Genome-wide association studies (GWAS) have successfully identified genetic susceptible variants for complex diseases. However, the underlying mechanism of such association remains largely unknown. Most disease-associated genetic variants have been shown to reside in noncoding regions, leading to the hypothesis that regulation of gene expression may be the primary biological mechanism. Current methods to characterize gene expression mediating the effect of genetic variant on diseases, often analyzed one gene at a time and ignored the network structure. The impact of genetic variant can propagate to other genes along the links in the network, then to the final disease. There could be multiple pathways from the genetic variant to the final disease, with each having the chain structure since the first node is one specific SNP (Single Nucleotide Polymorphism) variant and the end is disease outcome. One key but inadequately addressed question is how to measure the between-node connection strength and rank the effects of such chain-type pathways, which can provide statistical evidence to give the priority of some pathways for potential drug development in a cost-effective manner. We first introduce the maximal correlation coefficient (MCC) to represent the between-node connection, and then integrate MCC with K shortest paths algorithm to rank and identify the potential pathways from genetic variant to disease. The pathway importance score (PIS) was further provided to quantify the importance of each pathway. We termed this method as “MCC-SP”. Various simulations are conducted to illustrate MCC is a better measurement of the between-node connection strength than other quantities including Pearson correlation, Spearman correlation, distance correlation, mutual information, and maximal information coefficient. Finally, we applied MCC-SP to analyze one real dataset from the Religious Orders Study and the Memory and Aging Project, and successfully detected 2 typical pathways from APOE genotype to Alzheimer’s disease (AD) through gene expression enriched in Alzheimer’s disease pathway. MCC-SP has powerful and robust performance in identifying the pathway(s) from the genetic variant to the disease. The source code of MCC-SP is freely available at GitHub ( https://github.com/zhuyuchen95/ADnet ).

中文翻译：

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MCC-SP：一种强大的整合方法，可用于识别从遗传变异到复杂疾病的因果途径。

全基因组关联研究（GWAS）已成功鉴定出复杂疾病的遗传易感变异。但是，这种关联的基本机制仍然未知。多数与疾病相关的遗传变异已显示位于非编码区，从而导致以下假设：基因表达的调控可能是主要的生物学机制。当前表征介导遗传变异对疾病影响的基因表达的方法，经常一次分析一个基因而忽略了网络结构。遗传变异的影响可以沿着网络中的链接传播到其他基因，然后传播到最终疾病。从遗传变异到最终疾病可能有多种途径，由于第一个结点是一个特定的SNP（单核苷酸多态性）变体，并且每个结局都具有链结构，最终是疾病的结果。一个关键但未充分解决的问题是如何测量节点间的连接强度并对此类链型途径的影响进行排名，这可以提供统计依据，以具有成本效益的方式优先考虑某些途径用于潜在的药物开发。我们首先引入最大相关系数（MCC）来表示节点之间的连接，然后将MCC与K最短路径算法集成在一起，对从遗传变异到疾病的潜在路径进行排序和识别。还提供了途径重要性评分（​​PIS）来量化每种途径的重要性。我们称这种方法为“ MCC-SP”。进行了各种仿真，以说明MCC是节点间连接强度的更好度量，它比包括Pearson相关性，Spearman相关性，距离相关性，互信息和最大信息系数在内的其他数量更好。最后，我们使用MCC-SP分析了来自宗教秩序研究和记忆与衰老项目的一个真实数据集，并通过丰富阿尔茨海默氏病途径的基因表达成功地检测了从APOE基因型到阿尔茨海默氏病（AD）的2种典型途径。MCC-SP在鉴定从遗传变异到疾病的途径方面具有强大的功能。MCC-SP的源代码可从GitHub（https://github.com/zhuyuchen95/ADnet）免费获得。