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@article{garud2015sweeps,
author = {Garud, Nandita R. AND Messer, Philipp W. AND Buzbas, Erkan O. AND Petrov, Dmitri A.},
journal = {PLoS Genetics},
publisher = {Public Library of Science},
title = {Recent selective sweeps in {North American} \textit{Drosophila melanogaster} show signatures of soft sweeps},
year = {2015},
month = {02},
volume = {11},
url = {https://doi.org/10.1371/journal.pgen.1005004},
pages = {1-32},
abstract = {Author Summary Evolutionary adaptation is a process in which beneficial mutations increase in frequency in response to selective pressures. If these mutations were previously rare or absent from the population, adaptation should generate a characteristic signature in the genetic diversity around the adaptive locus, known as a selective sweep. Such selective sweeps can be distinguished into hard selective sweeps, where only a single adaptive mutation rises in frequency, or soft selective sweeps, where multiple adaptive mutations at the same locus sweep through the population simultaneously. Here we design a new statistical method that can identify both hard and soft sweeps in population genomic data and apply this method to a Drosophila melanogaster population genomic dataset consisting of 145 sequenced strains collected in North Carolina. We find that selective sweeps were abundant in the recent history of this population. Interestingly, we also find that practically all of the strongest and most recent sweeps show patterns that are more consistent with soft rather than hard sweeps. We discuss the implications of these findings for the discovery and quantification of adaptation from population genomic data in Drosophila and other species with large population sizes.},
number = {2},
doi = {10.1371/journal.pgen.1005004}
}
@article {kim2002detecting,
author = {Kim, Yuseob and Stephan, Wolfgang},
title = {Detecting a Local Signature of Genetic Hitchhiking Along a Recombining Chromosome},
volume = {160},
number = {2},
pages = {765--777},
year = {2002},
publisher = {Genetics},
abstract = {The theory of genetic hitchhiking predicts that the level of genetic variation is greatly reduced at the site of strong directional selection and increases as the recombinational distance from the site of selection increases. This characteristic pattern can be used to detect recent directional selection on the basis of DNA polymorphism data. However, the large variance of nucleotide diversity in samples of moderate size imposes difficulties in detecting such patterns. We investigated the patterns of genetic variation along a recombining chromosome by constructing ancestral recombination graphs that are modified to incorporate the effect of genetic hitchhiking. A statistical method is proposed to test the significance of a local reduction of variation and a skew of the frequency spectrum caused by a hitchhiking event. This method also allows us to estimate the strength and the location of directional selection from DNA sequence data.},
issn = {0016-6731},
URL = {https://www.genetics.org/content/160/2/765},
eprint = {https://www.genetics.org/content/160/2/765.full.pdf},
journal = {Genetics}
}
@article {mcvean2004ldhat,
author = {McVean, Gilean A. T. and Myers, Simon R. and Hunt, Sarah and Deloukas, Panos and Bentley, David R. and Donnelly, Peter},
title = {The Fine-Scale Structure of Recombination Rate Variation in the Human Genome},
volume = {304},
number = {5670},
pages = {581--584},
year = {2004},
doi = {10.1126/science.1092500},
publisher = {American Association for the Advancement of Science},
abstract = {The nature and scale of recombination rate variation are largely unknown for most species. In humans, pedigree analysis has documented variation at the chromosomal level, and sperm studies have identified specific hotspots in which crossing-over events cluster. To address whether this picture is representative of the genome as a whole, we have developed and validated a method for estimating recombination rates from patterns of genetic variation. From extensive single-nucleotide polymorphism surveys in European and African populations, we find evidence for extreme local rate variation spanning four orders in magnitude, in which 50\% of all recombination events take place in less than 10\% of the sequence. We demonstrate that recombination hotspots are a ubiquitous feature of the human genome, occurring on average every 200 kilobases or less, but recombination occurs preferentially outside genes.},
issn = {0036-8075},
URL = {https://science.sciencemag.org/content/304/5670/581},
eprint = {https://science.sciencemag.org/content/304/5670/581.full.pdf},
journal = {Science}
}
@article{beichman2018review,
author = {Beichman, Annabel C. and Huerta-Sanchez, Emilia and Lohmueller, Kirk E.},
title = {Using Genomic Data to Infer Historic Population Dynamics of Nonmodel Organisms},
journal = {Annual Review of Ecology, Evolution, and Systematics},
volume = {49},
number = {1},
pages = {433-456},
year = {2018},
doi = {10.1146/annurev-ecolsys-110617-062431},
URL = { https://doi.org/10.1146/annurev-ecolsys-110617-062431 },
eprint = { https://doi.org/10.1146/annurev-ecolsys-110617-062431 } ,
abstract = { Genome sequence data are now being routinely obtained from many nonmodel organisms. These data contain a wealth of information about the demographic history of the populations from which they originate. Many sophisticated statistical inference procedures have been developed to infer the demographic history of populations from this type of genomic data. In this review, we discuss the different statistical methods available for inference of demography, providing an overview of the underlying theory and logic behind each approach. We also discuss the types of data required and the pros and cons of each method. We then discuss how these methods have been applied to a variety of nonmodel organisms. We conclude by presenting some recommendations for researchers looking to use genomic data to infer demographic history. }
}
@article{andrews2016radseq,
abstract = {RADseq has fuelled studies in ecological, evolutionary and conservation genomics by using next-generation sequencing to uncover hundreds or thousands of polymorphic loci across the genome in a single, simple and cost-effective experiment. RADseq does not require any prior genomic information for the taxa being studied, and is therefore particularly advantageous for studies of non-model organisms.Numerous technical variations on RADseq have been developed, which promise to increase the flexibility and decrease the cost and effort of genomics studies. Differences among the methods lead to important considerations for all steps of genomics studies, from the types of scientific questions that can be addressed and the costs of library preparation and sequencing to the types of bias and error that are inherent in the resulting data.Allele dropout, PCR duplicates and variance in depth of coverage among loci are important sources of error and bias in RADseq studies, and the prevalence of these phenomena will vary across RADseq methods.Other important considerations when designing a RADseq study include the number, length and coverage of loci needed to address the research question; the availability of prior genomic resources; the budget; and the consistency of data across sequencing runs and laboratories.There is no single best or most flexible RADseq method. Researchers must consider the trade-offs of the different methods, and choose the approach that is best suited to their study goals.},
author = {Andrews, Kimberly R and Good, Jeffrey M and Miller, Michael R and Luikart, Gordon and Hohenlohe, Paul A},
doi = {10.1038/nrg.2015.28},
issn = {1471-0064},
journal = {Nat. Rev. Genet.},
number = {2},
pages = {81--92},
title = {{Harnessing the power of RADseq for ecological and evolutionary genomics}},
url = {https://doi.org/10.1038/nrg.2015.28},
volume = {17},
year = {2016}
}
@article {hey2004IM,
author = {Hey, Jody and Nielsen, Rasmus},
title = {Multilocus Methods for Estimating Population Sizes, Migration Rates and Divergence Time, With Applications to the Divergence of \textit{Drosophila pseudoobscura} and \textit{D. persimilis}},
volume = {167},
number = {2},
pages = {747--760},
year = {2004},
doi = {10.1534/genetics.103.024182},
publisher = {Genetics},
abstract = {The genetic study of diverging, closely related populations is required for basic questions on demography and speciation, as well as for biodiversity and conservation research. However, it is often unclear whether divergence is due simply to separation or whether populations have also experienced gene flow. These questions can be addressed with a full model of population separation with gene flow, by applying a Markov chain Monte Carlo method for estimating the posterior probability distribution of model parameters. We have generalized this method and made it applicable to data from multiple unlinked loci. These loci can vary in their modes of inheritance, and inheritance scalars can be implemented either as constants or as parameters to be estimated. By treating inheritance scalars as parameters it is also possible to address variation among loci in the impact via linkage of recurrent selective sweeps or background selection. These methods are applied to a large multilocus data set from Drosophila pseudoobscura and D. persimilis. The species are estimated to have diverged \~{}500,000 years ago. Several loci have nonzero estimates of gene flow since the initial separation of the species, with considerable variation in gene flow estimates among loci, in both directions between the species.},
issn = {0016-6731},
URL = {https://www.genetics.org/content/167/2/747},
eprint = {https://www.genetics.org/content/167/2/747.full.pdf},
journal = {Genetics}
}
@article{durvasula2017african,
Author = {Durvasula, Arun and Fulgione, Andrea and Gutaker, Rafal M and Alacakaptan, Selen Irez and Flood, P{\'a}draic J and Neto, C{\'e}lia and Tsuchimatsu, Takashi and Burbano, Hern{\'a}n A and Pic{\'o}, F Xavier and Alonso-Blanco, Carlos and others},
Date-Added = {2019-11-25 16:09:47 -0800},
Date-Modified = {2019-11-25 16:09:47 -0800},
Journal = {Proceedings of the National Academy of Sciences},
Number = {20},
Pages = {5213--5218},
Publisher = {National Acad Sciences},
Title = {African genomes illuminate the early history and transition to selfing in \textit{Arabidopsis thaliana}},
Volume = {114},
Year = {2017}}
@article{sheehan2016deep,
Author = {Sheehan, Sara and Song, Yun S},
Date-Added = {2019-11-25 16:08:27 -0800},
Date-Modified = {2019-11-25 16:08:27 -0800},
Journal = {PLoS Computational Biology},
Number = {3},
Pages = {e1004845},
Publisher = {Public Library of Science},
Title = {Deep learning for population genetic inference},
Volume = {12},
Year = {2016}}
@article{tennessen2012evolution,
Author = {Tennessen, Jacob A and Bigham, Abigail W and O'Connor, Timothy D and Fu, Wenqing and Kenny, Eimear E and Gravel, Simon and McGee, Sean and Do, Ron and Liu, Xiaoming and Jun, Goo and others},
Date-Added = {2019-11-25 16:07:39 -0800},
Date-Modified = {2019-11-25 16:07:39 -0800},
Journal = {Science},
Number = {6090},
Pages = {64--69},
Publisher = {American Association for the Advancement of Science},
Title = {Evolution and functional impact of rare coding variation from deep sequencing of human exomes},
Volume = {337},
Year = {2012}}
@article{kong2010fine,
Author = {Kong, Augustine and Thorleifsson, Gudmar and Gudbjartsson, Daniel F and Masson, Gisli and Sigurdsson, Asgeir and Jonasdottir, Aslaug and Walters, G Bragi and Jonasdottir, Adalbjorg and Gylfason, Arnaldur and Kristinsson, Kari Th and others},
Date-Added = {2019-11-25 14:10:27 -0800},
Date-Modified = {2019-11-25 14:10:27 -0800},
Journal = {Nature},
Number = {7319},
Pages = {1099},
Publisher = {Nature Publishing Group},
Title = {Fine-scale recombination rate differences between sexes, populations and individuals},
Volume = {467},
Year = {2010}}
@article{international2007second,
Author = {{International HapMap Consortium} and others},
Date-Added = {2019-11-25 14:09:36 -0800},
Date-Modified = {2019-11-25 14:09:36 -0800},
Journal = {Nature},
Number = {7164},
Pages = {851},
Publisher = {Nature Publishing Group},
Title = {A second generation human haplotype map of over 3.1 million {SNPs}},
Volume = {449},
Year = {2007}}
@article{beichman2017comparison,
Author = {Beichman, Annabel C and Phung, Tanya N and Lohmueller, Kirk E},
Date-Added = {2019-11-25 13:38:43 -0800},
Date-Modified = {2019-11-25 13:38:43 -0800},
Journal = {G3: Genes, Genomes, Genetics},
Number = {11},
Pages = {3605--3620},
Publisher = {G3: Genes, Genomes, Genetics},
Title = {Comparison of single genome and allele frequency data reveals discordant demographic histories},
Volume = {7},
Year = {2017}}
@article{gutenkunst2009inferring,
Author = {Gutenkunst, Ryan N and Hernandez, Ryan D and Williamson, Scott H and Bustamante, Carlos D},
Date-Added = {2019-11-25 13:36:47 -0800},
Date-Modified = {2019-11-25 13:36:47 -0800},
Journal = {PLoS Genetics},
Number = {10},
Pages = {e1000695},
Publisher = {Public Library of Science},
Title = {Inferring the joint demographic history of multiple populations from multidimensional {SNP} frequency data},
Volume = {5},
Year = {2009}}
@article{sugden2018localization,
Author = {Sugden, Lauren Alpert and Atkinson, Elizabeth G and Fischer, Annie P and Rong, Stephen and Henn, Brenna M and Ramachandran, Sohini},
Date-Added = {2019-11-22 10:40:22 -0800},
Date-Modified = {2019-11-22 10:40:22 -0800},
Journal = {Nature Communications},
Number = {1},
Pages = {703},
Publisher = {Nature Publishing Group},
Title = {Localization of adaptive variants in human genomes using averaged one-dependence estimation},
Volume = {9},
Year = {2018}}
@article{kern2018diplos,
Author = {Kern, Andrew D and Schrider, Daniel R},
Date-Added = {2019-11-22 10:39:49 -0800},
Date-Modified = {2019-11-22 10:39:49 -0800},
Journal = {G3: Genes, Genomes, Genetics},
Number = {6},
Pages = {1959--1970},
Publisher = {G3: Genes, Genomes, Genetics},
Title = {{diploS/HIC}: an updated approach to classifying selective sweeps},
Volume = {8},
Year = {2018}}
@article{degiorgio2016sweepfinder2,
Author = {DeGiorgio, Michael and Huber, Christian D and Hubisz, Melissa J and Hellmann, Ines and Nielsen, Rasmus},
Date-Added = {2019-11-22 10:38:45 -0800},
Date-Modified = {2019-11-22 10:38:45 -0800},
Journal = {Bioinformatics},
Number = {12},
Pages = {1895--1897},
Publisher = {Oxford University Press},
Title = {SweepFinder2: increased sensitivity, robustness and flexibility},
Volume = {32},
Year = {2016}}
@article{alachiotis2012omegaplus,
Author = {Alachiotis, Nikolaos and Stamatakis, Alexandros and Pavlidis, Pavlos},
Date-Added = {2019-11-22 10:38:11 -0800},
Date-Modified = {2019-11-22 10:38:11 -0800},
Journal = {Bioinformatics},
Number = {17},
Pages = {2274--2275},
Publisher = {Oxford University Press},
Title = {{OmegaPlus}: a scalable tool for rapid detection of selective sweeps in whole-genome datasets},
Volume = {28},
Year = {2012}}
@article{eyre2009estimating,
Author = {Eyre-Walker, Adam and Keightley, Peter D},
Date-Added = {2019-11-22 10:34:54 -0800},
Date-Modified = {2019-11-22 10:34:54 -0800},
Journal = {Molecular Biology and Evolution},
Number = {9},
Pages = {2097--2108},
Publisher = {Oxford University Press},
Title = {Estimating the rate of adaptive molecular evolution in the presence of slightly deleterious mutations and population size change},
Volume = {26},
Year = {2009}}
@article{kim2017inference,
Author = {Kim, Bernard Y and Huber, Christian D and Lohmueller, Kirk E},
Date-Added = {2019-11-22 10:34:02 -0800},
Date-Modified = {2019-11-22 10:34:02 -0800},
Journal = {Genetics},
Number = {1},
Pages = {345--361},
Publisher = {Genetics Soc America},
Title = {Inference of the distribution of selection coefficients for new nonsynonymous mutations using large samples},
Volume = {206},
Year = {2017}}
@article{terhorst2017robust,
Author = {Terhorst, Jonathan and Kamm, John A and Song, Yun S},
Date-Added = {2019-11-22 10:32:59 -0800},
Date-Modified = {2019-11-22 10:32:59 -0800},
Journal = {Nature Genetics},
Number = {2},
Pages = {303},
Publisher = {Nature Publishing Group},
Title = {Robust and scalable inference of population history from hundreds of unphased whole genomes},
Volume = {49},
Year = {2017}}
@article{liu2015exploring,
Author = {Liu, Xiaoming and Fu, Yun-Xin},
Date-Added = {2019-11-22 10:32:41 -0800},
Date-Modified = {2019-11-22 10:32:41 -0800},
Journal = {Nature Genetics},
Number = {5},
Pages = {555},
Publisher = {Nature Publishing Group},
Title = {Exploring population size changes using {SNP} frequency spectra},
Volume = {47},
Year = {2015}}
@article{schiffels2014inferring,
Author = {Schiffels, Stephan and Durbin, Richard},
Date-Added = {2019-11-22 10:32:22 -0800},
Date-Modified = {2019-11-22 10:32:22 -0800},
Journal = {Nature Genetics},
Number = {8},
Pages = {919},
Publisher = {Nature Publishing Group},
Title = {Inferring human population size and separation history from multiple genome sequences},
Volume = {46},
Year = {2014}}
@article{li2011inference,
Author = {Li, Heng and Durbin, Richard},
Date-Added = {2019-11-22 10:32:04 -0800},
Date-Modified = {2019-11-22 10:32:04 -0800},
Journal = {Nature},
Number = {7357},
Pages = {493},
Publisher = {Nature Publishing Group},
Title = {Inference of human population history from individual whole-genome sequences},
Volume = {475},
Year = {2011}}
@article{haller2019tree,
Author = {Haller, Benjamin C and Galloway, Jared and Kelleher, Jerome and Messer, Philipp W and Ralph, Peter L},
Journal = {Molecular Ecology Resources},
Number = {2},
Pages = {552--566},
Publisher = {Wiley Online Library},
Title = {Tree-sequence recording in {SLiM} opens new horizons for forward-time simulation of whole genomes},
Volume = {19},
Year = {2019}}
@article{kelleher2018efficient,
Abstract = {Author summary Sexually reproducing organisms are related to the others in their species by the complex web of parent-offspring relationships that constitute the pedigree. In this paper, we describe a way to record all of these relationships, as well as how genetic material is passed down through the pedigree, during a forwards-time population genetic simulation. To make effective use of this information, we describe both efficient storage methods for this embellished pedigree as well as a way to remove all information that is irrelevant to the genetic history of a given set of individuals, which dramatically reduces the required amount of storage space. Storing this information allows us to produce whole-genome sequence from simulations of large populations in which we have not explicitly recorded new genomic mutations; we find that this results in computational run times of up to 50 times faster than simulations forced to explicitly carry along that information.},
Added-At = {2018-12-09T17:30:18.000+0100},
Author = {Kelleher, Jerome and Thornton, Kevin R. and Ashander, Jaime and Ralph, Peter L.},
Biburl = {https://www.bibsonomy.org/bibtex/2a4f12f0bb2d13faee628fec0fc2679b8/peter.ralph},
Description = {Efficient pedigree recording for fast population genetics simulation},
Doi = {10.1371/journal.pcbi.1006581},
Interhash = {b855fb1dfdb8542b91b86dff2be80c63},
Intrahash = {a4f12f0bb2d13faee628fec0fc2679b8},
Journal = {PLoS Computational Biology},
Keywords = {ARG methods myown simulation software tree_sequence},
Month = {11},
Number = 11,
Pages = {1-21},
Publisher = {Public Library of Science},
Timestamp = {2018-12-09T17:30:18.000+0100},
Title = {Efficient pedigree recording for fast population genetics simulation},
Url = {https://doi.org/10.1371/journal.pcbi.1006581},
Volume = 14,
Year = 2018,
Bdsk-Url-1 = {https://doi.org/10.1371/journal.pcbi.1006581}}
@article{adrion2020predicting,
author = {Adrion, Jeffrey R and Galloway, Jared G and Kern, Andrew D},
title = {Predicting the Landscape of Recombination Using Deep Learning},
journal = {Molecular Biology and Evolution},
year = {2020},
month = {02},
issn = {0737-4038},
doi = {10.1093/molbev/msaa038},
url = {https://doi.org/10.1093/molbev/msaa038},
note = {msaa038},
eprint = {https://academic.oup.com/mbe/advance-article-pdf/doi/10.1093/molbev/msaa038/32926636/msaa038.pdf}}
@article{kelleher2016efficient,
Author = {Kelleher, Jerome and Etheridge, Alison M and McVean, Gilean},
Date-Added = {2018-11-09 11:18:34 -0800},
Date-Modified = {2018-11-09 11:18:34 -0800},
Journal = {PLoS Computational Biology},
Number = {5},
Pages = {e1004842},
Publisher = {Public Library of Science},
Title = {Efficient coalescent simulation and genealogical analysis for large sample sizes},
Volume = {12},
Year = {2016}}
@article{lin2013fast,
Author = {Lin, Kao and Futschik, Andreas and Li, Haipeng},
Date-Added = {2018-11-09 10:35:44 -0800},
Date-Modified = {2018-11-09 10:35:44 -0800},
Journal = {Genetics},
Pages = {473--484},
Publisher = {Genetics Soc America},
Title = {A fast estimate for the population recombination rate based on regression},
Volume = {194},
Number = {2},
Year = {2013}}
@article{chan2012genome,
Author = {Chan, Andrew H and Jenkins, Paul A and Song, Yun S},
Date-Added = {2018-11-09 10:29:39 -0800},
Date-Modified = {2018-11-09 10:29:39 -0800},
Journal = {PLoS Genetics},
Number = {12},
Pages = {e1003090},
Publisher = {Public Library of Science},
Title = {Genome-wide fine-scale recombination rate variation in \textit{Drosophila melanogaster}},
Volume = {8},
Year = {2012}}
@article{haller2018tree,
Author = {Haller, Benjamin C and Galloway, Jared and Kelleher, Jerome and Messer, Philipp W and Ralph, Peter L},
Journal = {Molecular ecology resources},
Publisher = {Wiley Online Library},
Title = {Tree-sequence recording in {SLiM} opens new horizons for forward-time simulation of whole genomes},
Year = {2018}}
@article{kelleher2019inferring,
Added-At = {2019-09-11T22:02:19.000+0200},
Author = {Kelleher, Jerome and Wong, Yan and Wohns, Anthony W. and Fadil, Chaimaa and Albers, Patrick K. and McVean, Gil},
Biburl = {https://www.bibsonomy.org/bibtex/2d3c11b81ec3003c314454ff82fc67927/peter.ralph},
Doi = {10.1038/s41588-019-0483-y},
Interhash = {595cf803356d1c356a806a7eb973190e},
Intrahash = {d3c11b81ec3003c314454ff82fc67927},
Issn = {15461718},
Journal = {Nature Genetics},
Keywords = {methods tree_sequence tree_sequence_inference},
Number = 9,
Pages = {1330--1338},
Refid = {Kelleher2019},
Timestamp = {2019-09-11T22:02:19.000+0200},
Title = {Inferring whole-genome histories in large population datasets},
Url = {https://doi.org/10.1038/s41588-019-0483-y},
Volume = 51,
Year = 2019,
Bdsk-Url-1 = {https://doi.org/10.1038/s41588-019-0483-y}}
@article{haller2019slim,
Author = {Haller, Benjamin C and Messer, Philipp W},
Journal = {Molecular Biology and Evolution},
Number = {3},
Pages = {632--637},
Publisher = {Oxford University Press},
Title = {{SLiM} 3: forward genetic simulations beyond the {Wright--Fisher} model},
Volume = {36},
Year = {2019}}
@article{ragsdale2019models,
title={Models of archaic admixture and recent history from two-locus statistics},
author={Ragsdale, Aaron P and Gravel, Simon},
journal={PLoS Genetics},
volume={15},
number={6},
pages={e1008204},
year={2019},
publisher={Public Library of Science}
}
@article{browning2018ancestry,
title={Ancestry-specific recent effective population size in the {Americas}},
author={Browning, Sharon R and Browning, Brian L and Daviglus, Martha L and Durazo-Arvizu, Ramon A and Schneiderman, Neil and Kaplan, Robert C and Laurie, Cathy C},
journal={PLoS Genetics},
volume={14},
number={5},
pages={e1007385},
year={2018},
publisher={Public Library of Science}
}
@article{li2006inferring,
title={Inferring the demographic history and rate of adaptive substitution in \textit{Drosophila}},
author={Li, Haipeng and Stephan, Wolfgang},
journal={PLoS Genetics},
volume={2},
number={10},
pages={e166},
year={2006},
publisher={Public Library of Science}
}
@article{koster2012snakemake,
title={Snakemake---a scalable bioinformatics workflow engine},
author={K{\"o}ster, Johannes and Rahmann, Sven},
journal={Bioinformatics},
volume={28},
number={19},
pages={2520--2522},
year={2012},
publisher={Oxford University Press}
}
@article{comeron2012many,
title={The many landscapes of recombination in \textit{Drosophila melanogaster}},
author={Comeron, Josep M and Ratnappan, Ramesh and Bailin, Samuel},
journal={PLoS Genetics},
volume={8},
number={10},
pages={e1002905},
year={2012},
publisher={Public Library of Science}
}
@article{excoffier2013robust,
title={Robust Demographic Inference from Genomic and {SNP} Data},
author={Excoffier, Laurent and Dupanloup, Isabelle and Huerta-S\'anchez, Emilia and Sousa, Vitor C and Foll, Matthieu},
journal={PLoS Genetics},
volume={9},
issue={10},
pages={e1003905},
year={2013},
publisher={Public Library of Science}
}
@article{danecek20111000,
title={The variant call format and {VCFtools}},
author={Danecek, Petr and Auton, Adam and Abecasis, Goncalo and Albers, Cornelis A and Banks, Eric and DePristo, Mark A and Handsaker, Robert E and Lunter, Gerton and Marth, Gabor T and Sherry, Stephen T and others},
journal={Bioinformatics},
volume={27},
number={15},
pages={2156--2158},
year={2011},
publisher={Oxford University Press}
}
@article{langley2012genomic,
title={Genomic variation in natural populations of \textit{Drosophila melanogaster}},
author={Langley, Charles H and Stevens, Kristian and Cardeno, Charis and Lee, Yuh Chwen G and Schrider, Daniel R and Pool, John E and Langley, Sasha A and Suarez, Charlyn and Corbett-Detig, Russell B and Kolaczkowski, Bryan and others},
journal={Genetics},
volume={192},
number={2},
pages={533--598},
year={2012},
publisher={Genetics Soc America}
}
@article{moult1995large,
title={A large-scale experiment to assess protein structure prediction methods},
author={Moult, John and Pedersen, Jan T and Judson, Richard and Fidelis, Krzysztof},
journal={Proteins: Structure, Function, and Bioinformatics},
volume={23},
number={3},
pages={ii--iv},
year={1995},
publisher={Wiley Online Library}
}
@article{russakovsky2015imagenet,
title={Imagenet large scale visual recognition challenge},
author={Russakovsky, Olga and Deng, Jia and Su, Hao and Krause, Jonathan and Satheesh, Sanjeev and Ma, Sean and Huang, Zhiheng and Karpathy, Andrej and Khosla, Aditya and Bernstein, Michael and others},
journal={International Journal of Computer Vision},
volume={115},
number={3},
pages={211--252},
year={2015},
publisher={Springer}
}
@article{tataru2017inference,
title={Inference of distribution of fitness effects and proportion of adaptive substitutions from polymorphism data},
author={Tataru, Paula and Mollion, Ma{\'e}va and Gl{\'e}min, Sylvain and Bataillon, Thomas},
journal={Genetics},
volume={207},
number={3},
pages={1103--1119},
year={2017},
publisher={Genetics Soc America}
}
@article{tataru2020polydfe,
title={polyDFE: inferring the distribution of fitness effects and properties of beneficial mutations from polymorphism data},
author={Tataru, Paula and Bataillon, Thomas},
journal={Statistical population genomics},
pages={125--146},
year={2020},
publisher={Springer US}
}
@article{galtier2016adaptive,
title={Adaptive protein evolution in animals and the effective population size hypothesis},
author={Galtier, Nicolas},
journal={PLoS genetics},
volume={12},
number={1},
pages={e1005774},
year={2016},
publisher={Public Library of Science San Francisco, CA USA}
}
@article {Fortier703918,
author = {Fortier, Alyssa Lyn and Coffman, Alec J. and Struck, Travis J. and Irby, Megan N. and Burguete, Jose E. L. and Ragsdale, Aaron P. and Gutenkunst, Ryan N.},
title = {{DFEnitely} different: Genome-wide characterization of differences in mutation fitness effects between populations},
elocation-id = {703918},
year = {2019},
doi = {10.1101/703918},
publisher = {Cold Spring Harbor Laboratory},
abstract = {The effect of a mutation on fitness may differ between populations, depending on environmental and genetic context. Experimental studies have shown that such differences exist, but little is known about the broad patterns of such differences or the factors that drive them. To quantify genome-wide patterns of differences in mutation fitness effects, we extended the concept of a distribution of fitness effects (DFE) to a joint DFE between populations. To infer the joint DFE, we fit parametric models that included demographic history to genomic data summarized by the joint allele frequency spectrum. Using simulations, we showed that our approach is statistically powerful and robust to many forms of model misspecification. We then applied our approach to populations of Drosophila melanogaster, wild tomatoes, and humans. We found that mutation fitness effects are overall least correlated between populations in tomatoes and most correlated in humans, corresponding to overall genetic differentiation. In D. melanogaster and tomatoes, mutations in genes involved in immunity and stress response showed the lowest correlation of fitness effects, consistent with environmental influence. In D. melanogaster and humans, deleterious mutations showed a lower correlation of fitness effects than tolerated mutations, hinting at the complexity of the joint DFE. Together, our results show that the joint DFE can be reliably inferred and that it offers extensive insight into the genetics of population divergence.},
URL = {https://www.biorxiv.org/content/early/2019/07/16/703918},
eprint = {https://www.biorxiv.org/content/early/2019/07/16/703918.full.pdf},
journal = {bioRxiv}
}
@article {Vecchyo770966,
author = {Ortega-Del Vecchyo, Diego and Lohmueller, Kirk E. and Novembre, John},
title = {Haplotype-based inference of the distribution of fitness effects},
elocation-id = {770966},
year = {2019},
doi = {10.1101/770966},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Recent genome sequencing studies with large sample sizes in humans have discovered a vast quantity of low-frequency variants, providing an important source of information to analyze how selection is acting on human genetic variation. In order to estimate the strength of natural selection acting on low-frequency variants, we have developed a likelihood-based method that uses the lengths of pairwise identity-by-state between haplotypes carrying low-frequency variants. We show that in some non-equilibrium populations (such as those that have had recent population expansions) it is possible to distinguish between positive or negative selection acting on a set of variants. With our new framework, one can infer a fixed selection intensity acting on a set of variants at a particular frequency, or a distribution of selection coefficients for standing variants and new mutations. We apply our method to the UK10K phased haplotype dataset of 3,781 individuals and find a similar proportion of neutral, moderately deleterious, and deleterious variants compared to previous estimates made using the site frequency spectrum. We discuss several interpretations for this result, including that selective constraints have remained constant over time.},
URL = {https://www.biorxiv.org/content/early/2019/09/16/770966},
eprint = {https://www.biorxiv.org/content/early/2019/09/16/770966.full.pdf},
journal = {bioRxiv}
}
@article{Huang2019,
abstract = {A central challenge in human genomics is to understand the cellular, evolutionary, and clinical significance of genetic variants. Here we introduce a unified population-genetic and machine-learning model, called L inear A llele- S pecific S election I nferenc E ( LASSIE ), for estimating the fitness effects of all potential single-nucleotide variants, based on polymorphism data and predictive genomic features. We applied LASSIE to 51 high-coverage genome sequences annotated with 33 genomic features, and constructed a map of allele-specific selection coefficients across all protein-coding sequences in the human genome. We show that this map is informative about both human evolution and disease.},
author = {Huang, Yi-Fei and Siepel, Adam},
doi = {10.1101/gr.245522.118},
file = {:Users/vicentediegoortegadelvecchyo/Dropbox/Documents/DissertationThesis/PurifyingSelection/Papers/Huang{\_}Siepel2019.pdf:pdf},
issn = {1088-9051},
journal = {Genome Research},
pages = {gr.245522.118},
title = {{Estimation of allele-specific fitness effects across human protein-coding sequences and implications for disease}},
year = {2019}
}
@article{Boyko:2008cr,
abstract = {Quantifying the distribution of fitness effects among newly arising mutations in the human genome is key to resolving important debates in medical and evolutionary genetics. Here, we present a method for inferring this distribution using Single Nucleotide Polymorphism (SNP) data from a population with non-stationary demographic history (such as that of modern humans). Application of our method to 47,576 coding SNPs found by direct resequencing of 11,404 protein coding-genes in 35 individuals (20 European Americans and 15 African Americans) allows us to assess the relative contribution of demographic and selective effects to patterning amino acid variation in the human genome. We find evidence of an ancient population expansion in the sample with African ancestry and a relatively recent bottleneck in the sample with European ancestry. After accounting for these demographic effects, we find strong evidence for great variability in the selective effects of new amino acid replacing mutations. In both populations, the patterns of variation are consistent with a leptokurtic distribution of selection coefficients (e.g., gamma or log-normal) peaked near neutrality. Specifically, we predict 27-29{\%} of amino acid changing (nonsynonymous) mutations are neutral or nearly neutral (vertical bar s vertical bar {\textless} 0.01{\%}), 30-42{\%} are moderately deleterious (0.01{\%}, vertical bar s vertical bar, 1{\%}), and nearly all the remainder are highly deleterious or lethal (vertical bar s vertical bar. 1{\%}). Our results are consistent with 10-20{\%} of amino acid differences between humans and chimpanzees having been fixed by positive selection with the remainder of differences being neutral or nearly neutral. Our analysis also predicts that many of the alleles identified via whole-genome association mapping may be selectively neutral or (formerly) positively selected, implying that deleterious genetic variation affecting disease phenotype may be missed by this widely used approach for mapping genes underlying complex traits.},
address = {185 BERRY ST, STE 1300, SAN FRANCISCO, CA 94107 USA},
author = {Boyko, Adam R. and Williamson, Scott H. and Indap, Amit R. and Degenhardt, Jeremiah D. and Hernandez, Ryan D. and Lohmueller, Kirk E. and Adams, Mark D. and Schmidt, Steffen and Sninsky, John J. and Sunyaev, Shamil R. and White, Thomas J. and Nielsen, Rasmus and Clark, Andrew G. and Bustamante, Carlos D.},
doi = {DOI 10.1371/journal.pgen.1000083},
file = {:Users/vicentediegoortegadelvecchyo/Library/Application Support/Mendeley Desktop/Downloaded/Boyko et al. - 2008 - Assessing the evolutionary impact of amino acid mutations in the human genome.pdf:pdf;:Users/vicentediegoortegadelvecchyo/Library/Application Support/Mendeley Desktop/Downloaded/Boyko et al. - 2008 - Assessing the evolutionary impact of amino acid mutations in the human genome(2).pdf:pdf},
isbn = {1553-7404 (Electronic)$\backslash$r1553-7390 (Linking)},
issn = {15537390},
journal = {PLoS Genetics},
month = {may},
number = {5},
pages = {e1000083},
pmid = {18516229},
publisher = {PUBLIC LIBRARY SCIENCE},
title = {{Assessing the evolutionary impact of amino acid mutations in the human genome}},
volume = {4},
year = {2008}
}
@article{Barroso2019,
abstract = {Understanding the causes and consequences of recombination landscape evolution is a fundamental goal in genetics that requires recombination maps from across the tree of life. Such maps can be obtained from population genomic datasets, but require large sample sizes. Alternative methods are therefore necessary to research organisms where such datasets cannot be generated easily, such as non-model or ancient species. Here we extend the sequentially Markovian coalescent model to jointly infer demography and the spatial variation in recombination rate. Using extensive simulations and sequence data from humans, fruit-flies and a fungal pathogen, we demonstrate that iSMC accurately infers recombination maps under a wide range of scenarios�remarkably, even from a single pair of unphased genomes. We exploit this possibility and reconstruct the recombination maps of ancient hominins. We report that the ancient and modern maps are correlated in a manner that reflects the established phylogeny of Neanderthals, Denisovans, and modern human populations.},
author = {Barroso, Gustavo V. and Puzovi{\'{c}}, Nata{\v{s}}a and Dutheil, Julien Y.},
doi = {10.1371/journal.pgen.1008449},
file = {:Users/vicentediegoortegadelvecchyo/Dropbox/Documents/LIIGH/Proyectos/stdpopsim/Papers/Barroso{\_}etal2019.pdf:pdf},
isbn = {1111111111},
journal = {PLoS Genetics},
number = {11},
pages = {e1008449},
title = {{Inference of recombination maps from a single pair of genomes and its application to ancient samples}},
url = {https://dx.plos.org/10.1371/journal.pgen.1008449},
volume = {15},
year = {2019}
}
@article{ralph2020efficiently,
title={Efficiently summarizing relationships in large samples: a general duality between statistics of genealogies and genomes},
author={Ralph, Peter and Thornton, Kevin and Kelleher, Jerome},
journal={Genetics},
volume={215},
number={3},
pages={779--797},
year={2020},
publisher={Oxford University Press}
}
@article{kamm2019efficiently,
author = {Kamm, Jack and Terhorst, Jonathan and Durbin, Richard and Song, Yun S.},
doi = {10.1080/01621459.2019.1635482},
issn = {1537-274X},
journal = {Journal of the American Statistical Association},
month = {Jul},
pages = {1–16},
publisher = {Informa UK Limited},
title = {Efficiently Inferring the Demographic History of Many Populations With Allele Count Data},
url = {http://dx.doi.org/10.1080/01621459.2019.1635482},
year = {2019}
}
@article{huber2018gene,
author = {Huber, Christian D. and Durvasula, Arun and Hancock, Angela M. and Lohmueller, Kirk E.},
doi = {10.1038/s41467-018-05281-7},
issn = {2041-1723},
journal = {Nature Communications},
month = {7},
number = {1},
publisher = {Springer Science and Business Media LLC},
title = {Gene expression drives the evolution of dominance},
url = {http://dx.doi.org/10.1038/s41467-018-05281-7},
volume = {9},
year = {2018}
}
@article{locke2011comparative,
author = {Locke, Devin P. and Hillier, LaDeana W. and Warren, Wesley C. and Worley, Kim C. and Nazareth, Lynne V. and Muzny, Donna M. and Yang, Shiaw-Pyng and Wang, Zhengyuan and Chinwalla, Asif T. and Minx, Pat and et al.},
doi = {10.1038/nature09687},
issn = {1476-4687},
journal = {Nature},
month = {Jan},
number = {7331},
pages = {529–533},
publisher = {Springer Science and Business Media LLC},
title = {Comparative and demographic analysis of orang-utan genomes},
url = {http://dx.doi.org/10.1038/nature09687},
volume = {469},
year = {2011}
}
@article{salome2011recombination,
title={The recombination landscape in \textit{Arabidopsis thaliana} {F2} populations},
volume={108},
ISSN={1365-2540},
url={http://dx.doi.org/10.1038/hdy.2011.95},
DOI={10.1038/hdy.2011.95},
number={4},
journal={Heredity},
publisher={Springer Science and Business Media LLC},
author={Salom\'{e}, P A and Bomblies, K and Fitz, J and Laitinen, R A E and Warthmann, N and Yant, L and Weigel, D},
year={2011},
month={Nov},
pages={447--455}
}
@article {campbell2016pedigree,
author = {Campbell, Christopher L. and Bh{\'e}rer, Claude and Morrow, Bernice E. and Boyko, Adam R. and Auton, Adam},
title = {A Pedigree-Based Map of Recombination in the Domestic Dog Genome},
volume = {6},
number = {11},
pages = {3517--3524},
year = {2016},
doi = {10.1534/g3.116.034678},
publisher = {G3: Genes, Genomes, Genetics},
abstract = {Meiotic recombination in mammals has been shown to largely cluster into hotspots, which are targeted by the chromatin modifier PRDM9. The canid family, including wolves and dogs, has undergone a series of disrupting mutations in this gene, rendering PRDM9 inactive. Given the importance of PRDM9, it is of great interest to learn how its absence in the dog genome affects patterns of recombination placement. We have used genotypes from domestic dog pedigrees to generate sex-specific genetic maps of recombination in this species. On a broad scale, we find that placement of recombination events in dogs is consistent with that in mice and apes, in that the majority of recombination occurs toward the telomeres in males, while female crossing over is more frequent and evenly spread along chromosomes. It has been previously suggested that dog recombination is more uniform in distribution than that of humans; however, we found that recombination in dogs is less uniform than in humans. We examined the distribution of recombination within the genome, and found that recombination is elevated immediately upstream of the transcription start site and around CpG islands, in agreement with previous studies, but that this effect is stronger in male dogs. We also found evidence for positive crossover interference influencing the spacing between recombination events in dogs, as has been observed in other species including humans and mice. Overall our data suggests that dogs have similar broad scale properties of recombination to humans, while fine scale recombination is similar to other species lacking PRDM9.},
URL = {https://www.g3journal.org/content/6/11/3517},
eprint = {https://www.g3journal.org/content/6/11/3517.full.pdf},
journal = {G3: Genes, Genomes, Genetics}
}
@article{jacobs2019multiple,
title={Multiple deeply divergent {Denisovan} ancestries in {Papuans}},
author={Jacobs, Guy S and Hudjashov, Georgi and Saag, Lauri and Kusuma, Pradiptajati and Darusallam, Chelzie C and Lawson, Daniel J and Mondal, Mayukh and Pagani, Luca and Ricaut, Fran{\c{c}}ois-Xavier and Stoneking, Mark and others},
journal={Cell},
volume={177},
number={4},
pages={1010--1021},
year={2019},
publisher={Elsevier}
}
@article{crow1988inbreeding,
author = {Crow, James F. and Denniston, Carter},
journal = {Evolution},
number = 3,
pages = {482--495},
publisher = {Society for the Study of Evolution},
title = {Inbreeding and Variance Effective Population Numbers},
url = {http://www.jstor.org/stable/2409033},
volume = 42,
year = 1988
}
@book{wakeley2005coalescent,
address = {Greenwood Village, CO},
author = {Wakeley, John},
publisher = {Roberts and Company},
title = {Coalescent Theory, an Introduction},
url = {http://www.coalescentheory.com/},
year = 2005
}
@book{kemeny2012denumerable,
title={Denumerable Markov chains},
author={Kemeny, John G and Snell, J Laurie and Knapp, Anthony W},
volume={40},
year={2012},
publisher={Springer Science \& Business Media}
}
@article {uricchio2014robust,
author = {Uricchio, Lawrence H. and Hernandez, Ryan D.},
title = {Robust Forward Simulations of Recurrent Hitchhiking},
volume = {197},
number = {1},
pages = {221--236},
year = {2014},
doi = {10.1534/genetics.113.156935},
publisher = {Genetics},
issn = {0016-6731},
URL = {https://www.genetics.org/content/197/1/221},
eprint = {https://www.genetics.org/content/197/1/221.full.pdf},
journal = {Genetics}
}
@article{gladstein2019substructured,
author = {Gladstein, Ariella L and Hammer, Michael F},
doi = {10.1093/molbev/msz047},
editor = {Rogers, RebekahEditor},
issn = {1537-1719},
journal = {Molecular Biology and Evolution},
month = {Mar},
number = {6},
pages = {1162--1171},
publisher = {Oxford University Press (OUP)},
title = {Substructured Population Growth in the {Ashkenazi Jews} Inferred with {Approximate Bayesian Computation}},
url = {http://dx.doi.org/10.1093/molbev/msz047},
volume = {36},
year = {2019}
}
@article{nater2017morphometric,
title={Morphometric, behavioral, and genomic evidence for a new orangutan
species},
author={Nater, Alexander and Mattle-Greminger, Maja P and Nurcahyo,
Anton and Nowak, Matthew G and De Manuel, Marc and Desai, Tariq and
Groves, Colin and Pybus, Marc and Sonay, Tugce Bilgin and Roos,
Christian and others},
journal={Current Biology},
volume={27},
number={22},
pages={3487--3498},
year={2017},
publisher={Elsevier}
}
@Article{Robinson2023,
Author="Robinson, J. and Kyriazis, C. C. and Yuan, S. C. and Lohmueller, K. E. ",
Title="{{D}eleterious {V}ariation in {N}atural {P}opulations and {I}mplications for {C}onservation {G}enetics}",
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author = {Zhou, Ying and Tian, Xiaowen and Browning, Brian L and Browning, Sharon R},
title = "{POPdemog: visualizing population demographic history from simulation scripts}",
journal = {Bioinformatics},
volume = {34},
number = {16},
pages = {2854-2855},
year = {2018},
month = {03},
abstract = "{We present POPdemog, an R package which converts coalescent simulation program input parameters into a visual representation of the demographic model. This package is useful for preparing figures, for checking that demographic simulation parameters have been correctly specified, and for understanding demographic models that other researchers have used to simulate genetic data. The POPdemog package supports the ms, msa, msHot, MaCS, msprime, scrm and Cosi2 programs, and includes options for customizing the output figures.The POPdemog package and its tutorial can be freely downloaded from https://github.com/YingZhou001/POPdemog.Supplementary data are available at Bioinformatics online.}",
issn = {1367-4803},
doi = {10.1093/bioinformatics/bty184},
url = {https://doi.org/10.1093/bioinformatics/bty184},
eprint = {https://academic.oup.com/bioinformatics/article-pdf/34/16/2854/48918390/bioinformatics\_34\_16\_2854.pdf},
}
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publisher={Nature Publishing Group UK London}
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