| pubmed-article:16783633 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:16783633 | lifeskim:mentions | umls-concept:C0017262 | lld:lifeskim |
| pubmed-article:16783633 | lifeskim:mentions | umls-concept:C0683224 | lld:lifeskim |
| pubmed-article:16783633 | lifeskim:mentions | umls-concept:C1710191 | lld:lifeskim |
| pubmed-article:16783633 | lifeskim:mentions | umls-concept:C1552617 | lld:lifeskim |
| pubmed-article:16783633 | lifeskim:mentions | umls-concept:C0282443 | lld:lifeskim |
| pubmed-article:16783633 | lifeskim:mentions | umls-concept:C2911684 | lld:lifeskim |
| pubmed-article:16783633 | lifeskim:mentions | umls-concept:C0185117 | lld:lifeskim |
| pubmed-article:16783633 | pubmed:issue | 6 | lld:pubmed |
| pubmed-article:16783633 | pubmed:dateCreated | 2006-6-19 | lld:pubmed |
| pubmed-article:16783633 | 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. | lld:pubmed |
| pubmed-article:16783633 | pubmed:language | eng | lld:pubmed |
| pubmed-article:16783633 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:16783633 | pubmed:citationSubset | IM | lld:pubmed |
| pubmed-article:16783633 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:16783633 | pubmed:month | Jun | lld:pubmed |
| pubmed-article:16783633 | pubmed:issn | 0938-8990 | lld:pubmed |
| pubmed-article:16783633 | pubmed:author | pubmed-author:WangPingP | lld:pubmed |
| pubmed-article:16783633 | pubmed:author | pubmed-author:KendziorskiCh... | lld:pubmed |
| pubmed-article:16783633 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:16783633 | pubmed:volume | 17 | lld:pubmed |
| pubmed-article:16783633 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:16783633 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:16783633 | pubmed:pagination | 509-17 | lld:pubmed |
| pubmed-article:16783633 | pubmed:meshHeading | pubmed-meshheading:16783633... | lld:pubmed |
| pubmed-article:16783633 | pubmed:meshHeading | pubmed-meshheading:16783633... | lld:pubmed |
| pubmed-article:16783633 | pubmed:meshHeading | pubmed-meshheading:16783633... | lld:pubmed |
| pubmed-article:16783633 | pubmed:meshHeading | pubmed-meshheading:16783633... | lld:pubmed |
| pubmed-article:16783633 | pubmed:meshHeading | pubmed-meshheading:16783633... | lld:pubmed |
| pubmed-article:16783633 | pubmed:year | 2006 | lld:pubmed |
| pubmed-article:16783633 | pubmed:articleTitle | A review of statistical methods for expression quantitative trait loci mapping. | lld:pubmed |
| pubmed-article:16783633 | pubmed:affiliation | Department of Biostatistics and Medical Informatics, University of Wisconsin, 1300 University Avenue (6729 MSC), Madison, WI 53706, USA. kendzior@biostat.wisc.edu | lld:pubmed |
| pubmed-article:16783633 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:16783633 | pubmed:publicationType | Review | lld:pubmed |
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