Prioritization of schizophrenia risk genes by a network-regularized logistic regression method

Wen Zhang, Jhin Rong Lin, Rubén Nogales-Cadenas, Quanwei Zhang, Ying Cai, Zhengdong D. Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Schizophrenia (SCZ) is a severe mental disorder with a large genetic component. While recent large-scale microarray- and sequencing-based genome wide association studies have made significant progress toward finding SCZ risk variants and genes of subtle effect, the interactions among them were not considered in those studies. Using a protein-protein interaction network both in our regression model and to generate a SCZ gene subnetwork, we developed an analytical framework with Logit-Lapnet, the graphical Laplacian-regularized logistic regression, for whole exome sequencing (WES) data analysis to detect SCZ gene subnetworks. Using simulated data from sequencing-based association study, we compared the performances of Logit-Lapnet with other logistic regression (LR)-based models. We use Logit-Lapnet to prioritize genes according to their coefficients and select top-ranked genes as seeds to generate the gene sub-network that is associated to SCZ. The comparison demonstrated not only the applicability but also better performance of Logit-Lapnet to score disease risk genes using sequencing-based association data. We applied our method to SCZ whole exome sequencing data and selected top-ranked risk genes, the majority of which are either known SCZ genes or genes potentially associated with SCZ. We then used the seed genes to construct SCZ gene subnetworks. This result demonstrates that by ranking gene according to their disease contributions our method scores and thus prioritizes disease risk genes for further investigation. An implementation of our approach in MATLAB is freely available for download at: http://zdzlab.einstein.yu.edu/1/publications/ LapNet-MATLAB.zip.

Original languageEnglish (US)
Title of host publicationBioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings
PublisherSpringer Verlag
Pages434-445
Number of pages12
Volume9656
ISBN (Print)9783319317434
DOIs
StatePublished - 2016
Event4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016 - Granada, Spain
Duration: Apr 20 2016Apr 22 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9656
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other4th International Work-Conference on Bioinformatics and Biomedical Engineering, IWBBIO 2016
CountrySpain
CityGranada
Period4/20/164/22/16

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

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  • Cite this

    Zhang, W., Lin, J. R., Nogales-Cadenas, R., Zhang, Q., Cai, Y., & Zhang, Z. D. (2016). Prioritization of schizophrenia risk genes by a network-regularized logistic regression method. In Bioinformatics and Biomedical Engineering - 4th International Conference, IWBBIO 2016, Proceedings (Vol. 9656, pp. 434-445). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9656). Springer Verlag. https://doi.org/10.1007/978-3-319-31744-1_39