Application of structural equation models to construct genetic networks using differentially expressed genes and single-nucleotide polymorphisms

Understanding the genetic basis of human variation is an important goal of biomedical research. In this study, we used structural equation models (SEMs) to construct genetic networks to model how specific single-nucleotide polymorphisms (SNPs) from two genes known to cause acute myeloid leukemia (AM...

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Detalles Bibliográficos
Autores principales: Lee, Seungmook, Jhun, Mina, Lee, Eun-Kyung, Park, Taesung
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Proceedings
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2367521/
https://www.ncbi.nlm.nih.gov/pubmed/18466578
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author Lee, Seungmook
Jhun, Mina
Lee, Eun-Kyung
Park, Taesung
author_facet Lee, Seungmook
Jhun, Mina
Lee, Eun-Kyung
Park, Taesung
author_sort Lee, Seungmook
collection PubMed
description Understanding the genetic basis of human variation is an important goal of biomedical research. In this study, we used structural equation models (SEMs) to construct genetic networks to model how specific single-nucleotide polymorphisms (SNPs) from two genes known to cause acute myeloid leukemia (AML) by somatic mutation, runt-related transcription factor 1 (RUNX1) and ets variant gene 6 (ETV6), affect expression levels of other genes and how RUNX1 and ETV6 are related to each other. The SEM approach allows us to compare several candidate models from which an explanatory genetic network can be constructed.
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spelling pubmed-23675212008-05-06 Application of structural equation models to construct genetic networks using differentially expressed genes and single-nucleotide polymorphisms Lee, Seungmook Jhun, Mina Lee, Eun-Kyung Park, Taesung BMC Proc Proceedings Understanding the genetic basis of human variation is an important goal of biomedical research. In this study, we used structural equation models (SEMs) to construct genetic networks to model how specific single-nucleotide polymorphisms (SNPs) from two genes known to cause acute myeloid leukemia (AML) by somatic mutation, runt-related transcription factor 1 (RUNX1) and ets variant gene 6 (ETV6), affect expression levels of other genes and how RUNX1 and ETV6 are related to each other. The SEM approach allows us to compare several candidate models from which an explanatory genetic network can be constructed. BioMed Central 2007-12-18 /pmc/articles/PMC2367521/ /pubmed/18466578 Text en Copyright © 2007 Lee et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Proceedings
Lee, Seungmook
Jhun, Mina
Lee, Eun-Kyung
Park, Taesung
Application of structural equation models to construct genetic networks using differentially expressed genes and single-nucleotide polymorphisms
title Application of structural equation models to construct genetic networks using differentially expressed genes and single-nucleotide polymorphisms
title_full Application of structural equation models to construct genetic networks using differentially expressed genes and single-nucleotide polymorphisms
title_fullStr Application of structural equation models to construct genetic networks using differentially expressed genes and single-nucleotide polymorphisms
title_full_unstemmed Application of structural equation models to construct genetic networks using differentially expressed genes and single-nucleotide polymorphisms
title_short Application of structural equation models to construct genetic networks using differentially expressed genes and single-nucleotide polymorphisms
title_sort application of structural equation models to construct genetic networks using differentially expressed genes and single-nucleotide polymorphisms
topic Proceedings
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2367521/
https://www.ncbi.nlm.nih.gov/pubmed/18466578
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