Linked Life Data

16355252

Source:http://linkedlifedata.com/resource/pubmed/id/16355252

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2006-6-5
pubmed:abstractText
Previously, we observed that without using prior information about individual sampling locations, a clustering algorithm applied to multilocus genotypes from worldwide human populations produced genetic clusters largely coincident with major geographic regions. It has been argued, however, that the degree of clustering is diminished by use of samples with greater uniformity in geographic distribution, and that the clusters we identified were a consequence of uneven sampling along genetic clines. Expanding our earlier dataset from 377 to 993 markers, we systematically examine the influence of several study design variables--sample size, number of loci, number of clusters, assumptions about correlations in allele frequencies across populations, and the geographic dispersion of the sample--on the "clusteredness" of individuals. With all other variables held constant, geographic dispersion is seen to have comparatively little effect on the degree of clustering. Examination of the relationship between genetic and geographic distance supports a view in which the clusters arise not as an artifact of the sampling scheme, but from small discontinuous jumps in genetic distance for most population pairs on opposite sides of geographic barriers, in comparison with genetic distance for pairs on the same side. Thus, analysis of the 993-locus dataset corroborates our earlier results: if enough markers are used with a sufficiently large worldwide sample, individuals can be partitioned into genetic clusters that match major geographic subdivisions of the globe, with some individuals from intermediate geographic locations having mixed membership in the clusters that correspond to neighboring regions.
pubmed:grant
pubmed:commentsCorrections
http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-10835412, http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-11855957, http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-11954565, http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-12205564, http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-12493913, http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-12557124, http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-12600278, http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-12890924, http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-12930761, http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-14631557, http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-15073024, http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-15342553, http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-15601537, http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-15625622, http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-16243969, http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-7510853, http://linkedlifedata.com/resource/pubmed/commentcorrection/16355252-9326336
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1553-7404
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
1
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
e70
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Clines, clusters, and the effect of study design on the inference of human population structure.
pubmed:affiliation
Department of Human Genetics, Bioinformatics Program, and the Life Sciences Institute, University of Michigan, Ann Arbor, Michigan, USA. rnoah@umich.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural