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Article: Identification of linked regions using high-density SNP genotype data in linkage analysis

TitleIdentification of linked regions using high-density SNP genotype data in linkage analysis
Authors
Issue Date2008
PublisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/
Citation
Bioinformatics, 2008, v. 24 n. 1, p. 86-93 How to Cite?
AbstractMotivation: With the knowledge of large number of SNPs in human genome and the fast development in high-throughput genotyping technologies, identification of linked regions in linkage analysis through allele sharing status determination will play an ever important role, while consideration of recombination fractions becomes unnecessary. Results: In this study, we have developed a rule-based program that identifies linked regions for underlined diseases using allele sharing information among family members. Our program uses high-density SNP genotype data and works in the face of genotyping errors. It works on nuclear family structures with two or more siblings. The program graphically displays allele sharing status for all members in a pedigree and identifies regions that are potentially linked to the underlined diseases according to user-specified inheritance mode and penetrance. Extensive simulations based on the ×2 model for recombination show that our program identifies linked regions with high sensitivity and accuracy. Graphical display of allele sharing status helps to detect misspecification of inheritance mode and penetrance, as well as mislabeling or misdiagnosis. Allele sharing determination may represent the future direction of linkage analysis due to its better adaptation to high-density SNP genotyping data. © 2007 The Author(s).
Persistent Identifierhttp://hdl.handle.net/10722/80155
ISSN
2015 Impact Factor: 5.766
2015 SCImago Journal Rankings: 4.643
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLin, Gen_HK
dc.contributor.authorWang, Zen_HK
dc.contributor.authorWang, Len_HK
dc.contributor.authorLau, YLen_HK
dc.contributor.authorYang, Wen_HK
dc.date.accessioned2010-09-06T08:03:02Z-
dc.date.available2010-09-06T08:03:02Z-
dc.date.issued2008en_HK
dc.identifier.citationBioinformatics, 2008, v. 24 n. 1, p. 86-93en_HK
dc.identifier.issn1367-4803en_HK
dc.identifier.urihttp://hdl.handle.net/10722/80155-
dc.description.abstractMotivation: With the knowledge of large number of SNPs in human genome and the fast development in high-throughput genotyping technologies, identification of linked regions in linkage analysis through allele sharing status determination will play an ever important role, while consideration of recombination fractions becomes unnecessary. Results: In this study, we have developed a rule-based program that identifies linked regions for underlined diseases using allele sharing information among family members. Our program uses high-density SNP genotype data and works in the face of genotyping errors. It works on nuclear family structures with two or more siblings. The program graphically displays allele sharing status for all members in a pedigree and identifies regions that are potentially linked to the underlined diseases according to user-specified inheritance mode and penetrance. Extensive simulations based on the ×2 model for recombination show that our program identifies linked regions with high sensitivity and accuracy. Graphical display of allele sharing status helps to detect misspecification of inheritance mode and penetrance, as well as mislabeling or misdiagnosis. Allele sharing determination may represent the future direction of linkage analysis due to its better adaptation to high-density SNP genotyping data. © 2007 The Author(s).en_HK
dc.languageengen_HK
dc.publisherOxford University Press. The Journal's web site is located at http://bioinformatics.oxfordjournals.org/en_HK
dc.relation.ispartofBioinformaticsen_HK
dc.rightsBioinformatics. Copyright © Oxford University Press.en_HK
dc.titleIdentification of linked regions using high-density SNP genotype data in linkage analysisen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1367-4803&volume=24&issue=1&spage=86&epage=93&date=2008&atitle=Identification+Of+Linked+Regions+Using+High-density+Snp+Genotype+Data+In+Linkage+Analysisen_HK
dc.identifier.emailLau, YL:lauylung@hkucc.hku.hken_HK
dc.identifier.emailYang, W:yangwl@hkucc.hku.hken_HK
dc.identifier.authorityLau, YL=rp00361en_HK
dc.identifier.authorityYang, W=rp00524en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1093/bioinformatics/btm552en_HK
dc.identifier.pmid18024969-
dc.identifier.scopuseid_2-s2.0-37549056571en_HK
dc.identifier.hkuros140524en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-37549056571&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume24en_HK
dc.identifier.issue1en_HK
dc.identifier.spage86en_HK
dc.identifier.epage93en_HK
dc.identifier.isiWOS:000251865000012-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridLin, G=7401699810en_HK
dc.identifier.scopusauthoridWang, Z=8211361600en_HK
dc.identifier.scopusauthoridWang, L=7409188146en_HK
dc.identifier.scopusauthoridLau, YL=7201403380en_HK
dc.identifier.scopusauthoridYang, W=23101349500en_HK
dc.identifier.citeulike2139173-

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