DEMOGRAPHIC HISTORY AND LINKAGE DISEQUILIBRIUM IN HUMAN-POPULATIONS

Authors
Citation
M. Laan et S. Paabo, DEMOGRAPHIC HISTORY AND LINKAGE DISEQUILIBRIUM IN HUMAN-POPULATIONS, Nature genetics, 17(4), 1997, pp. 435-438
Citations number
25
Journal title
ISSN journal
10614036
Volume
17
Issue
4
Year of publication
1997
Pages
435 - 438
Database
ISI
SICI code
1061-4036(1997)17:4<435:DHALDI>2.0.ZU;2-U
Abstract
In the human genome, linkage disequilibrium (LD)-the nonrandom associa tion of alleles at chromosomal loci(1)-has been studied mainly in regi ons surrounding disease genes on affected chromosomes2-6. Consequently , little information is available on the distribution of LD across ano nymous genomic regions in the general population. However, demographic history is expected to influence the extent of overall LD across the genome, so a population that has been of constant size will display hi gher levels of LD than a population that has expanded(7). In support o f this, the extent of LD between anonymous loci on chromosome 4 in chi mpanzees (as a model of a population of constant size) has been compar ed to that in Finns (as a model of an expanded population; refs 8,9) a nd found to exhibit more LD than in the latter population. In Europe, studies of mitochondrial (mt) DNA sequences have suggested that most p opulations have experienced expansion(10), whereas the Saami in northe rn Fenno-Scandinavia have been of constant size (Table 1). Thus, in no rthern Europe, populations with radically different demographic histor ies live in close geographic proximity to each other. We studied the a llelic associations between anonymous microsatellite loci on the X chr omosome in the Saami and neighbouring populations and found dramatical ly higher levels of LD in the Saami than in other populations in the r egion. This indicates that whereas recently expanded populations, such as the Finns, are well suited to map single disease genes affected by recent mutations, populations that have been of constant size, such a s the Saami, may be much better suited to map genes for complex traits that are caused by older mutations.