Linked Life Data

15133312

Source:http://linkedlifedata.com/resource/pubmed/id/15133312

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2004-5-10
pubmed:abstractText
Nonparametric linkage analysis is widely used to map susceptibility genes for complex diseases. This paper introduces six nonparametric statistics for measuring marker allele sharing among the affected members of a pedigree. We compare the power of these new statistics and three previous statistics to detect linkage with Mendelian diseases having recessive, additive, and dominant modes of inheritance. The nine statistics represent all possible combinations of three different IBD scoring functions and three different schemes for sampling genes among affecteds. Our results strongly suggest that the statistic T(rec)(blocks) is best for recessive traits, while the two statistics T(kin)(pairs) and T(all)(kin) vie for best for an additive trait. The best statistic for a dominant trait is less clear. The statistics T(kin)(pairs) and T(all)(kin) are equally promising for small sibships, but in extended pedigrees the statistics T(dom)(blocks) and T(dom)(pairs) appear best. For a complex trait, we advocate computing several of these statistics.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
0001-5652
pubmed:author
pubmed:copyrightInfo
Copyright 2004 S. Karger AG, Basel
pubmed:issnType
Print
pubmed:volume
57
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
49-58
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Powerful allele sharing statistics for nonparametric linkage analysis.
pubmed:affiliation
Section on Biostatistics, Department of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC 27157-1063, USA. elange@wfubmc.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.