disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data DOI Creative Commons
Chris C. R. Smith, Andrew D. Kern

BMC Bioinformatics, Год журнала: 2023, Номер 24(1)

Опубликована: Окт. 11, 2023

Spatial genetic variation is shaped in part by an organism's dispersal ability. We present a deep learning tool, disperseNN2, for estimating the mean per-generation distance from georeferenced polymorphism data. Our neural network performs feature extraction on pairs of genotypes, and uses geographic information that comes with each sample. These attributes led disperseNN2 to outperform state-of-the-art method does not use explicit spatial information: relative absolute error was reduced 33% 48% using sample sizes 10 100 individuals, respectively. particularly useful non-model organisms or systems sparse genomic resources, as it unphased, single nucleotide polymorphisms its input. The software open source available https://github.com/kr-colab/disperseNN2 , documentation located at https://dispersenn2.readthedocs.io/en/latest/ .

Язык: Английский

Estimation of spatial demographic maps from polymorphism data using a neural network DOI Creative Commons
Chris C. R. Smith,

Gilia Patterson,

Peter L. Ralph

и другие.

Molecular Ecology Resources, Год журнала: 2024, Номер 24(7)

Опубликована: Авг. 16, 2024

Abstract A fundamental goal in population genetics is to understand how variation arrayed over natural landscapes. From first principles we know that common features such as heterogeneous densities and barriers dispersal should shape genetic space, however there are few tools currently available can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data increasingly accessible, presenting an opportunity study across geographic space myriad species. We present a new inference method uses geo‐referenced SNPs deep neural network estimate spatially maps of density rate. Our trains on simulated input output pairings, where the consists genotypes sampling locations generated from continuous simulator, map true demographic parameters. benchmark our tool against existing methods discuss qualitative differences between different approaches; particular, program unique because it infers magnitude both well their landscape, does so using SNP data. Similar constrained estimating relative migration rates, or require identity‐by‐descent blocks input. applied empirical North American grey wolves, for which estimated mostly reasonable parameters, but was affected by incomplete spatial sampling. Genetic based like ours complement other, direct past demography, believe will serve valuable applications conservation, ecology evolutionary biology. An open source software package implementing https://github.com/kr‐colab/mapNN .

Язык: Английский

Процитировано

1
A Next Generation of Hierarchical Bayesian Analyses of Hybrid Zones Enables Model‐Based Quantification of Variation in Introgression in R DOI Creative Commons
Zachariah Gompert, Devon A. DeRaad, C. Alex Buerkle

и другие.

Ecology and Evolution, Год журнала: 2024, Номер 14(11)

Опубликована: Ноя. 1, 2024

ABSTRACT Hybrid zones, where genetically distinct groups of organisms meet and interbreed, offer valuable insights into the nature species speciation. Here, we present a new R package, bgchm , for population genomic analyses hybrid zones. This package extends updates existing bgc software combines Bayesian hierarchical clines with methods estimating indexes, interpopulation ancestry proportions, geographic clines. Compared to software, offers enhanced efficiency through Hamiltonian Monte Carlo sampling ability work genotype likelihoods combined approach, enabling inference diverse types genetic data sets. The also facilitates quantification introgression patterns across genomes, which is crucial understanding reproductive isolation speciation genetics. We first describe models underlying then provide an overview illustrate its use analysis simulated empirical show that generates accurate estimates model parameters under variety conditions, especially when loci analyzed are highly informative. includes relatively robust genome‐wide variability in clines, has not been focus previous methods. how both selection drift contribute among additional information can be used help distinguish these contributions. conclude by describing promises limitations comparing other cline analyses, identifying areas fruitful future development.

Язык: Английский

Процитировано

1
Patterns of Gene Flow in Anopheles coluzzii Populations From Two African Oceanic Islands DOI Creative Commons
Melina Campos, Gordana Rašić, J. Viegas

и другие.

Evolutionary Applications, Год журнала: 2024, Номер 17(11)

Опубликована: Ноя. 1, 2024

ABSTRACT The malaria vector Anopheles coluzzii is widespread across West Africa and the sole species on islands of São Tomé Príncipe. Our interest in population genetics this these part an assessment their suitability for a field trial involving release genetically engineered A. . construct includes two genes that encode anti‐Plasmodium peptides, along with Cas9‐based gene drive. We investigated flow among subpopulations each island to estimate dispersal rates between sites. Sampling covered known range both islands. Spatial autocorrelation suggests 7 km be likely extent species, whereas estimates based convolutional neural network were roughly 3 km. This difference highlights complexity dynamics value using multiple approaches. analysis also revealed weak heterogeneity populations within but did identify areas weakly resistant or permissive flow. Overall, exist as single Mendelian populations. expect low‐threshold drive has minimal fitness impact should, once introduced, spread relatively unimpeded island.

Язык: Английский

Процитировано

1
Evolutionary consequences of long-distance dispersal in mosquitoes DOI Creative Commons
Thomas L. Schmidt

Current Opinion in Insect Science, Год журнала: 2024, Номер unknown, С. 101325 - 101325

Опубликована: Дек. 1, 2024

Язык: Английский

Процитировано

1
disperseNN2: a neural network for estimating dispersal distance from georeferenced polymorphism data DOI Creative Commons
Chris C. R. Smith, Andrew D. Kern

BMC Bioinformatics, Год журнала: 2023, Номер 24(1)

Опубликована: Окт. 11, 2023

Spatial genetic variation is shaped in part by an organism's dispersal ability. We present a deep learning tool, disperseNN2, for estimating the mean per-generation distance from georeferenced polymorphism data. Our neural network performs feature extraction on pairs of genotypes, and uses geographic information that comes with each sample. These attributes led disperseNN2 to outperform state-of-the-art method does not use explicit spatial information: relative absolute error was reduced 33% 48% using sample sizes 10 100 individuals, respectively. particularly useful non-model organisms or systems sparse genomic resources, as it unphased, single nucleotide polymorphisms its input. The software open source available https://github.com/kr-colab/disperseNN2 , documentation located at https://dispersenn2.readthedocs.io/en/latest/ .

Язык: Английский

Процитировано

3