scholarly journals Inferring Selective Constraint from Population Genomic Data Suggests Recent Regulatory Turnover in the Human Brain

2015 ◽  
Vol 7 (12) ◽  
pp. 3511-3528 ◽  
Author(s):  
Daniel R. Schrider ◽  
Andrew D. Kern
Keyword(s):  
Human Brain ◽  
Genomic Data ◽  
Selective Constraint ◽  
Population Genomic

scholarly journals Population genomic data in spider mites point to a role for local adaptation in shaping range shifts

2020 ◽  
Vol 13 (10) ◽  
pp. 2821-2835
Author(s):  
Lei Chen ◽  
Jing‐Tao Sun ◽  
Peng‐Yu Jin ◽  
Ary A. Hoffmann ◽  
Xiao‐Li Bing ◽  
...  
Keyword(s):  
Local Adaptation ◽  
Genomic Data ◽  
Spider Mites ◽  
Range Shifts ◽  
Population Genomic

scholarly journals Inferring Adaptive Introgression Using Hidden Markov Models

2020 ◽  
Author(s):  
Jesper Svedberg ◽  
Vladimir Shchur ◽  
Solomon Reinman ◽  
Rasmus Nielsen ◽  
Russell Corbett-Detig
Keyword(s):  
Markov Models ◽  
Hidden Markov ◽  
Genomic Data ◽  
Diverse Populations ◽  
Powerful Method ◽  
Adaptive Genetic Variation ◽  
Adaptive Introgression ◽  
Population Genomic ◽  
Significant Interest ◽  
Selection Parameters

AbstractAdaptive introgression - the flow of adaptive genetic variation between species or populations - has attracted significant interest in recent years and it has been implicated in a number of cases of adaptation, from pesticide resistance and immunity, to local adaptation. Despite this, methods for identification of adaptive introgression from population genomic data are lacking. Here, we present Ancestry_HMM-S, a Hidden Markov Model based method for identifying genes undergoing adaptive introgression and quantifying the strength of selection acting on them. Through extensive validation, we show that this method performs well on moderately sized datasets for realistic population and selection parameters. We apply Ancestry_HMM-S to a dataset of an admixed Drosophila melanogaster population from South Africa and we identify 17 loci which show signatures of adaptive introgression, four of which have previously been shown to confer resistance to insecticides. Ancestry_HMM-S provides a powerful method for inferring adaptive introgression in datasets that are typically collected when studying admixed populations. This method will enable powerful insights into the genetic consequences of admixture across diverse populations. Ancestry_HMM-S can be downloaded from https://github.com/jesvedberg/Ancestry_HMM-S/.

scholarly journals Estimating Seven Coefficients of Pairwise Relatedness Using Population-Genomic Data

Genetics ◽  
2017 ◽  
Vol 206 (1) ◽  
pp. 105-118 ◽  
Author(s):  
Matthew S. Ackerman ◽  
Parul Johri ◽  
Ken Spitze ◽  
Sen Xu ◽  
Thomas G. Doak ◽  
...  
Keyword(s):  
Genomic Data ◽  
Population Genomic

scholarly journals Equitable Expanded Carrier Screening Needs Indigenous Clinical and Population Genomic Data

2020 ◽  
Vol 107 (2) ◽  
pp. 175-182
Author(s):  
Simon Easteal ◽  
Ruth M. Arkell ◽  
Renzo F. Balboa ◽  
Shayne A. Bellingham ◽  
Alex D. Brown ◽  
...  
Keyword(s):  
Genomic Data ◽  
Carrier Screening ◽  
Expanded Carrier Screening ◽  
Population Genomic

FIFS: A data mining method for informative marker selection in high dimensional population genomic data

2017 ◽  
Vol 90 ◽  
pp. 146-154 ◽  
Author(s):  
Ioannis Kavakiotis ◽  
Patroklos Samaras ◽  
Alexandros Triantafyllidis ◽  
Ioannis Vlahavas
Keyword(s):  
Data Mining ◽  
Genomic Data ◽  
High Dimensional ◽  
Mining Method ◽  
Informative Marker ◽  
Data Mining Method ◽  
Marker Selection ◽  
Population Genomic

scholarly journals The Recombination Landscape in Wild House Mice Inferred Using Population Genomic Data

Genetics ◽  
2017 ◽  
Vol 207 (1) ◽  
pp. 297-309 ◽  
Author(s):  
Tom R. Booker ◽  
Rob W. Ness ◽  
Peter D. Keightley
Keyword(s):  
Genomic Data ◽  
House Mice ◽  
Population Genomic ◽  
Wild House Mice

scholarly journals An evaluation of seed zone delineation using phenotypic and population genomic data on black alderAlnus glutinosa

2014 ◽  
Vol 51 (5) ◽  
pp. 1218-1227 ◽  
Author(s):  
Hanne De Kort ◽  
Joachim Mergeay ◽  
Kristine Vander Mijnsbrugge ◽  
Guillaume Decocq ◽  
Simona Maccherini ◽  
...  
Keyword(s):  
Genomic Data ◽  
Population Genomic ◽  
Seed Zone

scholarly journals A new test suggests that balancing selection maintains hundreds of non-synonymous polymorphisms in the human genome

2021 ◽  
Author(s):  
Vivak Soni ◽  
Michiel Vos ◽  
Adam Eyre-Walker
Keyword(s):  
Genetic Diversity ◽  
Human Genome ◽  
Balancing Selection ◽  
Genomic Data ◽  
Demographic Changes ◽  
Simple Test ◽  
Direct Estimate ◽  
Population Genomic

AbstractThe role that balancing selection plays in the maintenance of genetic diversity remains unresolved. Here we introduce a new test, based on the McDonald-Kreitman test, in which the number of polymorphisms that are shared between populations is contrasted to those that are private at selected and neutral sites. We show that this simple test is robust to a variety of demographic changes, and that it can also give a direct estimate of the number of shared polymorphisms that are directly maintained by balancing selection. We apply our method to population genomic data from humans and conclude that more than a thousand non-synonymous polymorphisms are subject to balancing selection.

scholarly journals Longitudinal samples of bacterial genomes potentially bias evolutionary analyses

2017 ◽  
Author(s):  
B.J. Arnold ◽  
W.P. Hanage
Keyword(s):  
Genomic Data ◽  
Real Data ◽  
Sampling Bias ◽  
Bacterial Genomes ◽  
Clock Rate ◽  
Rare Mutations ◽  
Constant Size ◽  
Population Genomic ◽  
Genomic Studies ◽  
Longitudinal Sampling

AbstractSamples of bacteria collected over a period of time are attractive for several reasons, including the ability to estimate the molecular clock rate and to detect fluctuations in allele frequencies over time. However, longitudinal datasets are occasionally used in analyses that assume samples were collected contemporaneously. Using both simulations and genomic data from Neisseria gonorrhoeae, Streptococcus mutans, Campylobacter jejuni, and Helicobacter pylori, we show that longitudinal samples (spanning more than a decade in real data) may suffer from considerable bias that inflates estimates of recombination and the number of rare mutations in a sample of genomic sequences. While longitudinal data are frequently accounted for using the serial coalescent, many studies use other programs or metrics, such as Tajima’s D, that are sensitive to these sampling biases and contain genomic data collected across many years. Notably, longitudinal samples from a population of constant size may exhibit evidence of exponential growth. We suggest that population genomic studies of bacteria should routinely account for temporal diversity in samples or provide evidence that longitudinal sampling bias does not affect conclusions.

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