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Integrative regression network for genomic association study.

ABSTRACT: The increasing availability of multiple types of genomic profiles measured from the same cancer patients has provided numerous opportunities for investigating genomic mechanisms underlying cancer. In particular, association studies of gene expression traits with respect to multi-layered genomic features are highly useful for uncovering the underlying mechanism. Conventional correlation-based association tests are limited because they are prone to revealing indirect associations. Moreover, integration of multiple types of genomic features raises another challenge.In this study, we propose a new framework for association studies called integrative regression network that identifies genomic associations on multiple high-dimensional genomic profiles by taking into account the associations between as well as within profiles. We employed high-dimensional regression techniques to first identify the associations between different genomic profiles. Based on the resulting regression coefficients, a regression network was constructed within each profile. For example, two methylation features having similar regression coefficients with respect to a number of gene expression traits are likely to be involved in the same biological process and therefore we define an edge between two methylation features in the regression network. To extract more reliable associations, multiple sparse structured regression techniques were applied and the resulting multiple networks were merged as the integrative regression network using a similarity network fusion technique.Experiments were carried out using four different sparse structured regression methods on five cancer types from TCGA. The advantages and disadvantages of each regression method were also explored. We find there was large inconsistency in the results from different regression methods, which supports the need to extract the proposed integrative regression network from multiple complimentary regression techniques. Fusing multiple regression networks by using similarity measurements led to the identification of significant gene pairs and a resulting network with better topological properties.We developed and validated the integrative regression network scheme on multi-layered genomic profiles from TCGA. Our method facilitates identification of the strong signals as well as weaker signals by fusing information from different regression techniques. It could be extended to integrate results obtained from different cancer types as well.

SUBMITTER: Vangimalla RR

PROVIDER: S-EPMC4989890 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Integrative regression network for genomic association study.

Vangimalla Reddy Rani RR Jeong Hyun-Hwan HH Sohn Kyung-Ah KA

BMC medical genomics 20160812

<h4>Background</h4>The increasing availability of multiple types of genomic profiles measured from the same cancer patients has provided numerous opportunities for investigating genomic mechanisms underlying cancer. In particular, association studies of gene expression traits with respect to multi-layered genomic features are highly useful for uncovering the underlying mechanism. Conventional correlation-based association tests are limited because they are prone to revealing indirect association ...[more]

PMID: 27535739

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Project description:BackgroundCashmere is a keratinized product derived from the secondary hair follicles (SHFs) of cashmere goat skins. The cashmere fiber stops growing following the transition from the actively proliferating anagen stage to the apoptosis-driven catagen stage. However, little is known regarding the molecular mechanisms responsible for the occurrence of apoptosis in SHFs, especially as pertains to the role of non-coding RNAs (ncRNAs) and their interactions with other molecules. Hair follicle (HF) degeneration is caused by localized apoptosis in the skin, while anti-apoptosis pathways may coexist in adjacent HFs. Thus, elucidating the molecular interactions responsible for apoptosis and anti-apoptosis in the skin will provide insights into HF regression.ResultsWe used multiple-omics approaches to systematically identify long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and mRNAs expressed in cashmere goat skins in two crucial phases (catagen vs. anagen) of HF growth. Skin samples were collected from three cashmere goats at the anagen (September) and catagen (February) stages, and six lncRNA libraries and six miRNA libraries were constructed for further analysis. We identified 1122 known and 403 novel lncRNAs in the goat skins, 173 of which were differentially expressed between the anagen and catagen stages. We further identified 3500 gene-encoding transcripts that were differentially expressed between these two phases. We also identified 411 known miRNAs and 307 novel miRNAs, including 72 differentially expressed miRNAs. We further investigated the target genes of lncRNAs via both cis- and trans-regulation during HF growth. Our data suggest that lncRNAs and miRNAs act synergistically in the HF growth transition, and the catagen inducer factors (TGF?1 and BDNF) were regulated by miR-873 and lnc108635596 in the lncRNA-miRNA-mRNA networks.ConclusionThis study enriches the repertoire of ncRNAs in goats and other mammals, and contributes to a better understanding of the molecular mechanisms of ncRNAs involved in the regulation of HF growth and regression in goats and other hair-producing species.

Dataset Information

Integrative regression network for genomic association study.

Publications

Integrative regression network for genomic association study.

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