The linkage disequilibrium maps of three human chromosomes across four populations reflect their demographic history and a common underlying recombination pattern

Francisco M. De La Vega(Sigmovir Biosystems (United States)), Hadar Isaac, Andrew Collins(University of Southampton), Charles Scafe, Bjarni V. Halldórsson(deCODE Genetics (Iceland)), Xiaoping Su(St. Jude Children's Research Hospital), Ross A. Lippert(Massachusetts Institute of Technology), Yibin Wang, Marion Laig-Webster, Ryan T. Koehler, Janet Ziegle, L T Wogan, Junko Stevens, Kyle M. Leinen, Sheri J. Olson, Karl Guegler, Xiaoqing You, Lily H. Xu, Heinz G. Hemken, Francis Kalush, Mitsuo Itakura(Tokushima University), Yi Zheng(Chinese Center For Disease Control and Prevention), Guy de Thé(Institut Pasteur), Stephen J. O’Brien(National Institutes of Health), Andrew G. Clark(Cornell University), Sorin Istrail, Michael W. Hunkapiller, Eugene G. Spier, Dennis A. Gilbert
Genome Research
March 21, 2005
Cited by 116Open Access
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Abstract

The extent and patterns of linkage disequilibrium (LD) determine the feasibility of association studies to map genes that underlie complex traits. Here we present a comparison of the patterns of LD across four major human populations (African-American, Caucasian, Chinese, and Japanese) with a high-resolution single-nucleotide polymorphism (SNP) map covering almost the entire length of chromosomes 6, 21, and 22. We constructed metric LD maps formulated such that the units measure the extent of useful LD for association mapping. LD reaches almost twice as far in chromosome 6 as in chromosomes 21 or 22, in agreement with their differences in recombination rates. By all measures used, out-of-Africa populations showed over a third more LD than African-Americans, highlighting the role of the population's demography in shaping the patterns of LD. Despite those differences, the long-range contour of the LD maps is remarkably similar across the four populations, presumably reflecting common localization of recombination hot spots. Our results have practical implications for the rational design and selection of SNPs for disease association studies.


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