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Inference of modules associated to eQTLs.

Kreimer A, Litvin O, Hao K, Molony C, Pe'er D, Pe'er I - Nucleic Acids Res. (2012)

Bottom Line: The modules are significantly more, larger and denser than found in permuted data.Quantification of the confidence in a module as a likelihood score, allows us to detect transcripts that do not reach genome-wide significance level.This and further phenotypic analysis provide a validation for our methodology.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Columbia University, New York 10032, USA. anat.kreimer@gmail.com

ABSTRACT
Cataloging the association of transcripts to genetic variants in recent years holds the promise for functional dissection of regulatory structure of human transcription. Here, we present a novel approach, which aims at elucidating the joint relationships between transcripts and single-nucleotide polymorphisms (SNPs). This entails detection and analysis of modules of transcripts, each weakly associated to a single genetic variant, together exposing a high-confidence association signal between the module and this 'main' SNP. To explore how transcripts in a module are related to causative loci for that module, we represent such dependencies by a graphical model. We applied our method to the existing data on genetics of gene expression in the liver. The modules are significantly more, larger and denser than found in permuted data. Quantification of the confidence in a module as a likelihood score, allows us to detect transcripts that do not reach genome-wide significance level. Topological analysis of each module identifies novel insights regarding the flow of causality between the main SNP and transcripts. We observe similar annotations of modules from two sources of information: the enrichment of a module in gene subsets and locus annotation of the genetic variants. This and further phenotypic analysis provide a validation for our methodology.

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The number of (a) association pairs (b) modules and (c) large modules in real data compared with 100 permuted data sets. Although only 93 out of the 100 permutated data sets have fewer association pairs than in the real data, all of them have fewer (large) modules.
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gks269-F2: The number of (a) association pairs (b) modules and (c) large modules in real data compared with 100 permuted data sets. Although only 93 out of the 100 permutated data sets have fewer association pairs than in the real data, all of them have fewer (large) modules.

Mentions: The first step detects 67 540 association pairs of a SNP s and a transcript t whose expression level is putatively associated with s (nominal association P < 10−5, see ‘Materials and Methods’ section for details). The distribution of the number of pairs in the permuted data (Figure 2a) demonstrates that the observed number of association pairs is consistent with the expectation (P ≈ 0.07). We eliminate 623 pairs that include transcripts whose association statistic is strongly distorted, as observed by permutation (see ‘Materials and Methods’ section for details). We proceed with analyzing the remaining 66 917 association pairs.Figure 2.


Inference of modules associated to eQTLs.

Kreimer A, Litvin O, Hao K, Molony C, Pe'er D, Pe'er I - Nucleic Acids Res. (2012)

The number of (a) association pairs (b) modules and (c) large modules in real data compared with 100 permuted data sets. Although only 93 out of the 100 permutated data sets have fewer association pairs than in the real data, all of them have fewer (large) modules.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3401454&req=5

gks269-F2: The number of (a) association pairs (b) modules and (c) large modules in real data compared with 100 permuted data sets. Although only 93 out of the 100 permutated data sets have fewer association pairs than in the real data, all of them have fewer (large) modules.
Mentions: The first step detects 67 540 association pairs of a SNP s and a transcript t whose expression level is putatively associated with s (nominal association P < 10−5, see ‘Materials and Methods’ section for details). The distribution of the number of pairs in the permuted data (Figure 2a) demonstrates that the observed number of association pairs is consistent with the expectation (P ≈ 0.07). We eliminate 623 pairs that include transcripts whose association statistic is strongly distorted, as observed by permutation (see ‘Materials and Methods’ section for details). We proceed with analyzing the remaining 66 917 association pairs.Figure 2.

Bottom Line: The modules are significantly more, larger and denser than found in permuted data.Quantification of the confidence in a module as a likelihood score, allows us to detect transcripts that do not reach genome-wide significance level.This and further phenotypic analysis provide a validation for our methodology.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Columbia University, New York 10032, USA. anat.kreimer@gmail.com

ABSTRACT
Cataloging the association of transcripts to genetic variants in recent years holds the promise for functional dissection of regulatory structure of human transcription. Here, we present a novel approach, which aims at elucidating the joint relationships between transcripts and single-nucleotide polymorphisms (SNPs). This entails detection and analysis of modules of transcripts, each weakly associated to a single genetic variant, together exposing a high-confidence association signal between the module and this 'main' SNP. To explore how transcripts in a module are related to causative loci for that module, we represent such dependencies by a graphical model. We applied our method to the existing data on genetics of gene expression in the liver. The modules are significantly more, larger and denser than found in permuted data. Quantification of the confidence in a module as a likelihood score, allows us to detect transcripts that do not reach genome-wide significance level. Topological analysis of each module identifies novel insights regarding the flow of causality between the main SNP and transcripts. We observe similar annotations of modules from two sources of information: the enrichment of a module in gene subsets and locus annotation of the genetic variants. This and further phenotypic analysis provide a validation for our methodology.

Show MeSH