Limits...
A statistical framework for joint eQTL analysis in multiple tissues.

Flutre T, Wen X, Pritchard J, Stephens M - PLoS Genet. (2013)

Bottom Line: By modeling the sharing of active eQTLs among tissues, this framework increases power to detect eQTLs that are present in more than one tissue compared with "tissue-by-tissue" analyses that examine each tissue separately.Conversely, by modeling the inactivity of eQTLs in some tissues, the framework allows the proportion of eQTLs shared across different tissues to be formally estimated as parameters of a model, addressing the difficulties of accounting for incomplete power when comparing overlaps of eQTLs identified by tissue-by-tissue analyses.Further, the results suggest that, in contrast to previous analyses of the same data, the majority of eQTLs detectable in these data are shared among all three tissues.

View Article: PubMed Central - PubMed

Affiliation: Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America.

ABSTRACT
Mapping expression Quantitative Trait Loci (eQTLs) represents a powerful and widely adopted approach to identifying putative regulatory variants and linking them to specific genes. Up to now eQTL studies have been conducted in a relatively narrow range of tissues or cell types. However, understanding the biology of organismal phenotypes will involve understanding regulation in multiple tissues, and ongoing studies are collecting eQTL data in dozens of cell types. Here we present a statistical framework for powerfully detecting eQTLs in multiple tissues or cell types (or, more generally, multiple subgroups). The framework explicitly models the potential for each eQTL to be active in some tissues and inactive in others. By modeling the sharing of active eQTLs among tissues, this framework increases power to detect eQTLs that are present in more than one tissue compared with "tissue-by-tissue" analyses that examine each tissue separately. Conversely, by modeling the inactivity of eQTLs in some tissues, the framework allows the proportion of eQTLs shared across different tissues to be formally estimated as parameters of a model, addressing the difficulties of accounting for incomplete power when comparing overlaps of eQTLs identified by tissue-by-tissue analyses. Applying our framework to re-analyze data from transformed B cells, T cells, and fibroblasts, we find that it substantially increases power compared with tissue-by-tissue analysis, identifying 63% more genes with eQTLs (at FDR = 0.05). Further, the results suggest that, in contrast to previous analyses of the same data, the majority of eQTLs detectable in these data are shared among all three tissues.

Show MeSH

Related in: MedlinePlus

Example of an eQTL with weak, yet consistent effects.A. Boxplots of the PC-corrected expression levels from gene ASCC1 (Ensembl id ENSG00000138303) in all three cell types, color-coded by genotype class at SNP rs1678614. B. Forest plot of estimated standardized effect sizes of this eQTL. Note that none of the  from the tissue-by-tissue analysis are significant at FDR = 0.05.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3649995&req=5

pgen-1003486-g004: Example of an eQTL with weak, yet consistent effects.A. Boxplots of the PC-corrected expression levels from gene ASCC1 (Ensembl id ENSG00000138303) in all three cell types, color-coded by genotype class at SNP rs1678614. B. Forest plot of estimated standardized effect sizes of this eQTL. Note that none of the from the tissue-by-tissue analysis are significant at FDR = 0.05.

Mentions: In many cases, the eQTLs detected by but not by the tissue-by-tissue analysis have modest effects that are consistent across tissues. Because their effects are modest in each tissue, they fail to reach the threshold for statistical significance in any single tissue, and so the tissue-by-tissue analysis misses them. But because their effects are consistent across tissues, the joint analysis is able to detect them. Figure 4 shows an example illustrating this (gene ASCC1, Ensembl id ENSG00000138303, with SNP rs1678614). The PC-corrected phenotypes already indicate that this gene-SNP pair looks like a consistent eQTL (Figure 4A), and its effect size estimates are highly concordant across tissues (Figure 4B). However, as indicated by the on the forest plot, this eQTL is not called by the tissue-by-tissue analysis in any tissue (all the exceed ). In contrast, the joint analysis pools information across tissues to conclude that there is strong evidence for an eQTL (), and that it is likely an eQTL in all three tissues (probability 1 assigned to the consistent configuration , conditional on it being an eQTL).


A statistical framework for joint eQTL analysis in multiple tissues.

Flutre T, Wen X, Pritchard J, Stephens M - PLoS Genet. (2013)

Example of an eQTL with weak, yet consistent effects.A. Boxplots of the PC-corrected expression levels from gene ASCC1 (Ensembl id ENSG00000138303) in all three cell types, color-coded by genotype class at SNP rs1678614. B. Forest plot of estimated standardized effect sizes of this eQTL. Note that none of the  from the tissue-by-tissue analysis are significant at FDR = 0.05.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3649995&req=5

pgen-1003486-g004: Example of an eQTL with weak, yet consistent effects.A. Boxplots of the PC-corrected expression levels from gene ASCC1 (Ensembl id ENSG00000138303) in all three cell types, color-coded by genotype class at SNP rs1678614. B. Forest plot of estimated standardized effect sizes of this eQTL. Note that none of the from the tissue-by-tissue analysis are significant at FDR = 0.05.
Mentions: In many cases, the eQTLs detected by but not by the tissue-by-tissue analysis have modest effects that are consistent across tissues. Because their effects are modest in each tissue, they fail to reach the threshold for statistical significance in any single tissue, and so the tissue-by-tissue analysis misses them. But because their effects are consistent across tissues, the joint analysis is able to detect them. Figure 4 shows an example illustrating this (gene ASCC1, Ensembl id ENSG00000138303, with SNP rs1678614). The PC-corrected phenotypes already indicate that this gene-SNP pair looks like a consistent eQTL (Figure 4A), and its effect size estimates are highly concordant across tissues (Figure 4B). However, as indicated by the on the forest plot, this eQTL is not called by the tissue-by-tissue analysis in any tissue (all the exceed ). In contrast, the joint analysis pools information across tissues to conclude that there is strong evidence for an eQTL (), and that it is likely an eQTL in all three tissues (probability 1 assigned to the consistent configuration , conditional on it being an eQTL).

Bottom Line: By modeling the sharing of active eQTLs among tissues, this framework increases power to detect eQTLs that are present in more than one tissue compared with "tissue-by-tissue" analyses that examine each tissue separately.Conversely, by modeling the inactivity of eQTLs in some tissues, the framework allows the proportion of eQTLs shared across different tissues to be formally estimated as parameters of a model, addressing the difficulties of accounting for incomplete power when comparing overlaps of eQTLs identified by tissue-by-tissue analyses.Further, the results suggest that, in contrast to previous analyses of the same data, the majority of eQTLs detectable in these data are shared among all three tissues.

View Article: PubMed Central - PubMed

Affiliation: Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America.

ABSTRACT
Mapping expression Quantitative Trait Loci (eQTLs) represents a powerful and widely adopted approach to identifying putative regulatory variants and linking them to specific genes. Up to now eQTL studies have been conducted in a relatively narrow range of tissues or cell types. However, understanding the biology of organismal phenotypes will involve understanding regulation in multiple tissues, and ongoing studies are collecting eQTL data in dozens of cell types. Here we present a statistical framework for powerfully detecting eQTLs in multiple tissues or cell types (or, more generally, multiple subgroups). The framework explicitly models the potential for each eQTL to be active in some tissues and inactive in others. By modeling the sharing of active eQTLs among tissues, this framework increases power to detect eQTLs that are present in more than one tissue compared with "tissue-by-tissue" analyses that examine each tissue separately. Conversely, by modeling the inactivity of eQTLs in some tissues, the framework allows the proportion of eQTLs shared across different tissues to be formally estimated as parameters of a model, addressing the difficulties of accounting for incomplete power when comparing overlaps of eQTLs identified by tissue-by-tissue analyses. Applying our framework to re-analyze data from transformed B cells, T cells, and fibroblasts, we find that it substantially increases power compared with tissue-by-tissue analysis, identifying 63% more genes with eQTLs (at FDR = 0.05). Further, the results suggest that, in contrast to previous analyses of the same data, the majority of eQTLs detectable in these data are shared among all three tissues.

Show MeSH
Related in: MedlinePlus