Title
Identification of linked regions using high-density SNP genotype data in linkage analysis.
Abstract
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.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 chi(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.http://paed.hku.hk/uploadarea/yangwl/html/index.html
Year
DOI
Venue
2008
10.1093/bioinformatics/btm552
Bioinformatics
Keywords
Field
DocType
allele sharing status determination,allele sharing information,allele sharing determination,snp genotyping data,allele sharing status,linkage analysis,genotyping error,high-density snp genotype data,rule-based program,program graphically display,high throughput,indexation,human genome
Nuclear family,Genotyping,Allele,Biology,Inheritance Mode,SNP genotyping,Single-nucleotide polymorphism,Bioinformatics,Genetics,SNP,Penetrance
Journal
Volume
Issue
ISSN
24
1
1367-4811
Citations 
PageRank 
References 
9
0.91
6
Authors
5
Name
Order
Citations
PageRank
Guohui Lin190.91
Zhanyong Wang2507.04
Lusheng Wang32433224.97
Yu Lung Lau4122.47
Wanling Yang5323.29