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Joint study of genetic regulators for expression traits related to breast cancer.

Zheng T, Wang S, Cong L, Ding Y, Ionita-Laza I, Lo SH - BMC Proc (2007)

Bottom Line: We also derived evidence on interacting genetic regulatory loci shared by a number of these transcripts.Interesting inter-regulation patterns and significant overlaps of genetic regulators between transcripts were observed.Interaction association results returned more expression quantitative trait locus hotspots that are significant.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistics, Columbia University, New York, New York 10027, USA. tzheng@stat.columbia.edu

ABSTRACT

Background: The mRNA expression levels of genes have been shown to have discriminating power for the classification of breast cancer. Studying the heritability of gene expression levels on breast cancer related transcripts can lead to the identification of shared common regulators and inter-regulation patterns, which would be important for dissecting the etiology of breast cancer.

Results: We applied multilocus association genome-wide scans to 18 breast cancer related transcripts and combined the results with traditional linkage scans. Regulatory hotspots for these transcripts were identified and some inter-regulation patterns were observed. We also derived evidence on interacting genetic regulatory loci shared by a number of these transcripts.

Conclusion: In this paper, by restricting to a set of related genes, we were able to employ a more detailed multilocus approach that evaluates both marginal and interaction association signals at each single-nucleotide polymorphism. Interesting inter-regulation patterns and significant overlaps of genetic regulators between transcripts were observed. Interaction association results returned more expression quantitative trait locus hotspots that are significant.

No MeSH data available.


Related in: MedlinePlus

Hierarchical clustering of transcripts. Clustering is based on the phenotype (expression values), common interacting loci pairs, qGTD return frequencies, and overall return frequencies.
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Figure 3: Hierarchical clustering of transcripts. Clustering is based on the phenotype (expression values), common interacting loci pairs, qGTD return frequencies, and overall return frequencies.

Mentions: In Figure 3, we clustered transcripts using four sets of information (the phenotype, the number of shared interacting regulatory pairs, the qGTD return frequencies, and the overall return frequencies). BRCA1 and RAD51AP1 are found to share much more interacting regulatory loci than other transcript pairs. Also notably, the grouping based on interacting regulatory activities is different from that based on overall regulatory activities. Consistent clustering similarity between phenotype and association results was observed only on the strongest correlated pairs: BRCA1 and RAD51AP1, TP53I11 and RPPM. Such similarity is much weaker for the interaction association signals (shared interacting loci pairs and qGTD return frequencies).


Joint study of genetic regulators for expression traits related to breast cancer.

Zheng T, Wang S, Cong L, Ding Y, Ionita-Laza I, Lo SH - BMC Proc (2007)

Hierarchical clustering of transcripts. Clustering is based on the phenotype (expression values), common interacting loci pairs, qGTD return frequencies, and overall return frequencies.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2367474&req=5

Figure 3: Hierarchical clustering of transcripts. Clustering is based on the phenotype (expression values), common interacting loci pairs, qGTD return frequencies, and overall return frequencies.
Mentions: In Figure 3, we clustered transcripts using four sets of information (the phenotype, the number of shared interacting regulatory pairs, the qGTD return frequencies, and the overall return frequencies). BRCA1 and RAD51AP1 are found to share much more interacting regulatory loci than other transcript pairs. Also notably, the grouping based on interacting regulatory activities is different from that based on overall regulatory activities. Consistent clustering similarity between phenotype and association results was observed only on the strongest correlated pairs: BRCA1 and RAD51AP1, TP53I11 and RPPM. Such similarity is much weaker for the interaction association signals (shared interacting loci pairs and qGTD return frequencies).

Bottom Line: We also derived evidence on interacting genetic regulatory loci shared by a number of these transcripts.Interesting inter-regulation patterns and significant overlaps of genetic regulators between transcripts were observed.Interaction association results returned more expression quantitative trait locus hotspots that are significant.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistics, Columbia University, New York, New York 10027, USA. tzheng@stat.columbia.edu

ABSTRACT

Background: The mRNA expression levels of genes have been shown to have discriminating power for the classification of breast cancer. Studying the heritability of gene expression levels on breast cancer related transcripts can lead to the identification of shared common regulators and inter-regulation patterns, which would be important for dissecting the etiology of breast cancer.

Results: We applied multilocus association genome-wide scans to 18 breast cancer related transcripts and combined the results with traditional linkage scans. Regulatory hotspots for these transcripts were identified and some inter-regulation patterns were observed. We also derived evidence on interacting genetic regulatory loci shared by a number of these transcripts.

Conclusion: In this paper, by restricting to a set of related genes, we were able to employ a more detailed multilocus approach that evaluates both marginal and interaction association signals at each single-nucleotide polymorphism. Interesting inter-regulation patterns and significant overlaps of genetic regulators between transcripts were observed. Interaction association results returned more expression quantitative trait locus hotspots that are significant.

No MeSH data available.


Related in: MedlinePlus