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Gene Flow

Gene Flow: The transfer of genetic information from one population or species to another through the production of fertile offspring.
This process can lead to changes in the genetic makeup of the recipient population or species and is an important mechanism of evolutionary change.
Gene flow can occur through various means, such as pollen dispersal, seed dispersal, or the movement of individuals between populations.
It is a key factor in maintaining genetic diversity within and between populations and can have significant implications for conservation biology, agriculture, and the study of speciation.

Most cited protocols related to «Gene Flow»

1
Allele Counts and Admixture Analysis of Dog Breeds
Allele counts for the dog breeds and wild canids reported in Boyko et al. Boyko:2010fk were downloaded from http://genome-mirror.bscb.cornell.edu/ on July 30, 2011. These data consist of counts of reference and alternate alleles at 61,468 sites in 85 dog breeds and wild canids. We removed the Jackal and Scottish Deerhound for having relatively high amounts of missing data, and the village dogs because it is unclear if they represent a coherent population. We also removed all SNPs on the X chromosome. This left us with 60,615 SNPs in 82 populations. We ran TreeMix with a window size ( ) of 500. This corresponds to a window size of approximately 20 Mb. For all TreeMix analyses, we set the coyote as the outgroup.
The ascertainment scheme used for SNP discovery in dogs was complicated [68] (link). The largest set of SNPs were ascertained by virtue of being different between the boxer and poodle assemblies. This should lead to an overestimation of the distance between the boxer and the poodle in our analysis. Indeed, in Figure 5B, a considerable negative residual between the boxer and poodle is visible. Another set of SNPs were ascertained by being heterozygous within a boxer individual, and a third set were ascertained by comparison between a boxer and wild canids. These latter SNPs should lead to an overestimation of the distance between the boxer and the wolf in our analysis (as we see for the poodle); in fact, we infer migration between the boxer and the wolf. This ascertainment issue may have led us to underestimate the amount of gene flow in the comparison.
Pickrell J.K, & Pritchard J.K. (2012). Inference of Population Splits and Mixtures from Genome-Wide Allele Frequency Data. PLoS Genetics, 8(11), e1002967.
Publication 2012
Alleles Breeding Canidae Canis familiaris Coyotes Gene Flow Genome Heterozygote Jackals Population Group Single Nucleotide Polymorphism Wolves X Chromosome
2
Genetic Ancestry of Native Americans
The DNA samples we analyzed were collected over several decades. For each sample, we verified that informed consent was obtained consistent with studies of population history and that institutional approval had been obtained in the country of collection. Ethical oversight and approval for this project was provided by the NHS National Research Ethics Service, Central London committee (Ref # 05/Q0505/31). The dataset is based on merging Illumina SNP array data newly generated for this study (including 273 Native American samples) with data from six other studies. We applied stringent data curation and validation procedures to the merged data set. We used local ancestry inference software to identify genome segments in each Native American and Siberian sample without evidence of recent European or African admixture, and created a dataset that masked segments of potentially non-Native origin. Most of analyses are performed on the masked data set; however, we confirmed major inferences on a subset of 163 Native American samples that had no evidence of European or African admixture. We used model-based clustering and neighbor-joining trees to obtain an overview of population relationships, and then tested whether proposed sets of four populations were consistent with having a simple tree relationship using the 4 Population Test, which we generalized via a Hotelling T-test. We analyzed the correlation in allele frequency differences across populations to infer the minimum number of gene flow events that occurred between Asia and America. We fit the patterns of correlation in allele frequency differences to proposed models of history—Admixture Graphs—that can incorporate population splits and mixtures.
Reich D., Patterson N., Campbell D., Tandon A., Mazieres S., Ray N., Parra M.V., Rojas W., Duque C., Mesa N., García L.F., Triana O., Blair S., Maestre A., Dib J.C., Bravi C.M., Bailliet G., Corach D., Hünemeier T., Bortolini M.C., Salzano F.M., Petzl-Erler M.L., Acuña-Alonzo V., Aguilar-Salinas C., Canizales-Quinteros S., Tusié-Luna T., Riba L., Rodríguez-Cruz M., Lopez-Alarcón M., Coral-Vazquez R., Canto-Cetina T., Silva-Zolezzi I., Fernandez-Lopez J.C., Contreras A.V., Jimenez-Sanchez G., Gómez-Vázquez M.J., Molina J., Carracedo Á., Salas A., Gallo C., Poletti G., Witonsky D.B., Alkorta-Aranburu G., Sukernik R.I., Osipova L., Fedorova S., Vasquez R., Villena M., Moreau C., Barrantes R., Pauls D., Excoffier L., Bedoya G., Rothhammer F., Dugoujon J.M., Larrouy G., Klitz W., Labuda D., Kidd J., Kidd K., Rienzo A.D., Freimer N.B., Price A.L, & Ruiz-Linares A. (2012). Reconstructing Native American Population History. Nature, 488(7411), 370-374.
Publication 2012
American Indian or Alaska Native Europeans Gene Flow Genome Negroid Races Reproduction Trees
3
Introgression Detection for Complex Phylogenies
The number of possible gene flow donor-recipient combinations increases rapidly with the number of populations or species. A unified test for introgression has been developed for a five taxon symmetric phylogeny, implemented in the DFOIL package (Pease & Hahn 2015 (link)). However, no such framework currently exists for datasets with six or more taxa. A common approach is to perform the D and ƒ4-ratio analyses on all four taxon subsamples from the dataset [e.g. (Green et al. 2010 ; Martin et al. 2013 (link); vonHoldt et al. 2016 (link); Kozak et al. 2018 ; Malinsky et al. 2018 )]. However, the number of analyses that need to be performed grows very quickly. Even with a fixed outgroup, the number of combinations is (n3) , i.e. n choose 3, where n is the number of taxa. For example, there are 1,140 different combinations of ((P1, P2), P3) in a dataset of 20 taxa, growing to 161,700 combinations in a dataset with 100 taxa. Interpreting the results of such a system of four taxon tests is not straightforward; the different subsets are not independent as soon as the taxa share drift (that is, they share branches on the phylogeny) and, therefore, a single gene flow event can be responsible for many elevated D and ƒ4-ratio results. At the same time, the correlations, especially of the ƒ4-ratio scores, can be informative about the timing of introgression events and about the specific donor-recipient combinations.
The ƒ-branch or ƒb metric was introduced in Malinsky et al. (2018) to disentangle correlated ƒ4-ratio results and assign gene flow evidence to specific, possibly internal, branches on a phylogeny by building upon the logic developed by Martin et al. (2013) (link), as illustrated in Fig. 1. Given a specific tree (with known or hypothesised relationships), the ƒb(P3) statistic reflects excess sharing of alleles between the population or species P3 and the descendants of the branch labelled b, relative to allele sharing between P3 and the descendants of the sister branch of b.
Formally: fb(P3)=medianA[minB[f4ratio(A,B;P3,O)]] where B refers to the populations or taxa descending from the branch b, and A refers to descendants from the sister branch of b. The calculation is over all positive ƒ4-ratio results which had A in the P1 and B in the P2 positions.
Malinsky M., Matschiner M, & Svardal H. (2020). Dsuite - fast D-statistics and related admixture evidence from VCF files. Molecular ecology resources, 21(2), 584-595.
Publication 2020
Alleles Gene Flow Population Group Tissue Donors Trees
4
Spatially Explicit DNA Sequence Clustering
Several spatial models for estimating genetic population structure from molecular marker loci have been introduced in the past few years (Wasser et al. 2004 (link); Guillot et al. 2005 (link); Francois et al. 2006 (link); Chen et al. 2007 ; Corander et al. 2008b ). A common feature of these models is to introduce a spatially explicit prior for cluster structure that will combine sample locations with likelihood of the genetic data to provide improved inferences about geographical boundaries to gene flow in the underlying population. A specific feature of the model introduced by Corander et al. (2008b) is that it allows analytical integration of the parameters in both the spatial prior and the likelihood of genetic data, which enables the use of highly efficient stochastic optimization methods to estimate the posterior mode over the space of clustering solutions, in contrast to standard Markov chain Monte Carlo methods, which can be extremely tedious to use for large and complex data sets. Here, we developed an implementation of the spatial prior combined with the Markovian sequence clustering model introduced by Corander and Tang (2007) (link) to enable spatially explicit clustering of DNA sequence data in the presence of geographical sample coordinates. This new implementation is provided by the spatial clustering module of the BAPS software version 6.0, which is freely available for research purposes at http://www.helsinki.fi/bsg/software/BAPS/, last accessed November 5, 2012. In addition to the earlier standard output from the spatial analysis, which includes both numerical and graphical representations of the estimated population structure, we have added an output format which provides a direct interface to the web portal http://www.spatialepidemiology.net/, last accessed November 5, 2012 where a user-defined Google Maps representation of the estimated clustering can be created. The zoomability of these maps provides a useful way to produce a series of spatial images at different levels of resolution.
As demonstrated in Willems et al. (2012) (link), a hierarchical approach to model-based DNA sequence clustering, where data from a cluster at particular stage of the hierarchy are reclustered in the next stage, provides a useful way of increasing statistical power to detect separate lineages residing within the data. To preserve the internal consistency of the outputs from different BAPS modules, we implemented the hierarchical clustering approach in a separate program that can be used in tandem with BAPS. This tool, hierBAPS, is freely available for research purposes at http://www.helsinki.fi/bsg/software/BAPS/, last accessed November 5, 2012. hierBAPS accepts standard multiple sequence alignments up to whole-genome level as an input and provides access to improved imaging of the hierarchical clustering results. Distinct from the standard prior used in BAPS for nonspatial clustering, hierBAPS uses a uniform prior on the number of clusters k, such that any particular clustering solution has the prior probability proportional to , where the denominator equals the Stirling number of the second kind and n is the number of objects to be clustered. Such a prior introduces an additional penalty for an increase in the number of clusters, because the Stirling number of the second kind increases rapidly as a function of k for a given n (until it reaches its maximum value, whereafter it decreases). Given a partition, hierBAPS uses the standard multinomial likelihood for each single-nucleotide polymorphism site in each cluster and a conjugate Dirichlet prior distribution for the frequencies of the distinct variants detected at the sequence site in question, similar to the basic clustering model in BAPS. For technical details about the distributional assumptions, see for example, Corander and Marttinen (2006) (link).
Cheng L., Connor T.R., Sirén J., Aanensen D.M, & Corander J. (2013). Hierarchical and Spatially Explicit Clustering of DNA Sequences with BAPS Software. Molecular Biology and Evolution, 30(5), 1224-1228.
Publication 2013
Biological Markers DNA, A-Form DNA Sequence Gene Flow Genome Knuckle pads, leuconychia and sensorineural deafness Microtubule-Associated Proteins Reproduction Sequence Alignment Single Nucleotide Polymorphism
5
FUSION Diabetes Genetics Study
The Finland-United States Investigation of Non-Insulin-Dependent Diabetes Mellitus Genetics (FUSION) Study consists of 1,161 Finnish individuals with type 2 diabetes (T2D) and 1,174 normal glucose tolerant Finnish controls. Samples were genotyped with the Illumina Human-Hap300 BeadChip (v1.1). In sum, 306,222 autosomal SNPs passed quality control (HWE P ≥ 10−6 in the total sample, call frequency ≥ 0.90 and MAF > 0.01)15 (link). In addition, 120 trios were genotyped with the same chip and haplotypes were estimated based on the most likely pattern of gene flow using Merlin20 (link) and compared with haplotypes estimated statistically using population information and the software program MaCH12 (link).
Howie B., Fuchsberger C., Stephens M., Marchini J, & Abecasis G.R. (2012). Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nature genetics, 44(8), 955-959.
Publication 2012
Diabetes Mellitus Diabetes Mellitus, Non-Insulin-Dependent DNA Chips Gene Flow Glucose Haplotypes Homo sapiens Single Nucleotide Polymorphism TRIO protein, human

Most recents protocols related to «Gene Flow»

1
Chloroplast Genome Assembly and Variation Analysis
Sequencing raw data results and raw data acquired from NCBI made up a total of 954 GB of information. Each sample had 10–50× sequencing coverage and the quality of the reads was examined using FastQC v.0.11.7 (Andrews et al., 2010 ). We used pipeline GetOrganelle V 1.7.3.4 (Jin et al., 2020 (link)) to extract and assemble the chloroplast genome. The program performs a de novo assembly using seed and genome sequences as references. Here, we used the Ribulose 1,5‐bisphosphate (RuBP) sequence from Zea mays and Gossypium hirsutum Coker 310 FR complete chloroplast genome (GenBank: NC_007944; Lee et al., 2006 (link)), as seed and reference sequences, respectively. The annotation of each genome was performed using GeSeq (Tillich et al., 2017 (link)) and CPGAVAS2 (Shi et al., 2019 (link)), using the Gossypium hirsutum Coker 310 genome as references. OrganellarGenomeDRAW (Greiner et al., 2019 (link)) was used to plot the chloroplast structure for each assembly and to compare the LSC‐IR‐SSC‐IR region boundaries. The genomic rearrangements and homologous regions were detected with the alignment of the chloroplast using Mauve algorithm in Mauve V 2.4.0 (Darling et al., 2010 (link)) with default parameters.
To identify single nucleotide variants (SNVs), Indels, and short tandem repeats (STRs) we aligned the raw reads with the reference genome of G. hirsutum cocker 310 FR using Burrows‐Wheeler Aligner 0.7.17‐r1188 (Li & Durbin, 2009 (link)). We used Samtools 1.10 (Li et al., 2009 (link)), Picard 2.6.2 (http://broadinstitute.github.io/picard/), and NGSEP 3.3.0 (Tello et al., 2019 (link)) as format converters, for sorting files and to identify genomic variants. The genomic variability was compared between samples and between the sum of genomic variants in wild, TI‐wild, landrace, TI‐landrace, and BL genomes. Nucleotide diversity (π), NST index, and the Tajima's D test were calculated using DnaSPv6 (Rozas et al., 2017 (link)); and synonym and nonsynonym substitution rates (dN/dS) were calculated with PAML through PAL2NAL (Suyama et al., 2006 (link); Xu & Yang, 2013 (link)). The genetic diversity and selection were compared between the groups with different degrees of management, and between these and the introgressed groups. We examined population structure by performing a Bayesian spatial analysis using the package RhierBAPS (Cheng et al., 2013 (link); Tonkin‐Hill et al., 2018 (link)). The haplotype diversity was calculated using DnaSPv6 (Rozas et al., 2017 (link)). In addition, we analyzed the evolutionary history and relationships among the haplotypes and gene flow by constructing a minimum‐spanning network of haplotypes using TCS in the software POPART (Leigh & Bryant, 2015 ).
Vega M., Quintero‐Corrales C., Mastretta‐Yanes A., Casas A., López‐Hilario V, & Wegier A. (2023). Multiple domestication events explain the origin of Gossypium hirsutum landraces in Mexico. Ecology and Evolution, 13(3), e9838.
Publication 2023
Biological Evolution Chloroplasts Gene Flow Gene Rearrangement Genetic Diversity Genome Genome, Chloroplast Gossypium Haplotypes INDEL Mutation Nucleotides ribulose Zea mays
2
Estimating Gene Flow with MIGRATE
To generate a measure of gene flow between the populations, we conducted analyses with MIGRATE using Bayesian inference [41 –43 (link)]. Beginning with the 301 K Set for the 116 unique individuals, we used PLINK to filter out alleles with a minor allele frequency under 50% (–maf 0.5) to target the most variable sites [44 (link)]. We evaluated four different migration models for the 1,143 resultant loci: a full migration model, two unidirectional migration models, and a panmixia model. For each locus, the program visited 10,000,000 steps per parameter (a*b*c) following a burn-in of 1,000,000 steps. We utilized the default static heating scheme of four chains with a Bayesian prior range of 0–0.01 for Θ and a range of 0–1,000,000 for M. We calculated log marginal likelihoods (lmL) based on the Bezier approximation scores to select the model that best described the migration patterns [45 (link)]. MIGRATE generates Θ values per population, representing the product of effective population size and mutation rate (4Ne*mu), and M, a mutation rate scaled migration estimation (Migration/mu). These measures can then be used to estimate how many migrants there are per four generations, and how these contribute to the genetic diversity of a population relative to mutation rate.
Dillon M.N., Thomas R., Mousseau T.A., Betz J.A., Kleiman N.J., Reiskind M.O, & Breen M. (2023). Population dynamics and genome-wide selection scan for dogs in Chernobyl. Canine Medicine and Genetics, 10, 1.
Publication 2023
Gene Flow Genes, vif Migrants
3
Modeling Gene Flow and Migration Events
To model the gene flow and identify the migration events between the selected blueberry species, we used TreeMix22 v.1.12 [45 ]). The programs infer population splitting and mixing patterns from genome-wide allele frequency data. For a given set of allele frequencies, the program will return the maximum likelihood tree for the collection of populations and attempt to infer potential gene flow from the residual covariance matrix. In this study, we used five migration events for modeling.
Manzanero B.R., Kulkarni K.P., Vorsa N., Reddy U.K., Natarajan P., Elavarthi S., Iorizzo M, & Melmaiee K. (2023). Genomic and evolutionary relationships among wild and cultivated blueberry species. BMC Plant Biology, 23, 126.
Publication 2023
Blueberries Gene Flow Genome Population Group Trees
4
Spatial Genetic Structure Analysis
We ran spatial auto-correlation analysis in SPAGEDI (Hardy and Vekemans, 2002 (link)) to test for the presence of fine-scale genetic structure at the five populations with more than 20 individuals (WX1, WX2, WX3, NPA2 and PXH1). Pairwise kinship coefficients were calculated between all individuals (Fij) within each population (Loiselle et al., 1995 (link); Kalisz et al., 2001 (link)), mean Fij was derived for each distance interval, d, and this was plotted against distance in metres. The software requires that the number of pairwise comparisons is kept constant across all distance intervals. Mean Fij(d) estimates were calculated for intervals defined as 0–20 m (d = 5 m), 21–50 m (d = 10 m), 51–100 m (d = 50 m or end-point) and 101–600 m (d = 100 m or end-point), and 95% confidence intervals (CI) associated with the null hypothesis of no genetic structure [Fij(d) = 0] were constructed using 1,000 random permutations. Significant positive or negative structure was inferred if the CIs did not overlap.
We then regressed the slope bLF(d) [linear regression of Fij (d) on ln (d)] to test whether there was significant deviation from the null hypothesis of no genetic structure [bLF(d) = 0]. To compare overall intensity of fine-scale genetic structure among populations, we also calculated the Sp statistic (Vekemans and Hardy, 2004 (link)), given by Sp = -bLF(d)/[1-F (d1)], where F(d1) is the average kinship coefficient between individuals of the first distance class (i.e., 0–20 m, d = 5 m), Fij.
Finally, we estimated the relative contribution of pollen (σp) and seed (σs) dispersal to total gene flow, σ (Heuertz, 2010 (link)). Using the average Fij(d) for all samples from each population, we regressed the residuals [f(d): Fij(d) - Fij(d)exp] on ln(d) by a polynomial regression of the third power: f(d) = a + b ln(d) + c [ln(d)]2 + d [ln(d)]3, where Fij(d)exp is the dependent variable of the linear regression equation at independent variable ln(d). The curvature of f(d) is given by the second derivative, k = 2c + 6d*ln (d1), where d1 is the average distance of the first distance class. A concave curve at short distances or k >0 suggests more restricted seed dispersal than pollen dispersal (σs ≪ σp), whereas a convex shape or k <0 suggests more restricted pollen dispersal or no particular restriction in seed dispersal (σs ≥ σp) (Vekemans and Hardy, 2004 (link)). Statistics were calculated in SPAGEDI (Hardy and Vekemans, 2002 (link)) and SPSS 22.0 (IBM Corp., New York, USA).
Zhang Z., Li J., Suddee S., Bouamanivong S., Averyanov L.V, & Gale S.W. (2023). Exploring island syndromes: Variable matrix permeability in Phalaenopsis pulcherrima (Orchidaceae), a specialist lithophyte of tropical Asian inselbergs. Frontiers in Plant Science, 14, 1097113.
Publication 2023
D-600 Gene Flow Genetic Diversity Genetic Structures Pollen Seed Dispersal
5
Exploring the Landscape Permeability of Phalaenopsis pulcherrima

Phalaenopsis pulcherrima is a lithophytic herb that is distributed primarily in the seasonal Asian tropics, from northeast India and Myanmar, through Thailand to Indochina and Hainan Island in South China, with a few outlying localities in Peninsular Malaysia, Sumatra and Borneo (Christenson, 2001 ; Kumar et al., 2018 ). Despite this wide range, the species is rare, being confined to open granitic platforms in monsoon forest (Figure 1A). Jin et al. (2012) (link) found it to achieve pollination through generalised food-deception of solitary bees and Zhang et al. (2019) (link) demonstrated that this, plus self-sterility, promote outcrossing and landscape-scale pollen flow, thereby enabling clusters occurring on the same inselberg to remain genetically connected. Although this generated high diversity at the population level, fine-scale structure was detected as a result of clonal propagation and localised gene flow via seed (Zhang et al., 2019 (link)). More recently, Hu et al. (2021) suggested that green- and red-coloured leaf morphs might be genetically differentiated, but sampling among morphs and across populations was low and inconsistent. The species’ ecological confinement to terrestrial habitat islands, as well as its geographic occurrence on both mainland and true island landmasses, makes it an ideal subject for investigating landscape permeability and colonisation history.
Zhang Z., Li J., Suddee S., Bouamanivong S., Averyanov L.V, & Gale S.W. (2023). Exploring island syndromes: Variable matrix permeability in Phalaenopsis pulcherrima (Orchidaceae), a specialist lithophyte of tropical Asian inselbergs. Frontiers in Plant Science, 14, 1097113.
Publication 2023
Asian Americans Bees Clone Cells Food Forests Gene Flow granite Morphine Permeability Phalaenopsis Plant Leaves Pollen Pollination Population Group Sterility, Reproductive

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More about "Gene Flow"

Gene flow is a fundamental concept in evolutionary biology, describing the transfer of genetic information between populations or species.
This process can lead to changes in the genetic makeup of the recipient population, ultimately driving evolutionary change.
Gene flow occurs through various means, such as pollen dispersal, seed dispersal, or the movement of individuals between populations.
It is a key factor in maintaining genetic diversity within and between populations, with significant implications for conservation biology, agriculture, and the study of speciation.
Arlequin v. 3.5.1.2 is a software package commonly used for the analysis of genetic and population structure data, which can provide insights into gene flow dynamics.
The ECL Western Blotting Detection Kit and RIPA protein lysis buffer are laboratory tools that can be employed to study the expression of genes and proteins involved in gene flow processes. β-actin is a commonly used reference gene or protein for normalization in gene expression studies.
The AllPrep DNA/RNA Mini Kit can be utilized to extract both DNA and RNA from biological samples, enabling the analysis of genetic and transcriptomic changes associated with gene flow.
The ABI BigDye Terminator v3.1 Cycle Sequencing Kit is a popular tool for DNA sequencing, which can be employed to investigate the genetic variation within and between populations.
Polyvinylidene difluoride (PVDF) membranes are frequently used in Western blotting techniques to study protein expression patterns related to gene flow.
By leveraging these tools and techniques, researchers can gain a deeper understanding of the mechanisms and consequences of gene flow, ultimately advancing the fields of evolutionary biology, conservation, and beyond.
PubCompare.ai's AI-driven platform can enhance the reproducibility and accuracy of gene flow analysis by providing access to relevant protocols from literature, preprints, and patents, as well as enabling seamless data analysis and improved research productivity.