scholarly journals Multilocus genetic analyses and spatial modeling reveal complex population structure and history in a widespread resident North American passerine (Perisoreus canadensis)

2017 ◽  
Vol 7 (23) ◽  
pp. 9869-9889 ◽  
Author(s):  
Kimberly M. Dohms ◽  
Brendan A. Graham ◽  
Theresa M. Burg
1993 ◽  
Vol 71 (8) ◽  
pp. 1064-1071 ◽  
Author(s):  
William J. Otrosina ◽  
Thomas E. Chase ◽  
Fields W. Cobb Jr. ◽  
Kari Korhonen

Isolates of Heterobasidion annosum (Fr.) Bref. representing North American S and P and European S, P, and F intersterility groups were subjected to isozyme analysis. European S, P, and F groups had more variability than the North American S and P groups in expected hterozygosity, number of alleles per locus, and percent polymorphic loci. In contrast with the North American S and P groups, the European intersterility groups could not be distinguished from each other on the basis of individual isozyme loci, although significant differences in allele frequencies exist between European S and P groups. This suggests that evolution proceeded at different rates in the intersterility groups, or intersterility barriers appeared later in the European populations relative to the North American populations of H. annosum. Changes in climate and host species associations during the Tertiary may have been a major factor in evolution of H. annosum intersterility groups. Key words: allozymes, forest tree hosts, playnological events, evolutionary relationships, Hymenomycetes, root disease.


2009 ◽  
Vol 106 (33) ◽  
pp. 13903-13908 ◽  
Author(s):  
A. R. Boyko ◽  
R. H. Boyko ◽  
C. M. Boyko ◽  
H. G. Parker ◽  
M. Castelhano ◽  
...  

2014 ◽  
Author(s):  
Prem Gopalan ◽  
Wei Hao ◽  
David M. Blei ◽  
John D. Storey

One of the major goals of population genetics is to quantitatively understand variation of genetic polymorphisms among individuals. To this end, researchers have developed sophisticated statistical methods to capture the complex population structure that underlies observed genotypes in humans, and such methods have been effective for analyzing modestly sized genomic data sets. However, the number of genotyped humans has grown significantly in recent years, and it is accelerating. In aggregate about 1M individuals have been genotyped to date. Analyzing these data will bring us closer to a nearly complete picture of human genetic variation; but existing methods for population genetics analysis do not scale to data of this size. To solve this problem we developed TeraStructure. TeraStructure is a new algorithm to fit Bayesian models of genetic variation in human populations on tera-sample-sized data sets (1012observed genotypes, e.g., 1M individuals at 1M SNPs). It is a principled approach to Bayesian inference that iterates between subsampling locations of the genome and updating an estimate of the latent population structure of the individuals. On data sets of up to 2K individuals, TeraStructure matches the existing state of the art in terms of both speed and accuracy. On simulated data sets of up to 10K individuals, TeraStructure is twice as fast as existing methods and has higher accuracy in recovering the latent population structure. On genomic data simulated at the tera-sample-size scales, TeraStructure continues to be accurate and is the only method that can complete its analysis.


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