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Article: Linked region detection using high-density SNP genotype data via the minimum recombinant model of pedigree haplotype inference

TitleLinked region detection using high-density SNP genotype data via the minimum recombinant model of pedigree haplotype inference
Authors
Issue Date2009
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcbioinformatics/
Citation
Bmc Bioinformatics, 2009, v. 10 How to Cite?
AbstractBackground: With the rapid development of high-throughput genotyping technologies, efficient methods for identifying linked regions using high-density SNP genotype data have become more and more important. Recently, a deterministic method that works very well on SNP genotyping data has been developed (Lin et al. Bioinformatics 2008, 24(1): 86-93). However, that program can only work on a limited number of family structures. In particular, the results (if any) will be poor when the genotype data for the whole chromosome of one of the parents in a nuclear family is missing. Results: We have developed a software package (LIden) for identifying linked regions using high-density SNP genotype data. We focus on handling the case where the genotype data for the whole chromosome of one of the parents in a nuclear family is missing. We use the minimum recombinant model for haplotype inference in pedigrees. Several local optimization algorithms are used to infer the haplotype of each individual and determine the linked regions based on the inferred haplotype data. We have developed a more flexible method to combine nuclear families to further refine (reduce the length of) the linked regions. Conclusion: Our new package (LIden) is efficient software for linked region detection using high-density SNP genotype data. LIden can handle some important cases where the existing programs do not work well. In particular, the new package can handle many cases where the genotype data of one of the two parents is missing for the entire chromosome. The running time of the program is O(mn), where m is the number of members in the family and n is the number of SNP sites in the chromosome. LIden is specifically suitable for handling big sized families. This research also demonstrates another practical use of the minimum recombinant model for haplotype inference in pedigrees. © 2009 Wang et al; licensee BioMed Central Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/79931
ISSN
2015 Impact Factor: 2.435
2015 SCImago Journal Rankings: 1.722
PubMed Central ID
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorWang, Len_HK
dc.contributor.authorWang, Zen_HK
dc.contributor.authorYang, Wen_HK
dc.date.accessioned2010-09-06T08:00:26Z-
dc.date.available2010-09-06T08:00:26Z-
dc.date.issued2009en_HK
dc.identifier.citationBmc Bioinformatics, 2009, v. 10en_HK
dc.identifier.issn1471-2105en_HK
dc.identifier.urihttp://hdl.handle.net/10722/79931-
dc.description.abstractBackground: With the rapid development of high-throughput genotyping technologies, efficient methods for identifying linked regions using high-density SNP genotype data have become more and more important. Recently, a deterministic method that works very well on SNP genotyping data has been developed (Lin et al. Bioinformatics 2008, 24(1): 86-93). However, that program can only work on a limited number of family structures. In particular, the results (if any) will be poor when the genotype data for the whole chromosome of one of the parents in a nuclear family is missing. Results: We have developed a software package (LIden) for identifying linked regions using high-density SNP genotype data. We focus on handling the case where the genotype data for the whole chromosome of one of the parents in a nuclear family is missing. We use the minimum recombinant model for haplotype inference in pedigrees. Several local optimization algorithms are used to infer the haplotype of each individual and determine the linked regions based on the inferred haplotype data. We have developed a more flexible method to combine nuclear families to further refine (reduce the length of) the linked regions. Conclusion: Our new package (LIden) is efficient software for linked region detection using high-density SNP genotype data. LIden can handle some important cases where the existing programs do not work well. In particular, the new package can handle many cases where the genotype data of one of the two parents is missing for the entire chromosome. The running time of the program is O(mn), where m is the number of members in the family and n is the number of SNP sites in the chromosome. LIden is specifically suitable for handling big sized families. This research also demonstrates another practical use of the minimum recombinant model for haplotype inference in pedigrees. © 2009 Wang et al; licensee BioMed Central Ltd.en_HK
dc.languageengen_HK
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedcentral.com/bmcbioinformatics/en_HK
dc.relation.ispartofBMC Bioinformaticsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong Licenseen_HK
dc.subject.meshGenetic Linkage-
dc.subject.meshHaplotypes-
dc.subject.meshPedigree-
dc.subject.meshPolymorphism, Single Nucleotide - genetics-
dc.subject.meshSoftware-
dc.titleLinked region detection using high-density SNP genotype data via the minimum recombinant model of pedigree haplotype inferenceen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1471-2105&volume=10&spage=216&epage=&date=2009&atitle=Linked+region+detection+using+high-density+SNP+genotype+data+via+the+minimum+recombinant+model+of+pedigree+haplotype+inferenceen_HK
dc.identifier.emailYang, W:yangwl@hkucc.hku.hken_HK
dc.identifier.authorityYang, W=rp00524en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/1471-2105-10-216en_HK
dc.identifier.pmid19604391-
dc.identifier.pmcidPMC2723091-
dc.identifier.scopuseid_2-s2.0-68649116052en_HK
dc.identifier.hkuros160939en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-68649116052&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume10en_HK
dc.identifier.isiWOS:000269418100001-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridWang, L=7409188146en_HK
dc.identifier.scopusauthoridWang, Z=8211361600en_HK
dc.identifier.scopusauthoridYang, W=23101349500en_HK
dc.identifier.citeulike5192187-

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