Clines, clusters, and the effect of study design on the inference of human population structure.
Noah A. Rosenberg,Saurabh Mahajan,Sohini Ramachandran,Chengfeng Zhao,Jonathan K. Pritchard,Marcus W. Feldman +5 more
TLDR
Analysis of the 993-locus dataset corroborates 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.Abstract:
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.read more
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A global reference for human genetic variation.
Adam Auton,Gonçalo R. Abecasis,David Altshuler,Richard Durbin,David R. Bentley,Aravinda Chakravarti,Andrew G. Clark,Peter Donnelly,Evan E. Eichler,Paul Flicek,Stacey Gabriel,Richard A. Gibbs,Eric D. Green,Matthew E. Hurles,Bartha Maria Knoppers,Jan O. Korbel,Eric S. Lander,Charles Lee,Hans Lehrach,Elaine R. Mardis,Gabor T. Marth,Gil McVean,Deborah A. Nickerson,Jeanette Schmidt,Stephen T. Sherry,Jun Wang,Richard K. Wilson,Eric Boerwinkle,Harsha Doddapaneni,Yi Han,Viktoriya Korchina,Christie Kovar,Sandra L. Lee,Donna M. Muzny,Jeffrey G. Reid,Yiming Zhu,Yuqi Chang,Qiang Feng,Qiang Feng,Xiaodong Fang,Xiaodong Fang,Xiaosen Guo,Xiaosen Guo,Min Jian,Min Jian,Hui Jiang,Hui Jiang,Xin Jin,Tianming Lan,Guoqing Li,Jingxiang Li,Yingrui Li,Shengmao Liu,Xiao Liu,Xiao Liu,Yao Lu,Xuedi Ma,Meifang Tang,Bo Wang,Guangbiao Wang,Honglong Wu,Renhua Wu,Xun Xu,Ye Yin,Dandan Zhang,Wenwei Zhang,Jiao Zhao,Meiru Zhao,Xiaole Zheng,Namrata Gupta,Neda Gharani,Lorraine Toji,Norman P. Gerry,Alissa M. 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Principal components analysis corrects for stratification in genome-wide association studies
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References
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Noah A. Rosenberg,Jonathan K. Pritchard,James L. Weber,Howard M. Cann,Kenneth K. Kidd,Lev A. Zhivotovsky,Marcus W. Feldman +6 more
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