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Journal articles on the topic 'Ancestral recombination graphs'

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Published: 9 March 2023
Last updated: 10 March 2023

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1

Song, Yun S., and Jotun Hein. "Constructing Minimal Ancestral Recombination Graphs." Journal of Computational Biology 12, no. 2 (March 2005): 147–69. http://dx.doi.org/10.1089/cmb.2005.12.147.

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2

Mahmoudi, Ali, Jere Koskela, Jerome Kelleher, Yao-ban Chan, and David Balding. "Bayesian inference of ancestral recombination graphs." PLOS Computational Biology 18, no. 3 (March 9, 2022): e1009960. http://dx.doi.org/10.1371/journal.pcbi.1009960.

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We present a novel algorithm, implemented in the software ARGinfer, for probabilistic inference of the Ancestral Recombination Graph under the Coalescent with Recombination. Our Markov Chain Monte Carlo algorithm takes advantage of the Succinct Tree Sequence data structure that has allowed great advances in simulation and point estimation, but not yet probabilistic inference. Unlike previous methods, which employ the Sequentially Markov Coalescent approximation, ARGinfer uses the Coalescent with Recombination, allowing more accurate inference of key evolutionary parameters. We show using simulations that ARGinfer can accurately estimate many properties of the evolutionary history of the sample, including the topology and branch lengths of the genealogical tree at each sequence site, and the times and locations of mutation and recombination events. ARGinfer approximates posterior probability distributions for these and other quantities, providing interpretable assessments of uncertainty that we show to be well calibrated. ARGinfer is currently limited to tens of DNA sequences of several hundreds of kilobases, but has scope for further computational improvements to increase its applicability.
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3

Kuhner, Mary K., and Jon Yamato. "Assessing Differences Between Ancestral Recombination Graphs." Journal of Molecular Evolution 80, no. 5-6 (April 5, 2015): 258–64. http://dx.doi.org/10.1007/s00239-015-9676-x.

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4

Rasmussen, Matthew D., Melissa J. Hubisz, Ilan Gronau, and Adam Siepel. "Genome-Wide Inference of Ancestral Recombination Graphs." PLoS Genetics 10, no. 5 (May 15, 2014): e1004342. http://dx.doi.org/10.1371/journal.pgen.1004342.

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5

Nguyen, Thao Thi Phuong, Vinh Sy Le, Hai Bich Ho, and Quang Si Le. "Building Ancestral Recombination Graphs for Whole Genomes." IEEE/ACM Transactions on Computational Biology and Bioinformatics 14, no. 2 (March 1, 2017): 478–83. http://dx.doi.org/10.1109/tcbb.2016.2542801.

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6

Kuhner, Mary K., and Jon Yamato. "A Consensus Method for Ancestral Recombination Graphs." Journal of Molecular Evolution 84, no. 2-3 (March 2017): 129–38. http://dx.doi.org/10.1007/s00239-017-9786-8.

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7

Vaughan, Timothy G., David Welch, Alexei J. Drummond, Patrick J. Biggs, Tessy George, and Nigel P. French. "Inferring Ancestral Recombination Graphs from Bacterial Genomic Data." Genetics 205, no. 2 (December 22, 2016): 857–70. http://dx.doi.org/10.1534/genetics.116.193425.

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8

Deng, Yun, Yun S. Song, and Rasmus Nielsen. "The distribution of waiting distances in ancestral recombination graphs." Theoretical Population Biology 141 (October 2021): 34–43. http://dx.doi.org/10.1016/j.tpb.2021.06.003.

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9

Heine, K., A. Beskos, A. Jasra, D. Balding, and M. De Iorio. "Bridging trees for posterior inference on ancestral recombination graphs." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474, no. 2220 (December 2018): 20180568. http://dx.doi.org/10.1098/rspa.2018.0568.

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We present a new Markov chain Monte Carlo algorithm, implemented in the software Arbores, for inferring the history of a sample of DNA sequences. Our principal innovation is a bridging procedure, previously applied only for simple stochastic processes, in which the local computations within a bridge can proceed independently of the rest of the DNA sequence, facilitating large-scale parallelization.
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10

Yang, Shuo, Shai Carmi, and Itsik Pe'er. "Rapidly Registering Identity-by-Descent Across Ancestral Recombination Graphs." Journal of Computational Biology 23, no. 6 (June 2016): 495–507. http://dx.doi.org/10.1089/cmb.2016.0016.

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11

Cámara, Pablo G., Arnold J. Levine, and Raúl Rabadán. "Inference of Ancestral Recombination Graphs through Topological Data Analysis." PLOS Computational Biology 12, no. 8 (August 17, 2016): e1005071. http://dx.doi.org/10.1371/journal.pcbi.1005071.

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12

Minichiello, Mark J., and Richard Durbin. "Mapping Trait Loci by Use of Inferred Ancestral Recombination Graphs." American Journal of Human Genetics 79, no. 5 (November 2006): 910–22. http://dx.doi.org/10.1086/508901.

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13

Hössjer, Ola, Linda Hartman, and Keith Humphreys. "Ancestral Recombination Graphs under Non-Random Ascertainment, with Applications to Gene Mapping." Statistical Applications in Genetics and Molecular Biology 8, no. 1 (January 9, 2009): 1–44. http://dx.doi.org/10.2202/1544-6115.1380.

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14

Wu, Yufeng. "Association Mapping of Complex Diseases with Ancestral Recombination Graphs: Models and Efficient Algorithms." Journal of Computational Biology 15, no. 7 (September 2008): 667–84. http://dx.doi.org/10.1089/cmb.2007.0116.

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15

Kim, Yuseob, and Wolfgang Stephan. "Detecting a Local Signature of Genetic Hitchhiking Along a Recombining Chromosome." Genetics 160, no. 2 (February 1, 2002): 765–77. http://dx.doi.org/10.1093/genetics/160.2.765.

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Abstract The theory of genetic hitchhiking predicts that the level of genetic variation is greatly reduced at the site of strong directional selection and increases as the recombinational distance from the site of selection increases. This characteristic pattern can be used to detect recent directional selection on the basis of DNA polymorphism data. However, the large variance of nucleotide diversity in samples of moderate size imposes difficulties in detecting such patterns. We investigated the patterns of genetic variation along a recombining chromosome by constructing ancestral recombination graphs that are modified to incorporate the effect of genetic hitchhiking. A statistical method is proposed to test the significance of a local reduction of variation and a skew of the frequency spectrum caused by a hitchhiking event. This method also allows us to estimate the strength and the location of directional selection from DNA sequence data.
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16

Parida, Laxmi. "Ancestral Recombinations Graph: A Reconstructability Perspective Using Random-Graphs Framework." Journal of Computational Biology 17, no. 10 (October 2010): 1345–70. http://dx.doi.org/10.1089/cmb.2009.0243.

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17

Schaefer, Nathan K., Beth Shapiro, and Richard E. Green. "An ancestral recombination graph of human, Neanderthal, and Denisovan genomes." Science Advances 7, no. 29 (July 2021): eabc0776. http://dx.doi.org/10.1126/sciadv.abc0776.

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Many humans carry genes from Neanderthals, a legacy of past admixture. Existing methods detect this archaic hominin ancestry within human genomes using patterns of linkage disequilibrium or direct comparison to Neanderthal genomes. Each of these methods is limited in sensitivity and scalability. We describe a new ancestral recombination graph inference algorithm that scales to large genome-wide datasets and demonstrate its accuracy on real and simulated data. We then generate a genome-wide ancestral recombination graph including human and archaic hominin genomes. From this, we generate a map within human genomes of archaic ancestry and of genomic regions not shared with archaic hominins either by admixture or incomplete lineage sorting. We find that only 1.5 to 7% of the modern human genome is uniquely human. We also find evidence of multiple bursts of adaptive changes specific to modern humans within the past 600,000 years involving genes related to brain development and function.
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18

Guo, Fangfang, Ignazio Carbone, and David A. Rasmussen. "Recombination-aware phylogeographic inference using the structured coalescent with ancestral recombination." PLOS Computational Biology 18, no. 8 (August 19, 2022): e1010422. http://dx.doi.org/10.1371/journal.pcbi.1010422.

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Movement of individuals between populations or demes is often restricted, especially between geographically isolated populations. The structured coalescent provides an elegant theoretical framework for describing how movement between populations shapes the genealogical history of sampled individuals and thereby structures genetic variation within and between populations. However, in the presence of recombination an individual may inherit different regions of their genome from different parents, resulting in a mosaic of genealogical histories across the genome, which can be represented by an Ancestral Recombination Graph (ARG). In this case, different genomic regions may have different ancestral histories and so different histories of movement between populations. Recombination therefore poses an additional challenge to phylogeographic methods that aim to reconstruct the movement of individuals from genealogies, although also a potential benefit in that different loci may contain additional information about movement. Here, we introduce the Structured Coalescent with Ancestral Recombination (SCAR) model, which builds on recent approximations to the structured coalescent by incorporating recombination into the ancestry of sampled individuals. The SCAR model allows us to infer how the migration history of sampled individuals varies across the genome from ARGs, and improves estimation of key population genetic parameters such as population sizes, recombination rates and migration rates. Using the SCAR model, we explore the potential and limitations of phylogeographic inference using full ARGs. We then apply the SCAR to lineages of the recombining fungus Aspergillus flavus sampled across the United States to explore patterns of recombination and migration across the genome.
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19

Larribe, Fabrice, Sabin Lessard, and Nicholas J. Schork. "Gene Mapping via the Ancestral Recombination Graph." Theoretical Population Biology 62, no. 2 (September 2002): 215–29. http://dx.doi.org/10.1006/tpbi.2002.1601.

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20

Lam, Fumei, Ryan Tarpine, and Sorin Istrail. "The Imperfect Ancestral Recombination Graph Reconstruction Problem: Upper Bounds for Recombination and Homoplasy." Journal of Computational Biology 17, no. 6 (June 2010): 767–81. http://dx.doi.org/10.1089/cmb.2009.0249.

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21

Mano, Shuhei. "Duality Between the Two-Locus Wright–Fisher Diffusion Model and the Ancestral Process with Recombination." Journal of Applied Probability 50, no. 1 (March 2013): 256–71. http://dx.doi.org/10.1239/jap/1363784437.

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Known results on the moments of the distribution generated by the two-locus Wright–Fisher diffusion model, and the duality between the diffusion process and the ancestral process with recombination are briefly summarized. A numerical method for computing moments using a Markov chain Monte Carlo simulation and a method to compute closed-form expressions of the moments are presented. By applying the duality argument, the properties of the ancestral recombination graph are studied in terms of the moments.
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22

Mano, Shuhei. "Duality Between the Two-Locus Wright–Fisher Diffusion Model and the Ancestral Process with Recombination." Journal of Applied Probability 50, no. 01 (March 2013): 256–71. http://dx.doi.org/10.1017/s0021900200013243.

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Known results on the moments of the distribution generated by the two-locus Wright–Fisher diffusion model, and the duality between the diffusion process and the ancestral process with recombination are briefly summarized. A numerical method for computing moments using a Markov chain Monte Carlo simulation and a method to compute closed-form expressions of the moments are presented. By applying the duality argument, the properties of the ancestral recombination graph are studied in terms of the moments.
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23

Pardoux, Etienne, and Majid Salamat. "On the Height and Length of the Ancestral Recombination Graph." Journal of Applied Probability 46, no. 3 (September 2009): 669–89. http://dx.doi.org/10.1239/jap/1253279845.

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The goal of this paper is to provide formulae for the expectation and variance of the height and length of the ancestral recombination graph (ARG). While the formula for the expectation of the height is known (see, e.g. Krone and Neuhauser (1997)), the other formulae seem to be new. We obtain in particular (see Theorem 4.1) a very simple formula which expresses the expectation of the length of the ARG as a linear combination of the expectations of both the length of the coalescent tree and the height of the ARG. Finally, we study the speed at which the ARG comes down from infinity.
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24

Pardoux, Etienne, and Majid Salamat. "On the Height and Length of the Ancestral Recombination Graph." Journal of Applied Probability 46, no. 03 (September 2009): 669–89. http://dx.doi.org/10.1017/s0021900200005817.

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The goal of this paper is to provide formulae for the expectation and variance of the height and length of the ancestral recombination graph (ARG). While the formula for the expectation of the height is known (see, e.g. Krone and Neuhauser (1997)), the other formulae seem to be new. We obtain in particular (see Theorem 4.1) a very simple formula which expresses the expectation of the length of the ARG as a linear combination of the expectations of both the length of the coalescent tree and the height of the ARG. Finally, we study the speed at which the ARG comes down from infinity.
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25

Birkner, Matthias, Jochen Blath, and Bjarki Eldon. "An Ancestral Recombination Graph for Diploid Populations with Skewed Offspring Distribution." Genetics 193, no. 1 (November 12, 2012): 255–90. http://dx.doi.org/10.1534/genetics.112.144329.

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26

Sanderson, Michael. "ReCombinatorics: The Algorithmics of Ancestral Recombination Graphs and Explicit Phylogenetic Networks. By Dan Gusfield; with contributions from, Charles H. Langley, Yun S. Song, and Yufeng Wu. Cambridge (Massachusetts): MIT Press. $60.00. xix + 580 p.; ill.; index. ISBN: 978-0-262-02752-6. 2014." Quarterly Review of Biology 90, no. 3 (September 2015): 344–45. http://dx.doi.org/10.1086/682642.

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27

Wilton, P. R., S. Carmi, and A. Hobolth. "The SMC' Is a Highly Accurate Approximation to the Ancestral Recombination Graph." Genetics 200, no. 1 (March 17, 2015): 343–55. http://dx.doi.org/10.1534/genetics.114.173898.

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28

Parida, Laxmi, Marta Melé, Francesc Calafell, and Jaume Bertranpetit. "Estimating the Ancestral Recombinations Graph (ARG) as Compatible Networks of SNP Patterns." Journal of Computational Biology 15, no. 9 (November 2008): 1133–53. http://dx.doi.org/10.1089/cmb.2008.0065.

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29

Lessard, Sabin, and Amir R. Kermany. "Fixation Probability in a Two-Locus Model by the Ancestral Recombination–Selection Graph." Genetics 190, no. 2 (November 17, 2011): 691–707. http://dx.doi.org/10.1534/genetics.111.136309.

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30

Nordborg, Magnus. "Linkage Disequilibrium, Gene Trees and Selfing: An Ancestral Recombination Graph With Partial Self-Fertilization." Genetics 154, no. 2 (February 1, 2000): 923–29. http://dx.doi.org/10.1093/genetics/154.2.923.

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Abstract It is shown that partial self-fertilization can be introduced into neutral population genetic models with recombination as a simple change in the scaling of the parameters. This means that statistical and computational methods that have been developed under the assumption of random mating can be used without modification, provided the appropriate parameter changes are made. An important prediction is that all forms of linkage disequilibrium will be more extensive in selfing species. The implications of this are discussed.
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31

Leocard, Stephanie, and Etienne Pardoux. "Evolution of the ancestral recombination graph along the genome in case of selective sweep." Journal of Mathematical Biology 61, no. 6 (January 14, 2010): 819–41. http://dx.doi.org/10.1007/s00285-009-0321-4.

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32

Wang, Ying, and Bruce Rannala. "Bayesian inference of fine-scale recombination rates using population genomic data." Philosophical Transactions of the Royal Society B: Biological Sciences 363, no. 1512 (October 7, 2008): 3921–30. http://dx.doi.org/10.1098/rstb.2008.0172.

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Recently, several statistical methods for estimating fine-scale recombination rates using population samples have been developed. However, currently available methods that can be applied to large-scale data are limited to approximated likelihoods. Here, we developed a full-likelihood Markov chain Monte Carlo method for estimating recombination rate under a Bayesian framework. Genealogies underlying a sampling of chromosomes are effectively modelled by using marginal individual single nucleotide polymorphism genealogies related through an ancestral recombination graph. The method is compared with two existing composite-likelihood methods using simulated data. Simulation studies show that our method performs well for different simulation scenarios. The method is applied to two human population genetic variation datasets that have been studied by sperm typing. Our results are consistent with the estimates from sperm crossover analysis.
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33

Hubisz, Melissa J., Amy L. Williams, and Adam Siepel. "Mapping gene flow between ancient hominins through demography-aware inference of the ancestral recombination graph." PLOS Genetics 16, no. 8 (August 6, 2020): e1008895. http://dx.doi.org/10.1371/journal.pgen.1008895.

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34

Kermany, Amir R., and Sabin Lessard. "Effect of epistasis and linkage on fixation probability in three-locus models: An ancestral recombination–selection graph approach." Theoretical Population Biology 82, no. 2 (September 2012): 131–45. http://dx.doi.org/10.1016/j.tpb.2012.05.002.

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35

Olson, H. A., I. Carbone, and D. M. Benson. "Phylogenetic History of Phytophthora cryptogea and P. drechsleri Isolates from Floriculture Crops in North Carolina Greenhouses." Phytopathology® 101, no. 11 (November 2011): 1373–84. http://dx.doi.org/10.1094/phyto-11-10-0302.

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The evolutionary history of Phytophthora cryptogea and P. drechsleri isolates previously collected from floriculture crops in North Carolina commercial greenhouses was explored with coalescent- and parsimony-based analyses. Initially, 68 isolates representing 13 location–host groups were sequenced at multiple loci. Sequences of all isolates within a group were identical. A subset of isolates were selected, cloned to resolve heterozygous sites, and analyzed with SNAP Workbench. The internal transcribed spacer (ITS) region of the ribosomal DNA and cytochrome oxidase II gene genealogies were congruent and indicated that P. cryptogea and P. drechsleri are sister species diverged from a common ancestor with no evidence of gene flow. In contrast, genealogies inferred from β-tubulin (β-tub) and translation elongation factor 1α (EF-1α) genes were in conflict with these loci. Coalescent analysis based on a nonrecombining partition in β-tub and EF-1α showed an initial (older) split between P. cryptogea and P. drechsleri, with a later (recent) event separating the remaining P. cryptogea haplotypes from P. drechsleri. A parsimony-based minimal ancestral recombination graph inferred recombination between P. cryptogea and P. drechsleri isolates in the ITS region and β-tub, suggesting genetic exchange between species. Also, putative recombination between A1 and A2 mating types of P. cryptogea suggests that sexual reproduction has occurred in the history of these P. cryptogea isolates.
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36

Bose, Aritra, Filippo Utro, Daniel E. Platt, and Laxmi Parida. "Multiple Loci Selection with Multi-Way Epistasis in Coalescence with Recombination." Algorithms 14, no. 5 (April 25, 2021): 136. http://dx.doi.org/10.3390/a14050136.

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As studies move into deeper characterization of the impact of selection through non-neutral mutations in whole genome population genetics, modeling for selection becomes crucial. Moreover, epistasis has long been recognized as a significant component in understanding the evolution of complex genetic systems. We present a backward coalescent model, EpiSimRA, that accommodates multiple loci selection, with multi-way (k-way) epistasis for any arbitrary k. Starting from arbitrary extant populations with epistatic sites, we trace the Ancestral Recombination Graph (ARG), sampling relevant recombination and coalescent events. Our framework allows for studying different complex evolutionary scenarios in the presence of selective sweeps, positive and negative selection with multiway epistasis. We also present a forward counterpart of the coalescent model based on a Wright-Fisher (WF) process, which we use as a validation framework, comparing the hallmarks of the ARG between the two. We provide the first framework that allows a nose-to-nose comparison of multiway epistasis in a coalescent simulator with its forward counterpart with respect to the hallmarks of the ARG. We demonstrate, through extensive experiments, that EpiSimRA is consistently superior in terms of performance (seconds vs. hours) in comparison to the forward model without compromising on its accuracy.
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37

Hejase, Hussein A., Ayelet Salman-Minkov, Leonardo Campagna, Melissa J. Hubisz, Irby J. Lovette, Ilan Gronau, and Adam Siepel. "Genomic islands of differentiation in a rapid avian radiation have been driven by recent selective sweeps." Proceedings of the National Academy of Sciences 117, no. 48 (November 16, 2020): 30554–65. http://dx.doi.org/10.1073/pnas.2015987117.

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Numerous studies of emerging species have identified genomic “islands” of elevated differentiation against a background of relative homogeneity. The causes of these islands remain unclear, however, with some signs pointing toward “speciation genes” that locally restrict gene flow and others suggesting selective sweeps that have occurred within nascent species after speciation. Here, we examine this question through the lens of genome sequence data for five species of southern capuchino seedeaters, finch-like birds from South America that have undergone a species radiation during the last ∼50,000 generations. By applying newly developed statistical methods for ancestral recombination graph inference and machine-learning methods for the prediction of selective sweeps, we show that previously identified islands of differentiation in these birds appear to be generally associated with relatively recent, species-specific selective sweeps, most of which are predicted to be soft sweeps acting on standing genetic variation. Many of these sweeps coincide with genes associated with melanin-based variation in plumage, suggesting a prominent role for sexual selection. At the same time, a few loci also exhibit indications of possible selection against gene flow. These observations shed light on the complex manner in which natural selection shapes genome sequences during speciation.
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38

Campagna, Leonardo, Ziyi Mo, Adam Siepel, and J. Albert C. Uy. "Selective sweeps on different pigmentation genes mediate convergent evolution of island melanism in two incipient bird species." PLOS Genetics 18, no. 11 (November 1, 2022): e1010474. http://dx.doi.org/10.1371/journal.pgen.1010474.

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Insular organisms often evolve predictable phenotypes, like flightlessness, extreme body sizes, or increased melanin deposition. The evolutionary forces and molecular targets mediating these patterns remain mostly unknown. Here we study the Chestnut-bellied Monarch (Monarcha castaneiventris) from the Solomon Islands, a complex of closely related subspecies in the early stages of speciation. On the large island of Makira M. c. megarhynchus has a chestnut belly, whereas on the small satellite islands of Ugi, and Santa Ana and Santa Catalina (SA/SC) M. c. ugiensis is entirely iridescent blue-black (i.e., melanic). Melanism has likely evolved twice, as the Ugi and SA/SC populations were established independently. To investigate the genetic basis of melanism on each island we generated whole genome sequence data from all three populations. Non-synonymous mutations at the MC1R pigmentation gene are associated with melanism on SA/SC, while ASIP, an antagonistic ligand of MC1R, is associated with melanism on Ugi. Both genes show evidence of selective sweeps in traditional summary statistics and statistics derived from the ancestral recombination graph (ARG). Using the ARG in combination with machine learning, we inferred selection strength, timing of onset and allele frequency trajectories. MC1R shows evidence of a recent, strong, soft selective sweep. The region including ASIP shows more complex signatures; however, we find evidence for sweeps in mutations near ASIP, which are comparatively older than those on MC1R and have been under relatively strong selection. Overall, our study shows convergent melanism results from selective sweeps at independent molecular targets, evolving in taxa where coloration likely mediates reproductive isolation with the neighboring chestnut-bellied subspecies.
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39

Ignatieva, Anastasia, Rune B. Lyngsø, Paul A. Jenkins, and Jotun Hein. "KwARG: parsimonious reconstruction of ancestral recombination graphs with recurrent mutation." Bioinformatics, May 10, 2021. http://dx.doi.org/10.1093/bioinformatics/btab351.

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Abstract Motivation The reconstruction of possible histories given a sample of genetic data in the presence of recombination and recurrent mutation is a challenging problem, but can provide key insights into the evolution of a population. We present KwARG, which implements a parsimony-based greedy heuristic algorithm for finding plausible genealogical histories (ancestral recombination graphs) that are minimal or near-minimal in the number of posited recombination and mutation events. Results Given an input dataset of aligned sequences, KwARG outputs a list of possible candidate solutions, each comprising a list of mutation and recombination events that could have generated the dataset; the relative proportion of recombinations and recurrent mutations in a solution can be controlled via specifying a set of ‘cost’ parameters. We demonstrate that the algorithm performs well when compared against existing methods. Availability and implementation The software is available at https://github.com/a-ignatieva/kwarg. Supplementary information Supplementary data are available at Bioinformatics online.
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40

Y. C. Brandt, Débora, Xinzhu Wei, Yun Deng, Andrew H. Vaughn, and Rasmus Nielsen. "Evaluation of methods for estimating coalescence times using ancestral recombination graphs." Genetics, March 25, 2022. http://dx.doi.org/10.1093/genetics/iyac044.

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Abstract The ancestral recombination graph is a structure that describes the joint genealogies of sampled DNA sequences along the genome. Recent computational methods have made impressive progress toward scalably estimating whole-genome genealogies. In addition to inferring the ancestral recombination graph, some of these methods can also provide ancestral recombination graphs sampled from a defined posterior distribution. Obtaining good samples of ancestral recombination graphs is crucial for quantifying statistical uncertainty and for estimating population genetic parameters such as effective population size, mutation rate, and allele age. Here, we use standard neutral coalescent simulations to benchmark the estimates of pairwise coalescence times from 3 popular ancestral recombination graph inference programs: ARGweaver, Relate, and tsinfer+tsdate. We compare (1) the true coalescence times to the inferred times at each locus; (2) the distribution of coalescence times across all loci to the expected exponential distribution; (3) whether the sampled coalescence times have the properties expected of a valid posterior distribution. We find that inferred coalescence times at each locus are most accurate in ARGweaver, and often more accurate in Relate than in tsinfer+tsdate. However, all 3 methods tend to overestimate small coalescence times and underestimate large ones. Lastly, the posterior distribution of ARGweaver is closer to the expected posterior distribution than Relate’s, but this higher accuracy comes at a substantial trade-off in scalability. The best choice of method will depend on the number and length of input sequences and on the goal of downstream analyses, and we provide guidelines for the best practices.
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41

Alberti, Frederic, Carolin Herrmann, and Ellen Baake. "Selection, recombination, and the ancestral initiation graph." Theoretical Population Biology, September 2021. http://dx.doi.org/10.1016/j.tpb.2021.08.001.

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42

Parida, Laxmi, Pier Francesco Palamara, and Asif Javed. "A minimal descriptor of an ancestral recombinations graph." BMC Bioinformatics 12, S1 (February 15, 2011). http://dx.doi.org/10.1186/1471-2105-12-s1-s6.

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43

Arenas, Miguel. "The importance and application of the ancestral recombination graph." Frontiers in Genetics 4 (2013). http://dx.doi.org/10.3389/fgene.2013.00206.

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44

Hejase, Hussein A., Ziyi Mo, Leonardo Campagna, and Adam Siepel. "A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph." Molecular Biology and Evolution 39, no. 1 (November 22, 2021). http://dx.doi.org/10.1093/molbev/msab332.

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Abstract:
Abstract Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep-learning framework, we developed a novel method to detect and quantify positive selection: Selection Inference using the Ancestral recombination graph (SIA). Built on a Long Short-Term Memory (LSTM) architecture, a particular type of a Recurrent Neural Network (RNN), SIA can be trained to explicitly infer a full range of selection coefficients, as well as the allele frequency trajectory and time of selection onset. We benchmarked SIA extensively on simulations under a European human demographic model, and found that it performs as well or better as some of the best available methods, including state-of-the-art machine-learning and ARG-based methods. In addition, we used SIA to estimate selection coefficients at several loci associated with human phenotypes of interest. SIA detected novel signals of selection particular to the European (CEU) population at the MC1R and ABCC11 loci. In addition, it recapitulated signals of selection at the LCT locus and several pigmentation-related genes. Finally, we reanalyzed polymorphism data of a collection of recently radiated southern capuchino seedeater taxa in the genus Sporophila to quantify the strength of selection and improved the power of our previous methods to detect partial soft sweeps. Overall, SIA uses deep learning to leverage the ARG and thereby provides new insight into how selective sweeps shape genomic diversity.
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45

Del Amparo, Roberto, Luis Daniel González-Vázquez, Laura Rodríguez-Moure, Ugo Bastolla, and Miguel Arenas. "Consequences of Genetic Recombination on Protein Folding Stability." Journal of Molecular Evolution, December 3, 2022. http://dx.doi.org/10.1007/s00239-022-10080-2.

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Abstract:
AbstractGenetic recombination is a common evolutionary mechanism that produces molecular diversity. However, its consequences on protein folding stability have not attracted the same attention as in the case of point mutations. Here, we studied the effects of homologous recombination on the computationally predicted protein folding stability for several protein families, finding less detrimental effects than we previously expected. Although recombination can affect multiple protein sites, we found that the fraction of recombined proteins that are eliminated by negative selection because of insufficient stability is not significantly larger than the corresponding fraction of proteins produced by mutation events. Indeed, although recombination disrupts epistatic interactions, the mean stability of recombinant proteins is not lower than that of their parents. On the other hand, the difference of stability between recombined proteins is amplified with respect to the parents, promoting phenotypic diversity. As a result, at least one third of recombined proteins present stability between those of their parents, and a substantial fraction have higher or lower stability than those of both parents. As expected, we found that parents with similar sequences tend to produce recombined proteins with stability close to that of the parents. Finally, the simulation of protein evolution along the ancestral recombination graph with empirical substitution models commonly used in phylogenetics, which ignore constraints on protein folding stability, showed that recombination favors the decrease of folding stability, supporting the convenience of adopting structurally constrained models when possible for inferences of protein evolutionary histories with recombination.
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