scholarly journals A Composite-Likelihood Method for Detecting Incomplete Selective Sweep from Population Genomic Data

Genetics ◽  
2015 ◽  
Vol 200 (2) ◽  
pp. 633-649 ◽  
Author(s):  
Ha My T. Vy ◽  
Yuseob Kim
Keyword(s):  
Selective Sweep ◽  
Genomic Data ◽  
Composite Likelihood ◽  
Likelihood Method ◽  
Population Genomic

scholarly journals Flexible Mixture Model Approaches That Accommodate Footprint Size Variability for Robust Detection of Balancing Selection

2020 ◽  
Vol 37 (11) ◽  
pp. 3267-3291 ◽  
Author(s):  
Xiaoheng Cheng ◽  
Michael DeGiorgio
Keyword(s):  
Pain Perception ◽  
Balancing Selection ◽  
Genomic Data ◽  
Composite Likelihood ◽  
Ratio Test ◽  
Data Set ◽  
Robust Detection ◽  
Population Genomic ◽  
Genomic Regions

Abstract Long-term balancing selection typically leaves narrow footprints of increased genetic diversity, and therefore most detection approaches only achieve optimal performances when sufficiently small genomic regions (i.e., windows) are examined. Such methods are sensitive to window sizes and suffer substantial losses in power when windows are large. Here, we employ mixture models to construct a set of five composite likelihood ratio test statistics, which we collectively term B statistics. These statistics are agnostic to window sizes and can operate on diverse forms of input data. Through simulations, we show that they exhibit comparable power to the best-performing current methods, and retain substantially high power regardless of window sizes. They also display considerable robustness to high mutation rates and uneven recombination landscapes, as well as an array of other common confounding scenarios. Moreover, we applied a specific version of the B statistics, termed B2, to a human population-genomic data set and recovered many top candidates from prior studies, including the then-uncharacterized STPG2 and CCDC169–SOHLH2, both of which are related to gamete functions. We further applied B2 on a bonobo population-genomic data set. In addition to the MHC-DQ genes, we uncovered several novel candidate genes, such as KLRD1, involved in viral defense, and SCN9A, associated with pain perception. Finally, we show that our methods can be extended to account for multiallelic balancing selection and integrated the set of statistics into open-source software named BalLeRMix for future applications by the scientific community.

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

scholarly journals Composite likelihood method for inferring local pedigrees

PLoS Genetics ◽  
2017 ◽  
Vol 13 (8) ◽  
pp. e1006963 ◽  
Author(s):  
Amy Ko ◽  
Rasmus Nielsen
Keyword(s):  
Composite Likelihood ◽  
Likelihood Method

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
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