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A Metric Linkage Disequilibrium Map of a Human Chromosome
Author(s) -
Tapper W. J.,
Maniatis N.,
Morton N. E.,
Collins A.
Publication year - 2003
Publication title -
annals of human genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.537
H-Index - 77
eISSN - 1469-1809
pISSN - 0003-4800
DOI - 10.1046/j.1469-1809.2003.00050.x
Subject(s) - genetics , centimorgan , linkage disequilibrium , biology , genetic linkage , chromosome , single nucleotide polymorphism , recombination , linkage (software) , gene mapping , haplotype , allele , gene , genotype
Summary We used LDMAP (Maniatis et al . 2002) to analyse SNP data spanning chromosome 22 (Dawson et al . 2002), to obtain a whole‐chromosome metric LD map. The LD map, with map distances analogous to the centiMorgan scale of linkage maps, identifies regions of high LD as plateaus (‘blocks’) and characterises steps which define the relationship between these regions. From this map we estimate that block regions comprise between 32% and 55% of the euchromatic portion of chromosome 22 and that increasing marker density within steps may increase block coverage. Steps are regions of low LD which correspond to areas of variable recombination intensity. The intensity of recombination is related to the height of the step and thus intense recombination hot‐spots can be distinguished from more randomly distributed historical events. The LD maps are more closely related to the high‐resolution linkage map (Kong et al . 2002) than average measures of ρ with recombination accounting for between 34% and 52% of the variance in patterns of LD (r = 0.58 – 0.71, p = 0.0001) . Step regions are closely correlated with a range of sequence motifs including GT/CA repeats. The LD map identifies holes in which greater marker density is required and defines the optimal SNP spacing for positional cloning, which suggests that some multiple of around 50,000 SNPs will be required to efficiently screen Caucasian genomes. Further analyses which investigate selection of informative SNPs and the effect of SNP allele frequency and marker density will refine this estimate.