Linked Life Data

9250355

Source:http://linkedlifedata.com/resource/pubmed/id/9250355

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 3
pubmed:dateCreated
1997-10-10
pubmed:abstractText
The Haseman & Elston (1972) sibling-pair regression method has been used to detect and estimate the variance contribution to observed values of a quantitative trait by allelic variation in specific candidate genes. The procedure was developed under a model with a single biallelic trait locus. This assumption does not hold for several known systems. In this paper we prove that for candidate gene analysis the Haseman-Elston procedure extends to the case of multiple trait loci, each possibly having more than two alleles. Simulation experiments comparing single-locus to two-locus models show that fitting the extended regression equations maintains nominal significance levels, but the power to detect linkage to trait variation is not improved by including additional loci. These results indicate that the original proposal is statistically robust to violations of the underlying genetic model. Practical issues associated with quantifying the relative variance contribution by individual loci are also discussed. Applications of the extended regression equations to lipoprotein(a) and high density lipoprotein cholesterol are given for illustration.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
0003-4800
pubmed:author
pubmed:issnType
Print
pubmed:volume
61
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
263-74
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
1997
pubmed:articleTitle
Extension of the Haseman-Elston method to multiple alleles and multiple loci: theory and practice for candidate genes.
pubmed:affiliation
Department of Statistical Science, Southern Methodist University, Dallas, TX 75275, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.