Linked Life Data

18719944

Source:http://linkedlifedata.com/resource/pubmed/id/18719944

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2008-10-9
pubmed:abstractText
For genome-wide association studies, it has been increasingly recognized that the popular locus-by-locus search for DNA variants associated with disease susceptibility may not be effective, especially when there are interactions between or among multiple loci, for which a multi-loci search strategy may be more productive. However, even if computationally feasible, a genome-wide search over all possible multiple loci requires exploring a huge model space and making costly adjustment for multiple testing, leading to reduced statistical power. On the other hand, there are accumulating data suggesting that protein products of many disease-causing genes tend to interact with each other, or cluster in the same biological pathway. To incorporate this prior knowledge and existing data on gene networks, we propose a gene network-based method to improve statistical power over that of the exhaustive search by giving higher weights to models involving genes nearby in a network. We use simulated data under realistic scenarios, including a large-scale human protein-protein interaction network and 23 known ataxia-causing genes, to demonstrate potential gain by our proposed method when disease-genes are clustered in a network.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1432-1203
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
124
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
225-34
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Network-based model weighting to detect multiple loci influencing complex diseases.
pubmed:affiliation
Division of Biostatistics, MMC 303, School of Public Health, University of Minnesota, Minneapolis, MN 55455-0392, USA. weip@biostat.umn.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural