Dataset Information

Genome-wide gene-gene interaction analysis for next-generation sequencing.

ABSTRACT: The critical barrier in interaction analysis for next-generation sequencing (NGS) data is that the traditional pairwise interaction analysis that is suitable for common variants is difficult to apply to rare variants because of their prohibitive computational time, large number of tests and low power. The great challenges for successful detection of interactions with NGS data are (1) the demands in the paradigm of changes in interaction analysis; (2) severe multiple testing; and (3) heavy computations. To meet these challenges, we shift the paradigm of interaction analysis between two SNPs to interaction analysis between two genomic regions. In other words, we take a gene as a unit of analysis and use functional data analysis techniques as dimensional reduction tools to develop a novel statistic to collectively test interaction between all possible pairs of SNPs within two genome regions. By intensive simulations, we demonstrate that the functional logistic regression for interaction analysis has the correct type 1 error rates and higher power to detect interaction than the currently used methods. The proposed method was applied to a coronary artery disease dataset from the Wellcome Trust Case Control Consortium (WTCCC) study and the Framingham Heart Study (FHS) dataset, and the early-onset myocardial infarction (EOMI) exome sequence datasets with European origin from the NHLBI's Exome Sequencing Project. We discovered that 6 of 27 pairs of significantly interacted genes in the FHS were replicated in the independent WTCCC study and 24 pairs of significantly interacted genes after applying Bonferroni correction in the EOMI study.

SUBMITTER: Zhao J

PROVIDER: S-EPMC4755380 | biostudies-literature | 2016 Mar

REPOSITORIES: biostudies-literature

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Genome-wide gene-gene interaction analysis for next-generation sequencing.

Zhao Jinying J Zhu Yun Y Xiong Momiao M

European journal of human genetics : EJHG 20150715 3

The critical barrier in interaction analysis for next-generation sequencing (NGS) data is that the traditional pairwise interaction analysis that is suitable for common variants is difficult to apply to rare variants because of their prohibitive computational time, large number of tests and low power. The great challenges for successful detection of interactions with NGS data are (1) the demands in the paradigm of changes in interaction analysis; (2) severe multiple testing; and (3) heavy comput ...[more]

PMID: 26173972

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Project description:PurposeEndotoxin-induced uveitis (EIU) is a well-established mouse model for studying human acute inflammatory uveitis. The purpose of this study is to investigate the genome-wide retinal transcriptome profile of EIU.MethodsThe anterior segment of the mice was examined with a slit-lamp, and clinical scores were evaluated simultaneously. The histological changes in the posterior segment of the eyes were evaluated with hematoxylin and eosin (H&E) staining. A high throughput RNA sequencing (RNA-seq) strategy using the Illumina Hiseq 2500 platform was applied to characterize the retinal transcriptome profile from lipopolysaccharide (LPS)-treated and untreated mice. The validation of the differentially expressed genes (DEGs) was analyzed with real-time PCR.ResultsAt the 24th hour after challenge, the clinical score of the LPS group was significantly higher (3.83±0.75, mean ± standard deviation [SD]) than that of the control group (0.08±0.20, mean ± SD; p<0.001). The histological evaluation showed a large number of inflammatory cells infiltrated into the vitreous cavity in the LPS group compared with the control group. A total of 478 DEGs were identified with RNA-seq. Among these genes, 406 were upregulated and 72 were downregulated in the LPS group. Gene Ontology (GO) enrichment showed three significantly enriched upregulated terms. Twenty-one upregulated and seven downregulated pathways were remarkably enriched by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Eleven inflammatory response-, complement system-, fibrinolytic system-, and cell stress-related genes were validated to show similar results as the RNA-seq.ConclusionsWe first reported the retinal transcriptome profile of the EIU mouse with RNA-seq. The results indicate that the abnormal changes in the inflammatory response-, complement system-, fibrinolytic system-, and cell stress-related genes occurred concurrently in EIU. These genes may play an important role in the pathogenesis of EIU. This study will lead to a better understanding of the underlying mechanisms and shed light on discovering novel therapeutic targets for ocular inflammation.

Dataset Information

Genome-wide gene-gene interaction analysis for next-generation sequencing.

Publications

Genome-wide gene-gene interaction analysis for next-generation sequencing.

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