Identification of Genetic Interaction Networks Via an Evolutionary Algorithm Evolved Bayesian Network

Overview

Journal BioData Min

Publisher Biomed Central

Specialty Biology

Date 2016 May 12

PMID 27168765

Citations 6

Authors

Ruowang Li

Scott M Dudek

Dokyoon Kim

Molly A Hall

Yuki Bradford

Peggy L Peissig

Murray H Brilliant

James G Linneman

Catherine A McCarty

Le Bao

Marylyn D Ritchie

Affiliations

Soon will be listed here.

Abstract

Background: The future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With the fast paced improvement of high-throughput methods for genome sequencing, a tremendous amount of genetics data have already been generated. The next hurdle for precision medicine is to have sufficient computational tools for analyzing large sets of data. Genome-Wide Association Studies (GWAS) have been the primary method to assess the relationship between single nucleotide polymorphisms (SNPs) and disease traits. While GWAS is sufficient in finding individual SNPs with strong main effects, it does not capture potential interactions among multiple SNPs. In many traits, a large proportion of variation remain unexplained by using main effects alone, leaving the door open for exploring the role of genetic interactions. However, identifying genetic interactions in large-scale genomics data poses a challenge even for modern computing.

Results: For this study, we present a new algorithm, Grammatical Evolution Bayesian Network (GEBN) that utilizes Bayesian Networks to identify interactions in the data, and at the same time, uses an evolutionary algorithm to reduce the computational cost associated with network optimization. GEBN excelled in simulation studies where the data contained main effects and interaction effects. We also applied GEBN to a Type 2 diabetes (T2D) dataset obtained from the Marshfield Personalized Medicine Research Project (PMRP). We were able to identify genetic interactions for T2D cases and controls and use information from those interactions to classify T2D samples. We obtained an average testing area under the curve (AUC) of 86.8 %. We also identified several interacting genes such as INADL and LPP that are known to be associated with T2D.

Conclusions: Developing the computational tools to explore genetic associations beyond main effects remains a critically important challenge in human genetics. Methods, such as GEBN, demonstrate the utility of considering genetic interactions, as they likely explain some of the missing heritability.

Citing Articles

Identifying large-scale interaction atlases using probabilistic graphs and external knowledge.

Chanumolu S, Otu H J Clin Transl Sci. 2022; 6(1):e27.

PMID: 35321220 PMC: 8922291. DOI: 10.1017/cts.2022.18.

Novel EDGE encoding method enhances ability to identify genetic interactions.

Hall M, Wallace J, Lucas A, Bradford Y, Verma S, Muller-Myhsok B PLoS Genet. 2021; 17(6):e1009534.

PMID: 34086673 PMC: 8208534. DOI: 10.1371/journal.pgen.1009534.

Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network.

Guo Y, Zhong Z, Yang C, Hu J, Jiang Y, Liang Z BMC Bioinformatics. 2019; 20(1):444.

PMID: 31455207 PMC: 6712799. DOI: 10.1186/s12859-019-3022-z.

Collective feature selection to identify crucial epistatic variants.

Verma S, Lucas A, Zhang X, Veturi Y, Dudek S, Li B BioData Min. 2018; 11:5.

PMID: 29713383 PMC: 5907720. DOI: 10.1186/s13040-018-0168-6.

Analysis of high-resolution 3D intrachromosomal interactions aided by Bayesian network modeling.

Zhang X, Branciamore S, Gogoshin G, Rodin A, Riggs A Proc Natl Acad Sci U S A. 2017; 114(48):E10359-E10368.

PMID: 29133398 PMC: 5715735. DOI: 10.1073/pnas.1620425114.

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