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Springer Nature [academic journals on nature.com], European Journal of Human Genetics, 4(13), p. 452-462, 2004

DOI: 10.1038/sj.ejhg.5201340

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X-chromosome as a marker for population history: linkage disequilibrium and haplotype study in Eurasian populations

Journal article published in 2004 by Maris Laan ORCID, Victor Wiebe, Elza Khusnutdinova, Maido Remm, Svante Pääbo
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Linkage disequilibrium (LD) structure is still unpredictable because the interplay of regional recombination rate and demographic history is poorly understood. We have compared the distribution of LD across two genomic regions differing in crossing-over activity -- Xq13 (0.166 cM/Mb) and Xp22 (1.3 cM/Mb) -- in 15 Eurasian populations. Demographic events predicted to increase the LD level -- genetic drift, bottleneck and admixture - had a very strong impact on extent and patterns of regional LD across Xq13 compared to Xp22. The haplotype distribution of the DXS1225-DXS8082 microsatellites from Xq13 exhibiting strong association in all populations was remarkably influenced by population history. European populations shared one common haplotype with a frequency of 25-40%. The Volga-Ural populations studied, living at the geographic borderline of Europe, showed elevated LD as well as harboring a significant fraction of haplotypes originating from East Asia, thus reflecting their past migrations and admixture. In the young Kuusamo isolate from Finland, a bottleneck has led to allelic associations between loci and shifted the haplotype distribution, but has much less affected single microsatellite allele frequencies compared to the main Finnish population. The data show that the footprint of a demographic event is longer preserved in haplotype distribution within a region of low crossing-over rate, than in the information content of a single marker, or between actively recombining markers. As the knowledge of LD patterns is often chosen to assist association mapping of common disease, our conclusions emphasize the importance of understanding the history, structure and variation of a study population.