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A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies.

Stegle O, Parts L, Durbin R, Winn J - PLoS Comput. Biol. (2010)

Bottom Line: We compare the performance of VBQTL with alternative methods for dealing with confounding variability on eQTL mapping datasets from simulations, yeast, mouse, and human.Employing Bayesian complexity control and joint modelling is shown to result in more precise estimates of the contribution of different confounding factors resulting in additional associations to measured transcript levels compared to alternative approaches.We present a threefold larger collection of cis eQTLs than previously found in a whole-genome eQTL scan of an outbred human population.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institutes Tübingen, Tübingen, Germany. oliver.stegle@tuebingen.mpg.de

ABSTRACT
Gene expression measurements are influenced by a wide range of factors, such as the state of the cell, experimental conditions and variants in the sequence of regulatory regions. To understand the effect of a variable of interest, such as the genotype of a locus, it is important to account for variation that is due to confounding causes. Here, we present VBQTL, a probabilistic approach for mapping expression quantitative trait loci (eQTLs) that jointly models contributions from genotype as well as known and hidden confounding factors. VBQTL is implemented within an efficient and flexible inference framework, making it fast and tractable on large-scale problems. We compare the performance of VBQTL with alternative methods for dealing with confounding variability on eQTL mapping datasets from simulations, yeast, mouse, and human. Employing Bayesian complexity control and joint modelling is shown to result in more precise estimates of the contribution of different confounding factors resulting in additional associations to measured transcript levels compared to alternative approaches. We present a threefold larger collection of cis eQTLs than previously found in a whole-genome eQTL scan of an outbred human population. Altogether, 27% of the tested probes show a significant genetic association in cis, and we validate that the additional eQTLs are likely to be real by replicating them in different sets of individuals. Our method is the next step in the analysis of high-dimensional phenotype data, and its application has revealed insights into genetic regulation of gene expression by demonstrating more abundant cis-acting eQTLs in human than previously shown. Our software is freely available online at http://www.sanger.ac.uk/resources/software/peer/.

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General additive model for sources of gene expression variability.The  matrix  of measured gene expression levels of  genes from  individuals is modelled by additive contributions from components  and observation noise . Here, the components capture the signal due to primary effect of the genetic state , known factors  and hidden factors . Some examples of possible underlying sources of variation are given above the model boxes. The groupings represent some standard genetic association models commonly used.
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pcbi-1000770-g001: General additive model for sources of gene expression variability.The matrix of measured gene expression levels of genes from individuals is modelled by additive contributions from components and observation noise . Here, the components capture the signal due to primary effect of the genetic state , known factors and hidden factors . Some examples of possible underlying sources of variation are given above the model boxes. The groupings represent some standard genetic association models commonly used.

Mentions: An important issue in such studies is additional variation in expression data that is not due to the genetic state, as illustrated in Figure 1. Intracellular fluctuations, environmental conditions, and experimental procedures are factors that all can have a strong effect on the measured transcript levels [2], [8]–[10] and thereby obscure the association signal. When measured, correct estimation of the additional variation due to these known factors allows for a more sensitive analysis of the genetic effect. For example, it has been reported that additional human eQTLs can be found when including the known factors of age, and blood cell counts in the model [7]. It is also standard procedure to correct for batch effects, such as image artefacts or sample preparation differences [11].


A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies.

Stegle O, Parts L, Durbin R, Winn J - PLoS Comput. Biol. (2010)

General additive model for sources of gene expression variability.The  matrix  of measured gene expression levels of  genes from  individuals is modelled by additive contributions from components  and observation noise . Here, the components capture the signal due to primary effect of the genetic state , known factors  and hidden factors . Some examples of possible underlying sources of variation are given above the model boxes. The groupings represent some standard genetic association models commonly used.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2865505&req=5

pcbi-1000770-g001: General additive model for sources of gene expression variability.The matrix of measured gene expression levels of genes from individuals is modelled by additive contributions from components and observation noise . Here, the components capture the signal due to primary effect of the genetic state , known factors and hidden factors . Some examples of possible underlying sources of variation are given above the model boxes. The groupings represent some standard genetic association models commonly used.
Mentions: An important issue in such studies is additional variation in expression data that is not due to the genetic state, as illustrated in Figure 1. Intracellular fluctuations, environmental conditions, and experimental procedures are factors that all can have a strong effect on the measured transcript levels [2], [8]–[10] and thereby obscure the association signal. When measured, correct estimation of the additional variation due to these known factors allows for a more sensitive analysis of the genetic effect. For example, it has been reported that additional human eQTLs can be found when including the known factors of age, and blood cell counts in the model [7]. It is also standard procedure to correct for batch effects, such as image artefacts or sample preparation differences [11].

Bottom Line: We compare the performance of VBQTL with alternative methods for dealing with confounding variability on eQTL mapping datasets from simulations, yeast, mouse, and human.Employing Bayesian complexity control and joint modelling is shown to result in more precise estimates of the contribution of different confounding factors resulting in additional associations to measured transcript levels compared to alternative approaches.We present a threefold larger collection of cis eQTLs than previously found in a whole-genome eQTL scan of an outbred human population.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institutes Tübingen, Tübingen, Germany. oliver.stegle@tuebingen.mpg.de

ABSTRACT
Gene expression measurements are influenced by a wide range of factors, such as the state of the cell, experimental conditions and variants in the sequence of regulatory regions. To understand the effect of a variable of interest, such as the genotype of a locus, it is important to account for variation that is due to confounding causes. Here, we present VBQTL, a probabilistic approach for mapping expression quantitative trait loci (eQTLs) that jointly models contributions from genotype as well as known and hidden confounding factors. VBQTL is implemented within an efficient and flexible inference framework, making it fast and tractable on large-scale problems. We compare the performance of VBQTL with alternative methods for dealing with confounding variability on eQTL mapping datasets from simulations, yeast, mouse, and human. Employing Bayesian complexity control and joint modelling is shown to result in more precise estimates of the contribution of different confounding factors resulting in additional associations to measured transcript levels compared to alternative approaches. We present a threefold larger collection of cis eQTLs than previously found in a whole-genome eQTL scan of an outbred human population. Altogether, 27% of the tested probes show a significant genetic association in cis, and we validate that the additional eQTLs are likely to be real by replicating them in different sets of individuals. Our method is the next step in the analysis of high-dimensional phenotype data, and its application has revealed insights into genetic regulation of gene expression by demonstrating more abundant cis-acting eQTLs in human than previously shown. Our software is freely available online at http://www.sanger.ac.uk/resources/software/peer/.

Show MeSH