Limits...
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/.

Show MeSH
Validation of VBeQTLs by comparison to standard eQTLs.(a,b,d,e) Venn diagrams depicting overlap of probes with a standard eQTL or VBeQTL in the CEU population and probes with an eQTL in other populations. (c,f) Standard and VBeQTL location and strength relative to the transcription start site.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC2865505&req=5

pcbi-1000770-g006: Validation of VBeQTLs by comparison to standard eQTLs.(a,b,d,e) Venn diagrams depicting overlap of probes with a standard eQTL or VBeQTL in the CEU population and probes with an eQTL in other populations. (c,f) Standard and VBeQTL location and strength relative to the transcription start site.

Mentions: We repeated this genome-wide experiment on pooled populations. Due to the increased sample size, it was possible to detect additional associations. We found 2696 genes with a VBeQTL compared to 1045 genes with a standard eQTL at the 0.1% FPR (Figure 6a). The VBeQTLs in the pooled sample cover of all the considered probes, suggesting that the number of human genes whose expression levels are affected by common cis-acting genetic variation may be significantly higher than previously shown [24], [25]. This additional abundance of associations suggests that detection of cis eQTLs has not been saturated and larger sample sizes may lead to evidence of even more extensive cis regulation by common polymorphisms.


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)

Validation of VBeQTLs by comparison to standard eQTLs.(a,b,d,e) Venn diagrams depicting overlap of probes with a standard eQTL or VBeQTL in the CEU population and probes with an eQTL in other populations. (c,f) Standard and VBeQTL location and strength relative to the transcription start site.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi-1000770-g006: Validation of VBeQTLs by comparison to standard eQTLs.(a,b,d,e) Venn diagrams depicting overlap of probes with a standard eQTL or VBeQTL in the CEU population and probes with an eQTL in other populations. (c,f) Standard and VBeQTL location and strength relative to the transcription start site.
Mentions: We repeated this genome-wide experiment on pooled populations. Due to the increased sample size, it was possible to detect additional associations. We found 2696 genes with a VBeQTL compared to 1045 genes with a standard eQTL at the 0.1% FPR (Figure 6a). The VBeQTLs in the pooled sample cover of all the considered probes, suggesting that the number of human genes whose expression levels are affected by common cis-acting genetic variation may be significantly higher than previously shown [24], [25]. This additional abundance of associations suggests that detection of cis eQTLs has not been saturated and larger sample sizes may lead to evidence of even more extensive cis regulation by common polymorphisms.

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