发掘通路中的其他基因.我们将讨论该算法在模拟数据和实际数据中的计算效果,证明该算法在遗传相互作用数据应用中的有效性.
In biological systems, genetic interaction refers to the phenomenon that the phenotypes of two genes are mutated at same time differ from their respective mutant phenotypic superposition effects. In recent years, with the development of high-throughput technology, highthroughput screening of genetic interactions is achieved, resulting in a large number of genetic interactions. Pathway genes refer to a group of genes that they cooperate with each other on the same biological pathway, and they together complete a life process. The study of the pathway genes in genetic interaction data can know how genes interact with each other to influence a phenotype.It is an important way to understand the structure and function of biological pathways, the evolution of biological systems, and the study of complex diseases. However, due to gene pleiotropic and difficult to deal with high-dimensional data and other issues, how to find the pathway genes in genetic interaction data is very challenging. In this paper, we study the correlation between genes by calculating the conditional independence of gene, that is to say removing the effects of known genes in the pathway. The known genes will be used as seed genes to excavate other genes.We will discuss the effect of the algorithm in simulated data and real data to prove the validity in the application of genetic interaction data.
出处
《生物数学学报》
2018年第1期120-126,共7页
Journal of Biomathematics
基金
中国博士后科学基金(2016M602091)
关键词
遗传相互作用数据
基因通路
偏相关系数
Genetic interaction data
Pathway genes
Partial correlation coefficient