Identification of linked regions using high-density SNP genotype data in linkage analysis.
BACKGROUND
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 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.


BACKGROUND
http://paed.hku.hk/uploadarea/yangwl/html/index.html
Lin
Guohui
G
Department of Computing Science, University of Alberta, Edmonton, Alberta T6G 2E8, Canada.
Wang
Zhanyong
Z
Wang
Lusheng
L
Lau
Yu-Lung
YL
Yang
Wanling
W
D002874:Chromosome Mapping / Q000379:methods*; D004252:DNA Mutational Analysis / Q000379:methods*; D030541:Databases, Genetic; D005819:Genetic Markers / Q000235:genetics*; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D005820:Genetic Testing / Q000379:methods*; D005838:Genotype; D006801:Humans; D015810:Linkage Disequilibrium
D005819:Genetic Markers