Copyright © 2003 The American Society of Human Genetics. All rights reserved.
The American Journal of Human Genetics, Volume 73, Issue 2, 285-300, 1 August 2003
doi:10.1086/377138
Linkage Disequilibrium and Inference of Ancestral Recombination in 538 Single-Nucleotide Polymorphism Clusters across the Human Genome
Andrew G. Clark1, 3, 
, 
, Rasmus Nielsen2, James Signorovitch2, Tara C. Matise4, Stephen Glanowski3, Jeremy Heil3, Emily S. Winn-Deen3, 5, Arthur L. Holden6 and Eric Lai7
1 Department of Molecular Biology and Genetics, Cornell University, Ithaca, NY
2 Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY
3 Department of Celera Genomics, Rockville, MD
4 Department of Genetics, Rutgers University, Piscataway, NJ
5 Roche Molecular Systems, Pleasanton, CA
6 First Genetic Trust, Deerfield, IL; and
7 GlaxoSmithKline, Research Triangle Park, NC
Address for correspondence and reprints: Dr. Andrew G. Clark, Department of Molecular Biology and Genetics, 107 Biotechnology Building, Cornell University, Ithaca, NY 14853Abstract
The prospect of using linkage disequilibrium (LD) for fine-scale mapping in humans has attracted considerable attention, and, during the validation of a set of single-nucleotide polymorphisms (SNPs) for linkage analysis, a set of data for 4,833 SNPs in 538 clusters was produced that provides a rich picture of local attributes of LD across the genome. LD estimates may be biased depending on the means by which SNPs are first identified, and a particular problem of ascertainment bias arises when SNPs identified in small heterogeneous panels are subsequently typed in larger population samples. Understanding and correcting ascertainment bias is essential for a useful quantitative assessment of the landscape of LD across the human genome. Heterogeneity in the population recombination rate, ρ=4Nr, along the genome reflects how variable the density of markers will have to be for optimal coverage. We find that ascertainment-corrected ρ varies along the genome by more than two orders of magnitude, implying great differences in the recombinational history of different portions of our genome. The distribution of ρˆ is unimodal, and we show that this is compatible with a wide range of mixtures of hotspots in a background of variable recombination rate. Although ρˆ is significantly correlated across the three population samples, some regions of the genome exhibit population-specific spikes or troughs in ρ that are too large to be explained by sampling. This result is consistent with differences in the genealogical depth of local genomic regions, a finding that has direct bearing on the design and utility of LD mapping and on the National Institutes of Health HapMap project.
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