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Controlling for false positive findings of trans-hubs in expression quantitative trait loci mapping.

Peng J, Wang P, Tang H - BMC Proc (2007)

Bottom Line: In the fast-developing field of expression quantitative traits loci (eQTL) studies, much interest has been concentrated on detecting genomic regions containing transcriptional regulators that influence multiple expression phenotypes (trans-hubs).After correlations among expressions were accounted for, the previously detected trans-hubs are no longer significant.Our results suggest that conclusions regarding regulation hot spots should be treated with great caution.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistics, University of California, Davis, California 95616, USA. jie@wald.ucdavis.edu

ABSTRACT
In the fast-developing field of expression quantitative traits loci (eQTL) studies, much interest has been concentrated on detecting genomic regions containing transcriptional regulators that influence multiple expression phenotypes (trans-hubs). In this paper, we develop statistical methods for eQTL mapping and propose a new procedure for investigating candidate trans-hubs. We use data from the Genetic Analysis Workshop 15 to illustrate our methods. After correlations among expressions were accounted for, the previously detected trans-hubs are no longer significant. Our results suggest that conclusions regarding regulation hot spots should be treated with great caution.

No MeSH data available.


Related in: MedlinePlus

Distribution of trans-hit along the genome. The x-axis represents the genome order of the 1197 markers. The y-axis represents the number of trans-hits in a 5-Mb neighborhood region of each marker. Markers on different chromosomes are separated by vertical gray lines. The left panel is for the original expression data. The right panel is for the residual analysis with respect to 9p13.3. The positions of DSCR2 (21q22.3) and MAP3k6 (1p36.11), which show strong evidence of trans-linkage to 9p13.3 region, are indicated in the left panel.
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Figure 1: Distribution of trans-hit along the genome. The x-axis represents the genome order of the 1197 markers. The y-axis represents the number of trans-hits in a 5-Mb neighborhood region of each marker. Markers on different chromosomes are separated by vertical gray lines. The left panel is for the original expression data. The right panel is for the residual analysis with respect to 9p13.3. The positions of DSCR2 (21q22.3) and MAP3k6 (1p36.11), which show strong evidence of trans-linkage to 9p13.3 region, are indicated in the left panel.

Mentions: Following Morley et al. [4], we define cis-regulators as the eQTL that map within 5 megabases (Mb) of the target gene and all other eQTL are defined as trans-regulators. To illustrate the proposed trans-hub investigation method, we examine the trans-eQTL events (based on the original expression data) at the genome-wide 0.05 significant level to harvest enough eQTL hits for deriving candidate trans-hubs. The numbers of trans-hits and cis-hits at different significance levels are summarized in Table 3. The number of trans-hits dropped dramatically as the significance levels became more stringent, indicating an overall lower confidence of the trans-linkage detection. The genomic locations of the eQTLs detected at the 0.05 significant level suggest two possible hot spots, one at 9p13.3 (30.8 Mb to 35.1 Mb) with 13 trans-hits, and another at 14q32 (94.7 Mb to 98.2 Mb) with 11 trans-hits (Figure 1, left panel). The latter region, 14q32, has also been recognized as a candidate trans-hub by Morley et al. [4]. If regulators for expression phenotypes were independently and randomly distributed along the genome, the probability of the maximum number of hits being at least 13 or 11 are both less than 1 × 10-6. However, based on permutation results (in the original data), the chance of observing a trans-hub with at least 13 hits is about 11.2%, and the chance of at least 11 hits is as high as 22.4%. These numbers imply that the false detections of trans-eQTL are clustered instead of uniformly distributed along the genome. We hypothesize that this is partly due to the expression correlations.


Controlling for false positive findings of trans-hubs in expression quantitative trait loci mapping.

Peng J, Wang P, Tang H - BMC Proc (2007)

Distribution of trans-hit along the genome. The x-axis represents the genome order of the 1197 markers. The y-axis represents the number of trans-hits in a 5-Mb neighborhood region of each marker. Markers on different chromosomes are separated by vertical gray lines. The left panel is for the original expression data. The right panel is for the residual analysis with respect to 9p13.3. The positions of DSCR2 (21q22.3) and MAP3k6 (1p36.11), which show strong evidence of trans-linkage to 9p13.3 region, are indicated in the left panel.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2367467&req=5

Figure 1: Distribution of trans-hit along the genome. The x-axis represents the genome order of the 1197 markers. The y-axis represents the number of trans-hits in a 5-Mb neighborhood region of each marker. Markers on different chromosomes are separated by vertical gray lines. The left panel is for the original expression data. The right panel is for the residual analysis with respect to 9p13.3. The positions of DSCR2 (21q22.3) and MAP3k6 (1p36.11), which show strong evidence of trans-linkage to 9p13.3 region, are indicated in the left panel.
Mentions: Following Morley et al. [4], we define cis-regulators as the eQTL that map within 5 megabases (Mb) of the target gene and all other eQTL are defined as trans-regulators. To illustrate the proposed trans-hub investigation method, we examine the trans-eQTL events (based on the original expression data) at the genome-wide 0.05 significant level to harvest enough eQTL hits for deriving candidate trans-hubs. The numbers of trans-hits and cis-hits at different significance levels are summarized in Table 3. The number of trans-hits dropped dramatically as the significance levels became more stringent, indicating an overall lower confidence of the trans-linkage detection. The genomic locations of the eQTLs detected at the 0.05 significant level suggest two possible hot spots, one at 9p13.3 (30.8 Mb to 35.1 Mb) with 13 trans-hits, and another at 14q32 (94.7 Mb to 98.2 Mb) with 11 trans-hits (Figure 1, left panel). The latter region, 14q32, has also been recognized as a candidate trans-hub by Morley et al. [4]. If regulators for expression phenotypes were independently and randomly distributed along the genome, the probability of the maximum number of hits being at least 13 or 11 are both less than 1 × 10-6. However, based on permutation results (in the original data), the chance of observing a trans-hub with at least 13 hits is about 11.2%, and the chance of at least 11 hits is as high as 22.4%. These numbers imply that the false detections of trans-eQTL are clustered instead of uniformly distributed along the genome. We hypothesize that this is partly due to the expression correlations.

Bottom Line: In the fast-developing field of expression quantitative traits loci (eQTL) studies, much interest has been concentrated on detecting genomic regions containing transcriptional regulators that influence multiple expression phenotypes (trans-hubs).After correlations among expressions were accounted for, the previously detected trans-hubs are no longer significant.Our results suggest that conclusions regarding regulation hot spots should be treated with great caution.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Statistics, University of California, Davis, California 95616, USA. jie@wald.ucdavis.edu

ABSTRACT
In the fast-developing field of expression quantitative traits loci (eQTL) studies, much interest has been concentrated on detecting genomic regions containing transcriptional regulators that influence multiple expression phenotypes (trans-hubs). In this paper, we develop statistical methods for eQTL mapping and propose a new procedure for investigating candidate trans-hubs. We use data from the Genetic Analysis Workshop 15 to illustrate our methods. After correlations among expressions were accounted for, the previously detected trans-hubs are no longer significant. Our results suggest that conclusions regarding regulation hot spots should be treated with great caution.

No MeSH data available.


Related in: MedlinePlus