Article: PLINK: A tool set for whole-genome association and population-based linkage analyses

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TitlePLINK: A tool set for whole-genome association and population-based linkage analyses
AuthorsPurcell, S1 4
Neale, B2 4
ToddBrown, K
Thomas, L
Ferreira, MAR
Bender, D4
Maller, J4
Sklar, P4
De Bakker, PIW4
Daly, MJ4
Sham, PC3
Issue Date2007
PublisherCell Press. The Journal's web site is located at http://www.cell.com/AJHG/
CitationAmerican Journal Of Human Genetics, 2007, v. 81 n. 3, p. 559-575 [How to Cite?]
DOI: http://dx.doi.org/10.1086/519795
AbstractWhole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis. © 2007 by The American Society of Human Genetics. All rights reserved.
ISSN0002-9297
2011 Impact Factor: 10.603
2011 SCImago Journal Rankings: 2.479
DOIhttp://dx.doi.org/10.1086/519795
ISI Accession Number IDWOS:000249128200012
ReferencesReferences in Scopus
DC Field
Value
dc.contributor.authorPurcell, S
dc.contributor.authorNeale, B
dc.contributor.authorToddBrown, K
dc.contributor.authorThomas, L
dc.contributor.authorFerreira, MAR
dc.contributor.authorBender, D
dc.contributor.authorMaller, J
dc.contributor.authorSklar, P
dc.contributor.authorDe Bakker, PIW
dc.contributor.authorDaly, MJ
dc.contributor.authorSham, PC
dc.date.accessioned2010-09-06T08:19:14Z
dc.date.available2010-09-06T08:19:14Z
dc.date.issued2007
dc.description.abstractWhole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis. © 2007 by The American Society of Human Genetics. All rights reserved.
dc.description.natureLink_to_subscribed_fulltext
dc.identifier.citationAmerican Journal Of Human Genetics, 2007, v. 81 n. 3, p. 559-575 [How to Cite?]
DOI: http://dx.doi.org/10.1086/519795
dc.identifier.citeulike3292454
dc.identifier.doihttp://dx.doi.org/10.1086/519795
dc.identifier.epage575
dc.identifier.hkuros151791
dc.identifier.isiWOS:000249128200012
dc.identifier.issn0002-9297
2011 Impact Factor: 10.603
2011 SCImago Journal Rankings: 2.479
dc.identifier.issue3
dc.identifier.openurl
dc.identifier.pmid17701901
dc.identifier.scopuseid_2-s2.0-34548292504
dc.identifier.spage559
dc.identifier.urihttp://hdl.handle.net/10722/81557
dc.identifier.volume81
dc.languageeng
dc.publisherCell Press. The Journal's web site is located at http://www.cell.com/AJHG/
dc.publisher.placeUnited States
dc.relation.ispartofAmerican Journal of Human Genetics
dc.relation.referencesReferences in Scopus
dc.rightsAmerican Journal of Human Genetics. Copyright © University of Chicago Press.
dc.titlePLINK: A tool set for whole-genome association and population-based linkage analyses
dc.typeArticle
Author Affiliations
  1. Massachusetts General Hospital
  2. King's College London
  3. The University of Hong Kong
  4. Broad Institute