Abstract

Molecular Interaction Network Approach (MINA) was used to elucidate candidate disease genes. The approach was implemented to identify novel gene association with commonly known autoimmune diseases [1]. In MINA, we evaluated the hypothesis that “network proximity” within a whole genome molecular interaction network can be used to inform the search for multigene inheritance. There are now numerous examples of gene discoveries based upon network proximity between novel and previously identified disease genes (Yin et al., 2017 [2], Wang et al., 2011 [3], and Barrenas et al., 2009 [4]). This study extends the application of interaction networks to the interrogation of Genome Wide Association studies: first, by showing that a group of nine autoimmune diseases (AuD) genes “seed genes”, are connected in a highly non-random manner within a whole genome network; and second, by showing that the minimal number of connecting genes required to connect a maximal number of AuD candidate genes are highly enriched as candidate genes for AuD predisposing mutations. The findings imply that a threshold number of candidate genes for any heritable disorder can be used to “seed” a molecular interaction network that•Serves to validate the disease status of closely associated seed genes•Identifies genes that are highly enriched as novel candidate disease genes•Provides a strategy for elucidation of epistatic gene x gene interactionsThe method could provide a critical toll for understanding the genetic architecture of common traits and disorders.

Highlights

  • Our study design is based on the identification and association analysis of a very small number of candidate genes where the statistical cost of multiple testing is greatly reduced and which allows for cheap and rapid testing candidate genes by testing targeted single nucleotide polymorphisms (SNPs) in case: control study

  • Genetic studies implicates set of genes that are well established for multipile and overlapping autoimmune diseases (AuD) including Type-1 diabetes (T1D) [5,6,7,8,9]

  • A meta-analysis of 18 AuD-GWAS identified a total of nine genes that are common among two or more of the following seven AuD: Celiac disease (CeD), Crohn’s disease (CD), Multiple sclerosis (MS), Rheumatoid arthritis (RA), Systemic lupus erythematosus (SLE), PSO and T1D [5]

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Summary

Introduction

[1] The Ingenuity Pathway Analysis (IPA) software: http://www.ingenuity.com 2 Ingenuity Pathway Analysis (IPA) core tool created and score-ranked networks interconnecting seed genes 3 Largest, highest-scoring network from IPA output selected 4 Candidate Genes (connecting genes), their location, and all their genotyped SNPs are identified.

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