From regionally predictable to locally complex population structure in a freshwater top predator: river systems are not always the unit of connectivity in Northern PikeEsox lucius

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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Mycelial biomass and concentration of loline alkaloids driven by complex population structure in Epichloë uncinata and meadow fescue (Schedonorus pratensis)

Mycologia ◽

10.1080/00275514.2020.1746607 ◽

2020 ◽

Vol 112 (3) ◽

pp. 474-490 ◽

Cited By ~ 2

Author(s):

G. Cagnano ◽

I. Lenk ◽

N. Roulund ◽

C. S. Jensen ◽

M. P. Cox ◽

...

Keyword(s):

Population Structure ◽

Mycelial Biomass ◽

Meadow Fescue ◽

Complex Population ◽

Loline Alkaloids

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netview p: a network visualization tool to unravel complex population structure using genome-wide SNPs

Molecular Ecology Resources ◽

10.1111/1755-0998.12442 ◽

2015 ◽

Vol 16 (1) ◽

pp. 216-227 ◽

Cited By ~ 47

Author(s):

Eike J. Steinig ◽

Markus Neuditschko ◽

Mehar S. Khatkar ◽

Herman W. Raadsma ◽

Kyall R. Zenger

Keyword(s):

Population Structure ◽

Network Visualization ◽

Visualization Tool ◽

Genome Wide ◽

Complex Population

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Conservation implications of complex population structure: lessons from the loggerhead turtle (Caretta caretta)

Molecular Ecology ◽

10.1111/j.1365-294x.2005.02598.x ◽

2005 ◽

Vol 14 (8) ◽

pp. 2389-2402 ◽

Cited By ~ 151

Author(s):

B. W. BOWEN ◽

A. L. BASS ◽

L. SOARES ◽

R. J. TOONEN

Keyword(s):

Population Structure ◽

Loggerhead Turtle ◽

Caretta Caretta ◽

Conservation Implications ◽

Complex Population

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Complex population structure of the Atlantic puffin revealed by whole genome analyses

10.1101/2020.11.05.351874 ◽

2020 ◽

Author(s):

Oliver Kersten ◽

Bastiaan Star ◽

Deborah M. Leigh ◽

Tycho Anker-Nilssen ◽

Hallvard Strøm ◽

...

Keyword(s):

Population Structure ◽

Gene Flow ◽

Isolation By Distance ◽

Spatial Scales ◽

Secondary Contact ◽

Whole Genome ◽

Genome Data ◽

Complex Population ◽

Genome Analyses ◽

Atlantic Puffin

AbstractThe factors underlying gene flow and genomic population structure in vagile seabirds are notoriously difficult to understand due to their complex ecology with diverse dispersal barriers and extensive periods at sea. Yet, such understanding is vital for conservation management of seabirds that are globally declining at alarming rates. Here, we elucidate the population structure of the Atlantic puffin (Fratercula arctica) by assembling its reference genome and analyzing genome-wide resequencing data of 72 individuals from 12 colonies. We identify four large, genetically distinct clusters, observe isolation-by-distance between colonies within these clusters, and obtain evidence for a secondary contact zone. These observations disagree with the current taxonomy, and show that a complex set of contemporary biotic factors impede gene flow over different spatial scales. Our results highlight the power of whole genome data to reveal unexpected population structure in vagile marine seabirds and its value for seabird taxonomy, evolution and conservation.

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New kinship and FST estimates reveal higher levels of differentiation in the global human population

10.1101/653279 ◽

2019 ◽

Cited By ~ 2

Author(s):

Alejandro Ochoa ◽

John D. Storey

Keyword(s):

Population Structure ◽

Genetic Relatedness ◽

Genetic Distances ◽

Recent Common Ancestor ◽

Human Populations ◽

Population Genetic Analysis ◽

Most Recent Common Ancestor ◽

Complex Population ◽

Population Structures ◽

Modern Population

Kinship coefficients and FST, which measure genetic relatedness and the overall population structure, respectively, have important biomedical applications. However, existing estimators are only accurate under restrictive conditions that most natural population structures do not satisfy. We recently derived new kinship and FST estimators for arbitrary population structures [1, 2]. Our estimates on human datasets reveal a complex population structure driven by founder effects due to dispersal from Africa and admixture. Notably, our new approach estimates larger FST values of 26% for native worldwide human populations and 23% for admixed Hispanic individuals, whereas the existing approach estimates 9.8% and 2.6%, respectively. While previous work correctly measured FST between subpopulation pairs, our generalized FST measures genetic distances among all individuals and their most recent common ancestor (MRCA) population, revealing that genetic differentiation is greater than previously appreciated. This analysis demonstrates that estimating kinship and FST under more realistic assumptions is important for modern population genetic analysis.

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