references.bib

@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}",
   Journal="Annu Rev Anim Biosci",
   Year="2023",
   Volume="11",
   Pages="93--114",
   Month="Feb"
}

@article{Zhou2018,
    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},
}

@Article{Hsieh2021,
  title={Evidence for opposing selective forces operating on human-specific duplicated TCAF genes in neanderthals and humans},
  author={Hsieh, PingHsun and Dang, Vy and Vollger, Mitchell R and Mao, Yafei and Huang, Tzu-Hsueh and Dishuck, Philip C and Baker, Carl and Cantsilieris, Stuart and Lewis, Alexandra P and Munson, Katherine M and others},
  journal={Nature Communications},
  volume={12},
  number={1},
  pages={5118},
  year={2021},
  publisher={Nature Publishing Group UK London}
}


@Article{Speidel2021,
   Author="Speidel, L.  and Cassidy, L.  and Davies, R. W.  and Hellenthal, G.  and Skoglund, P.  and Myers, S. R. ",
   Title="{{I}nferring {P}opulation {H}istories for {A}ncient {G}enomes {U}sing {G}enome-{W}ide {G}enealogies}",
   Journal="Mol Biol Evol",
   Year="2021",
   Volume="38",
   Number="9",
   Pages="3497--3511",
   Month="Aug"
}

@Article{Gower2021,
   Author="Gower, G.  and Picazo, P. I.  and Fumagalli, M.  and Racimo, F. ",
   Title="{{D}etecting adaptive introgression in human evolution using convolutional neural networks}",
   Journal="Elife",
   Year="2021",
   Volume="10",
   Month="May"
}

@Article{Harris2013,
   Author="Harris, K.  and Nielsen, R. ",
   Title="{{I}nferring demographic history from a spectrum of shared haplotype lengths}",
   Journal="PLoS Genet",
   Year="2013",
   Volume="9",
   Number="6",
   Pages="e1003521",
   Month="Jun"
}
@Article{Beichman2017,
   Author="Beichman, A. C.  and Phung, T. N.  and Lohmueller, K. E. ",
   Title="{{C}omparison of {S}ingle {G}enome and {A}llele {F}requency {D}ata {R}eveals {D}iscordant {D}emographic {H}istories}",
   Journal="G3 (Bethesda)",
   Year="2017",
   Volume="7",
   Number="11",
   Pages="3605--3620",
   Month="Nov"
}
@Article{LiDurbin2011,
   Author="Li, H.  and Durbin, R. ",
   Title="{{I}nference of human population history from individual whole-genome sequences}",
   Journal="Nature",
   Year="2011",
   Volume="475",
   Number="7357",
   Pages="493--496",
   Month="Jul"
}

@article{Cury2022,
     author = {Cury, J. and Haller, B. C. and Achaz, G. and Jay, F.},
     title = {Simulation of bacterial populations with {SLiM}},
     journal = {Peer Community Journal},
     eid = {e7},
     publisher = {Peer Community In},
     volume = {2},
     year = {2022},
     doi = {10.24072/pcjournal.72},
     url = {https://peercommunityjournal.org/articles/10.24072/pcjournal.72/}
}

@Article{Hinrichs2006ucsc,
   Author="Hinrichs, A. S.  and Karolchik, D.  and Baertsch, R.  and Barber, G. P.  and Bejerano, G.  and Clawson, H.  and Diekhans, M.  and Furey, T. S.  and Harte, R. A.  and Hsu, F.  and Hillman-Jackson, J.  and Kuhn, R. M.  and Pedersen, J. S.  and Pohl, A.  and Raney, B. J.  and Rosenbloom, K. R.  and Siepel, A.  and Smith, K. E.  and Sugnet, C. W.  and Sultan-Qurraie, A.  and Thomas, D. J.  and Trumbower, H.  and Weber, R. J.  and Weirauch, M.  and Zweig, A. S.  and Haussler, D.  and Kent, W. J. ",
   Title="{{T}he {U}{C}{S}{C} {G}enome {B}rowser {D}atabase: update 2006}",
   Journal="Nucleic Acids Res",
   Year="2006",
   Volume="34",
   Number="Database issue",
   Pages="D590--598",
   Month="Jan"
}

@Article{Kumar2022,
   Author="Kumar, S.  and Suleski, M.  and Craig, J. M.  and Kasprowicz, A. E.  and Sanderford, M.  and Li, M.  and Stecher, G.  and Hedges, S. B. ",
   Title="{{T}ime{T}ree 5: {A}n {E}xpanded {R}esource for {S}pecies {D}ivergence {T}imes}",
   Journal="Mol Biol Evol",
   Year="2022",
   Month="Aug"
}

@Article{Wijnker2013,
   Author="Wijnker, E.  and Velikkakam James, G.  and Ding, J.  and Becker, F.  and Klasen, J. R.  and Rawat, V.  and Rowan, B. A.  and de Jong, D. F.  and de Snoo, C. B.  and Zapata, L.  and Huettel, B.  and de Jong, H.  and Ossowski, S.  and Weigel, D.  and Koornneef, M.  and Keurentjes, J. J.  and Schneeberger, K. ",
   Title="{{T}he genomic landscape of meiotic crossovers and gene conversions in \textit{{Arabidopsis} thaliana}}",
   Journal="Elife",
   Year="2013",
   Volume="2",
   Pages="e01426",
   Month="Dec"
}

@Article{Gay2007,
   Author="Gay, J.  and Myers, S.  and McVean, G. ",
   Title="{{E}stimating meiotic gene conversion rates from population genetic data}",
   Journal="Genetics",
   Year="2007",
   Volume="177",
   Number="2",
   Pages="881--894",
   Month="Oct"
}

@Article{Gophna2022,
   Author="Gophna, U.  and Altman-Price, N. ",
   Title="{Horizontal Gene Transfer in Archaea-From Mechanisms to Genome Evolution}",
   Journal="Annu Rev Microbiol",
   Year="2022",
   Volume="76",
   Pages="481--502",
   Month="Sep"
}

@Article{Thomas2005,
   Author="Thomas, C. M.  and Nielsen, K. M. ",
   Title="{{M}echanisms of, and barriers to, horizontal gene transfer between bacteria}",
   Journal="Nat Rev Microbiol",
   Year="2005",
   Volume="3",
   Number="9",
   Pages="711--721",
   Month="Sep"
}

@Article{Didelot2010,
   Author="Didelot, X.  and Maiden, M. C. ",
   Title="{{I}mpact of recombination on bacterial evolution}",
   Journal="Trends Microbiol",
   Year="2010",
   Volume="18",
   Number="7",
   Pages="315--322",
   Month="Jul"
}

@Article{Liu2015,
   Author="Liu, X.  and Fu, Y. X. ",
   Title="{{C}orrigendum: {E}xploring population size changes using {S}{N}{P} frequency spectra}",
   Journal="Nat Genet",
   Year="2015",
   Volume="47",
   Number="9",
   Pages="1099",
   Month="Sep"
}

@Article{Comeron2012,
   Author="Comeron, Josep M.  and Ratnappan, Ramesh  and Bailin, Samuel",
   Title="{{T}he many landscapes of recombination in \textit{{Drosophila} melanogaster}}",
   Journal="PLoS Genet",
   Year="2012",
   Volume="8",
   Number="10",
   Pages="e1002905"
}

@Article{Didelot2012,
   Author="Didelot, X.  and Meric, G.  and Falush, D.  and Darling, A. E. ",
   Title="{Impact of homologous and non-homologous recombination in the genomic evolution of \textit{{Escherichia} coli}}",
   Journal="BMC Genomics",
   Year="2012",
   Volume="13",
   Pages="256",
   Month="Jun"
}

@Article{Wielgoss2011,
   Author="Wielgoss, S.  and Barrick, J. E.  and Tenaillon, O.  and Cruveiller, S.  and Chane-Woon-Ming, B.  and Medigue, C.  and Lenski, R. E.  and Schneider, D. ",
   Title={Mutation Rate Inferred From Synonymous Substitutions in a Long-Term Evolution Experiment With \textit{{Escherichia} coli}},
   Journal="G3 (Bethesda)",
   Year="2011",
   Volume="1",
   Number="3",
   Pages="183--186",
   Month="Aug"
}

@Article{Tennessen2012,
   Author="Tennessen, J. A.  and Bigham, A. W.  and O'Connor, T. D.  and Fu, W.  and Kenny, E. E.  and Gravel, S.  and McGee, S.  and Do, R.  and Liu, X.  and Jun, G.  and Kang, H. M.  and Jordan, D.  and Leal, S. M.  and Gabriel, S.  and Rieder, M. J.  and Abecasis, G.  and Altshuler, D.  and Nickerson, D. A.  and Boerwinkle, E.  and Sunyaev, S.  and Bustamante, C. D.  and Bamshad, M. J.  and Akey, J. M. ",
   Title="{{E}volution and functional impact of rare coding variation from deep sequencing of human exomes}",
   Journal="Science",
   Year="2012",
   Volume="337",
   Number="6090",
   Pages="64--69",
   Month="Jul"
}

@Article{Eldon2015,
   Author="Eldon, B.  and Birkner, M.  and Blath, J.  and Freund, F. ",
   Title="{{C}an the site-frequency spectrum distinguish exponential population growth from multiple-merger coalescents?}",
   Journal="Genetics",
   Year="2015",
   Volume="199",
   Number="3",
   Pages="841--856",
   Month="Mar"
}

@InCollection{Jukes1969,
  author =       {Jukes, T. H. and Cantor, C. R. },
  title =        {Evolution of protein molecules},
  booktitle =    {Mammalian Protein Metabolism},
  pages =        {21-132},
  publisher =    {Academic Press},
  year =         1969,
  editor =       {H.N. Munro},
  address =      {New York}
}

@article{Korunes2017,
	author = {Korunes, Katharine L. and Noor, Mohamed A. F.},
	title = {Gene conversion and linkage: effects on genome evolution and speciation},
	journal = {Molecular Ecology},
	volume = {26},
	number = {1},
	pages = {351-364},
	keywords = {gene conversion, linked selection, recombination, speciation},
	doi = {https://doi.org/10.1111/mec.13736},
	abstract = {Abstract Crossing over is well known to have profound effects on patterns of genetic diversity and genome evolution. Far less direct attention has been paid to another distinct outcome of meiotic recombination: noncrossover gene conversion (NCGC). Crossing over and NCGC both shuffle combinations of alleles, and this degradation of linkage disequilibrium (LD) has major evolutionary consequences, ranging from immediate effects on nucleotide diversity to long-term consequences that shape genome evolution, species formation and species persistence. Unlike simple crossing over, NCGC has the potential to alter allele frequencies. Gene conversion can also occur in genomic regions where crossing over does not, and it purportedly exhibits more uniform rates across genomes. Considerable progress has been made towards understanding the mechanisms of gene conversion, and this progress enables us to begin exploring how gene conversion affects processes such as molecular evolution and interspecies gene flow. These topics are timely with the recent shift in focus from a primarily neutral null model of molecular evolution and speciation to one incorporating base levels of selection, making it all the more crucial to understand the basis and evolutionary implications of linkage. Here, we discuss the impact of gene conversion on genome structure and evolution and the current methods for detecting these events. We provide a comprehensive review of how gene conversion breaks down LD and affects both short- and long-term evolutionary processes, and we contrast its impact to that expected from crossing over alone.},
	year = {2017}
}

@Article{Keightley2009,
   Author="Keightley, P. D.  and Trivedi, U.  and Thomson, M.  and Oliver, F.  and Kumar, S.  and Blaxter, M. L. ",
   Title="{Analysis of the genome sequences of three \textit{{Drosophila} melanogaster} spontaneous mutation accumulation lines}",
   Journal="Genome Res",
   Year="2009",
   Volume="19",
   Number="7",
   Pages="1195--1201",
   Month="Jul"
}

@Article{Sharakhova2007,
   Author="Sharakhova, M. V.  and Hammond, M. P.  and Lobo, N. F.  and Krzywinski, J.  and Unger, M. F.  and Hillenmeyer, M. E.  and Bruggner, R. V.  and Birney, E.  and Collins, F. H. ",
   Title="{Update of the \textit{{Anopheles} gambiae} {PEST} genome assembly}",
   Journal="Genome Biol",
   Year="2007",
   Volume="8",
   Number="1",
   Pages="R5"
}

@article{clarkson2020genome,
  title={Genome variation and population structure among 1142 mosquitoes of the {African} malaria vector species \textit{{Anopheles} gambiae} and \textit{{Anopheles} coluzzii}},
  author={Clarkson, Chris S and Miles, Alistair and Harding, Nicholas J and Lucas, Eric R and Battey, CJ and Amaya-Romero, Jorge Edouardo and Kern, Andrew D and Fontaine, Michael C and Donnelly, Martin J and Lawniczak, Mara KN and others},
  journal={Genome research},
  volume={30},
  number={10},
  pages={1533--1546},
  year={2020},
  publisher={Cold Spring Harbor Lab}
}

@Article{Miles2017,
   Author="Miles, A.  and Harding, N. J.  and Botta, G.  and Clarkson, C. S.  and Antao, T.  and Kozak, K.  and Schrider, D. R.  and Kern, A. D.  and Redmond, S.  and Sharakhov, I.  and Pearson, R. D.  and Bergey, C.  and Fontaine, M. C.  and Donnelly, M. J.  and Lawniczak, M. K. N.  and Kwiatkowski, D. P.  and Donnelly, M. J.  and Ayala, D.  and Besansky, N. J.  and Burt, A.  and Caputo, B.  and Della Torre, A.  and Fontaine, M. C.  and Godfray, H. C. J.  and Hahn, M. W.  and Kern, A. D.  and Kwiatkowski, D. P.  and Lawniczak, M. K. N.  and Midega, J.  and Neafsey, D. E.  and O'Loughlin, S.  and Pinto, J.  and Riehle, M. M.  and Sharakhov, I.  and Vernick, K. D.  and Weetman, D.  and Wilding, C. S.  and White, B. J.  and Troco, A. D.  and Pinto, J.  and Diabaté, A.  and O'Loughlin, S.  and Burt, A.  and Costantini, C.  and Rohatgi, K. R.  and Besansky, N. J.  and Elissa, N.  and Pinto, J.  and Coulibaly, B.  and Riehle, M. M.  and Vernick, K. D.  and Pinto, J.  and Dinis, J.  and Midega, J.  and Mbogo, C.  and Bejon, P.  and Wilding, C. S.  and Weetman, D.  and Mawejje, H. D.  and Donnelly, M. J.  and Weetman, D.  and Wilding, C. S.  and Donnelly, M. J.  and Stalker, J.  and Rockett, K.  and Drury, E.  and Mead, D.  and Jeffreys, A.  and Hubbart, C.  and Rowlands, K.  and Isaacs, A. T.  and Jyothi, D.  and Malangone, C.  and Vauterin, P.  and Jeffery, B.  and Wright, I.  and Hart, L.  and Kluczy?ski, K.  and Cornelius, V.  and MacInnis, B.  and Henrichs, C.  and Giacomantonio, R.  and Kwiatkowski, D. P.  and Cornelius, V.  and MacInnis, B.  and Henrichs, C.  and Giacomantonio, R.  and Kwiatkowski, D. P. ",
   Title="{{G}enetic diversity of the {A}frican malaria vector {A}nopheles gambiae}",
   Journal="Nature",
   Year="2017",
   Volume="552",
   Number="7683",
   Pages="96--100",
   Month="12"
}

@article{Excoffier2013,
    doi = {10.1371/journal.pgen.1003905},
    author = {Excoffier, Laurent AND Dupanloup, Isabelle AND Huerta-Sánchez, Emilia AND Sousa, Vitor C. AND Foll, Matthieu},
    journal = {PLOS Genetics},
    publisher = {Public Library of Science},
    title = {Robust Demographic Inference from Genomic and {SNP} Data},
    year = {2013},
    month = {10},
    volume = {9},
    url = {https://doi.org/10.1371/journal.pgen.1003905},
    pages = {1-17},
    abstract = {We introduce a flexible and robust simulation-based framework to infer demographic parameters from the site frequency spectrum (SFS) computed on large genomic datasets. We show that our composite-likelihood approach allows one to study evolutionary models of arbitrary complexity, which cannot be tackled by other current likelihood-based methods. For simple scenarios, our approach compares favorably in terms of accuracy and speed with , the current reference in the field, while showing better convergence properties for complex models. We first apply our methodology to non-coding genomic SNP data from four human populations. To infer their demographic history, we compare neutral evolutionary models of increasing complexity, including unsampled populations. We further show the versatility of our framework by extending it to the inference of demographic parameters from SNP chips with known ascertainment, such as that recently released by Affymetrix to study human origins. Whereas previous ways of handling ascertained SNPs were either restricted to a single population or only allowed the inference of divergence time between a pair of populations, our framework can correctly infer parameters of more complex models including the divergence of several populations, bottlenecks and migration. We apply this approach to the reconstruction of African demography using two distinct ascertained human SNP panels studied under two evolutionary models. The two SNP panels lead to globally very similar estimates and confidence intervals, and suggest an ancient divergence (>110 Ky) between Yoruba and San populations. Our methodology appears well suited to the study of complex scenarios from large genomic data sets.},
    number = {10},

}

@article{Gutenkunst2009,
    doi = {10.1371/journal.pgen.1000695},
    author = {Gutenkunst, Ryan N. AND Hernandez, Ryan D. AND Williamson, Scott H. AND Bustamante, Carlos D.},
    journal = {PLOS Genetics},
    publisher = {Public Library of Science},
    title = {Inferring the Joint Demographic History of Multiple Populations from Multidimensional {SNP} Frequency Data},
    year = {2009},
    month = {10},
    volume = {5},
    url = {https://doi.org/10.1371/journal.pgen.1000695},
    pages = {1-11},
    abstract = {Demographic models built from genetic data play important roles in illuminating prehistorical events and serving as null models in genome scans for selection. We introduce an inference method based on the joint frequency spectrum of genetic variants within and between populations. For candidate models we numerically compute the expected spectrum using a diffusion approximation to the one-locus, two-allele Wright-Fisher process, involving up to three simultaneous populations. Our approach is a composite likelihood scheme, since linkage between neutral loci alters the variance but not the expectation of the frequency spectrum. We thus use bootstraps incorporating linkage to estimate uncertainties for parameters and significance values for hypothesis tests. Our method can also incorporate selection on single sites, predicting the joint distribution of selected alleles among populations experiencing a bevy of evolutionary forces, including expansions, contractions, migrations, and admixture. We model human expansion out of Africa and the settlement of the New World, using 5 Mb of noncoding DNA resequenced in 68 individuals from 4 populations (YRI, CHB, CEU, and MXL) by the Environmental Genome Project. We infer divergence between West African and Eurasian populations 140 thousand years ago (95% confidence interval: 40–270 kya). This is earlier than other genetic studies, in part because we incorporate migration. We estimate the European (CEU) and East Asian (CHB) divergence time to be 23 kya (95% c.i.: 17–43 kya), long after archeological evidence places modern humans in Europe. Finally, we estimate divergence between East Asians (CHB) and Mexican-Americans (MXL) of 22 kya (95% c.i.: 16.3–26.9 kya), and our analysis yields no evidence for subsequent migration. Furthermore, combining our demographic model with a previously estimated distribution of selective effects among newly arising amino acid mutations accurately predicts the frequency spectrum of nonsynonymous variants across three continental populations (YRI, CHB, CEU).},
    number = {10},

}

@Article{Tajima1989,
   Author="Tajima, F. ",
   Title="{{S}tatistical method for testing the neutral mutation hypothesis by {D}{N}{A} polymorphism}",
   Journal="Genetics",
   Year="1989",
   Volume="123",
   Number="3",
   Pages="585--595",
   Month="Nov"
}
@article{Watterson1975,
    title = {On the number of segregating sites in genetical models without recombination},
    journal = {Theoretical Population Biology},
    volume = {7},
    number = {2},
    pages = {256-276},
    year = {1975},
    issn = {0040-5809},
    doi = {https://doi.org/10.1016/0040-5809(75)90020-9},
    url = {https://www.sciencedirect.com/science/article/pii/0040580975900209},
    author = {G.A. Watterson},
    abstract = {The distribution is obtained for the number of segregating sites observed in a sample from a population which is subject to recurring, new, mutations but not subject to recombination. After allowance is made for the different effective population sizes, the results apply approximately to three population models, due to Wright, Burrows and Cockerham, and Moran. Included as extreme special cases are the distributions of the number of segregating sites in the whole population and of the number of heterozygous sites in a diploid individual. Some results of Fisher, Haldane, Kimura, and Ewens concerning the means of the distributions for different models are confirmed, but the variances, and the distributions themselves, are new.}
}

@article{Baumdicker2022,
    author = {Baumdicker, Franz and Bisschop, Gertjan and Goldstein, Daniel and Gower, Graham and Ragsdale, Aaron P and Tsambos, Georgia and Zhu, Sha and Eldon, Bjarki and Ellerman, E Castedo and Galloway, Jared G and Gladstein, Ariella L and Gorjanc, Gregor and Guo, Bing and Jeffery, Ben and Kretzschumar, Warren W and Lohse, Konrad and Matschiner, Michael and Nelson, Dominic and Pope, Nathaniel S and Quinto-Cortés, Consuelo D and Rodrigues, Murillo F and Saunack, Kumar and Sellinger, Thibaut and Thornton, Kevin and van Kemenade, Hugo and Wohns, Anthony W and Wong, Yan and Gravel, Simon and Kern, Andrew D and Koskela, Jere and Ralph, Peter L and Kelleher, Jerome},
    title = "{Efficient ancestry and mutation simulation with msprime 1.0}",
    journal = {Genetics},
    volume = {220},
    number = {3},
    year = {2021},
    month = {12},
    abstract = "{Stochastic simulation is a key tool in population genetics, since the models involved are often analytically intractable and simulation is usually the only way of obtaining ground-truth data to evaluate inferences. Because of this, a large number of specialized simulation programs have been developed, each filling a particular niche, but with largely overlapping functionality and a substantial duplication of effort. Here, we introduce msprime version 1.0, which efficiently implements ancestry and mutation simulations based on the succinct tree sequence data structure and the tskit library. We summarize msprime’s many features, and show that its performance is excellent, often many times faster and more memory efficient than specialized alternatives. These high-performance features have been thoroughly tested and validated, and built using a collaborative, open source development model, which reduces duplication of effort and promotes software quality via community engagement.}",
    issn = {1943-2631},
    doi = {10.1093/genetics/iyab229},
    url = {https://doi.org/10.1093/genetics/iyab229},
    note = {iyab229},
    eprint = {https://academic.oup.com/genetics/article-pdf/220/3/iyab229/43780247/iyab229.pdf},
}

@article{Ragsdale2020,
  title    = {Lessons Learned from Bugs in Models of Human History},
  journal  = {The American Journal of Human Genetics},
  volume   = {107},
  number   = {4},
  pages    = {583-588},
  year     = {2020},
  issn     = {0002-9297},
  doi      = {https://doi.org/10.1016/j.ajhg.2020.08.017},
  url      = {https://www.sciencedirect.com/science/article/pii/S000292972030286X},
  author   = {Aaron P. Ragsdale and Dominic Nelson and Simon Gravel and Jerome Kelleher},
  abstract = {Simulation plays a central role in population genomics studies. Recent years have seen rapid improvements in software efficiency that make it possible to simulate large genomic regions for many individuals sampled from large numbers of populations. As the complexity of the demographic models we study grows, however, there is an ever-increasing opportunity to introduce bugs in their implementation. Here, we describe two errors made in defining population genetic models using the msprime coalescent simulator that have found their way into the published record. We discuss how these errors have affected downstream analyses and give recommendations for software developers and users to reduce the risk of such errors.}
}

@article{Adrion2020,
  article_type = {journal},
  title        = {A community-maintained standard library of population genetic models},
  author       = {Adrion, Jeffrey R and Cole, Christopher B and Dukler, Noah and Galloway, Jared G and Gladstein, Ariella L and Gower, Graham and Kyriazis, Christopher C and Ragsdale, Aaron P and Tsambos, Georgia and Baumdicker, Franz and Carlson, Jedidiah and Cartwright, Reed A and Durvasula, Arun and Gronau, Ilan and Kim, Bernard Y and McKenzie, Patrick and Messer, Philipp W and Noskova, Ekaterina and Ortega-Del Vecchyo, Diego and Racimo, Fernando and Struck, Travis J and Gravel, Simon and Gutenkunst, Ryan N and Lohmueller, Kirk E and Ralph, Peter L and Schrider, Daniel R and Siepel, Adam and Kelleher, Jerome and Kern, Andrew D},
  editor       = {Coop, Graham and Wittkopp, Patricia J and Novembre, John and Sethuraman, Arun and Mathieson, Sara},
  volume       = 9,
  year         = 2020,
  month        = {jun},
  pub_date     = {2020-06-23},
  pages        = {e54967},
  citation     = {eLife 2020;9:e54967},
  doi          = {10.7554/eLife.54967},
  url          = {https://doi.org/10.7554/eLife.54967},
  abstract     = {The explosion in population genomic data demands ever more complex modes of analysis, and increasingly, these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here, we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.},
  keywords     = {simulation, reproducibility, open source},
  journal      = {eLife},
  issn         = {2050-084X},
  publisher    = {eLife Sciences Publications, Ltd}
}

@article{Ellegren2014,
  abstract        = {High-throughput sequencing technologies are revolutionizing the life sciences. The past 12 months have seen a burst of genome sequences from non-model organisms, in each case representing a fundamental source of data of significant importance to biological research. This has bearing on several aspects of evolutionary biology, and we are now beginning to see patterns emerging from these studies. These include significant heterogeneity in the rate of recombination that affects adaptive evolution and base composition, the role of population size in adaptive evolution, and the importance of expansion of gene families in lineage-specific adaptation. Moreover, resequencing of population samples (population genomics) has enabled the identification of the genetic basis of critical phenotypes and cast light on the landscape of genomic divergence during speciation. {\textcopyright} 2013 Elsevier Ltd.},
  author          = {Ellegren, Hans},
  doi             = {10.1016/j.tree.2013.09.008},
  file            = {:Users/rgutenk/Documents/Literature/Ellegren - 2014 - Genome sequencing and population genomics in non-model organisms.pdf:pdf},
  issn            = {01695347},
  journal         = {Trends Ecol. Evol.},
  keywords        = {Adaptive evolution,Ecological genomics,Evolutionary genomics,Genome sequencing,Molecular evolution,Population genomics,Positive selection,Speciation genetics},
  mendeley-groups = {AddingSpecies},
  number          = {1},
  pages           = {51--63},
  pmid            = {24139972},
  publisher       = {Elsevier Ltd},
  title           = {{Genome sequencing and population genomics in non-model organisms}},
  url             = {http://dx.doi.org/10.1016/j.tree.2013.09.008},
  volume          = {29},
  year            = {2014}
}
@article{Schrider2018,
  abstract        = {As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly being developed to best utilize genomic sequence data. In this review we discuss a new paradigm that has emerged in computational population genomics: that of supervised machine learning (ML). We review the fundamentals of ML, discuss recent applications of supervised ML to population genetics that outperform competing methods, and describe promising future directions in this area. Ultimately, we argue that supervised ML is an important and underutilized tool that has considerable potential for the world of evolutionary genomics.},
  author          = {Schrider, Daniel R. and Kern, Andrew D.},
  doi             = {10.1016/j.tig.2017.12.005},
  issn            = {13624555},
  journal         = {Trends Genet.},
  mendeley-groups = {AddingSpecies},
  number          = {4},
  pages           = {301--312},
  pmid            = {29331490},
  publisher       = {Elsevier Ltd},
  title           = {Supervised Machine Learning for Population Genetics: {A} New Paradigm},
  url             = {http://dx.doi.org/10.1016/j.tig.2017.12.005},
  volume          = {34},
  year            = {2018}
}

@article{Csillery2010,
  abstract        = {Understanding the forces that influence natural variation within and among populations has been a major objective of evolutionary biologists for decades. Motivated by the growth in computational power and data complexity, modern approaches to this question make intensive use of simulation methods. Approximate Bayesian Computation (ABC) is one of these methods. Here we review the foundations of ABC, its recent algorithmic developments, and its applications in evolutionary biology and ecology. We argue that the use of ABC should incorporate all aspects of Bayesian data analysis: formulation, fitting, and improvement of a model. ABC can be a powerful tool to make inferences with complex models if these principles are carefully applied.},
  author          = {Csill{\'{e}}ry, Katalin and Blum, Michael G B and Gaggiotti, Oscar E and Fran{\c{c}}ois, Olivier},
  doi             = {10.1016/j.tree.2010.04.001},
  file            = {:Users/rgutenk/Documents/Literature/Csill{\'{e}}ry et al. - 2010 - Approximate Bayesian Computation (ABC) in practice.pdf:pdf},
  issn            = {0169-5347},
  journal         = {Trends Ecol. Evol.},
  keywords        = {Africa,Algorithms,Animals,Bayes Theorem,Biodiversity,Biological Evolution,Biostatistics,Demography,Drosophila melanogaster,Drosophila melanogaster: genetics,Genetic,Models},
  mendeley-groups = {AddingSpecies},
  month           = {jul},
  number          = {7},
  pages           = {410--8},
  pmid            = {20488578},
  title           = {{Approximate Bayesian Computation (ABC) in practice.}},
  url             = {http://www.ncbi.nlm.nih.gov/pubmed/20488578},
  volume          = {25},
  year            = {2010}
}

@article{Hohenlohe2021,
  abstract        = {Biodiversity is under threat worldwide. Over the past decade, the field of population genomics has developed across nonmodel organisms, and the results of this research have begun to be applied in conservation and management of wildlife species. Genomics tools can provide precise estimates of basic features of wildlife populations, such as effective population size, inbreeding, demographic history and population structure, that are critical for conservation efforts. Moreover, population genomics studies can identify particular genetic loci and variants responsible for inbreeding depression or adaptation to changing environments, allowing for conservation efforts to estimate the capacity of populations to evolve and adapt in response to environmental change and to manage for adaptive variation. While connections from basic research to applied wildlife conservation have been slow to develop, these connections are increasingly strengthening. Here we review the primary areas in which population genomics approaches can be applied to wildlife conservation and management, highlight examples of how they have been used, and provide recommendations for building on the progress that has been made in this field.},
  author          = {Hohenlohe, Paul A. and Funk, W. Chris and Rajora, Om P.},
  doi             = {10.1111/mec.15720},
  file            = {:Users/rgutenk/Documents/Literature/Funk - 2021 - Population genomics for wildlife conservation and management.pdf:pdf},
  issn            = {1365294X},
  journal         = {Mol. Ecol.},
  keywords        = {adaptive capacity,conservation units,effective population size,genetic rescue,inbreeding depression,population connectivity},
  mendeley-groups = {AddingSpecies},
  number          = {1},
  pages           = {62--82},
  pmid            = {33145846},
  title           = {{Population genomics for wildlife conservation and management}},
  volume          = {30},
  year            = {2021}
}


@article{Teixeira2021,
  abstract        = {The current rate of species extinction is rapidly approaching unprecedented highs, and life on Earth presently faces a sixth mass extinction event driven by anthropogenic activity, climate change, and ecological collapse. The field of conservation genetics aims at preserving species by using their levels of genetic diversity, usually measured as neutral genome-wide diversity, as a barometer for evaluating population health and extinction risk. A fundamental assumption is that higher levels of genetic diversity lead to an increase in fitness and long-term survival of a species. Here, we argue against the perceived importance of neutral genetic diversity for the conservation of wild populations and species. We demonstrate that no simple general relationship exists between neutral genetic diversity and the risk of species extinction. Instead, a better understanding of the properties of functional genetic diversity, demographic history, and ecological relationships is necessary for developing and implementing effective conservation genetic strategies.},
  author          = {Teixeira, Jo{\~{a}}o C. and Huber, Christian D.},
  doi             = {10.1073/pnas.2015096118},
  file            = {:Users/rgutenk/Documents/Literature/Huber - 2021 - The inflated significance of neutral genetic diversity in conservation genetics.pdf:pdf},
  issn            = {10916490},
  journal         = {Proc. Natl. Acad. Sci. U. S. A.},
  keywords        = {Adaptive potential,Conservation genetics,Genetic load,Inbreeding depression,Species extinction},
  mendeley-groups = {AddingSpecies},
  number          = {10},
  pages           = {1--10},
  pmid            = {33608481},
  title           = {{The inflated significance of neutral genetic diversity in conservation genetics}},
  volume          = {118},
  year            = {2021}
}

@article{Nachman2002,
  abstract        = {Recent data from humans and other species provide convincing evidence of variation in recombination rate in different genomic regions. Comparison of physical and genetic maps reveals variation on a scale of megabases, with substantial differences between sexes. Recombination is often suppressed near centromeres and elevated near telomeres, but neither of these observations is true for all chromosomes. In humans, patterns of linkage disequilibrium and experimental measures of recombination from sperm-typing reveal dramatic hotspots of recombination on a scale of kilobases. Genome-wide variation in the amount of crossing-over may be due to variation in the density of hotspots, the intensity of hotspots, or both. Theoretical models of selection and linkage predict that genetic variation will be reduced in regions of low recombination, and this prediction is supported by data from several species. Heterogeneity in rates of crossing-over provides both an opportunity and a challenge for identifying disease genes: as associations occur in blocks, genomic regions containing disease loci may be identified with relatively few markers, yet identifying the causal mutations is unlikely to be achieved through associations alone.},
  author          = {Nachman, Michael W.},
  doi             = {10.1016/S0959-437X(02)00358-1},
  file            = {:Users/rgutenk/Documents/Literature/Nachman - 2002 - Variation in recombination rate across the genome evidence and implications.pdf:pdf},
  issn            = {0959437X},
  journal         = {Curr. Opin. Genet. Dev.},
  mendeley-groups = {AddingSpecies},
  number          = {6},
  pages           = {657--663},
  pmid            = {12433578},
  title           = {{Variation in recombination rate across the genome: Evidence and implications}},
  volume          = {12},
  year            = {2002}
}

@article{Loog2021,
  abstract        = {Demographic processes directly affect patterns of genetic variation within contemporary populations as well as future generations, allowing for demographic inference from patterns of both present-day and past genetic variation. Advances in laboratory procedures, sequencing and genotyping technologies in the past decades have resulted in massive increases in high-quality genome-wide genetic data from present-day populations and allowed retrieval of genetic data from archaeological material, also known as ancient DNA. This has resulted in an explosion of work exploring past changes in population size, structure, continuity and movement. However, as genetic processes are highly stochastic, patterns of genetic variation only indirectly reflect demographic histories. As a result, past demographic processes need to be reconstructed using an inferential approach. This usually involves comparing observed patterns of variation with model expectations from theoretical population genetics. A large number of approaches have been developed based on different population genetic models that each come with assumptions about the data and underlying demography. In this article I review some of the key models and assumptions underlying the most commonly used approaches for past demographic inference and their consequences for our ability to link the inferred demographic processes to the archaeological and climate records. This article is part of the theme issue 'Cross-disciplinary approaches to prehistoric demography'.},
  author          = {Loog, Liisa},
  doi             = {10.1098/rstb.2019.0719rstb20190719},
  file            = {:Users/rgutenk/Documents/Literature/Loog - 2021 - Sometimes hidden but always there the assumptions underlying genetic inference of demographic histories.pdf:pdf},
  issn            = {14712970},
  journal         = {Philos. Trans. R. Soc. B Biol. Sci.},
  keywords        = {ancient DNA,archaeology,demographic modelling,population genetics,population history,statistical modelling},
  mendeley-groups = {AddingSpecies},
  number          = {1816},
  pmid            = {33250022},
  title           = {Sometimes hidden but always there: {The} assumptions underlying genetic inference of demographic histories: {Demographic} inference from genetic {DNA}},
  volume          = {376},
  year            = {2021}
}

@article{Hsieh2016a,
  author          = {Hsieh, PingHsun and Veeramah, Krishna R and Lachance, Joseph and Tishkoff, Sarah A and Wall, Jeffrey D and Hammer, Michael F and Gutenkunst, Ryan N},
  file            = {:Users/rgutenk/Documents/Literature/Hsieh et al. - 2016 - Whole genome sequence analyses of Western Central African Pygmy hunter-gatherers reveal a complex demographic hist.pdf:pdf},
  journal         = {Genome Res.},
  mendeley-groups = {AddingSpecies},
  pages           = {279----290},
  title           = {{Whole genome sequence analyses of Western Central African Pygmy hunter-gatherers reveal a complex demographic history and identify candidate genes under positive natural selection}},
  volume          = {26},
  year            = {2016}
}


@article{Teshima2006,
  abstract        = {The beneficial substitution of an allele shapes patterns of genetic variation at linked sites. Thus, in principle, adaptations can be mapped by looking for the signature of directional selection in polymorphism data. In practice, such efforts are hampered by the need for an accurate characterization of the demographic history of the species and of the effects of positive selection. In an attempt to circumvent these difficulties, researchers are increasingly taking a purely empirical approach, in which a large number of genomic regions are ordered by summaries of the polymorphism data, and loci with extreme values are considered to be likely targets of positive selection. We evaluated the reliability of the "empirical" approach, focusing on applications to human data and to maize. To do so, we considered a coalescent model of directional selection in a sensible demographic setting, allowing for selection on standing variation as well as on a new mutation. Our simulations suggest that while empirical approaches will identify several interesting candidates, they will also miss many - in some cases, most - loci of interest. The extent of the trade-off depends on the mode of positive selection and the demographic history of the population. Specifically, the false-discovery rate is higher when directional selection involves a recessive rather than a co-dominant allele, when it acts on a previously neutral rather than a new allele, and when the population has experienced a population bottleneck rather than maintained a constant size. One implication of these results is that, insofar as attributes of the beneficial mutation (e.g., the dominance coefficient) affect the power to detect targets of selection, genomic scans will yield an unrepresentative subset of loci that contribute to adaptations. {\textcopyright}2006 by Cold Spring Harbor Laboratory Press.},
  author          = {Teshima, Kosuke M. and Coop, Graham and Przeworski, Molly},
  doi             = {10.1101/gr.5105206},
  file            = {:Users/rgutenk/Documents/Literature/Teshima, Coop, Przeworski - 2006 - How reliable are empirical genomic scans for selective sweeps.pdf:pdf},
  issn            = {10889051},
  journal         = {Genome Res.},
  mendeley-groups = {AddingSpecies},
  number          = {6},
  pages           = {702--712},
  pmid            = {16687733},
  title           = {{How reliable are empirical genomic scans for selective sweeps?}},
  volume          = {16},
  year            = {2006}
}

@article{Beichman2018,
  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.},
  author          = {Beichman, Annabel C. and Huerta-Sanchez, Emilia and Lohmueller, Kirk E.},
  doi             = {10.1146/annurev-ecolsys-110617-062431},
  file            = {:Users/rgutenk/Documents/Literature/Beichman, Huerta-sanchez, Lohmueller - 2018 - Using Genomic Data to Infer Historic Population Dynamics of Nonmodel Organisms.pdf:pdf},
  issn            = {15452069},
  journal         = {Annu. Rev. Ecol. Evol. Syst.},
  keywords        = {coalescent,demographic inference,nonmodel organisms,statistical inference,whole-genome sequence data},
  mendeley-groups = {AddingSpecies},
  pages           = {433--456},
  title           = {{Using genomic data to infer historic population dynamics of nonmodel organisms}},
  volume          = {49},
  year            = {2018}
}

@article{Blischak2020,
  abstract        = {Demographic inference using the site frequency spectrum (SFS) is a common way to understand historical events affecting genetic variation. However, most methods for estimating demography from the SFS assume random mating within populations, precluding these types of analyses in inbred populations. To address this issue, we developed a model for the expected SFS that includes inbreeding by parameterizing individual genotypes using beta-binomial distributions. We then take the convolution of these genotype probabilities to calculate the expected frequency of biallelic variants in the population. Using simulations, we evaluated the model's ability to coestimate demography and inbreeding using one- and two-population models across a range of inbreeding levels. We also applied our method to two empirical examples, American pumas (Puma concolor) and domesticated cabbage (Brassica oleracea var. capitata), inferring models both with and without inbreeding to compare parameter estimates and model fit. Our simulations showed that we are able to accurately coestimate demographic parameters and inbreeding even for highly inbred populations (F = 0.9). In contrast, failing to include inbreeding generally resulted in inaccurate parameter estimates in simulated data and led to poor model fit in our empirical analyses. These results show that inbreeding can have a strong effect on demographic inference, a pattern that was especially noticeable for parameters involving changes in population size. Given the importance of these estimates for informing practices in conservation, agriculture, and elsewhere, our method provides an important advancement for accurately estimating the demographic histories of these species.},
  author          = {Blischak, Paul D. and Barker, Michael S. and Gutenkunst, Ryan N. and Falush, Daniel},
  doi             = {10.1093/molbev/msaa042},
  file            = {:Users/rgutenk/Documents/Literature/Blischak, Barker, Gutenkunst - 2020 - Inferring the Demographic History of Inbred Species from Genome-Wide SNP Frequency Data.pdf:pdf},
  issn            = {15371719},
  journal         = {Mol. Biol. Evol.},
  keywords        = {conservation,demography,domestication,inbreeding,site frequency spectrum},
  mendeley-groups = {AddingSpecies},
  number          = {7},
  pages           = {2124--2136},
  pmid            = {32068861},
  title           = {Inferring the Demographic History of Inbred Species from Genome-Wide {SNP} Frequency Data},
  volume          = {37},
  year            = {2020}
}

@article{Montano2016,
  abstract        = {Genetic estimates of effective population size (Ne) are an established means to develop informed conservation policies. Another key goal to pursue the conservation of endangered species is keeping the connectivity across fragmented environments, to which genetic inferences of gene flow and dispersal greatly contribute. Most current statistical tools for estimating such population demographic parameters are based on Kingman's coalescent (KC). However, KC is inappropriate for taxa displaying skewed reproductive variance, a property widely observed in natural species. Coalescent models that consider skewed reproductive success - called multiple merger coalescents (MMCs) - have been shown to substantially improve estimates of Ne when the distribution of offspring per capita is highly skewed. MMCs predictions of standard population genetic parameters, including the rate of loss of genetic variation and the fixation probability of strongly selected alleles, substantially depart from KC predictions. These extended models also allow studying gene genealogies in a spatial continuum, providing a novel theoretical framework to investigate spatial connectivity. Therefore, development of statistical tools based on MMCs should substantially improve estimates of population demographic parameters with major conservation implications.},
  author          = {Montano, Valeria},
  doi             = {10.1098/rsbl.2016.0211},
  file            = {:Users/rgutenk/Documents/Literature/Montano, Montano - 2016 - Coalescent inferences in conservation genetics should the exception become the rule.pdf:pdf},
  issn            = {1744957X},
  journal         = {Biol. Lett.},
  keywords        = {Coalescent theory,Conservation recommendations,Demographic inferences},
  mendeley-groups = {AddingSpecies},
  number          = {6},
  pmid            = {27330172},
  title           = {{Coalescent inferences in conservation genetics: Should the exception become the rule?}},
  volume          = {12},
  year            = {2016}
}



@article{Cheng2018,
  abstract = {Understanding plant evolution and diversity in a phylogenomic context is an enormous challenge due, in part, to limited availability of genome-scale data across phylodiverse species. The 10KP (10,000 Plants) Genome Sequencing Project will sequence and characterize representative genomes from every major clade of embryophytes, green algae, and protists (excluding fungi) within the next 5 years. By implementing and continuously improving leading-edge sequencing technologies and bioinformatics tools, 10KP will catalogue the genome content of plant and protist diversity and make these data freely available as an enduring foundation for future scientific discoveries and applications. 10KP is structured as an international consortium, open to the global community, including botanical gardens, plant research institutes, universities, and private industry. Our immediate goal is to establish a policy framework for this endeavor, the principles of which are outlined here.},
  author   = {Cheng, Shifeng and Melkonian, Michael and Smith, Stephen A. and Brockington, Samuel and Archibald, John M. and Delaux, Pierre-Marc and Li, Fay-Wei and Melkonian, Barbara and Mavrodiev, Evgeny V. and Sun, Wenjing and Fu, Yuan and Yang, Huanming and Soltis, Douglas E. and Graham, Sean W. and Soltis, Pamela S. and Liu, Xin and Xu, Xun and Wong, Gane Ka-Shu},
  doi      = {10.1093/gigascience/giy013},
  issn     = {2047-217X},
  journal  = {Gigascience},
  number   = {7},
  volume   = {3},
  year     = {2018},
  title    = {{10KP}: A phylodiverse genome sequencing plan}
}

@article{Rhie2021,
  abstract = {High-quality and complete reference genome assemblies are fundamental for the application of genomics to biology, disease, and biodiversity conservation. However, such assemblies are available for only a few non-microbial species1,2,3,4. To address this issue, the international Genome 10K (G10K) consortium5,6 has worked over a five-year period to evaluate and develop cost-effective methods for assembling highly accurate and nearly complete reference genomes. Here we present lessons learned from generating assemblies for 16 species that represent six major vertebrate lineages. We confirm that long-read sequencing technologies are essential for maximizing genome quality, and that unresolved complex repeats and haplotype heterozygosity are major sources of assembly error when not handled correctly. Our assemblies correct substantial errors, add missing sequence in some of the best historical reference genomes, and reveal biological discoveries. These include the identification of many false gene duplications, increases in gene sizes, chromosome rearrangements that are specific to lineages, a repeated independent chromosome breakpoint in bat genomes, and a canonical GC-rich pattern in protein-coding genes and their regulatory regions. Adopting these lessons, we have embarked on the Vertebrate Genomes Project (VGP), an international effort to generate high-quality, complete reference genomes for all of the roughly 70,000 extant vertebrate species and to help to enable a new era of discovery across the life sciences.},
  author   = {Rhie, Arang and McCarthy, Shane A. and Fedrigo, Olivier and Damas, Joana and Formenti, Giulio and Koren, Sergey and Uliano-Silva, Marcela and Chow, William and Fungtammasan, Arkarachai and Kim, Juwan and Lee, Chul and Ko, Byung June and Chaisson, Mark and Gedman, Gregory L. and Cantin, Lindsey J. and Thibaud-Nissen, Francoise and Haggerty, Leanne and Bista, Iliana and Smith, Michelle and Haase, Bettina and Mountcastle, Jacquelyn and Winkler, Sylke and Paez, Sadye and Howard, Jason and Vernes, Sonja C. and Lama, Tanya M. and Grutzner, Frank and Warren, Wesley C. and Balakrishnan, Christopher N. and Burt, Dave and George, Julia M. and Biegler, Matthew T. and Iorns, David and Digby, Andrew and Eason, Daryl and Robertson, Bruce and Edwards, Taylor and Wilkinson, Mark and Turner, George and Meyer, Axel and Kautt, Andreas F. and Franchini, Paolo and Detrich III, H. William and Svardal, Hannes and Wagner, Maximilian and Naylor, Gavin J. P. and Pippel, Martin and Malinsky, Milan and Mooney, Mark and Simbirsky, Maria and Hannigan, Brett T. and Pesout, Trevor and Houck, Marlys and Misuraca, Ann and Kingan, Sarah B. and Hall, Richard and Kronenberg, Zev and Sović, Ivan and Dunn, Christopher and Ning, Zemin and Hastie, Alex and Lee, Joyce and Selvaraj, Siddarth and Green, Richard E. and Putnam, Nicholas H. and Gut, Ivo and Ghurye, Jay and Garrison, Erik and Sims, Ying and Collins, Joanna and Pelan, Sarah and Torrance, James and Tracey, Alan and Wood, Jonathan and Dagnew, Robel E. and Guan, Dengfeng and London, Sarah E. and Clayton, David F. and Mello, Claudio V. and Friedrich, Samantha R. and Lovell, Peter V. and Osipova, Ekaterina and Al-Ajli, Farooq O. and Secomandi, Simona and Kim, Heebal and Theofanopoulou, Constantina and Hiller, Michael and Zhou, Yang and Harris, Robert S. and Makova, Kateryna D. and Medvedev, Paul and Hoffman, Jinna and Masterson, Patrick and Clark, Karen and Martin, Fergal and Howe, Kevin and Flicek, Paul Walenz, Brian P. and Kwak, Woori and Clawson, Hiram and Diekhans, Mark and Nassar, Luis and Paten, Benedict and Kraus, Robert H. S. and Crawford, Andrew J. and Gilbert, M. Thomas P. and Zhang, Guojie and Venkatesh, Byrappa and Murphy, Robert W. and Koepfli, Klaus-Peter and Shapiro, Beth and Johnson, Warren E. and Di Palma, Federica and Marques-Bonet, Tomas and Teeling, Emma C. and Warnow, Tandy and Marshall Graves, Jennifer and Ryder, Oliver A. and Haussler, David and O’Brien, Stephen J. and Korlach, Jonas and Lewin, Harris A. and Howe, Kerstin and Myers, Eugene W. and Durbin, Richard and Phillippy, Adam M. and Jarvis, Erich D.},
  issn     = {1476-4687},
  journal  = {Nature},
  title    = {Towards complete and error-free genome assemblies of all vertebrate species},
  year     = {2021},
  volume   = {592},
  number   = {7856},
  pages    = {737--746},
  doi      = {10.1038/s41586-021-03451-0}
}

@article{ensembl2021,
  author   = {Howe, Kevin L and Achuthan, Premanand and Allen, James and Allen, Jamie and Alvarez-Jarreta, Jorge and Amode, M Ridwan and Armean, Irina M and Azov, Andrey G and Bennett, Ruth and Bhai, Jyothish and Billis, Konstantinos and Boddu, Sanjay and Charkhchi, Mehrnaz and Cummins, Carla and Da Rin Fioretto, Luca and Davidson, Claire and Dodiya, Kamalkumar and El Houdaigui, Bilal and Fatima, Reham and Gall, Astrid and Garcia Giron, Carlos and Grego, Tiago and Guijarro-Clarke, Cristina and Haggerty, Leanne and Hemrom, Anmol and Hourlier, Thibaut and Izuogu, Osagie G and Juettemann, Thomas and Kaikala, Vinay and Kay, Mike and Lavidas, Ilias and Le, Tuan and Lemos, Diana and Gonzalez Martinez, Jose and Marugán, José Carlos and Maurel, Thomas and McMahon, Aoife C and Mohanan, Shamika and Moore, Benjamin and Muffato, Matthieu and Oheh, Denye N and Paraschas, Dimitrios and Parker, Anne and Parton, Andrew and Prosovetskaia, Irina and Sakthivel, Manoj P and Salam, Ahamed I Abdul and Schmitt, Bianca M and Schuilenburg, Helen and Sheppard, Dan and Steed, Emily and Szpak, Michal and Szuba, Marek and Taylor, Kieron and Thormann, Anja and Threadgold, Glen and Walts, Brandon and Winterbottom, Andrea and Chakiachvili, Marc and Chaubal, Ameya and De Silva, Nishadi and Flint, Bethany and Frankish, Adam and Hunt, Sarah E and IIsley, Garth R and Langridge, Nick and Loveland, Jane E and Martin, Fergal J and Mudge, Jonathan M and Morales, Joanella and Perry, Emily and Ruffier, Magali and Tate, John and Thybert, David and Trevanion, Stephen J and Cunningham, Fiona and Yates, Andrew D and Zerbino, Daniel R and Flicek, Paul},
  title    = {{Ensembl 2021}},
  journal  = {Nucleic Acids Research},
  volume   = {49},
  number   = {D1},
  pages    = {D884-D891},
  year     = {2020},
  month    = {11},
  abstract = {{The Ensembl project (https://www.ensembl.org) annotates genomes and disseminates genomic data for vertebrate species. We create detailed and comprehensive annotation of gene structures, regulatory elements and variants, and enable comparative genomics by inferring the evolutionary history of genes and genomes. Our integrated genomic data are made available in a variety of ways, including genome browsers, search interfaces, specialist tools such as the Ensembl Variant Effect Predictor, download files and programmatic interfaces. Here, we present recent Ensembl developments including two new website portals. Ensembl Rapid Release (http://rapid.ensembl.org) is designed to provide core tools and services for genomes as soon as possible and has been deployed to support large biodiversity sequencing projects. Our SARS-CoV-2 genome browser (https://covid-19.ensembl.org) integrates our own annotation with publicly available genomic data from numerous sources to facilitate the use of genomics in the international scientific response to the COVID-19 pandemic. We also report on other updates to our annotation resources, tools and services. All Ensembl data and software are freely available without restriction.}},
  issn     = {0305-1048},
  doi      = {10.1093/nar/gkaa942},
  url      = {https://doi.org/10.1093/nar/gkaa942},
  eprint   = {https://academic.oup.com/nar/article-pdf/49/D1/D884/35364073/gkaa942.pdf}
}

TODO: update demes citation
@article{Gower2022,
  title   = {Demes},
  url     = {https://github.com/popsim-consortium/demes-python},
  year    = {2022},
  volume  = {},
  journal = {bioRxiv},
  number  = {},
  pages   = {},
  author  = {Gower, Graham}
}

@article{Amarasinghe2020,
  title   = {Opportunities and challenges in long-read sequencing data analysis},
  author  = {Amarasinghe, Shanika L. and Su, Shian and Dong, Xueyi and Zappia, Luke and Ritchie, Matthew E. and Gouil, Quentin},
  journal = {Genome Biology},
  year    = {2020},
  volume  = {21},
  doi     = {https://doi.org/10.1186/s13059-020-1935-5}
}

@article{Haller2018,
  title   = {Tree-sequence recording in {SLiM} opens new horizons for forward-time simulation of whole genomes},
  author  = {Haller, B C and Galloway, J and Kelleher, J and Messer, P W and Ralph, P L},
  journal = {Molecular Ecology Resources},
  year    = {2018},
  url     = {https://doi.org/10.1111/1755-0998.12968},
  doi     = {10.1111/1755-0998.12968}
}

@article{Haller2019,
  title   = {{SLiM} 3: Forward genetic simulations beyond the {Wright}--{Fisher} model.},
  author  = {Haller, Benjamin  C. and Messer, Philipp W.},
  journal = {Molecular Biology and Evolution},
  year    = {2019},
  volume  = {36},
  number  = {3},
  pages   = {632-637}
}


@article{Lewin2022,
  author  = {Harris A. Lewin  and Stephen Richards  and Erez Lieberman Aiden  and Miguel L. Allende  and John M. Archibald  and Miklós Bálint  and Katharine B. Barker  and Bridget Baumgartner  and Katherine Belov  and Giorgio Bertorelle  and Mark L. Blaxter  and Jing Cai  and Nicolette D. Caperello  and Keith Carlson  and Juan Carlos Castilla-Rubio  and Shu-Miaw Chaw  and Lei Chen  and Anna K. Childers  and Jonathan A. Coddington  and Dalia A. Conde  and Montserrat Corominas  and Keith A. Crandall  and Andrew J. Crawford  and Federica DiPalma  and Richard Durbin  and ThankGod E. Ebenezer  and Scott V. Edwards  and Olivier Fedrigo  and Paul Flicek  and Giulio Formenti  and Richard A. Gibbs  and M. Thomas P. Gilbert  and Melissa M. Goldstein  and Jennifer Marshall Graves  and Henry T. Greely  and Igor V. Grigoriev  and Kevin J. Hackett  and Neil Hall  and David Haussler  and Kristofer M. Helgen  and Carolyn J. Hogg  and Sachiko Isobe  and Kjetill Sigurd Jakobsen  and Axel Janke  and Erich D. Jarvis  and Warren E. Johnson  and Steven J. M. Jones  and Elinor K. Karlsson  and Paul J. Kersey  and Jin-Hyoung Kim  and W. John Kress  and Shigehiro Kuraku  and Mara K. N. Lawniczak  and James H. Leebens-Mack  and Xueyan Li  and Kerstin Lindblad-Toh  and Xin Liu  and Jose V. Lopez  and Tomas Marques-Bonet  and Sophie Mazard  and Jonna A. K. Mazet  and Camila J. Mazzoni  and Eugene W. Myers  and Rachel J. O’Neill  and Sadye Paez  and Hyun Park  and Gene E. Robinson  and Cristina Roquet  and Oliver A. Ryder  and Jamal S. M. Sabir  and H. Bradley Shaffer  and Timothy M. Shank  and Jacob S. Sherkow  and Pamela S. Soltis  and Boping Tang  and Leho Tedersoo  and Marcela Uliano-Silva  and Kun Wang  and Xiaofeng Wei  and Regina Wetzer  and Julia L. Wilson  and Xun Xu  and Huanming Yang  and Anne D. Yoder  and Guojie Zhang },
  title   = {The {Earth} {BioGenome} {Project} 2020: {Starting} the clock},
  journal = {Proceedings of the National Academy of Sciences},
  volume  = {119},
  number  = {4},
  pages   = {e2115635118},
  year    = {2022},
  doi     = {10.1073/pnas.2115635118},
  url     = {https://www.pnas.org/doi/abs/10.1073/pnas.2115635118},
  eprint  = {https://www.pnas.org/doi/pdf/10.1073/pnas.2115635118}
}


@article{Hartfield2022,
  title   = {Using singleton densities to detect recent selection in \textit{{Bos} taurus}},
  author  = {Hartfield, M and Aagaard Poulsen, N and Guldbrandtsen, B and Bataillon, T},
  year    = {2022},
  journal = {Evolution Letters},
  doi     = {10.1002/evl3.263},
  url     = {https://doi.org/10.1002/evl3.263}
}

@article{kern2018neutral,
  title={The neutral theory in light of natural selection},
  author={Kern, Andrew D and Hahn, Matthew W},
  journal={Molecular biology and evolution},
  volume={35},
  number={6},
  pages={1366--1371},
  year={2018},
  publisher={Oxford University Press}
}

@article{jensen2019importance,
  title={The importance of the neutral theory in 1968 and 50 years on: a response to Kern and Hahn 2018},
  author={Jensen, Jeffrey D and Payseur, Bret A and Stephan, Wolfgang and Aquadro, Charles F and Lynch, Michael and Charlesworth, Deborah and Charlesworth, Brian},
  journal={Evolution},
  volume={73},
  number={1},
  pages={111--114},
  year={2019},
  publisher={Wiley Online Library}
}

@article{comeron2014background,
  title={Background selection as baseline for nucleotide variation across the {Drosophila} genome},
  author={Comeron, Josep M},
  journal={PLoS Genetics},
  volume={10},
  number={6},
  pages={e1004434},
  year={2014},
  publisher={Public Library of Science San Francisco, USA}
}

@article{zheng1996integrated,
  title={An integrated genetic map of the {African} human malaria vector mosquito, \textit{{Anopheles} gambiae}},
  author={Zheng, Liangbiao and Benedict, Mark Q and Cornel, Anton J and Collins, Frank H and Kafatos, Fotis C},
  journal={Genetics},
  volume={143},
  number={2},
  pages={941--952},
  year={1996},
  publisher={Oxford University Press}
}

@article{Gaynor2020,
  author   = {Gaynor, R Chris and Gorjanc, Gregor and Hickey, John M},
  title    = {{AlphaSimR: an R package for breeding program simulations}},
  journal  = {G3 Genes|Genomes|Genetics},
  volume   = {11},
  number   = {2},
  year     = {2020},
  month    = {12},
  abstract = {{This paper introduces AlphaSimR, an R package for stochastic simulations of plant and animal breeding programs. AlphaSimR is a highly flexible software package able to simulate a wide range of plant and animal breeding programs for diploid and autopolyploid species. AlphaSimR is ideal for testing the overall strategy and detailed design of breeding programs. AlphaSimR utilizes a scripting approach to building simulations that is particularly well suited for modeling highly complex breeding programs, such as commercial breeding programs. The primary benefit of this scripting approach is that it frees users from preset breeding program designs and allows them to model nearly any breeding program design. This paper lists the main features of AlphaSimR and provides a brief example simulation to show how to use the software.}},
  issn     = {2160-1836},
  doi      = {10.1093/g3journal/jkaa017},
  url      = {https://doi.org/10.1093/g3journal/jkaa017},
  note     = {jkaa017},
  eprint   = {https://academic.oup.com/g3journal/article-pdf/11/2/jkaa017/37042131/jkaa017.pdf}
}

@article{MacLeod2013,
  author  = {MacLeod, I M and Larkin, D M and Lewin, H A and Hayes, B J and Goddard, M E},
  title   = {Inferring Demography from Runs of Homozygosity in Whole-Genome Sequence, with Correction for Sequence Errors},
  journal = {Molecular Biology and Evolution},
  volume  = {30},
  number  = {9},
  pages   = {2209-2223},
  year    = {2013},
  month   = {07},
  issn    = {0737-4038},
  doi     = {10.1093/molbev/mst125},
  url     = {https://doi.org/10.1093/molbev/mst125},
  eprint  = {https://academic.oup.com/mbe/article-pdf/30/9/2209/13176911/mst125.pdf}
}

@article{Meuwissen2001,
  author  = {Meuwissen, T H E and Hayes, B J and Goddard, M E},
  title   = {Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps},
  journal = {Genetics},
  volume  = {157},
  number  = {4},
  pages   = {1819-1829},
  year    = {2001},
  month   = {04},
  issn    = {1943-2631},
  doi     = {10.1093/genetics/157.4.1819},
  url     = {https://doi.org/10.1093/genetics/157.4.1819},
  eprint  = {https://academic.oup.com/genetics/article-pdf/157/4/1819/42032331/genetics1819.pdf}
}

@article{MacLeod2014,
  author  = {MacLeod, I M and Hayes, B J and Goddard, M E},
  title   = {{The Effects of Demography and Long-Term Selection on the Accuracy of Genomic Prediction with Sequence Data}},
  journal = {Genetics},
  volume  = {198},
  number  = {4},
  pages   = {1671-1684},
  year    = {2014},
  month   = {09},
  issn    = {1943-2631},
  doi     = {10.1534/genetics.114.168344},
  url     = {https://doi.org/10.1534/genetics.114.168344},
  eprint  = {https://academic.oup.com/genetics/article-pdf/198/4/1671/42133899/genetics1671.pdf}
}

@article{Kelleher2016,
  title={Efficient coalescent simulation and genealogical analysis for large sample sizes},
  author={Kelleher, Jerome and Etheridge, Alison M and McVean, Gilean},
  journal={PLoS computational biology},
  volume={12},
  number={5},
  pages={e1004842},
  year={2016},
  publisher={Public Library of Science}
}

@article{Nelson2020,
  doi       = {10.1371/journal.pgen.1008619},
  author    = {Nelson, Dominic AND Kelleher, Jerome AND Ragsdale, Aaron P. AND Moreau, Claudia AND McVean, Gil AND Gravel, Simon},
  journal   = {PLOS Genetics},
  publisher = {Public Library of Science},
  title     = {Accounting for long-range correlations in genome-wide simulations of large cohorts},
  year      = {2020},
  month     = {05},
  volume    = {16},
  url       = {https://doi.org/10.1371/journal.pgen.1008619},
  pages     = {1-12},
  number    = {5}
}

@article{Bulmer1971,
  author  = {Bulmer, M. G.},
  title   = {The Effect of Selection on Genetic Variability},
  journal = {The American Naturalist},
  volume  = {105},
  number  = {943},
  pages   = {201-211},
  year    = {1971},
  doi     = {10.1086/282718},
  url     = {https://doi.org/10.1086/282718},
  eprint  = {https://doi.org/10.1086/282718}
}

@article{Lara2022,
  author  = {Lara, L A de C and Pocrnic, I and Thiago, de Paula Oliveira and Gaynor, R C and Gorjanc, G},
  title   = {Temporal and genomic analysis of additive genetic variance in breeding programmes},
  year    = {2022},
  doi     = {10.1038/s41437-021-00485-y},
  pages   = {21--32},
  volume  = {128},
  journal = {Heredity}
}


@article{Talenti2022,
  title   = {A cattle graph genome incorporating global breed diversity},
  author  = {Talenti, A and Powell, J and Hemmink, J D and Cook, E A J and Wragg, D and Jayaraman, S and Paxton, E and Ezeasor, C and Obishakin, E T and Agusi, E R and Tijjani, A and Marshall, K and Fisch, A and Ferreira, B R and Qasim, A and Chaudhry, U and Wiener, P and Toye, P and Morrison, L J and Connelley, T and Prendergast, J G D},
  journal = {Nature Communications},
  year    = {2022},
  doi     = {10.1038/s41467-022-28605-0},
  url     = {https://doi.org/10.1038/s41467-022-28605-0}
}

@article{Gaut2018,
  author  = {Gaut, B S and Seymour, D K and Liu, Q and Zhou, Y},
  title   = {Demography and its effects on genomic variation in crop domestication},
  journal = {Nature Plants},
  year    = {2018},
  doi     = {10.1038/s41477-018-0210-1},
  url     = {https://doi.org/10.1038/s41477-018-0210-1}
}

@article{Rosen2020,
  author  = {Rosen, Benjamin D and Bickhart, Derek M and Schnabel, Robert D and Koren, Sergey and Elsik, Christine G and Tseng, Elizabeth and Rowan, Troy N and Low, Wai Y and Zimin, Aleksey and Couldrey, Christine and Hall, Richard and Li, Wenli and Rhie, Arang and Ghurye, Jay and McKay, Stephanie D and Thibaud-Nissen, Françoise and Hoffman, Jinna and Murdoch, Brenda M and Snelling, Warren M and McDaneld, Tara G and Hammond, John A and Schwartz, John C and Nandolo, Wilson and Hagen, Darren E and Dreischer, Christian and Schultheiss, Sebastian J and Schroeder, Steven G and Phillippy, Adam M and Cole, John B and Van Tassell, Curtis P and Liu, George and Smith, Timothy P L and Medrano, Juan F},
  title   = {{De novo assembly of the cattle reference genome with single-molecule sequencing}},
  journal = {GigaScience},
  volume  = {9},
  number  = {3},
  year    = {2020},
  month   = {03},
  issn    = {2047-217X},
  doi     = {10.1093/gigascience/giaa021},
  url     = {https://doi.org/10.1093/gigascience/giaa021},
  note    = {giaa021},
  eprint  = {https://academic.oup.com/gigascience/article-pdf/9/3/giaa021/32932931/giaa021.pdf}
}


@article{Heaton2021,
  author  = {Heaton, Michael P and Smith, Timothy P L and Bickhart, Derek M and Vander Ley, Brian L and Kuehn, Larry A and Oppenheimer, Jonas and Shafer, Wade R and Schuetze, Fred T and Stroud, Brad and McClure, Jennifer C and Barfield, Jennifer P and Blackburn, Harvey D and Kalbfleisch, Theodore S and Davenport, Kimberly M and Kuhn, Kristen L and Green, Richard E and Shapiro, Beth and Rosen, Benjamin D},
  title   = {A Reference Genome Assembly of {Simmental} Cattle, \textit{{Bos} taurus taurus}},
  journal = {Journal of Heredity},
  volume  = {112},
  number  = {2},
  pages   = {184-191},
  year    = {2021},
  month   = {01},
  issn    = {0022-1503},
  doi     = {10.1093/jhered/esab002},
  url     = {https://doi.org/10.1093/jhered/esab002},
  eprint  = {https://academic.oup.com/jhered/article-pdf/112/2/184/39121213/esab002.pdf}
}


@article{Taylor2016,
  author  = {Jeremy F. Taylor  and Kristen H. Taylor  and Jared E. Decker },
  title   = {Holsteins are the genomic selection poster cows},
  journal = {Proceedings of the National Academy of Sciences},
  volume  = {113},
  number  = {28},
  pages   = {7690-7692},
  year    = {2016},
  doi     = {10.1073/pnas.1608144113},
  url     = {https://www.pnas.org/doi/abs/10.1073/pnas.1608144113},
  eprint  = {https://www.pnas.org/doi/pdf/10.1073/pnas.1608144113}
}

@article{VanRaden2020,
  title   = {Symposium review: How to implement genomic selection},
  journal = {Journal of Dairy Science},
  volume  = {103},
  number  = {6},
  pages   = {5291-5301},
  year    = {2020},
  issn    = {0022-0302},
  doi     = {https://doi.org/10.3168/jds.2019-17684},
  url     = {https://www.sciencedirect.com/science/article/pii/S002203022030309X},
  author  = {VanRaden, P M}
}

@article{Makanjouloa2020,
  title   = {Effect of genomic selection on rate of inbreeding and coancestry and effective population size of {Holstein} and {Jersey} cattle populations},
  author  = {Makanjuola, B O and Miglior, F and Abdalla, E A and Maltecca, C and Schenkel, F S and Baes, C F},
  year    = {2020},
  journal = {Journal of Dairy Science},
  doi     = {10.3168/jds.2019-18013},
  url     = {https://doi.org/10.3168/jds.2019-18013}
}

@article{Harland2017,
  author       = {Harland, Chad and Charlier, Carole and Karim, Latifa and Cambisano, Nadine and Deckers, Manon and Mni, Myriam and Mullaart, Erik and Coppieters, Wouter and Georges, Michel},
  title        = {Frequency of mosaicism points towards mutation-prone early cleavage cell divisions in cattle},
  elocation-id = {079863},
  year         = {2017},
  doi          = {10.1101/079863},
  url          = {https://www.biorxiv.org/content/early/2017/06/29/079863},
  eprint       = {https://www.biorxiv.org/content/early/2017/06/29/079863.full.pdf},
  journal      = {bioRxiv}
}

@article{Ma2015,
  doi       = {10.1371/journal.pgen.1005387},
  author    = {Ma, Li AND O'Connell, Jeffrey R. AND VanRaden, Paul M. AND Shen, Botong AND Padhi, Abinash AND Sun, Chuanyu AND Bickhart, Derek M. AND Cole, John B. AND Null, Daniel J. AND Liu, George E. AND Da, Yang AND Wiggans, George R.},
  journal   = {PLOS Genetics},
  publisher = {Public Library of Science},
  title     = {Cattle Sex-Specific Recombination and Genetic Control from a Large Pedigree Analysis},
  year      = {2015},
  month     = {11},
  volume    = {11},
  url       = {https://doi.org/10.1371/journal.pgen.1005387},
  pages     = {1-24},
  number    = {11}
}

@article{Obsteter2021,
  author  = {Ob\v{s}teter, J and Jenko, J and Gorjanc, G},
  title   = {Genomic Selection for Any Dairy Breeding Program via Optimized Investment in Phenotyping and Genotyping},
  journal = {Frontiers in Genetics},
  volume  = {12},
  year    = {2021},
  url     = {https://www.frontiersin.org/article/10.3389/fgene.2021.637017},
  doi     = {10.3389/fgene.2021.637017}
}

@article{Pombi2006,
  title    = {Variation in recombination rate across the {X} chromosome of \textit{{Anopheles} gambiae}},
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  number   = {5},
  pages    = {901-903},
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  doi      = {https://doi.org/10.4269/ajtmh.2006.75.901},
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  author   = {Pombi, March and Stump, Aram D. and Della Torre, Allesandra and Besansky, Nora J.},
}

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@article{Cutter2013,
  title    = {Genomic signatures of selection at linked sites: unifying the disparity among species},
  journal  = {Nature Reviews Genetcs},
  volume   = {14},
  number   = {4},
  pages    = {262-274},
  year     = {2013},
  doi      = {https://doi.org/10.1038/nrg3425},
  url      = {https://www.nature.com/articles/nrg3425},
  author   = {Cutter, A. D. and Payseur, B. A.}
}

@article{Dawson2002,
  title={A first-generation linkage disequilibrium map of human chromosome 22},
  author={Dawson, Elisabeth and Abecasis, Gon{\c{c}}alo R and Bumpstead, Suzannah and Chen, Yuan and Hunt, Sarah and Beare, David M and Pabial, Jagjit and Dibling, Thomas and Tinsley, Emma and Kirby, Susan and others},
  journal={Nature},
  volume={418},
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  pages={544-548},
  year={2002},
  publisher={Nature Publishing Group}
}

@article{Lynd2010,
  title={Field, genetic, and modeling approaches show strong positive selection acting upon an insecticide resistance mutation in \textit{{Anopheles} gambiae} ss},
  author={Lynd, Amy and Weetman, David and Barbosa, Susana and Egyir Yawson, Alexander and Mitchell, Sara and Pinto, Joao and Hastings, Ian and Donnelly, Martin J},
  journal={Molecular Biology and Evolution},
  volume={27},
  number={5},
  pages={1117-1125},
  year={2010},
  publisher={Oxford University Press}
}

@article{Begun1992,
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@article{Navascues2009,
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@article{Wolf2017,
  title={Making sense of genomic islands of differentiation in light of speciation},
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  journal={Nature Reviews Genetics},
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  number={2},
  pages={87-100},
  year={2017},
  publisher={Nature Publishing Group}
}

@article{Larson2013,
  title={A population genetics view of animal domestication},
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  year={2013},
  publisher={Elsevier}
}

@article{Chakraborty2016,
  title={Contiguous and accurate \textit{de novo} assembly of metazoan genomes with modest long read coverage},
  author={Chakraborty, Mahul and Baldwin-Brown, James G and Long, Anthony D and Emerson, JJ},
  journal={Nucleic acids research},
  volume={44},
  number={19},
  pages={e147--e147},
  year={2016},
  publisher={Oxford University Press}
}

@Inbook{Schiffels2020,
  author="Schiffels, Stephan and Wang, Ke",
  title="MSMC and MSMC2: The Multiple Sequentially Markovian Coalescent",
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  pages="147--166",
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}

@book{Dutheil2020,
  editor         = {Dutheil, Julien Y.},
  publisher      = {Springer US},
  title          = {Statistical Population Genomics},
  isbn           = {978-1-0716-0199-0},
  year           = {2020},
  address        = {New York, NY},

}

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   Title="{{T}he effects of deleterious mutations on evolution at linked sites}",
   Journal="Genetics",
   Year="2012",
   Volume="190",
   Number="1",
   Pages="5--22",
   Month="Jan"
}

@Article{McVicker2009,
   Author="McVicker, G.  and Gordon, D.  and Davis, C.  and Green, P. ",
   Title="{{W}idespread genomic signatures of natural selection in hominid evolution}",
   Journal="PLoS Genet",
   Year="2009",
   Volume="5",
   Number="5",
   Pages="e1000471",
   Month="May"
}

@article{Cesarani2022,
  title = {Multibreed genomic evaluation for production traits of dairy cattle in the {U}nited {S}tates using single-step genomic best linear unbiased predictor},
  journal = {Journal of Dairy Science},
  volume = {105},
  number = {6},
  pages = {5141-5152},
  year = {2022},
  doi = {https://doi.org/10.3168/jds.2021-21505},
  author = {Cesarani, A and Lourenco, D and Tsuruta, S and Legarra, A and Nicolazzi, E L and VanRaden, P M and Misztal, I},
}

@article{schrider2020background,
  title={Background selection does not mimic the patterns of genetic diversity produced by selective sweeps},
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  journal={Genetics},
  volume={216},
  number={2},
  pages={499--519},
  year={2020},
  publisher={Oxford University Press}
}

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}

@article{Eyre-Walker2007,
abstract = {The distribution of fitness effects (DFE) of new mutations is a fundamental entity in genetics that has implications ranging from the genetic basis of complex disease to the stability of the molecular clock. It has been studied by two different approaches: mutation accumulation and mutagenesis experiments, and the analysis of DNA sequence data. The proportion of mutations that are advantageous, effectively neutral and deleterious varies between species, and the DFE differs between coding and non-coding DNA. Despite these differences between species and genomic regions, some general principles have emerged: advantageous mutations are rare, and those that are strongly selected are exponentially distributed; and the DFE of deleterious mutations is complex and multi-modal.},
author = {Eyre-Walker, Adam and Keightley, Peter D},
doi = {10.1038/nrg2146},
file = {:Users/rgutenk/Documents/Literature//Eyre-Walker, Keightley - 2007 - The distribution of fitness effects of new mutations.pdf:pdf},
issn = {1471-0056},
journal = {Nat. Rev. Genet.},
keywords = {Amino Acid Substitution,Animals,DNA,DNA: genetics,Evolution,Genetic,Humans,Models,Molecular,Mutagenesis,Mutation,Polymorphism,Single Nucleotide},
mendeley-groups = {MIRA},
number = {8},
pages = {61061--8},
pmid = {17637733},
title = {The distribution of fitness effects of new mutations},
volume = {8},
year = {2007}
}

@article{Ma2013,
author = {Ma, Xin and Kelley, Joanna L. and Eilertson, Kirsten and Musharoff, Shaila and Degenhardt, Jeremiah D. and Martins, Andr{\'{e}} L. and Vinar, Tomas and Kosiol, Carolin and Siepel, Adam and Gutenkunst, Ryan N. and Bustamante, Carlos D.},
doi = {10.1371/journal.pone.0077175},
editor = {Kayser, Manfred},
issn = {1932-6203},
journal = {PLoS One},
mendeley-groups = {MIRA},
month = {oct},
number = {10},
pages = {e77175},
title = {Population genomic analysis reveals a rich speciation and demographic history of orang-utans (\textit{{Pongo} pygmaeus} and \textit{{Pongo} abelii})},
url = {http://dx.plos.org/10.1371/journal.pone.0077175},
volume = {8},
year = {2013}
}


@article{Huber2018,
abstract = {Dominance is a fundamental concept in molecular genetics and has implications for understanding patterns of genetic variation, evolution, and complex traits. However, despite its importance, the degree of dominance in natural populations is poorly quantified. Here, we leverage multiple mating systems in natural populations of Arabidopsis to co-estimate the distribution of fitness effects and dominance coefficients of new amino acid changing mutations. We find that more deleterious mutations are more likely to be recessive than less deleterious mutations. Further, this pattern holds across gene categories, but varies with the connectivity and expression patterns of genes. Our work argues that dominance arises as a consequence of the functional importance of genes and their optimal expression levels.},
author = {Huber, Christian D. and Durvasula, Arun and Hancock, Angela M. and Lohmueller, Kirk E.},
doi = {10.1038/s41467-018-05281-7},
file = {:Users/rgutenk/Documents/Literature/Huber et al. - 2018 - Gene expression drives the evolution of dominance.pdf:pdf},
isbn = {4146701805},
issn = {20411723},
journal = {Nat. Commun.},
number = {1},
pages = {2750},
publisher = {Springer US},
title = {Gene expression drives the evolution of dominance},
url = {http://dx.doi.org/10.1038/s41467-018-05281-7},
volume = {9},
year = {2018}
}

@article {kyriazis2022using,
	author = {Kyriazis, Christopher C. and Robinson, Jacqueline A. and Lohmueller, Kirk E.},
	title = {Using computational simulations to quantify genetic load and predict extinction risk},
	elocation-id = {2022.08.12.503792},
	year = {2022},
	doi = {10.1101/2022.08.12.503792},
	publisher = {Cold Spring Harbor Laboratory},
	abstract = {Small and isolated wildlife populations face numerous threats to extinction, among which is the deterioration of fitness due to an accumulation of deleterious genetic variation. Genomic tools are increasingly used to quantify the impacts of deleterious variation in small populations; however, these approaches remain limited by an inability to accurately predict the selective and dominance effects of individual mutations. Computational simulations of deleterious genetic variation offer an alternative and complementary tool that can help overcome these limitations, though such approaches have yet to be widely employed. In this Perspective, we aim to encourage conservation genomics researchers to adopt greater use of computational simulations to aid in quantifying and predicting the threat that deleterious genetic variation poses to extinction. We first provide an overview of the components of a simulation of deleterious genetic variation, describing the key parameters involved in such models. Next, we clarify several misconceptions about an essential simulation parameter, the distribution of fitness effects (DFE) of new mutations, and review recent debates over what the most appropriate DFE parameters are. We conclude by comparing modern simulation tools to those that have long been employed in population viability analysis, weighing the pros and cons of a {\textquoteleft}genomics-informed{\textquoteright} simulation approach, and discussing key areas for future research. Our aim is that this Perspective will facilitate broader use of computational simulations in conservation genomics, enabling a deeper understanding of the threat that deleterious genetic variation poses to biodiversity.Competing Interest StatementThe authors have declared no competing interest.},
	URL = {https://www.biorxiv.org/content/early/2022/08/15/2022.08.12.503792},
	eprint = {https://www.biorxiv.org/content/early/2022/08/15/2022.08.12.503792.full.pdf},
	journal = {bioRxiv}
}

@article{harris2016genetic,
    author = {Harris, Kelley and Nielsen, Rasmus},
    title = {The Genetic Cost of {Neanderthal} Introgression},
    journal = {Genetics},
    volume = {203},
    number = {2},
    pages = {881-891},
    year = {2016},
    month = {06},
    abstract = "{Approximately 2–4\% of genetic material in human populations outside Africa is derived from Neanderthals who interbred with anatomically modern humans. Recent studies have shown that this Neanderthal DNA is depleted around functional genomic regions; this has been suggested to be a consequence of harmful epistatic interactions between human and Neanderthal alleles. However, using published estimates of Neanderthal inbreeding and the distribution of mutational fitness effects, we infer that Neanderthals had at least 40\% lower fitness than humans on average; this increased load predicts the reduction in Neanderthal introgression around genes without the need to invoke epistasis. We also predict a residual Neanderthal mutational load in non-Africans, leading to a fitness reduction of at least 0.5\%. This effect of Neanderthal admixture has been left out of previous debate on mutation load differences between Africans and non-Africans. We also show that if many deleterious mutations are recessive, the Neanderthal admixture fraction could increase over time due to the protective effect of Neanderthal haplotypes against deleterious alleles that arose recently in the human population. This might partially explain why so many organisms retain gene flow from other species and appear to derive adaptive benefits from introgression.}",
    issn = {1943-2631},
    doi = {10.1534/genetics.116.186890},
    url = {https://doi.org/10.1534/genetics.116.186890},
    eprint = {https://academic.oup.com/genetics/article-pdf/203/2/881/42157267/genetics0881.pdf},
}


@article{browning2015accurate,
title = {Accurate Non-parametric Estimation of Recent Effective Population Size from Segments of Identity by Descent},
journal = {The American Journal of Human Genetics},
volume = {97},
number = {3},
pages = {404-418},
year = {2015},
issn = {0002-9297},
doi = {https://doi.org/10.1016/j.ajhg.2015.07.012},
url = {https://www.sciencedirect.com/science/article/pii/S0002929715002888},
author = {Sharon R. Browning and Brian L. Browning},
abstract = {Existing methods for estimating historical effective population size from genetic data have been unable to accurately estimate effective population size during the most recent past. We present a non-parametric method for accurately estimating recent effective population size by using inferred long segments of identity by descent (IBD). We found that inferred segments of IBD contain information about effective population size from around 4 generations to around 50 generations ago for SNP array data and to over 200 generations ago for sequence data. In human populations that we examined, the estimates of effective size were approximately one-third of the census size. We estimate the effective population size of European-ancestry individuals in the UK four generations ago to be eight million and the effective population size of Finland four generations ago to be 0.7 million. Our method is implemented in the open-source IBDNe software package.}
}


@article{ringbauer2018estimating,
    author = {Ringbauer, Harald and Kolesnikov, Alexander and Field, David L and Barton, Nicholas H},
    title = {Estimating Barriers to Gene Flow from Distorted Isolation-by-Distance Patterns},
    journal = {Genetics},
    volume = {208},
    number = {3},
    pages = {1231-1245},
    year = {2018},
    month = {03},
    abstract = "{Ringbauer et al. introduce a novel method to estimate barriers to gene flow in a two-dimensional population. Their inference scheme utilizes geographically...In continuous populations with local migration, nearby pairs of individuals have on average more similar genotypes than geographically well-separated pairs. A barrier to gene flow distorts this classical pattern of isolation by distance. Genetic similarity is decreased for sample pairs on different sides of the barrier and increased for pairs on the same side near the barrier. Here, we introduce an inference scheme that uses this signal to detect and estimate the strength of a linear barrier to gene flow in two dimensions. We use a diffusion approximation to model the effects of a barrier on the geographic spread of ancestry backward in time. This approach allows us to calculate the chance of recent coalescence and probability of identity by descent. We introduce an inference scheme that fits these theoretical results to the geographic covariance structure of bialleleic genetic markers. It can estimate the strength of the barrier as well as several demographic parameters. We investigate the power of our inference scheme to detect barriers by applying it to a wide range of simulated data. We also showcase an example application to an Antirrhinum majus (snapdragon) flower-color hybrid zone, where we do not detect any signal of a strong genome-wide barrier to gene flow.}",
    issn = {1943-2631},
    doi = {10.1534/genetics.117.300638},
    url = {https://doi.org/10.1534/genetics.117.300638},
    eprint = {https://academic.oup.com/genetics/article-pdf/208/3/1231/42167645/genetics1231.pdf},
}

@article{darwin2022sequence,
    title={Sequence locally, think globally: The {Darwin Tree of Life
        Project}},
    author={{Darwin Tree of Life Project Consortium}},
    journal={Proceedings of the National Academy of Sciences},
    volume={119},
    number={4},
    pages={e2115642118},
    year={2022},
    publisher={National Acad Sciences}
}

@article{Amarasinghe2021,
    author = {Amarasinghe, Shanika L and Ritchie, Matthew E and Gouil, Quentin},
    title = "{long-read-tools.org: an interactive catalogue of analysis methods for long-read sequencing data}",
    journal = {GigaScience},
    volume = {10},
    number = {2},
    year = {2021},
    month = {02},
    abstract = "{The data produced by long-read third-generation sequencers have unique characteristics compared to short-read sequencing data, often requiring tailored analysis tools for tasks ranging from quality control to downstream processing. The rapid growth in software that addresses these challenges for different genomics applications is difficult to keep track of, which makes it hard for users to choose the most appropriate tool for their analysis goal and for developers to identify areas of need and existing solutions to benchmark against.We describe the implementation of long-read-tools.org, an open-source database that organizes the rapidly expanding collection of long-read data analysis tools and allows its exploration through interactive browsing and filtering. The current database release contains 478 tools across 32 categories. Most tools are developed in Python, and the most frequent analysis tasks include base calling, de novo assembly, error correction, quality checking/filtering, and isoform detection, while long-read single-cell data analysis and transcriptomics are areas with the fewest tools available.Continued growth in the application of long-read sequencing in genomics research positions the long-read-tools.org database as an essential resource that allows researchers to keep abreast of both established and emerging software to help guide the selection of the most relevant tool for their analysis needs.}",
    issn = {2047-217X},
    doi = {10.1093/gigascience/giab003},
    url = {https://doi.org/10.1093/gigascience/giab003},
    note = {giab003},
    eprint = {https://academic.oup.com/gigascience/article-pdf/10/2/giab003/36332150/giab003.pdf},
}

@article{Benzer1961,
  author = {Seymour Benzer },
  title = {ON THE TOPOGRAPHY OF THE GENETIC FINE STRUCTURE},
  journal = {Proceedings of the National Academy of Sciences},
  volume = {47},
  number = {3},
  pages = {403-415},
  year = {1961},
  doi = {10.1073/pnas.47.3.403},
  URL = {https://www.pnas.org/doi/abs/10.1073/pnas.47.3.403},
  eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.47.3.403}
}

@article{Ellegren2003,
  title = {Mutation rate variation in the mammalian genome},
  journal = {Current Opinion in Genetics \& Development},
  volume = {13},
  number = {6},
  pages = {562-568},
  year = {2003},
  issn = {0959-437X},
  doi = {https://doi.org/10.1016/j.gde.2003.10.008},
  url = {https://www.sciencedirect.com/science/article/pii/S0959437X03001461},
  author = {Hans Ellegren and Nick GC Smith and Matthew T Webster},
  abstract = {Recent advances in the large-scale sequencing of mammalian genomes have provided a means to study divergence in not only genic sequences but also in the non-coding bulk of DNA. There is evidence of significant variation in the levels of divergence between presumably neutral regions, pointing at an underlying variation in the rate of mutation across the genome. Apparently, such variation occurs on different scales, including sequence context effects (the influence of neighboring nucleotides on the rate of mutation at individual sites), variation within chromosomes (on the scales of kilobases as well as megabases), and between chromosomes (among autosomes as well as between autosomes and sex chromosomes). An important aspect for further research in this area is to study whether there is an ultimate evolutionary explanation for mutation rate variation within mammalian genomes.}
}

@article{Supek2019,
  title = {Scales and mechanisms of somatic mutation rate variation across the human genome},
  journal = {DNA Repair},
  volume = {81},
  pages = {102647},
  year = {2019},
  note = {Cutting-edge Perspectives in Genomic Maintenance VI},
  issn = {1568-7864},
  doi = {https://doi.org/10.1016/j.dnarep.2019.102647},
  url = {https://www.sciencedirect.com/science/article/pii/S1568786419302009},
  author = {Fran Supek and Ben Lehner},
  keywords = {Mutation rates, Somatic cells, Tumors, Genomic instability, DNA mismatch repair, Chromatin},
  abstract = {Cancer genome sequencing has revealed that somatic mutation rates vary substantially across the human genome and at scales from megabase-sized domains to individual nucleotides. Here we review recent work that has both revealed the major mutation biases that operate across the genome and the molecular mechanisms that cause them. The default mutation rate landscape in mammalian genomes results in active genes having low mutation rates because of a combination of factors that increase DNA repair: early DNA replication, transcription, active chromatin modifications and accessible chromatin. Therefore, either an increase in the global mutation rate or a redistribution of mutations from inactive to active DNA can increase the rate at which consequential mutations are acquired in active genes. Several environmental carcinogens and intrinsic mechanisms operating in tumor cells likely cause cancer by this second mechanism: by specifically increasing the mutation rate in active regions of the genome.}
}