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Genetic Drift

Genetic Drift: The random fluctuations in the frequency of gene variants within a population over successive generations.
This stochastic process can lead to the loss of genetic diversity and the fixation of certain alleles, even in the absence of natural selection.
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Most cited protocols related to «Genetic Drift»

1
Evaluating Generalized UniFrac Distances
We use two simulation strategies to evaluate the power of the generalized UniFrac distances under various conditions. The first strategy is a modification of the simulation method proposed by Schloss (2008) (link), where we draw points (16S rRNA sequences) from a 2D circle with known densities (Fig. 1A). This strategy facilitates simulations of different community characteristics such as species evenness and species richness. The Euclidean distance between points is analogous of the genetic distance between the sequences. The diameter of the circle represents the maximum genetic divergence between any pair of sequences within a sample. The area of the circle is proportional to the richness and the density distribution of the circle is proportional to the evenness. By varying the centroid positions (o) and their radius (r), it is possible to vary the fraction of shared membership and species richness within each sample (Fig. 1B and D). By varying the point distribution on the circle (density proportional to rα, where α controls the degree of evenness and α = 0.5 for uniformly distribution), it is possible to change the species evenness (Fig. 1C). We also simulate scenarios where lineages of different abundance levels change by a k fold (Fig. 1E–G). These are achieved by simulating the community with point mass concentrated at the circle center (r1.0) and varying the point density in different regions of the 2D circle corresponding to abundant lineages (0–0.2r from the center; Fig. 1E), moderately abundant lineages (0.4r–0.8r from the center; Fig. 1F) and rare lineages (0.8r–1.0r from the center; Fig. 1G). We further bin the sampled points into small hexagons as ‘OTU’s before calculating the UniFrac distance [‘hexbin’ function from the R package ‘hexbin’ (Carr et al., 2011 )]. The phylogenetic tree of these ‘OTU’s is built using NJ algorithm (Neighbor Joining, ‘nj’ function in R) and rooted by midpoint rooting method. Generalized UniFrac distances are then calculated based on the NJ tree and ‘OTU’ abundances. Each replication consists of drawing 400 points from each community, a bin size of 0.015 units to form ‘OTUs’ (∼ 300 OTUs per sample), and the maximum distance between any two points is 0.3 units (r = 0.15), corresponding to typical phylum level divergence of 30% for 16S rRNA gene. These conditions allow us to simulate the sampling intensity and biodiversity found within a typical 16S rRNA gene targeted sequencing experiment (Schloss, 2008 (link)).

Two simulation strategies to evaluate the generalized UniFrac distances. (A–G), 2D circle-based simulation of microbial communities with different characteristics. (A) The microbial community is represented by a 2D circle. Points are drawn from the circle to simulate the 16S-based sampling process. These points are further binned into small hexagons as OTUs. UPGMA or NJ method is used to build the OTU phylogenetic tree. Six scenarios are investigated, where the difference occurs in: community membership (B), evenness (C), richness (D), most abundant lineages (E), moderately abundant lineages (F) and rare lineages (G). The affected lineages are indicated by a red circle or ring. H, tree-based simulation of microbial communities based on the phylogenetic tree and DM model. A real OTU phylogenetic tree from a throat microbial community dataset is used. These OTUs are roughly divided into 20 clusters (lineages) by performing PAM method using the OTU patristic distance matrix. Each cluster is subjected to abundance change in response to the environment. Counts are generated from a DM model.

The second set of simulations utilize a real upper respiratory tract microbiome dataset consisting of 60 samples and 856 OTUs from Charlson et al. (2010) (link) (Fig. 1H). A common way of modeling multivariate count data is to use the multinomial model. However, the multinomial model assumes fixed underlying proportions for each sample, which do not hold for real microbiome data due to high degree of heterogeneity among the samples. The real OTU count distribution (Supplementary Fig. S1A) exhibits more variance than expected from a multinomial model (Supplementary Fig. S1B). To realistically simulate the data, it is important to model extra-variation or overdispersion of the OTU counts. This can be achieved by using the Dirichlet-multinomial (DM) model (Mosimann, 1962 ), which assumes the underlying proportions of the multinomial model come from a Dirichlet distribution. The density function of a DM random variable N is given as

where is total count, k is the OTU number and proportion mean π = (π1,π2,⋯,πk) and dispersion θ are parameters. When θ = 0, it is reduced to multinomial model. We estimate the DM parameters π,θ using maximum likelihood method (‘dirmult’ function from R package ‘dirmult’). We then generate OTU counts using the DM model with the estimated parameters and 1000 counts per sample. Supplementary Figure S1C shows an OTU heatmap generated by the DM model, in which the overdispersion is similar to that of the real data. To study the power of UniFrac variants for identifying potential environmental factors, we let the abundance of a certain OTU cluster change in response to environment. We use the UPGMA tree of the OTUs based on the OTU distance matrix calculated under the K80 nucleotide substitution model (Felsenstein, 2004 ), QIIME (FastTree algorithm (?)) and partition the 856 OTUs into 20 clusters using Partitioning Around Medoids (PAM) (‘pam’ function from R package ‘cluster’) based on patristic distances (the length of the shortest path linking two OTUs on the tree). These OTU clusters are highlighted in different colors in Figure 1H.
We call the first strategy 2D circle-based simulation and the second tree-based simulation. For power calculation, we use 2000 replications.
Chen J., Bittinger K., Charlson E.S., Hoffmann C., Lewis J., Wu G.D., Collman R.G., Bushman F.D, & Li H. (2012). Associating microbiome composition with environmental covariates using generalized UniFrac distances. Bioinformatics, 28(16), 2106-2113.
Publication 2012
DNA Replication Genes Genetic Drift Genetic Heterogeneity Microbial Community Microbiome Nucleotides Pharynx Radius Reproduction Respiratory System Ribosomal RNA Genes RNA, Ribosomal, 16S Trees
2
Modeling Population Genetics Dynamics
To efficiently simulate the AFS, we adopt a diffusion approach. Such approaches have a long and distinguished history in population genetics, dating back to R. A. Fischer [28] –[30] . The diffusion approach is a continuous approximation to the population genetics of a discrete number of individuals evolving in discrete generations. An important underlying assumption is that per-generation changes in allele frequency are small. Consequently, the diffusion approximation applies when the effective population size is large and migration rates and selection coefficients are of order .
If we have samples from populations, the numbers of sampled sequences from each population are . (For diploids, is typically twice the number of individuals sampled from population 1.) Entry of the AFS records the number of diallelic polymorphic sites at which the derived allele was found in samples from population 1, from population 2, and so forth. (If ancestral alleles cannot be determined, then the “folded” AFS can be considered, in which entries correspond to the frequency of the minor allele.)
We model the evolution of , the density of derived mutations at relative frequencies in populations at time . (All run from 0 to 1.) Given an infinitely-many-sites mutational model [31] (link) and Wright-Fisher reproduction in each generation, the dynamics of for an arbitrary finite number of populations are governed by a linear diffusion equation: The first term models genetic drift, and the second term models selection and migration. Figure 1A illustrates the effects of different evolutionary forces on components of . Time is in units of , where is the time in generations and is a reference effective population size. The relative effective size of population is . The scaled migration rate is , where is the proportion of chromosomes per generation in population that are new migrants from population . (Thus migration is assumed to be conservative [32] (link)). Finally, the scaled selection coefficient is , where is the relative selective advantage or disadvantage of variants in population . Boundary conditions are no-flux except at two corners of the domain, where all population frequencies are 0 or 1; these are absorbing points corresponding to allele loss or fixation. Because the diffusion equation is linear, we can solve simultaneously for the evolution of all polymorphism by continually injecting density at low frequency in each population (at a rate proportional to the total mutation flux ), corresponding to novel mutations.
Changes in population size and migration alter the parameters in Equation 1, while population splits and mergers alter the dimensionality of . For example, if new population 3 is admixed with a proportion from population 1 and from population 2 then where denotes the Dirac delta function. To remove population 2, is integrated over : .
Given , the expected value of each entry of the AFS, , is found via a P-dimensional integral over all possible population allele frequencies of the probability of sampling derived alleles times the density of sites with those population allele frequencies. For SNP data obtained by resequencing, these probabilities are binomial, so In some cases of ascertained data [33] (link), the resulting bias can be corrected by modifying the above equation [11] (link),[34] (link).
Gutenkunst R.N., Hernandez R.D., Williamson S.H, & Bustamante C.D. (2009). Inferring the Joint Demographic History of Multiple Populations from Multidimensional SNP Frequency Data. PLoS Genetics, 5(10), e1000695.
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Publication 2009
Alleles Biological Evolution Chromosomes Diffusion Diploidy Genetic Drift Genetic Polymorphism Loss of Heterozygosity Migrants Mutation Neutrophil Reproduction
3
Weighted LD Analysis of Genetic Diversity
We primarily applied our weighted LD techniques to a data set of 940 individuals in 53 populations from the CEPH–Human Genome Diversity Cell Line Panel (HGDP) (Rosenberg et al. 2002 (link)) genotyped on an Illumina 650K SNP array (Li et al. 2008 (link)). To study the effect of SNP ascertainment, we also analyzed the same HGDP populations genotyped on the Affymetrix Human Origins Array (Patterson et al. 2012 (link)). For some analyses we also included HapMap Phase 3 data (International HapMap Consortium 2010 (link)) merged either with the Illumina HGDP data set, leaving ∼600,000 SNPs, or with the Indian data set of Reich et al. (2009) (link) including 16 Andaman Islanders (9 Onge and 7 Great Andamanese), leaving ∼500,000 SNPs.
We also constructed simulated admixed chromosomes from 112 CEU and 113 YRI phased HapMap individuals using the following procedure, described in Moorjani et al. (2011) (link). Given desired ancestry proportions α and β, the age n of the point admixture, and the number m of admixed individuals to simulate, we built each admixed chromosome as a composite of chromosomal segments from the source populations, choosing breakpoints via a Poisson process with rate constant n, and sampling blocks at random according to the specified mixture fractions. We stipulated that no individual haplotype could be reused at a given locus among the m simulated individuals, preventing unnaturally long identical-by-descent segments but effectively eliminating postadmixture genetic drift. For the short time scales we study (admixture occurring 200 or fewer generations ago), this approximation has little impact. We used this method to maintain some of the complications inherent in real data.
Loh P.R., Lipson M., Patterson N., Moorjani P., Pickrell J.K., Reich D, & Berger B. (2013). Inferring Admixture Histories of Human Populations Using Linkage Disequilibrium. Genetics, 193(4), 1233-1254.
Publication 2013
Cell Lines Chromosomes Genetic Drift Genome, Human Haplotypes HapMap Population Group Single Nucleotide Polymorphism
4
Constructing Hierarchical Population Tree from Genetic Data
Since the fineSTRUCTURE algorithm can identify fine subdivisions, it is often important in practice to have some indication of historical relationships amongst the inferred populations. We have found that performing inference under the full model using successively reducing values of (as is commonly done in ADMIXTURE and related algorithms) does not always perform well in this setting, e.g. by splitting off highly drifted groups. Instead, we recommend an approach that performs inference at the ‘natural’ (i.e. inferred) value of , and then generates a tree of relationships amongst these populations. We start with the maximum a posteriori (MAP) state, found by taking the MCMC iteration with the highest observed posterior likelihood and then performing a number of additional hill-climbing moves to identify any merges or splits that further improve the posterior probability. Starting from this ‘best’ partition, we successively merge populations, choosing the merge giving the highest probability for the merged group at each step, resulting in a bifurcating tree relating each of the populations together. One of the biggest discriminators between populations is within-population counts, which largely reflect genetic drift occurring after a split from other groups, and are thus uninformative in choosing among group merges. In order to allow populations that contain related individuals (i.e. with high ) to be merged more easily, during the tree creation we replace the count matrix with a modified count matrix with diagonal ‘flattened’ to be the next highest value in the row, where and . Although this ad hoc approach provides a key advantage over inference at specific for locating functional population units, we emphasize that this tree is not based on any model of population differentiation. Results may depend significantly on sample size, and so should be treated as an approximate guide to similarity, rather than a full population history. Despite these caveats, the tree empirically performs well in capturing relationships at multiple cases when the data is approximately hierarchical.
Lawson D.J., Hellenthal G., Myers S, & Falush D. (2012). Inference of Population Structure using Dense Haplotype Data. PLoS Genetics, 8(1), e1002453.
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Publication 2012
Genetic Drift Trees
5
Comparative Analysis of FST Estimators
FST was introduced by Wright (1951) as a measure of correlation of gene frequencies und suggested the first and simplest estimator, FSTW. For one allele at locus k, where is the observed variance of allele frequencies pi among the sampled populations i (i = 1, …, r) and is the mean allele frequency over all populations. The estimate of FSTW for multiple loci is calculated by taking the mean across k loci. This estimator has a theoretical range between zero and one and is known to overestimate the level of genetic differentiation especially at low values [18] (link).
The second estimator tested, FSTW&C, preserves Wright's definition of FST in terms of correlation of gene frequencies and is the most widely used estimators (cited approx. 7,000 times, source: Web-of-Science) [9] , [12] . It was proposed by Weir & Cockerham (1984) [18] (link), who showed that it provides a nearly unbiased estimate of FST at moderate population sample size (n = 15, 20 and 25) and small number of loci (k = 10). The estimates can also have negative values which do not have a biological meaning [19] , but they can compensate for overestimates especially at low levels of genetic differentiation. At a single locus k, FSTW&C is defined as where
Here, s2 is the observed variance of allele frequencies, n is the number of individuals per population, is the mean allele frequency over all populations, r is the number of sampled populations and is the mean observed heterozygosity. The overall estimate from all k loci is derived by
Recently, Reich and colleagues (2009) proposed a new unbiased estimator, FSTR, for bi-allelic loci and pairwise population comparison. In their study they used a very high number of loci, but small sample sizes per population. Therefore, we decided to test this estimator as well. Again FSTR is calculated as follows

where u is the allele count for population 1, v is the allele count for population 2, t and s are the total number of individuals for population 1 and 2, respectively [14] (link). The parameter is an unbiased estimate of the expected heterozygosity. An estimate over many loci is given by
Willing E.M., Dreyer C, & van Oosterhout C. (2012). Estimates of Genetic Differentiation Measured by FST Do Not Necessarily Require Large Sample Sizes When Using Many SNP Markers. PLoS ONE, 7(8), e42649.
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Publication 2012
Alleles Biopharmaceuticals Genetic Drift Heterozygote

Most recents protocols related to «Genetic Drift»

1
Genome-wide Association Study of Morph Coloration
We used plink v1.90 [76 (link)] for GWAS analysis. The SNP sites with minimum allele frequency (MAF) < 0.1, deletion rate of all individuals > 0.1, and Hardy Weinberg p < 10−5 were filtered, with green morphs as the control group and yellow morphs as the experimental group. GWAS analysis was performed using Fisher’s exact test with parameters “- assoc fisher”. Genetic differentiation (Fst) between the two morphs was calculated using a sliding window approach (window size 10 kb with step size 5 kb) using VCFtools v0.1.17 [77 (link)]. The analysis results were visualized using the R package qqman v0.1.4 [78 (link)].
Tang C.Y., Zhang X., Xu X., Sun S., Peng C., Song M.H., Yan C., Sun H., Liu M., Xie L., Luo S.J, & Li J.T. (2023). Genetic mapping and molecular mechanism behind color variation in the Asian vine snake. Genome Biology, 24, 46.
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Publication 2023
Deletion Mutation Genetic Drift Genome-Wide Association Study Morphine
2
Molecular Identification and Population Genetics of Fish
The captured fish were immediately preserved on ice and stored frozen at -20 °C until processing in the laboratory. For the molecular analysis, about 200 mg muscle tissues of each fish were collected and preserved in 95% ethanol, and stored at -20 °C until DNA extraction. For all specimens, genomic DNA was extracted from the muscle using a commercial DNA extraction kit (TIANGEN Biotech) following the manufacturer’s protocol. Total length (TL, to nearest mm) of fish was recorded. The specimen was first identified up to the species level with morphological methods, followed by the mtDNA gene, cytochrome C oxidase I (COI), as a genetic marker for species identification. To amplify the partial mtDNA COI fragment, PCR was carried out using the previously universal primers for COI, following the conditions as described41 (link). Each 25 μL PCR reaction system contained 2.5 μL of 10 × Buffer, 2.0 μL of dNTPs, 0.5 μL forward primer (10 mM), 0.5 μL backward primer (10 mM),0.5 μL of template DNA, 0.2 μL of Taq (TaKaRa), and 18.8 μL of ddH2O. The PCR thermocycling conditions were as follows: initial denaturation at 94 °C for 4 min followed by 30 cycles of denaturation at 94 °C for 30 s, annealing at 50 °C for 30 s, extension at 72 °C for 45 s, and final extension at 72 °C for 5 min. The quality of each PCR product was assessed by 1% agarose gel electrophoresis and observed under the UV light. All qualified PCR products were submitted for sequencing. Each COI sequence were aligned using Clustal W, and the alignments were subjected to population genetic structure analysis.
The level of genetic divergence between populations was evaluated by the genetic differentiation index (FST) between each pair of populations using Arlequin 3.5. The statistical significances of the pairwise FST values were evaluated through 1000 permutations. The haplotype network was constructed based on median-joining algorithm in PopART 12 to estimate the gene genealogies at the population level.
Xuan Z, & Wang W.X. (2023). Diversity of life history and population connectivity of threadfin fish Eleutheronema tetradactylum along the coastal waters of Southern China. Scientific Reports, 13, 3976.
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Publication 2023
Buffers DNA, Mitochondrial Electrophoresis, Agar Gel Ethanol Fishes Freezing Genes Genetic Drift Genetic Markers Genetic Structures Genome Haplotypes Muscle Tissue Oligonucleotide Primers Oxidase, Cytochrome-c Population Group Ultraviolet Rays
3
Mutation and Evolution of Cell Parameters
For mutation of maxact and λact parameters of a given cell, parameter values x were first log-transformed and subsequently mutated with a random error term:
xmut=lnx+ϵ
where
ϵ∼N(μ=0,σmut)
using σmut = 0.6 for the first 5 simulations and σmut = 0.2 afterwards. The higher initial choice of σmut is for efficiency reasons only; note that at the initial low values of λact and maxact, cells do not actively move and slight changes in these parameter values can therefore not affect fitness. Only through genetic drift do cells escape this “fitness plateau” into the motile regime where λact and maxact are high enough for migration. Choosing a higher initial σmut speeds up this process. It does not affect the main results or conclusions from the simulation but simply reduces the number of generations it takes for cells to escape the fitness plateau and start evolving.
Wortel I.M, & Textor J. (2023). Interpreting T-cell search “strategies” in the light of evolution under constraints. PLOS Computational Biology, 19(2), e1010918.
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Publication 2023
Cells Genetic Drift Mutation
4
Genomic Analysis of PfGARP in Malaria Parasites
Sequence analysis included 80 nucleotide sequences of PfGARP from Thai isolates, one clinical isolate from Guinea (isolate MDCU32) and 18 publicly available complete gene sequences whose isolate names, country of origins and their GenBank accession numbers are as follows: 3D7 (Netherlands from West Africa, AL844501), CD01 (Congo, LR129686), Dd2 (Indochina, LR131290), FC27 (Papua New Guinea, J03998), FCC1/HN (Hainan in China, AF251290), GA01 (Gambia, LR131386), GB4 (Ghana, LR131402), KH1 (Cambodia, LR131418), KH2 (Cambodia, LR131306), HB3 (Honduras, LR131338), IGH-CR14 (India, GG6656811), IT (Brazil, LR131322), KE01 (Kenya, LR131354), ML01 (Mali, LR131481), SD01 (Sudan, LR131466), SN01 (Senegal, LR131434), TG01 (Togo, LR131450), and UGT5.1 (Vietnam, KE124372). Of these, the 3D7, FC27and FCC1/HN sequences were determined by Sanger dideoxy-chain termination method whereas the remaining isolates were assembled sequences from next-generation sequencing platforms (Supplemental Table S1). Sequence alignment was performed by using the CLUSTAL_X program, taken into account appropriate codon match in the coding region by manual adjustment to maintain the reading frame. The sequence from the FC27 strain was used as a reference6 (link). Searching for nucleotide repeats was performed by using the Tandem Repeats Finder version 4.0 program with the default option. Nucleotide diversity (π), the rate of synonymous substitutions per synonymous site (dS) and the rate of nonsynonymous substitutions per nonsynonymous site (dN) were determined from the average values of sequence differences in all pairwise comparison of each taxon and the standard error was computed from 1000 bootstrap pseudoreplicates implemented in the MEGA 6.0 program41 (link). Haplotype diversity and its sampling variance were computed by taking into account the presence of gaps in the aligned sequences using the DnaSP version 5.10 program42 (link). Natural selection on codon substitution was determined by using fast unconstrained Bayesian approximation (FUBAR) method in the Datamonkey Web-Server43 (link),44 (link). Neighbor-joining phylogenetic tree based on nucleotide sequences was constructed by using maximum composite likelihood parameter whereas maximum likelihood tree was built using Tamura-Nei model with the rate variation model allowed for some sites to be evolutionarily invariable. The Arlequin 3.5.2.2 software was deployed to determine genetic differentiation between populations, the fixation index (FST), using analysis of molecular variance approach (AMOVA) akin to the Weir and Cockerham’s method but taken into account the number of mutations between haplotypes45 (link). One hundred permutations were deployed to determine the significance levels of the fixation indices. Prediction of linear B cell epitopes in PfGARP was performed by using a sequence similarity to known experimentally verified epitopes from the Immune Epitope DataBase (IEDB) implemented in the BepiBlast Web Server11 (link). Furthermore, linear B cell epitopes were also predicted based on protein language models implemented in BepiPred-3.012 (link). Potential HLA-class II-binding peptides were analyzed by using the IEDB recommended 2.22 algorithm with a default 12–18 amino acid residues option. The predicted HLA-class II-binding peptides were predicted based on the percentile rank < 10 and the IC50 threshold for HLA binding affinity ≤ 1000 nM14 (link). The analysis mainly concerned the common HLA class II haplotypes among Thai populations with allele frequency > 0.113 (link).
Rojrung R., Kuamsab N., Putaporntip C, & Jongwutiwes S. (2023). Analysis of sequence diversity in Plasmodium falciparum glutamic acid-rich protein (PfGARP), an asexual blood stage vaccine candidate. Scientific Reports, 13, 3951.
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Publication 2023
Amino Acids Codon Epitopes Epitopes, B-Lymphocyte Genes Genetic Drift Haplotypes Hereditary Nonpolyposis Colorectal Cancer Type 1 Mutation Natural Selection Nucleotides Peptides Population Group Proteins Reading Frames Sequence Alignment Sequence Analysis Strains Tandem Repeat Sequences Thai Trees
5
Phylogenetic Analysis of Viral Sequences in Aquatic Samples
We used a phylogenetic approach to taxonomically assign our putative viral sequences and infer their evolutionary histories. First, we aligned our putative viral sequences with complete sequences of related viruses available on NCBI/GenBank (August 2021) using MAFFT v.7.450, with the E-INS-i algorithm (Katoh and Standley 2013 (link)). While some of these sequences were partial, they are adequate for phylogenetic analysis (Geoghegan et al. 2021 ). We used background sequences from the similarity search as well as additional sequences listed by the International Committee of Viral Taxonomy (ICTV) (https://talk.ictvonline.org) for each viral family. These included the conserved RNA-dependent RNA polymerase (RdRp) for RNA viruses and the DNA polymerase and major capsid protein for DNA viruses. The amino acid sequence alignment was trimmed using TrimAl v.1.2 to remove ambiguously aligned regions with a gap threshold of 0.9 and a variable conserve value (Capella-Gutierrez, Silla-Martinez, and Gabaldon 2009 (link)). The best-fit model of amino acid substitution was estimated with the ‘ModelFinder Plus’ (-m MFP) flag in IQ-TREE (Nguyen et al. 2015 (link); Kalyaanamoorthy et al. 2017 (link)). Using these data, we estimated phylogenetic trees using a maximum likelihood approach with 1,000 bootstrap replicates using IQ-TREE. Trees were annotated using FigTree v1.4.4 (http://tree.bio.ed.ac.uk/software%20/figtree/).
Phylogenetic position and level of sequence similarity were used to determine whether a viral sequence was likely infecting fish (i.e. ‘vertebrate-associated’) or derived from diet or environment (i.e. ‘non-vertebrate’): the latter often exhibits considerable genetic divergence and hence are phylogenetically distinct (Shi et al. 2018 (link); Zhang et al. 2018 (link); Costa et al. 2021 ; Geoghegan et al. 2021 ). Vertebrate-associated viral sequences were classified as novel species according to similarity thresholds for each viral family as specified by the ICTV.
Costa V.A., Bellwood D.R., Mifsud J.C., Van Brussel K., Geoghegan J.L., Holmes E.C, & Harvey E. (2023). Limited cross-species virus transmission in a spatially restricted coral reef fish community. Virus Evolution, 9(1), vead011.
Publication 2023
Amino Acids Amino Acid Substitution Biological Evolution Capsid Proteins Diet DNA-Directed DNA Polymerase DNA Viruses Fishes Genetic Drift Ribs RNA-Directed RNA Polymerase RNA Viruses Satellite Viruses Sequence Alignment Speech Trees Vertebrates

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More about "Genetic Drift"

Genetic drift is a stochastic evolutionary process that describes the random fluctuations in the frequency of gene variants within a population over successive generations.
This phenomenon can lead to the loss of genetic diversity and the fixation of certain alleles, even in the absence of natural selection.
Arlequin, a software package for the analysis of genetic data, is a powerful tool that can be used to study the effects of genetic drift.
Arlequin version 3.5.1.2 and Arlequin version 3.5 are two commonly used versions of this software.
These tools allow researchers to perform a variety of analyses, including the calculation of genetic diversity indices, the estimation of population structure, and the testing of hypotheses related to genetic drift.
The TRIzol reagent and the RNeasy Mini Kit are commonly used for the extraction and purification of RNA, which is often used in genetic studies.
These techniques, combined with the use of Arlequin software, can provide valuable insights into the mechanisms of genetic drift and its impact on genetic diversity.
In addition to these tools, researchers may also utilize other common laboratory techniques, such as the use of penicillin/streptomycin and FBS (fetal bovine serum), to maintain and culture cells for genetic analyses.
PubCompare.ai is an innovative platform that can help researchers optimize their research protocols and locate the best protocols from literature, pre-prints, and patents through intelligent comparisons.
This can boost reproducibility and research efficiency, allowing researchers to better understand the power of genetic drift and its implications for their work.
By leveraging these tools and techniques, researchers can gain a deeper understanding of the complex processes involved in genetic drift, and use this knowledge to inform their research and drive scientific progress.
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