Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2006-12-13
pubmed:abstractText
Pre-eclampsia/eclampsia (PE/E) is a common and serious disorder of human pregnancy that is associated with substantial maternal and perinatal morbidity and mortality. The suspected aetiology of PE/E is complex, with susceptibility being attributable to multiple environmental factors and a large genetic component. By assuming that the underlying liability towards PE/E susceptibility is inherently quantitative, any PE/E susceptibility gene would represent a quantitative trait locus (QTL). This assumption enables a more refined and powerful variance components procedure using a threshold model for our PE/E statistical analysis. Using this more efficient linkage approach, we have now re-analysed our previously completed Australian/New Zealand genome scan data to identify two novel PE/E susceptibility QTLs on chromosomes 5q and 13q. We have obtained strong evidence of linkage on 5q with a peak logarithm-of-odds (LOD) score of 3.12 between D5S644 and D5S433 [at approximately 121 centimorgan (cM)] and strong evidence of linkage on 13q with a peak LOD score of 3.10 between D13S1265 and D13S173 (at approximately 123 cM). Objective identification and prioritization of positional candidate genes using the quantitative bioinformatics program GeneSniffer revealed highly plausible PE/E candidate genes encoding aminopeptidase enzymes and a placental peptide hormone on the 5q QTL and two type IV collagens on the 13q QTL regions, respectively.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
1360-9947
pubmed:author
pubmed:issnType
Print
pubmed:volume
13
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
61-7
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Identification of two novel quantitative trait loci for pre-eclampsia susceptibility on chromosomes 5q and 13q using a variance components-based linkage approach.
pubmed:affiliation
Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, TX 78245-0549, USA. mjohnson@darwin.sfbr.org
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural