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      Analysis pipeline for the epistasis search – statistical versus biological filtering

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          Abstract

          Gene–gene interactions may contribute to the genetic variation underlying complex traits but have not always been taken fully into account. Statistical analyses that consider gene–gene interaction may increase the power of detecting associations, especially for low-marginal-effect markers, and may explain in part the “missing heritability.” Detecting pair-wise and higher-order interactions genome-wide requires enormous computational power. Filtering pipelines increase the computational speed by limiting the number of tests performed. We summarize existing filtering approaches to detect epistasis, after distinguishing the purposes that lead us to search for epistasis. Statistical filtering includes quality control on the basis of single marker statistics to avoid the analysis of bad and least informative data, and limits the search space for finding interactions. Biological filtering includes targeting specific pathways, integrating various databases based on known biological and metabolic pathways, gene function ontology and protein–protein interactions. It is increasingly possible to target single-nucleotide polymorphisms that have defined functions on gene expression, though not belonging to protein-coding genes. Filtering can improve the power of an interaction association study, but also increases the chance of missing important findings.

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          Most cited references39

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            MINT: the Molecular INTeraction database

            The Molecular INTeraction database (MINT, ) aims at storing, in a structured format, information about molecular interactions (MIs) by extracting experimental details from work published in peer-reviewed journals. At present the MINT team focuses the curation work on physical interactions between proteins. Genetic or computationally inferred interactions are not included in the database. Over the past four years MINT has undergone extensive revision. The new version of MINT is based on a completely remodeled database structure, which offers more efficient data exploration and analysis, and is characterized by entries with a richer annotation. Over the past few years the number of curated physical interactions has soared to over 95 000. The whole dataset can be freely accessed online in both interactive and batch modes through web-based interfaces and an FTP server. MINT now includes, as an integrated addition, HomoMINT, a database of interactions between human proteins inferred from experiments with ortholog proteins in model organisms ().
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              DIP: the database of interacting proteins.

              The Database of Interacting Proteins (DIP; http://dip.doe-mbi.ucla.edu) is a database that documents experimentally determined protein-protein interactions. This database is intended to provide the scientific community with a comprehensive and integrated tool for browsing and efficiently extracting information about protein interactions and interaction networks in biological processes. Beyond cataloging details of protein-protein interactions, the DIP is useful for understanding protein function and protein-protein relationships, studying the properties of networks of interacting proteins, benchmarking predictions of protein-protein interactions, and studying the evolution of protein-protein interactions.
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                Author and article information

                Contributors
                Journal
                Front Genet
                Front Genet
                Front. Genet.
                Frontiers in Genetics
                Frontiers Media S.A.
                1664-8021
                30 April 2014
                2014
                : 5
                : 106
                Affiliations
                [1] 1Department of Epidemiology and Biostatistics, Case Western Reserve University Cleveland, OH, USA
                [2] 2 Department of Epidemiology and Biostatistics, Michigan State University East Lansing, MI, USA
                [3] 3 Department of Medicine, University of Washington Seattle, WA, USA
                [4] 4 Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park PA, USA
                Author notes

                Edited by: Mariza De Andrade, Mayo Clinic, USA

                Reviewed by: Frida Renstrom, Lund University, Sweden; Tao Wang, Albert Einstein College of Medicine, USA

                *Correspondence: Marylyn D. Ritchie, Department of Biochemistry and Molecular Biology, The Pennsylvania State University, 512A Wartik Lab, University Park, PA 16802, USA e-mail: marylyn.ritchie@ 123456psu.edu

                This article was submitted to Applied Genetic Epidemiology, a section of the journal Frontiers in Genetics.

                Article
                10.3389/fgene.2014.00106
                4012196
                24817878
                9e2cb452-3a62-4e13-80bb-d5764b3a30ae
                Copyright © 2014 Sun, Lu, Mukheerjee, Crane, Elston and Ritchie.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 11 January 2014
                : 10 April 2014
                Page count
                Figures: 0, Tables: 1, Equations: 0, References: 58, Pages: 7, Words: 0
                Categories
                Genetics
                Mini Review Article

                Genetics
                epistasis,genetic interaction,biological interaction,filtering pipeline,optimal search
                Genetics
                epistasis, genetic interaction, biological interaction, filtering pipeline, optimal search

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