Evaluation of sample size effect on the identification of haplotype blocks
1 Department of Bioinformatics, Division of Life Science Systems, Fujitsu Limited, 1-5-2, Higashishinbashi, Minato-ku, 105-7123, Tokyo, Japan
2 Section for Diabetes, Genotyping Division, Genetic Diversification Analysis Project, Japan Biological Information Consortium (JBIC), Tokyo, Japan
3 Division of Genetic Information, Institute for Genome Research, the University of Tokushima, 3-18-15, Kuramoto-cho, 770-8503, Tokushima, Japan
4 Division of R&D Solution, Fujitsu Nagano Systems Engineering Limited, 380-3813, Nagano, Japan
5 Department of Endocrinology and Metabolism, Kyoto Prefectural University of Medicine Graduate School of Medical Sciences, 465, Kajii-cho, Hirokoji-Kawaramachi, Kamigyo-ku, Kyoto, 602-8566, Japan
6 Department of Ophthalmology and Visual Neuroscience, Institute for Health Biosciences, the University of Tokushima, 3-18-15, Kuramoto-cho, Tokushima, 770-8503, Japan
BMC Bioinformatics 2007, 8:200 doi:10.1186/1471-2105-8-200Published: 14 June 2007
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.
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 θ 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.
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.