Linked Life Data

17049033

Source:http://linkedlifedata.com/resource/pubmed/id/17049033

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PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2006-10-19
pubmed:abstractText
Genetic epidemiology aims at identifying biological mechanisms responsible for human diseases. Genome-wide association studies, made possible by recent improvements in genotyping technologies, are now promisingly investigated. In these studies, common first-stage strategies focus on marginal effects but lead to multiple-testing and are unable to capture the possibly complex interplay between genetic factors. We have adapted the use of the local score statistic, already successfully applied to analyse long molecular sequences. Via sum statistics, this method captures local and possible distant dependences between markers. Dedicated to genome-wide association studies, it is fast to compute, able to handle large datasets, circumvents the the multiple-testing problem and outlines a set of genomic regions (segments) for further analyses. Applied to simulated and real data, our approach outperforms classical Bonferroni and FDR corrections for multiple-testing. It is implemented in a software termed LHiSA for Local High-scoring Segments for Association and available at: http://stat.genopole.cnrs.fr/software/lhisa.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1544-6115
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
5
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
Article22
pubmed:dateRevised
2010-11-18
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Detecting local high-scoring segments: a first-stage approach for genome-wide association studies.
pubmed:affiliation
Laboratoire Statistique et Génome. guedj@genopole.cnrs.fr
pubmed:publicationType
Journal Article