Linked Life Data

15611192

Source:http://linkedlifedata.com/resource/pubmed/id/15611192

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2004-12-21
pubmed:abstractText
The power of a genetic mapping study depends on the heritability of the trait, the number of individuals included in the analysis, and the genetic dissimilarity among them. In experiments that involve microarrays or other complex physiological assays, phenotyping can be expensive and time-consuming and may impose limits on the sample size. A random selection of individuals may not provide sufficient power to detect linkage until a large sample size is reached. We present an algorithm for selecting a subset of individuals solely on the basis of genotype data that can achieve substantial improvements in sensitivity compared to a random sample of the same size. The selective phenotyping method involves preferentially selecting individuals to maximize their genotypic dissimilarity. Selective phenotyping is most effective when prior knowledge of genetic architecture allows us to focus on specific genetic regions. However, it can also provide modest improvements in efficiency when applied on a whole-genome basis. Importantly, selective phenotyping does not reduce the efficiency of mapping as compared to a random sample in regions that are not considered in the selection process. In contrast to selective genotyping, inferences based solely on a selectively phenotyped population of individuals are representative of the whole population. The substantial improvement introduced by selective phenotyping is particularly useful when phenotyping is difficult or costly and thus limits the sample size in a genetic mapping study.
pubmed:grant
pubmed:commentsCorrections
http://linkedlifedata.com/resource/pubmed/commentcorrection/15611192-10712219, http://linkedlifedata.com/resource/pubmed/commentcorrection/15611192-11078464, http://linkedlifedata.com/resource/pubmed/commentcorrection/15611192-11418218, http://linkedlifedata.com/resource/pubmed/commentcorrection/15611192-11823790, http://linkedlifedata.com/resource/pubmed/commentcorrection/15611192-11923494, http://linkedlifedata.com/resource/pubmed/commentcorrection/15611192-12567189, http://linkedlifedata.com/resource/pubmed/commentcorrection/15611192-12586722, http://linkedlifedata.com/resource/pubmed/commentcorrection/15611192-12646919, http://linkedlifedata.com/resource/pubmed/commentcorrection/15611192-12671661, http://linkedlifedata.com/resource/pubmed/commentcorrection/15611192-12724300, http://linkedlifedata.com/resource/pubmed/commentcorrection/15611192-12930764, http://linkedlifedata.com/resource/pubmed/commentcorrection/15611192-2563713, http://linkedlifedata.com/resource/pubmed/commentcorrection/15611192-3692487, http://linkedlifedata.com/resource/pubmed/commentcorrection/15611192-7851788
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
0016-6731
pubmed:author
pubmed:issnType
Print
pubmed:volume
168
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2285-93
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Selective phenotyping for increased efficiency in genetic mapping studies.
pubmed:affiliation
Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706, USA.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural