Detecting Regulatory Gene-Environment Interactions with Unmeasured Environmental Factors

Nicoló Fusi, Christoph Lippert, Karsten Borgwardt, Neil D. Lawrence, Oliver Stegle
Bioinformatics, 29(11):1382-1389, 2013.

Abstract

Motivation: Genomic studies have revealed a substantial heritable component of the transcriptional state of the cell. To fully understand the genetic regulation of gene expression variability, it is important to study the effect of genotype in the context of external factors such as alternative environmental conditions. In model systems, explicit environmental perturbations have been considered for this purpose, allowing to directly test for environment-specific genetic effects. However, such experiments are limited to species that can be profiled in controlled environments, hampering their use in important systems such as human. Moreover, even in seemingly tightly regulated experimental conditions, subtle environmental perturbations cannot be ruled out, and hence unknown environmental influences are frequent. Here, we propose a model-based approach to simultaneously infer unmeasured environmental factors from gene expression profiles and use them in genetic analyses, identifying environment-specific associations between polymorphic loci and individual gene expression traits.

Results: In extensive simulation studies, we show that our method is able to accurately reconstruct environmental factors and their interactions with genotype in a variety of settings. We further illustrate the use of our model in a real-world dataset in which one environmental factor has been explicitly experimentally controlled. Our method is able to accurately reconstruct the true underlying environmental factor even if it’s not given as an input, allowing to detect genuine genotype-environment interactions. In addition to the known environmental factor, we find unmeasured factors involved in novel genotype-environment interactions. Our results suggest that interactions with both known and unknown environmental factors significantly contribute to gene expression variability.

Availability: Software available at http://pmbio.github.io/envGPLVM/.

Contact: [oliver.stegle@ebi.ac.uk](oliver.stegle@ebi.ac.uk), [nicolo.fusi@sheffield.ac.uk](nicolo.fusi@sheffield.ac.uk)

Cite this Paper

BibTeX
@Article{Fusi-detecting13, title = {Detecting Regulatory Gene-Environment Interactions with Unmeasured Environmental Factors}, author = {Fusi, Nicoló and Lippert, Christoph and Borgwardt, Karsten and Lawrence, Neil D. and Stegle, Oliver}, journal = {Bioinformatics}, pages = {1382--1389}, year = {2013}, volume = {29}, number = {11}, doi = {10.1093/bioinformatics/btt148}, url = {http://inverseprobability.com/publications/fusi-detecting13.html}, abstract = {Motivation: Genomic studies have revealed a substantial heritable component of the transcriptional state of the cell. To fully understand the genetic regulation of gene expression variability, it is important to study the effect of genotype in the context of external factors such as alternative environmental conditions. In model systems, explicit environmental perturbations have been considered for this purpose, allowing to directly test for environment-specific genetic effects. However, such experiments are limited to species that can be profiled in controlled environments, hampering their use in important systems such as human. Moreover, even in seemingly tightly regulated experimental conditions, subtle environmental perturbations cannot be ruled out, and hence unknown environmental influences are frequent. Here, we propose a model-based approach to simultaneously infer unmeasured environmental factors from gene expression profiles and use them in genetic analyses, identifying environment-specific associations between polymorphic loci and individual gene expression traits.

Results: In extensive simulation studies, we show that our method is able to accurately reconstruct environmental factors and their interactions with genotype in a variety of settings. We further illustrate the use of our model in a real-world dataset in which one environmental factor has been explicitly experimentally controlled. Our method is able to accurately reconstruct the true underlying environmental factor even if it’s not given as an input, allowing to detect genuine genotype-environment interactions. In addition to the known environmental factor, we find unmeasured factors involved in novel genotype-environment interactions. Our results suggest that interactions with both known and unknown environmental factors significantly contribute to gene expression variability.

Availability: Software available at http://pmbio.github.io/envGPLVM/.

Contact: [oliver.stegle@ebi.ac.uk](oliver.stegle@ebi.ac.uk), [nicolo.fusi@sheffield.ac.uk](nicolo.fusi@sheffield.ac.uk)} }
Endnote
%0 Journal Article %T Detecting Regulatory Gene-Environment Interactions with Unmeasured Environmental Factors %A Nicoló Fusi %A Christoph Lippert %A Karsten Borgwardt %A Neil D. Lawrence %A Oliver Stegle %J Bioinformatics %D 2013 %F Fusi-detecting13 %P 1382--1389 %R 10.1093/bioinformatics/btt148 %U http://inverseprobability.com/publications/fusi-detecting13.html %V 29 %N 11 %X Motivation: Genomic studies have revealed a substantial heritable component of the transcriptional state of the cell. To fully understand the genetic regulation of gene expression variability, it is important to study the effect of genotype in the context of external factors such as alternative environmental conditions. In model systems, explicit environmental perturbations have been considered for this purpose, allowing to directly test for environment-specific genetic effects. However, such experiments are limited to species that can be profiled in controlled environments, hampering their use in important systems such as human. Moreover, even in seemingly tightly regulated experimental conditions, subtle environmental perturbations cannot be ruled out, and hence unknown environmental influences are frequent. Here, we propose a model-based approach to simultaneously infer unmeasured environmental factors from gene expression profiles and use them in genetic analyses, identifying environment-specific associations between polymorphic loci and individual gene expression traits.

Results: In extensive simulation studies, we show that our method is able to accurately reconstruct environmental factors and their interactions with genotype in a variety of settings. We further illustrate the use of our model in a real-world dataset in which one environmental factor has been explicitly experimentally controlled. Our method is able to accurately reconstruct the true underlying environmental factor even if it’s not given as an input, allowing to detect genuine genotype-environment interactions. In addition to the known environmental factor, we find unmeasured factors involved in novel genotype-environment interactions. Our results suggest that interactions with both known and unknown environmental factors significantly contribute to gene expression variability.

Availability: Software available at http://pmbio.github.io/envGPLVM/.

Contact: [oliver.stegle@ebi.ac.uk](oliver.stegle@ebi.ac.uk), [nicolo.fusi@sheffield.ac.uk](nicolo.fusi@sheffield.ac.uk)
RIS
TY - JOUR TI - Detecting Regulatory Gene-Environment Interactions with Unmeasured Environmental Factors AU - Nicoló Fusi AU - Christoph Lippert AU - Karsten Borgwardt AU - Neil D. Lawrence AU - Oliver Stegle DA - 2013/04/03 ID - Fusi-detecting13 VL - 29 IS - 11 SP - 1382 EP - 1389 DO - 10.1093/bioinformatics/btt148 UR - http://inverseprobability.com/publications/fusi-detecting13.html AB - Motivation: Genomic studies have revealed a substantial heritable component of the transcriptional state of the cell. To fully understand the genetic regulation of gene expression variability, it is important to study the effect of genotype in the context of external factors such as alternative environmental conditions. In model systems, explicit environmental perturbations have been considered for this purpose, allowing to directly test for environment-specific genetic effects. However, such experiments are limited to species that can be profiled in controlled environments, hampering their use in important systems such as human. Moreover, even in seemingly tightly regulated experimental conditions, subtle environmental perturbations cannot be ruled out, and hence unknown environmental influences are frequent. Here, we propose a model-based approach to simultaneously infer unmeasured environmental factors from gene expression profiles and use them in genetic analyses, identifying environment-specific associations between polymorphic loci and individual gene expression traits.

Results: In extensive simulation studies, we show that our method is able to accurately reconstruct environmental factors and their interactions with genotype in a variety of settings. We further illustrate the use of our model in a real-world dataset in which one environmental factor has been explicitly experimentally controlled. Our method is able to accurately reconstruct the true underlying environmental factor even if it’s not given as an input, allowing to detect genuine genotype-environment interactions. In addition to the known environmental factor, we find unmeasured factors involved in novel genotype-environment interactions. Our results suggest that interactions with both known and unknown environmental factors significantly contribute to gene expression variability.

Availability: Software available at http://pmbio.github.io/envGPLVM/.

Contact: [oliver.stegle@ebi.ac.uk](oliver.stegle@ebi.ac.uk), [nicolo.fusi@sheffield.ac.uk](nicolo.fusi@sheffield.ac.uk) ER -
APA
Fusi, N., Lippert, C., Borgwardt, K., Lawrence, N.D. & Stegle, O.. (2013). Detecting Regulatory Gene-Environment Interactions with Unmeasured Environmental Factors. Bioinformatics 29(11):1382-1389 doi:10.1093/bioinformatics/btt148 Available from http://inverseprobability.com/publications/fusi-detecting13.html.

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