Linked Life Data

16451679

Source:http://linkedlifedata.com/resource/pubmed/id/16451679

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2010-1-19
pubmed:abstractText
A common dilemma arising in linkage studies of complex genetic diseases is the selection of positive signals, their follow-up with association studies and discrimination between true and false positive results. Several strategies for overcoming these issues have been devised. Using the Genetic Analysis Workshop 14 simulated dataset, we aimed to apply different analytical approaches and evaluate their performance in discerning real associations. We considered a) haplotype analyses, b) different methods adjusting for multiple testing, c) replication in a second dataset, and d) exhaustive genotyping of all markers in a sufficiently powered, large sample group. We found that haplotype-based analyses did not substantially improve over single-point analysis, although this may reflect the low levels of linkage disequilibrium simulated in the datasets provided. Multiple testing correction methods were in general found to be over-conservative. Replication of nominally positive results in a second dataset appears to be less stringent, resulting in the follow-up of false positives. Performing a comprehensive assay of all markers in a large, well-powered dataset appears to be the most effective strategy for complex disease gene identification.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1471-2156
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
6 Suppl 1
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
S66
pubmed:dateRevised
2010-9-20
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Will the real disease gene please stand up?
pubmed:affiliation
Epidemiology Unit, University of Manchester, Manchester, UK. neil.shephard@man.ac.uk
pubmed:publicationType
Journal Article