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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

Transcription hotspots identified by linkage and association scans. Linkage, the numbers of times that a SNP has LOD > 1.44 (nominal p-value = 0.01) for a transcript were counted and plotted as black vertical lines. Association, the numbers of times that a SNP is one of the top 30 association SNPs for a transcript were counted. The SNP-by-SNP transcription hotspots pattern is noisy. To have a clear pattern, these counts were aggregated into bins of ≤5 cM by chromosomes as in Morley et al. [4]. Bins with ≥5 genetic regulators identified (p = 4 × 10-3) were identified as eQTL hotspots (blue dotted lines are the selection thresholds).
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Figure 2: Transcription hotspots identified by linkage and association scans. Linkage, the numbers of times that a SNP has LOD > 1.44 (nominal p-value = 0.01) for a transcript were counted and plotted as black vertical lines. Association, the numbers of times that a SNP is one of the top 30 association SNPs for a transcript were counted. The SNP-by-SNP transcription hotspots pattern is noisy. To have a clear pattern, these counts were aggregated into bins of ≤5 cM by chromosomes as in Morley et al. [4]. Bins with ≥5 genetic regulators identified (p = 4 × 10-3) were identified as eQTL hotspots (blue dotted lines are the selection thresholds).

Mentions: Figure 2 displays aggregated linkage signals and association signals for the 18 expression traits. Such overlapping genetic regulators patterns are sometimes referred as hotspots in the literature. The linkage signal is fairly clean as shown in Figure 2. Therefore, no aggregation by bins (as in Morley et al. [4]) was done. eQTL hotspots were identified as clustered black lines. For association, counts of identified SNPs were aggregated into bins of ≤5 cM by chromosomes and bins with more than five top SNPs were identified as eQTL hotspots (p-value = 4 × 10-3, evaluated using the Poisson model outlined in Morley et al. [4]). In Figure 2, loci of breast cancer susceptibility genes are marked with red triangles. The identified hotspots overlap with these genes: linkage at 2q, 11q, and 17q; overall association at 1q, 2q, and 17q; interaction association at 8, 17p, and 20q. Linkage and association have two identified genetic regulatory loci in common, the locus of BARD1 (MIM 601593) on 2q34-35 and the locus of BRCA1 (MIM 113705) on 17q21. Both loci harbor important breast cancer genes. The overall association scans incorporate both marginal and interaction signals and thus correlate better with the marginal linkage scans. Figure 2 also displays the difference between the interaction association signals and the overall linkage signals, which demonstrates that different regulatory loci have different extent of interaction activities.


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)

Transcription hotspots identified by linkage and association scans. Linkage, the numbers of times that a SNP has LOD > 1.44 (nominal p-value = 0.01) for a transcript were counted and plotted as black vertical lines. Association, the numbers of times that a SNP is one of the top 30 association SNPs for a transcript were counted. The SNP-by-SNP transcription hotspots pattern is noisy. To have a clear pattern, these counts were aggregated into bins of ≤5 cM by chromosomes as in Morley et al. [4]. Bins with ≥5 genetic regulators identified (p = 4 × 10-3) were identified as eQTL hotspots (blue dotted lines are the selection thresholds).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Transcription hotspots identified by linkage and association scans. Linkage, the numbers of times that a SNP has LOD > 1.44 (nominal p-value = 0.01) for a transcript were counted and plotted as black vertical lines. Association, the numbers of times that a SNP is one of the top 30 association SNPs for a transcript were counted. The SNP-by-SNP transcription hotspots pattern is noisy. To have a clear pattern, these counts were aggregated into bins of ≤5 cM by chromosomes as in Morley et al. [4]. Bins with ≥5 genetic regulators identified (p = 4 × 10-3) were identified as eQTL hotspots (blue dotted lines are the selection thresholds).
Mentions: Figure 2 displays aggregated linkage signals and association signals for the 18 expression traits. Such overlapping genetic regulators patterns are sometimes referred as hotspots in the literature. The linkage signal is fairly clean as shown in Figure 2. Therefore, no aggregation by bins (as in Morley et al. [4]) was done. eQTL hotspots were identified as clustered black lines. For association, counts of identified SNPs were aggregated into bins of ≤5 cM by chromosomes and bins with more than five top SNPs were identified as eQTL hotspots (p-value = 4 × 10-3, evaluated using the Poisson model outlined in Morley et al. [4]). In Figure 2, loci of breast cancer susceptibility genes are marked with red triangles. The identified hotspots overlap with these genes: linkage at 2q, 11q, and 17q; overall association at 1q, 2q, and 17q; interaction association at 8, 17p, and 20q. Linkage and association have two identified genetic regulatory loci in common, the locus of BARD1 (MIM 601593) on 2q34-35 and the locus of BRCA1 (MIM 113705) on 17q21. Both loci harbor important breast cancer genes. The overall association scans incorporate both marginal and interaction signals and thus correlate better with the marginal linkage scans. Figure 2 also displays the difference between the interaction association signals and the overall linkage signals, which demonstrates that different regulatory loci have different extent of interaction activities.

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