Linked Life Data

16783633

Source:http://linkedlifedata.com/resource/pubmed/id/16783633

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2006-6-19
pubmed:abstractText
With high-throughput technologies now widely available, investigators can easily measure thousands of phenotypes for quantitative trait loci (QTL) mapping. Microarray measurements are particularly amenable to QTL mapping, as evidenced by a number of recent studies demonstrating utility across a broad range of biological endeavors. The early success stories have impelled a rapid increase in both the number and complexity of expression QTL (eQTL) experiments. Consequently, there is a need to consider the statistical principles involved in the design and analysis of these experiments and the methods currently being used. In this article we review these principles and methods and discuss the open questions most likely to yield significant progress toward increasing the amount of meaningful information obtained from eQTL mapping experiments.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0938-8990
pubmed:author
pubmed:issnType
Print
pubmed:volume
17
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
509-17
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
A review of statistical methods for expression quantitative trait loci mapping.
pubmed:affiliation
Department of Biostatistics and Medical Informatics, University of Wisconsin, 1300 University Avenue (6729 MSC), Madison, WI 53706, USA. kendzior@biostat.wisc.edu
pubmed:publicationType
Journal Article, Review