Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Learning genetic epistasis using Bayesian network scoring criteria

Jiang, X and Neapolitan, RE and Barmada, MM and Visweswaran, S (2011) Learning genetic epistasis using Bayesian network scoring criteria. BMC Bioinformatics, 12.

[img]
Preview
PDF
Published Version
Available under License : See the attached license file.

Download (520kB) | Preview
[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)

Abstract

Background: Gene-gene epistatic interactions likely play an important role in the genetic basis of many common diseases. Recently, machine-learning and data mining methods have been developed for learning epistatic relationships from data. A well-known combinatorial method that has been successfully applied for detecting epistasis is Multifactor Dimensionality Reduction (MDR). Jiang et al. created a combinatorial epistasis learning method called BNMBL to learn Bayesian network (BN) epistatic models. They compared BNMBL to MDR using simulated data sets. Each of these data sets was generated from a model that associates two SNPs with a disease and includes 18 unrelated SNPs. For each data set, BNMBL and MDR were used to score all 2-SNP models, and BNMBL learned significantly more correct models. In real data sets, we ordinarily do not know the number of SNPs that influence phenotype. BNMBL may not perform as well if we also scored models containing more than two SNPs. Furthermore, a number of other BN scoring criteria have been developed. They may detect epistatic interactions even better than BNMBL.Although BNs are a promising tool for learning epistatic relationships from data, we cannot confidently use them in this domain until we determine which scoring criteria work best or even well when we try learning the correct model without knowledge of the number of SNPs in that model.Results: We evaluated the performance of 22 BN scoring criteria using 28,000 simulated data sets and a real Alzheimer's GWAS data set. Our results were surprising in that the Bayesian scoring criterion with large values of a hyperparameter called α performed best. This score performed better than other BN scoring criteria and MDR at recall using simulated data sets, at detecting the hardest-to-detect models using simulated data sets, and at substantiating previous results using the real Alzheimer's data set.Conclusions: We conclude that representing epistatic interactions using BN models and scoring them using a BN scoring criterion holds promise for identifying epistatic genetic variants in data. In particular, the Bayesian scoring criterion with large values of a hyperparameter α appears more promising than a number of alternatives. © 2011 Jiang et al; licensee BioMed Central Ltd.


Share

Citation/Export:
Social Networking:
Share |

Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Jiang, Xxij6@pitt.eduXIJ6
Neapolitan, RE
Barmada, MMbarmada@pitt.eduBARMADA
Visweswaran, Sshv3@pitt.eduSHV3
Date: 5 April 2011
Date Type: Publication
Journal or Publication Title: BMC Bioinformatics
Volume: 12
DOI or Unique Handle: 10.1186/1471-2105-12-89
Schools and Programs: Dietrich School of Arts and Sciences > Intelligent Systems
Graduate School of Public Health > Human Genetics
School of Medicine > Biomedical Informatics
Refereed: Yes
Date Deposited: 31 Oct 2016 16:29
Last Modified: 02 Feb 2019 14:57
URI: http://d-scholarship.pitt.edu/id/eprint/30076

Metrics

Monthly Views for the past 3 years

Plum Analytics

Altmetric.com


Actions (login required)

View Item View Item