Linked Life Data

21835306

Source:http://linkedlifedata.com/resource/pubmed/id/21835306

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2011-8-12
pubmed:abstractText
Genomic association analyses of complex traits demand statistical tools that are capable of detecting small effects of common and rare variants and modeling complex interaction effects and yet are computationally feasible. In this work, we introduce a similarity-based regression method for assessing the main genetic and interaction effects of a group of markers on quantitative traits. The method uses genetic similarity to aggregate information from multiple polymorphic sites and integrates adaptive weights that depend on allele frequencies to accomodate common and uncommon variants. Collapsing information at the similarity level instead of the genotype level avoids canceling signals that have the opposite etiological effects and is applicable to any class of genetic variants without the need for dichotomizing the allele types. To assess gene-trait associations, we regress trait similarities for pairs of unrelated individuals on their genetic similarities and assess association by using a score test whose limiting distribution is derived in this work. The proposed regression framework allows for covariates, has the capacity to model both main and interaction effects, can be applied to a mixture of different polymorphism types, and is computationally efficient. These features make it an ideal tool for evaluating associations between phenotype and marker sets defined by linkage disequilibrium (LD) blocks, genes, or pathways in whole-genome analysis.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1537-6605
pubmed:author
pubmed:copyrightInfo
Copyright © 2011 The American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.
pubmed:issnType
Electronic
pubmed:day
12
pubmed:volume
89
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
277-88
pubmed:dateRevised
2011-10-14
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Studying gene and gene-environment effects of uncommon and common variants on continuous traits: a marker-set approach using gene-trait similarity regression.
pubmed:affiliation
Department of Statistics, North Carolina State University, Raleigh, NC 27695, USA. jung-ying_tzeng@ncsu.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural