Cargando…

Causal inference of regulator-target pairs by gene mapping of expression phenotypes

BACKGROUND: Correlations between polymorphic markers and observed phenotypes provide the basis for mapping traits in quantitative genetics. When the phenotype is gene expression, then loci involved in regulatory control can theoretically be implicated. Recent efforts to construct gene regulatory net...

Descripción completa

Detalles Bibliográficos
Autores principales: Kulp, David C, Jagalur, Manjunatha
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1481560/
https://www.ncbi.nlm.nih.gov/pubmed/16719927
http://dx.doi.org/10.1186/1471-2164-7-125
_version_ 1782128263246643200
author Kulp, David C
Jagalur, Manjunatha
author_facet Kulp, David C
Jagalur, Manjunatha
author_sort Kulp, David C
collection PubMed
description BACKGROUND: Correlations between polymorphic markers and observed phenotypes provide the basis for mapping traits in quantitative genetics. When the phenotype is gene expression, then loci involved in regulatory control can theoretically be implicated. Recent efforts to construct gene regulatory networks from genotype and gene expression data have shown that biologically relevant networks can be achieved from an integrative approach. In this paper, we consider the problem of identifying individual pairs of genes in a direct or indirect, causal, trans-acting relationship. RESULTS: Inspired by epistatic models of multi-locus quantitative trait (QTL) mapping, we propose a unified model of expression and genotype to identify quantitative trait genes (QTG) by extending the conventional linear model to include both genotype and expression of regulator genes and their interactions. The model provides mapping of specific genes in contrast to standard linkage approaches that implicate large QTL intervals typically containing tens of genes. In simulations, we found that the method can often detect weak trans-acting regulators amid the background noise of thousands of traits and is robust to transcription models containing multiple regulator genes. We reanalyze several pleiotropic loci derived from a large set of yeast matings and identify a likely alternative regulator not previously published. However, we also found that many regulators can not be so easily mapped due to the presence of cis-acting QTLs on the regulators, which induce close linkage among small neighborhoods of genes. QTG mapped regulator-target pairs linked to ARN1 were combined to form a regulatory module, which we observed to be highly enriched in iron homeostasis related genes and contained several causally directed links that had not been identified in other automatic reconstructions of that regulatory module. Finally, we also confirm the surprising, previously published results that regulators controlling gene expression are not enriched for transcription factors, but we do show that our more precise mapping model reveals functional enrichment for several other biological processes related to the regulation of the cell. CONCLUSION: By incorporating interacting expression and genotype, our QTG mapping method can identify specific regulator genes in contrast to standard QTL interval mapping. We have shown that the method can recover biologically significant regulator-target pairs and the approach leads to a general framework for inducing a regulatory module network topology of directed and undirected edges that can be used to identify leads in pathway analysis.
format Text
id pubmed-1481560
institution National Center for Biotechnology Information
language English
publishDate 2006
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-14815602006-06-22 Causal inference of regulator-target pairs by gene mapping of expression phenotypes Kulp, David C Jagalur, Manjunatha BMC Genomics Methodology Article BACKGROUND: Correlations between polymorphic markers and observed phenotypes provide the basis for mapping traits in quantitative genetics. When the phenotype is gene expression, then loci involved in regulatory control can theoretically be implicated. Recent efforts to construct gene regulatory networks from genotype and gene expression data have shown that biologically relevant networks can be achieved from an integrative approach. In this paper, we consider the problem of identifying individual pairs of genes in a direct or indirect, causal, trans-acting relationship. RESULTS: Inspired by epistatic models of multi-locus quantitative trait (QTL) mapping, we propose a unified model of expression and genotype to identify quantitative trait genes (QTG) by extending the conventional linear model to include both genotype and expression of regulator genes and their interactions. The model provides mapping of specific genes in contrast to standard linkage approaches that implicate large QTL intervals typically containing tens of genes. In simulations, we found that the method can often detect weak trans-acting regulators amid the background noise of thousands of traits and is robust to transcription models containing multiple regulator genes. We reanalyze several pleiotropic loci derived from a large set of yeast matings and identify a likely alternative regulator not previously published. However, we also found that many regulators can not be so easily mapped due to the presence of cis-acting QTLs on the regulators, which induce close linkage among small neighborhoods of genes. QTG mapped regulator-target pairs linked to ARN1 were combined to form a regulatory module, which we observed to be highly enriched in iron homeostasis related genes and contained several causally directed links that had not been identified in other automatic reconstructions of that regulatory module. Finally, we also confirm the surprising, previously published results that regulators controlling gene expression are not enriched for transcription factors, but we do show that our more precise mapping model reveals functional enrichment for several other biological processes related to the regulation of the cell. CONCLUSION: By incorporating interacting expression and genotype, our QTG mapping method can identify specific regulator genes in contrast to standard QTL interval mapping. We have shown that the method can recover biologically significant regulator-target pairs and the approach leads to a general framework for inducing a regulatory module network topology of directed and undirected edges that can be used to identify leads in pathway analysis. BioMed Central 2006-05-24 /pmc/articles/PMC1481560/ /pubmed/16719927 http://dx.doi.org/10.1186/1471-2164-7-125 Text en Copyright © 2006 Kulp and Jagalur; 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 Methodology Article
Kulp, David C
Jagalur, Manjunatha
Causal inference of regulator-target pairs by gene mapping of expression phenotypes
title Causal inference of regulator-target pairs by gene mapping of expression phenotypes
title_full Causal inference of regulator-target pairs by gene mapping of expression phenotypes
title_fullStr Causal inference of regulator-target pairs by gene mapping of expression phenotypes
title_full_unstemmed Causal inference of regulator-target pairs by gene mapping of expression phenotypes
title_short Causal inference of regulator-target pairs by gene mapping of expression phenotypes
title_sort causal inference of regulator-target pairs by gene mapping of expression phenotypes
topic Methodology Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1481560/
https://www.ncbi.nlm.nih.gov/pubmed/16719927
http://dx.doi.org/10.1186/1471-2164-7-125
work_keys_str_mv AT kulpdavidc causalinferenceofregulatortargetpairsbygenemappingofexpressionphenotypes
AT jagalurmanjunatha causalinferenceofregulatortargetpairsbygenemappingofexpressionphenotypes