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Indirect effect inference and application to GAW20 data

BACKGROUND: Association studies using a single type of omics data have been successful in identifying disease-associated genetic markers, but the underlying mechanisms are unaddressed. To provide a possible explanation of how these genetic factors affect the disease phenotype, integration of multipl...

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Autores principales: Li, Liming, Wang, Chan, Lu, Tianyuan, Lin, Shili, Hu, Yue-Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157197/
https://www.ncbi.nlm.nih.gov/pubmed/30255768
http://dx.doi.org/10.1186/s12863-018-0638-3
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author Li, Liming
Wang, Chan
Lu, Tianyuan
Lin, Shili
Hu, Yue-Qing
author_facet Li, Liming
Wang, Chan
Lu, Tianyuan
Lin, Shili
Hu, Yue-Qing
author_sort Li, Liming
collection PubMed
description BACKGROUND: Association studies using a single type of omics data have been successful in identifying disease-associated genetic markers, but the underlying mechanisms are unaddressed. To provide a possible explanation of how these genetic factors affect the disease phenotype, integration of multiple omics data is needed. RESULTS: We propose a novel method, LIPID (likelihood inference proposal for indirect estimation), that uses both single nucleotide polymorphism (SNP) and DNA methylation data jointly to analyze the association between a trait and SNPs. The total effect of SNPs is decomposed into direct and indirect effects, where the indirect effects are the focus of our investigation. Simulation studies show that LIPID performs better in various scenarios than existing methods. Application to the GAW20 data also leads to encouraging results, as the genes identified appear to be biologically relevant to the phenotype studied. CONCLUSIONS: The proposed LIPID method is shown to be meritorious in extensive simulations and in real-data analyses.
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spelling pubmed-61571972018-10-01 Indirect effect inference and application to GAW20 data Li, Liming Wang, Chan Lu, Tianyuan Lin, Shili Hu, Yue-Qing BMC Genet Research BACKGROUND: Association studies using a single type of omics data have been successful in identifying disease-associated genetic markers, but the underlying mechanisms are unaddressed. To provide a possible explanation of how these genetic factors affect the disease phenotype, integration of multiple omics data is needed. RESULTS: We propose a novel method, LIPID (likelihood inference proposal for indirect estimation), that uses both single nucleotide polymorphism (SNP) and DNA methylation data jointly to analyze the association between a trait and SNPs. The total effect of SNPs is decomposed into direct and indirect effects, where the indirect effects are the focus of our investigation. Simulation studies show that LIPID performs better in various scenarios than existing methods. Application to the GAW20 data also leads to encouraging results, as the genes identified appear to be biologically relevant to the phenotype studied. CONCLUSIONS: The proposed LIPID method is shown to be meritorious in extensive simulations and in real-data analyses. BioMed Central 2018-09-17 /pmc/articles/PMC6157197/ /pubmed/30255768 http://dx.doi.org/10.1186/s12863-018-0638-3 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Li, Liming
Wang, Chan
Lu, Tianyuan
Lin, Shili
Hu, Yue-Qing
Indirect effect inference and application to GAW20 data
title Indirect effect inference and application to GAW20 data
title_full Indirect effect inference and application to GAW20 data
title_fullStr Indirect effect inference and application to GAW20 data
title_full_unstemmed Indirect effect inference and application to GAW20 data
title_short Indirect effect inference and application to GAW20 data
title_sort indirect effect inference and application to gaw20 data
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6157197/
https://www.ncbi.nlm.nih.gov/pubmed/30255768
http://dx.doi.org/10.1186/s12863-018-0638-3
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