Linked Life Data

11822026

Source:http://linkedlifedata.com/resource/pubmed/id/11822026

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2002-2-14
pubmed:databankReference
pubmed:abstractText
In this report, we present a simple and powerful way to incorporate individual-specific liability classes into linkage analysis. The proposed method is applicable to both quantitative and qualitative traits. In linkage studies, we may have information about different covariates. Incorporation of these covariates along with the estimates of residual familial effects, age-at-onset effects, and susceptibility in the definition of liability classes can increase the power to detect genetic linkage. In this study, we show how one can form individual-specific liability classes and use these classes in standard linkage-analysis programs, such as the widely used LINKAGE package, to perform more powerful genetic linkage analysis. Our simulation study shows that this approach yields higher LOD scores and more-accurate estimates of the recombination fraction in the families showing linkage. The proposed method is also applied to kindreds collected, at the M. D. Anderson Cancer Center, through probands with childhood soft-tissue sarcoma. Confirmed germ-line mutations in the p53 tumor-suppressor gene have been identified in these families. Application of our method to these families yielded significantly higher LOD scores and more-accurate recombination fractions than did analysis that did not account for individual-specific covariate information.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
0002-9297
pubmed:author
pubmed:issnType
Print
pubmed:volume
70
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
813-7
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
Individual-specific liability groups in genetic linkage, with applications to kindreds with Li-Fraumeni syndrome.
pubmed:affiliation
Department of Epidemiology, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030, USA. sshete@mdanderson.org
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.