Results 11 to 20 of about 6,377,716 (133)

Molecular Population Genetics [PDF]

Genetics, 2017
AbstractMolecular population genetics aims to explain genetic variation and molecular evolution from population genetics principles. The field was born 50 years ago with the first measures of genetic variation in allozyme loci, continued with the nucleotide sequencing era, and is currently in the era of population genomics.
Casillas, Sònia, Barbadilla, Antonio
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Molecular Population Genetics [PDF]

Genetics, 2019
In the article by S. Casillas and A. Barbadilla (GENETICS 205: 1003–1035) entitled “Molecular Population Genetics,” on page 1010, the references provided in Table 1 were incomplete. For the measure/test vA, the reference to “Castellano et al.
Matthew W. Hahn
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Author Correction: Landscape of multi-nucleotide variants in 125,748 human exomes and 15,708 genomes

Nature Communications, 2021
A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-21077-8.
Qingbo Wang, Emma Pierce-Hoffman, Beryl B. Cummings, Jessica Alföldi, Laurent C. Francioli, Laura D. Gauthier, Andrew J. Hill, Anne H. O’Donnell-Luria, Genome Aggregation Database Production Team, Genome Aggregation Database Consortium, Konrad J. Karczewski, Daniel G. MacArthur   +11 more
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The population genetics of dN/dS. [PDF]

PLoS Genetics, 2008
Evolutionary pressures on proteins are often quantified by the ratio of substitution rates at non-synonymous and synonymous sites. The dN/dS ratio was originally developed for application to distantly diverged sequences, the differences among which ...
Sergey Kryazhimskiy, Joshua B Plotkin
doaj   +1 more source

A structural variation reference for medical and population genetics

Nature, 2020
Structural variants (SVs) rearrange large segments of DNA1 and can have profound consequences in evolution and human disease2,3. As national biobanks, disease-association studies, and clinical genetic testing have grown increasingly reliant on genome ...
Ryan L. Collins, H. Brand, K. Karczewski, Xuefang Zhao, Jessica Alföldi, L. Francioli, A. Khera, Chelsea Lowther, L. Gauthier, Harold Wang, N. Watts, M. Solomonson, Alexander Baumann, Ruchi Munshi, Mark Walker, Christopher W. Whelan, Yongqing Huang, Ted Brookings, Ted Sharpe, M. Stone, E. Valkanas, Jack M. Fu, G. Tiao, Kristen M. Laricchia, Valentín Ruano-Rubio, C. Stevens, N. Gupta, C. Cusick, L. Margolin, Irina M. Armean, E. Banks, Louis Bergelson, K. Cibulskis, K. Connolly, Miguel Covarrubias, Beryl B. Cummings, M. Daly, S. Donnelly, Y. Farjoun, S. Ferriera, S. Gabriel, Jeff Gentry, Thibault Jeandet, D. Kaplan, Christopher Llanwarne, E. Minikel, B. Neale, Sam Novod, A. O’Donnell-Luria, Nikelle Petrillo, T. Poterba, David Roazen, A. Saltzman, K. Samocha, Molly Schleicher, C. Seed, José Soto, Kathleen M. Tibbetts, C. Tolonen, C. Vittal, Gordon Wade, Arcturus Wang, Qingbo S. Wang, J. Ware, B. Weisburd, N. Whiffin, C. Salinas, Tariq Ahmad, C. Albert, D. Ardissino, G. Atzmon, J. Barnard, L. Beaugerie, E. Benjamin, M. Boehnke, L. Bonnycastle, E. Bottinger, D. Bowden, M. Bown, J. Chambers, J. Chan, D. Chasman, Judy H. Cho, M. Chung, Bruce Cohen, A. Correa, D. Dabelea, D. Darbar, R. Duggirala, J. Dupuis, P. Ellinor, R. Elosúa, J. Erdmann, T. Esko, M. Färkkilä, J. Florez, A. Franke, G. Getz, Benjamin Glaser, S. Glatt, David B. Goldstein, Clicerio Gonzalez, L. Groop, Christopher A. Haiman, Craig Hanis, Matthew Harms, M. Hiltunen, M. Holi, C. Hultman, M. Kallela, J. Kaprio, S. Kathiresan, Bong-Jo Kim, Y. J. Kim, G. Kirov, J. Kooner, S. Koskinen, H. Krumholz, S. Kugathasan, S. Kwak, M. Laakso, T. Lehtimäki, R. Loos, S. Lubitz, Ronald C. W. Ma, D. MacArthur, J. Marrugat, K. Mattila, S. Mccarroll, M. McCarthy, D. McGovern, R. McPherson, J. Meigs, O. Melander, A. Metspalu, P. M. Nilsson, M. O’Donovan, D. Öngür, L. Orozco, M. Owen, C. Palmer, A. Palotie, K. Park, C. Pato, A. Pulver, N. Rahman, A. Remes, J. Rioux, S. Ripatti, D. Roden, D. Saleheen, V. Salomaa, N. Samani, J. Scharf, H. Schunkert, M. B. Shoemaker, P. Sklar, H. Soininen, H. Sokol, T. Spector, P. Sullivan, J. Suvisaari, E. Tai, Y. Teo, Tuomi Tiinamaija, Ming Tsuang, D. Turner, Teresa Tusié-Luna, E. Vartiainen, H. Watkins, R. Weersma, M. Wessman, James G. Wilson, R. Xavier, K. Taylor, Henry J. Lin, S. Rich, W. Post, Y. Chen, J. Rotter, C. Nusbaum, A. Philippakis, E. Lander, M. Talkowski   +183 more
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Population-Matched Transcriptome Prediction Increases TWAS Discovery and Replication Rate

iScience, 2020
Summary: Most genome-wide association studies (GWAS) and transcriptome-wide association studies (TWAS) focus on European populations; however, these results cannot always be accurately applied to non-European populations due to genetic architecture ...
Elyse Geoffroy, Isabelle Gregga, Heather E. Wheeler   +2 more
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Archetypal Analysis for population genetics.

PLoS Computational Biology, 2022
The estimation of genetic clusters using genomic data has application from genome-wide association studies (GWAS) to demographic history to polygenic risk scores (PRS) and is expected to play an important role in the analyses of increasingly diverse ...
Julia Gimbernat-Mayol, Albert Dominguez Mantes, Carlos D Bustamante, Daniel Mas Montserrat, Alexander G Ioannidis   +4 more
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Spatial Population Genetics: It's About Time [PDF]

Annual Review of Ecology, Evolution and Systematics, 2019
Many important questions about the history and dynamics of organisms have a geographical component: How many are there, and where do they live? How do they move and interbreed across the landscape?
Gideon S. Bradburd, Peter L. Ralph
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Information-Geometric Optimization with Natural Selection

Entropy, 2020
Evolutionary algorithms, inspired by natural evolution, aim to optimize difficult objective functions without computing derivatives. Here we detail the relationship between classical population genetics of quantitative traits and evolutionary ...
Jakub Otwinowski, Colin H. LaMont, Armita Nourmohammad   +2 more
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Subclonal reconstruction of tumors using machine learning and population genetics

Nature Genetics, 2020
Most cancer genomic data are generated from bulk samples composed of mixtures of cancer subpopulations, as well as normal cells. Subclonal reconstruction methods based on machine learning aim to separate those subpopulations in a sample and infer their ...
G. Caravagna, Timon Heide, Marc J. Williams, L. Zapata, D. Nichol, K. Chkhaidze, W. Cross, George D. Cresswell, B. Werner, A. Acar, L. Chesler, Chris P. Barnes, G. Sanguinetti, T. Graham, A. Sottoriva   +14 more
semanticscholar   +1 more source