Background: Genome-wide association studies (GWAS) have successfully identified genetic susceptible variants for\ncomplex diseases. However, the underlying mechanism of such association remains largely unknown. Most diseaseassociated\ngenetic variants have been shown to reside in noncoding regions, leading to the hypothesis that\nregulation of gene expression may be the primary biological mechanism. Current methods to characterize gene\nexpression mediating the effect of genetic variant on diseases, often analyzed one gene at a time and ignored the\nnetwork structure. The impact of genetic variant can propagate to other genes along the links in the network, then\nto the final disease. There could be multiple pathways from the genetic variant to the final disease, with each\nhaving the chain structure since the first node is one specific SNP (Single Nucleotide Polymorphism) variant and\nthe end is disease outcome. One key but inadequately addressed question is how to measure the between-node\nconnection strength and rank the effects of such chain-type pathways, which can provide statistical evidence to\ngive the priority of some pathways for potential drug development in a cost-effective manner.\nResults: We first introduce the maximal correlation coefficient (MCC) to represent the between-node connection,\nand then integrate MCC with K shortest paths algorithm to rank and identify the potential pathways from genetic\nvariant to disease. The pathway importance score (PIS) was further provided to quantify the importance of each\npathway. We termed this method as â??MCC-SPâ?. Various simulations are conducted to illustrate MCC is a better\nmeasurement of the between-node connection strength than other quantities including Pearson correlation,\nSpearman correlation, distance correlation, mutual information, and maximal information coefficient. Finally, we\napplied MCC-SP to analyze one real dataset from the Religious Orders Study and the Memory and Aging Project,\nand successfully detected 2 typical pathways from APOE genotype to Alzheimerâ??s disease (AD) through gene\nexpression enriched in Alzheimerâ??s disease pathway....................
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