Linked Life Data

20199696

Source:http://linkedlifedata.com/resource/pubmed/id/20199696

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2010-5-3
pubmed:abstractText
We proposed hierarchical Poisson and binomial models for mapping multiple interacting quantitative trait loci (QTLs) for count traits in experimental crosses. We applied our methods to two counted reproductive traits, live fetuses (LF) and dead fetuses (DF) at 17 days gestation, in an F2 female mouse population. We treated observed number of corpora lutea (ovulation rate) as the baseline and the total trials in our Poisson and binomial models, respectively. We detected more than 10 QTLs for LF and DF, most having epistatic and pleiotropic effects. The epistatic effects were larger, involved more QTLs, and explained a larger proportion of phenotypic variance than the main effects. Our analyses revealed a complex network of multiple interacting QTLs for the reproductive traits, and increase our understanding of the genetic architecture of reproductive characters. The proposed statistical models and methods provide valuable tools for detecting multiple interacting QTLs for complex count phenotypes.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1469-5073
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
92
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
13-23
pubmed:dateRevised
2011-9-2
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Mapping interacting QTL for count phenotypes using hierarchical Poisson and binomial models: an application to reproductive traits in mice.
pubmed:affiliation
Department of Biostatistics, Section on Statistical Genetics, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural