scholarly journals Estimating recent migration and population size surfaces

2018 ◽  
Author(s):  
Hussein Al-Asadi ◽  
Desislava Petkova ◽  
Matthew Stephens ◽  
John Novembre
Keyword(s):  
Genetic Similarity ◽  
Human Genetics ◽  
Molecular Ecology ◽  
Geographic Distance ◽  
Evolutionary Genetics ◽  
Integrated Analysis ◽  
Parameter Estimates ◽  
Migration Rates ◽  
Population Sizes ◽  
Time Periods

AbstractIn many species a fundamental feature of genetic diversity is that genetic similarity decays with geographic distance; however, this relationship is often complex, and may vary across space and time. Methods to uncover and visualize such relationships have widespread use for analyses in molecular ecology, conservation genetics, evolutionary genetics, and human genetics. While several frameworks exist, a promising approach is to infer maps of how migration rates vary across geographic space. Such maps could, in principle, be estimated across time to reveal the full complexity of population histories. Here, we take a step in this direction: we present a method to infer separate maps of population sizes and migration rates for different time periods from a matrix of genetic similarity between every pair of individuals. Specifically, genetic similarity is measured by counting the number of long segments of haplotype sharing (also known as identity-by-descent tracts). By varying the length of these segments we obtain parameter estimates for qualitatively different time periods. Using simulations, we show that the method can reveal time-varying migration rates and population sizes, including changes that are not detectable when ignoring haplotypic structure. We apply the method to a dataset of contemporary European individuals (POPRES), and provide an integrated analysis of recent population structure and growth over the last ~3,000 years in Europe. Software implementing the methods is available at https://github.com/halasadi/MAPS.

scholarly journals Inferring time-dependent migration and coalescence patterns from genetic sequence and predictor data in structured populations

2018 ◽  
Author(s):  
Nicola F. Müller ◽  
Gytis Dudas ◽  
Tanja Stadler
Keyword(s):  
Population Dynamics ◽  
Phylogenetic Trees ◽  
Ebola Virus ◽  
Sequence Data ◽  
Geographic Distance ◽  
Structured Populations ◽  
Effective Population ◽  
Genetic Sequence ◽  
Migration Rates ◽  
Population Sizes

AbstractPopulation dynamics can be inferred from genetic sequence data using phylodynamic methods. These methods typically quantify the dynamics in unstructured populations or assume the parameters describing the dynamics to be constant through time in structured populations. Inference methods allowing for structured populations and parameters to vary through time involve many parameters which have to be inferred. Each of these parameters might be however only weakly informed by data. Here we introduce an approach that uses so-called predictors, such as geographic distance between locations, within a generalized linear model to inform the population dynamic parameters, namely the time-varying migration rates and effective population sizes under the marginal approximation of the structured coalescent. By using simulations, we show that we are able to reliably infer the parameters from phylogenetic trees. We then apply this framework to a previously described Ebola virus dataset. We infer incidence to be the strongest predictor for effective population size and geographic distance the strongest predictor for migration. This allows us to show not only on simulated data, but also on real data, that we are able to identify reasonable predictors. Overall, we provide a novel method that allows to identify predictors for migration rates and effective population sizes and to use these predictors to quantify migration rates and effective population sizes. Its implementation as part of the BEAST2 software package MASCOT allows to jointly infer population dynamics within structured populations, the phylogenetic tree, and evolutionary parameters.

scholarly journals Pairwise genetic interactions modulate lipid plasma levels and cellular uptake

2020 ◽  
Author(s):  
Magdalena Zimon ◽  
Yunfeng Huang ◽  
Anthi Trasta ◽  
Jimmy Z. Liu ◽  
Chia-Yen Chen ◽  
...  
Keyword(s):  
Complex Traits ◽  
Drug Target ◽  
Human Genetics ◽  
Large Population ◽  
Genetic Interactions ◽  
Model Systems ◽  
Lipid Lowering ◽  
Gene Pairs ◽  
Population Sizes ◽  
The Uk

SUMMARYGenetic interactions (GIs), the joint impact of different genes or variants on a phenotype, are foundational to the genetic architecture of complex traits. However, identifying GIs through human genetics is challenging since it necessitates very large population sizes, while findings from model systems not always translate to humans. Here, we combined exome-sequencing and genotyping in the UK Biobank with combinatorial RNA-interference (coRNAi) screening to systematically test for pairwise GIs between 30 lipid GWAS genes. Gene-based protein-truncating variant (PTV) burden analyses from 240,970 exomes revealed additive GIs for APOB with PCSK9 and LPL, respectively. Both, genetics and coRNAi identified additive GIs for 12 additional gene pairs. Overlapping non-additive GIs were detected only for TOMM40 at the APOE locus with SORT1 and NCAN. Our study identifies distinct gene pairs that modulate both, plasma and cellular lipid levels via additive and non-additive effects and nominates drug target pairs for improved lipid-lowering combination therapies.

Weak genetic structuring suggests historically high genetic connectivity among recently fragmented urban populations of the scincid lizard, Ctenotus fallens

2015 ◽  
Vol 63 (4) ◽  
pp. 279 ◽  
Author(s):  
Josef Krawiec ◽  
Siegfried L. Krauss ◽  
Robert A. Davis ◽  
Peter B. S. Spencer
Keyword(s):  
Genetic Diversity ◽  
Gene Flow ◽  
Large Population ◽  
Geographic Distance ◽  
Genetic Erosion ◽  
Genetic Connectivity ◽  
Genetic Structuring ◽  
Low Genetic Diversity ◽  
Population Sizes

Populations in fragmented urban remnants may be at risk of genetic erosion as a result of reduced gene flow and elevated levels of inbreeding. This may have serious genetic implications for the long-term viability of remnant populations, in addition to the more immediate pressures caused by urbanisation. The population genetic structure of the generalist skink Ctenotus fallens was examined using nine microsatellite markers within and among natural vegetation remnants within a highly fragmented urban matrix in the Perth metropolitan area in Western Australia. These data were compared with samples from a large unfragmented site on the edge of the urban area. Overall, estimates of genetic diversity and inbreeding within all populations were similar and low. Weak genetic differentiation, and a significant association between geographic and genetic distance, suggests historically strong genetic connectivity that decreases with geographic distance. Due to recent fragmentation, and genetic inertia associated with low genetic diversity and large population sizes, it is not possible from these data to infer current genetic connectivity levels. However, the historically high levels of gene flow that our data suggest indicate that a reduction in contemporary connectivity due to fragmentation in C. fallens is likely to result in negative genetic consequences in the longer term.

scholarly journals Correlation between geographic distance and genetic similarity in an international collection of bovine faecal Escherichia coli O157[ratio ]H7 isolates

2003 ◽  
Vol 131 (2) ◽  
pp. 923-930 ◽  
Author(s):  
M. A. DAVIS ◽  
D. D. HANCOCK ◽  
T. E. BESSER ◽  
D. H. RICE ◽  
C. J. HOVDE ◽  
...  
Keyword(s):  
Escherichia Coli ◽  
Gel Electrophoresis ◽  
Genetic Similarity ◽  
Genetic Relatedness ◽  
Geographic Distance ◽  
Similarity Coefficient ◽  
Pulsed Field Gel Electrophoresis ◽  
Dice Similarity Coefficient ◽  
Escherichia Coli O157 ◽  
Molecular Studies

Evidence from epidemiological and molecular studies of bovine Escherichia coli O157[ratio ]H7 suggests that strains are frequently transmitted across wide geographic distances. To test this hypothesis, we compared the geographic and genetic distance of a set of international bovine Escherichia coli O157[ratio ]H7 isolates using the Mantel correlation. For a measure of genetic relatedness, pulsed-field gel electrophoresis of six different restriction enzyme digests was used to generate an average Dice similarity coefficient for each isolate pair. Geographic distance was calculated using latitude and longitude data for isolate source locations. The Mantel correlation between genetic similarity and the logarithm of geographic distance in kilometers was −0·21 (P<0·001). The low magnitude of the Mantel correlation indicates that transmission over long distances is common. The occurrence of isolates from different continents on the same cluster of the dendrogram also supports the idea that Escherichia coli O157[ratio ]H7 strains can be transferred with considerable frequency over global distances.

Migratory Catch-Age Analysis

1990 ◽  
Vol 47 (12) ◽  
pp. 2315-2327 ◽  
Author(s):  
Terrance J. Quinn II ◽  
Richard B. Deriso ◽  
Philip R. Neal
Keyword(s):  
Auxiliary Information ◽  
Nonlinear Least Squares ◽  
Sensitivity Study ◽  
Error Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Migration Rates ◽  
Process Error ◽  
A New Technique ◽  
And Migration

We review techniques for estimating the abundance of migratory populations and develop a new technique based on catch-age data from geographic regions and our earlier technique, catch-age analysis with auxiliary information (Deriso et al. 1985, 1989). Data requirements are catch-age data over several years, some auxiliary information, and migration rates among regions. The model, containing parameters for year-class abundance, age selectivity, full-recruitment fishing mortality, and catchability, is fitted to data with a nonlinear least squares algorithm. We present a measurement error model and a process error model and favor the process error model because all model parameters can be jointly estimated. By application to data on Pacific halibut, the process error model converges readily and produces estimates with no significant bias. These estimates have relatively high precision compared to those from analyses which did not incorporate migration information. The error structure used in a model has a more significant impact on parameter estimates than migration rates. A sensitivity study of migration rates shows sensitivity of the order of the rates themselves.

scholarly journals Visualizing spatial population structure with estimated effective migration surfaces

2014 ◽  
Author(s):  
Desislava Petkova ◽  
John Novembre ◽  
Matthew Stephens
Keyword(s):  
Population Structure ◽  
Gene Flow ◽  
Genetic Similarity ◽  
Large Scale ◽  
Genetic Model ◽  
Isolation By Distance ◽  
Genetic Data ◽  
Migration Rates ◽  
Spatial Population ◽  
Spatial Population Structure

Genetic data often exhibit patterns that are broadly consistent with "isolation by distance" - a phenomenon where genetic similarity tends to decay with geographic distance. In a heterogeneous habitat, decay may occur more quickly in some regions than others: for example, barriers to gene flow can accelerate the genetic differentiation between groups located close in space. We use the concept of "effective migration" to model the relationship between genetics and geography: in this paradigm, effective migration is low in regions where genetic similarity decays quickly. We present a method to quantify and visualize variation in effective migration across the habitat, which can be used to identify potential barriers to gene flow, from geographically indexed large-scale genetic data. Our approach uses a population genetic model to relate underlying migration rates to expected pairwise genetic dissimilarities, and estimates migration rates by matching these expectations to the observed dissimilarities. We illustrate the potential and limitations of our method using simulations and data from elephant, human, and Arabidopsis thaliana populations. The resulting visualizations highlight important features of the spatial population structure that are difficult to discern using existing methods for summarizing genetic variation such as principal components analysis.

scholarly journals Evidence of the interplay of genetics and culture in Ethiopia

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Saioa López ◽  
Ayele Tarekegn ◽  
Gavin Band ◽  
Lucy van Dorp ◽  
Nancy Bird ◽  
...  
Keyword(s):  
Genetic Structure ◽  
Genetic Similarity ◽  
Cultural Practices ◽  
Geographic Distance ◽  
Human Populations ◽  
Genetic Studies ◽  
Sampling Protocols ◽  
Show Evidence ◽  
History Of ◽  
The Rich

AbstractThe rich linguistic, ethnic and cultural diversity of Ethiopia provides an unprecedented opportunity to understand the level to which cultural factors correlate with–and shape–genetic structure in human populations. Using primarily new genetic variation data covering 1,214 Ethiopians representing 68 different ethnic groups, together with information on individuals’ birthplaces, linguistic/religious practices and 31 cultural practices, we disentangle the effects of geographic distance, elevation, and social factors on the genetic structure of Ethiopians today. We provide evidence of associations between social behaviours and genetic differences among present-day peoples. We show that genetic similarity is broadly associated with linguistic affiliation, but also identify pronounced genetic similarity among groups from disparate language classifications that may in part be attributable to recent intermixing. We also illustrate how groups reporting the same culture traits are more genetically similar on average and show evidence of recent intermixing, suggesting that shared cultural traits may promote admixture. In addition to providing insights into the genetic structure and history of Ethiopia, we identify the most important cultural and geographic predictors of genetic differentiation and provide a resource for designing sampling protocols for future genetic studies involving Ethiopians.

scholarly journals Bayesian phylodynamics of avian influenza virus H9N2 in Asia with time-dependent predictors of migration

2018 ◽  
Author(s):  
Jing Yang ◽  
Nicola F. Müller ◽  
Remco Bouckaert ◽  
Bing Xu ◽  
Alexei J. Drummond
Keyword(s):  
Time Series ◽  
Avian Influenza ◽  
Wild Bird ◽  
Time Dependent ◽  
Poultry Production ◽  
Geographic Proximity ◽  
Effective Population ◽  
Migration Rates ◽  
Bird Populations ◽  
Population Sizes

AbstractModel-based phylodynamic approaches recently employed generalized linear models (GLMs) to uncover potential predictors of viral spread. Very recently some of these models have allowed both the predictors and their coefficients to be time-dependent. However, these studies mainly focused on predictors that are assumed to be constant through time. Here we inferred the phylodynamics of H9N2 viruses isolated in 12 Asian countries and regions under both discrete trait analysis (DTA) and structured coalescent (MASCOT) approaches. Using MASCOT we applied a new time-dependent GLM to uncover the underlying factors behind H9N2 spread. We curated a rich set of time-series predictors including annual international live poultry trade and national poultry production figures. This time-dependent phylodynamic prediction model was compared to commonly employed time-independent alternatives. Additionally the time-dependent MASCOT model allowed for the estimation of viral effective sub-population sizes and their changes through time and these effective population dynamics within each country were predicted by a GLM. International annual poultry trade is a strongly supported predictor of virus migration rates. There was also strong support for geographic proximity as a predictor of migration rate in all GLMs investigated. In time-dependent MASCOT models, national poultry production was also identified as a predictor of virus genetic diversity through time and this signal was obvious in mainland China and Bangladesh. Our application of a recently introduced time-dependent GLM predictors integrated rich time-series data in Bayesian phylodynamic prediction. We demonstrated the contribution of poultry trade and geographic proximity (potentially unheralded wild bird movements) to avian influenza spread in Asia. To gain a better understanding of the drivers of H9N2 spread, we suggest increased surveillance of the H9N2 virus in countries that are currently under-sampled as well as in wild bird populations in the most affected countries.Author summaryWhat drives the geographic dispersal and genetic diversity of H9N2 avian influenza virus in Asia? We used two model-based approaches, DTA and MASCOT, to reconstruct the phylogeographic dynamics of the virus. Further, multiple potential predictors were used to inform the virus spread and population dynamics by GLMs. Here, we maximised the power of time-series predictors in Bayesian phylodynamic prediction. For the first time, we were able to quantify the contribution of both time-series and constant predictors to both migration rates and effective population sizes in a structured population. We identified a positive association of international poultry trade and national poultry production time-series with virus migration rates and effective population sizes respectively. We also identify geographic proximity as a strongly supported driver to virus migration rates and this points to the potential role of wild bird populations in virus dispersal across countries. Our study is a practical exemplar of the use of temporal information in predictors to model heterogeneous spatial diffusion and population dynamic processes and provides direction to H9N2 control efforts in Asia.

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