Biomaterial Database

Evaluation of sample size effect on the identification of haplotype blocks. 2007

Dai Osabe, and Toshihito Tanahashi, and Kyoko Nomura, and Shuichi Shinohara, and Naoto Nakamura, and Toshikazu Yoshikawa, and Hiroshi Shiota, and Parvaneh Keshavarz, and Yuka Yamaguchi, and Kiyoshi Kunika, and Maki Moritani, and Hiroshi Inoue, and Mitsuo Itakura

Department of Bioinformatics, Division of Life Science Systems, Fujitsu Limited, Higashishinbashi, Minato-ku, Tokyo, Japan. d_osabe@jp.fujitsu.com <d_osabe@jp.fujitsu.com>

BACKGROUND Genome-wide maps of linkage disequilibrium (LD) and haplotypes have been created for different populations. Substantial sharing of the boundaries and haplotypes among populations was observed, but haplotype variations have also been reported across populations. Conflicting observations on the extent and distribution of haplotypes require careful examination. The mechanisms that shape haplotypes have not been fully explored, although the effect of sample size has been implicated. We present a close examination of the effect of sample size on haplotype blocks using an original computational simulation. RESULTS A region spanning 19.31 Mb on chromosome 20q was genotyped for 1,147 SNPs in 725 Japanese subjects. One region of 445 kb exhibiting a single strong LD value (average |D'|; 0.94) was selected for the analysis of sample size effect on haplotype structure. Three different block definitions (recombination-based, LD-based, and diversity-based) were exploited to create simulations for block identification with theta value from real genotyping data. As a result, it was quite difficult to estimate a haplotype block for data with less than 200 samples. Attainment of a reliable haplotype structure with 50 samples was not possible, although the simulation was repeated 10,000 times. CONCLUSIONS These analyses underscored the difficulties of estimating haplotype blocks. To acquire a reliable result, it would be necessary to increase sample size more than 725 and to repeat the simulation 3,000 times. Even in one genomic region showing a high LD value, the haplotype block might be fragile. We emphasize the importance of applying careful confidence measures when using the estimated haplotype structure in biomedical research.

UI	MeSH Term	Description	Entries
D008957	Models, Genetic	Theoretical representations that simulate the behavior or activity of genetic processes or phenomena. They include the use of mathematical equations, computers, and other electronic equipment.	Genetic Models,Genetic Model,Model, Genetic
D002874	Chromosome Mapping	Any method used for determining the location of and relative distances between genes on a chromosome.	Gene Mapping,Linkage Mapping,Genome Mapping,Chromosome Mappings,Gene Mappings,Genome Mappings,Linkage Mappings,Mapping, Chromosome,Mapping, Gene,Mapping, Genome,Mapping, Linkage,Mappings, Chromosome,Mappings, Gene,Mappings, Genome,Mappings, Linkage
D002890	Chromosomes, Human, Pair 20	A specific pair of GROUP F CHROMOSOMES of the human chromosome classification.	Chromosome 20
D003198	Computer Simulation	Computer-based representation of physical systems and phenomena such as chemical processes.	Computational Modeling,Computational Modelling,Computer Models,In silico Modeling,In silico Models,In silico Simulation,Models, Computer,Computerized Models,Computer Model,Computer Simulations,Computerized Model,In silico Model,Model, Computer,Model, Computerized,Model, In silico,Modeling, Computational,Modeling, In silico,Modelling, Computational,Simulation, Computer,Simulation, In silico,Simulations, Computer
D006239	Haplotypes	The genetic constitution of individuals with respect to one member of a pair of allelic genes, or sets of genes that are closely linked and tend to be inherited together such as those of the MAJOR HISTOCOMPATIBILITY COMPLEX.	Haplotype
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D012680	Sensitivity and Specificity	Binary classification measures to assess test results. Sensitivity or recall rate is the proportion of true positives. Specificity is the probability of correctly determining the absence of a condition. (From Last, Dictionary of Epidemiology, 2d ed)	Specificity,Sensitivity,Specificity and Sensitivity
D014644	Genetic Variation	Genotypic differences observed among individuals in a population.	Genetic Diversity,Variation, Genetic,Diversity, Genetic,Diversities, Genetic,Genetic Diversities,Genetic Variations,Variations, Genetic
D015233	Models, Statistical	Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc.	Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D015810	Linkage Disequilibrium	Nonrandom association of linked genes. This is the tendency of the alleles of two separate but already linked loci to be found together more frequently than would be expected by chance alone.	Disequilibrium, Linkage,Disequilibriums, Linkage,Linkage Disequilibriums