Linked Life Data

17635856

Source:http://linkedlifedata.com/resource/pubmed/id/17635856

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2007-8-23
pubmed:abstractText
Genome scans for identifying susceptibility loci for pulse pressure have produced inconclusive results. A heterogeneity-based genome search meta-analysis was applied to available genome-scan data on pulse pressure. A genome search meta-analysis divides the whole genome into 120 bins and identifies bins that rank high on average in terms of linkage statistics across genome scans unweighted or weighted by study size. The significance of each bin's average rank (right-sided test) and heterogeneity among studies (left-sided test) was calculated using a Monte Carlo test. The meta-analysis involved 7 genome scans, 3 consisting of subjects of European descent. Of the 120 bins, 5 bins had significant average rank (P(rank)<or=0.05) by either unweighted or weighted analyses, 4 of which (bins 21.2: 21q22.11 to 21q22.3, 18.3: 18q12.2 to 18q21.33, 18.4: 18q21.33 to 18q23, and 6.2: 6p22.3 to 6p21.1) were significant by both. In subjects of European descent, 3 bins (22.1: 22q11.1 to 22q12.3, 22.2: 22q12.3 to 22q13.3, 10.4: 10q22.1 to 10q23.32) had P(rank)<or=0.05 with both unweighted and weighted analyses. Bin 10.4 showed low heterogeneity (P(Q)=0.04). None of the bins showed low heterogeneity (P(Q)>0.05), indicating variation in the strength of association. Further investigation of these regions may help to direct the identification of candidate genes for pulse pressure variation.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1524-4563
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
50
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
557-64
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Genome-wide scans meta-analysis for pulse pressure.
pubmed:affiliation
Center for Clinical Evidence Synthesis, Institute for Clinical Research and Health Policy Studies, Department of Medicine, Tufts-New England Medical Center, Tufts University School of Medicine, Boston, MA 02111, USA. ezintzaras@nemc-tufts.org
pubmed:publicationType
Journal Article, Meta-Analysis