Genetic Diversity - Science topic

Any research about disease or insect resistance should take consideration on gene behavior. Ten decide the next step of gene editing procedure as human doctor: diagnosis problem and suggestion solutions. The question knocked door of general trends of genetic engineering application in agriculture and you should be more specific in your question of application genetic engineering in pest resistance in plants

1. Identifying Adaptive Traits

Genomic Data Analysis: Through whole-genome sequencing and other genomic approaches, scientists can identify specific genes or genomic regions associated with traits that enhance resilience to environmental changes, such as tolerance to temperature fluctuations, salinity levels, oxygen availability, and other stressors related to climate change.

Marker-Assisted Selection: By pinpointing genetic markers linked to these adaptive traits, conservationists and fisheries can focus on breeding programs that enhance the prevalence of these traits in fish populations, improving their ability to cope with future changes.

2. Understanding Population Structure and Connectivity

Genomic Data for Population Structure**: Genomic data helps determine the genetic structure of fish populations, including how subpopulations are connected or isolated. This information is critical for managing fish populations under climate change scenarios because isolated populations may have reduced genetic diversity and, therefore, a lower adaptive potential.

Conservation Strategies: By understanding which populations are genetically vulnerable due to low diversity or isolation, conservation efforts can be directed toward maintaining genetic flow between populations through habitat corridors or restocking from genetically diverse populations.

3. Monitoring Evolutionary Responses

Real-time Genomic Monitoring: Genomic tools allow researchers to monitor ongoing evolutionary changes in response to environmental stressors. This helps track how fish populations are adapting to changes such as rising sea temperatures, ocean acidification, or altered food availability.

Predictive Models: Genomic data can be used to create predictive models of how fish populations might respond to future environmental changes, enabling proactive conservation measures.

Genetic engineering plays a complex and multifaceted role in environmental conservation and biodiversity:

Positive contributions:

1. Conservation of endangered species: Genetic engineering can help increase population sizes and genetic diversity of endangered species.

2. Disease resistance: Engineered organisms can be designed to resist diseases, reducing the impact of disease outbreaks on populations.

3. Invasive species management: Genetic engineering can be used to control invasive species populations, mitigating their impact on native ecosystems.

4. Climate change mitigation: Engineered organisms can be designed to sequester carbon, produce biofuels, or enhance ecosystem resilience.

5. Bioremediation: Genetically engineered organisms can clean pollutants from contaminated environments.

Concerns and challenges:

1. Unintended consequences: Engineered organisms can have unforeseen effects on ecosystems, potentially disrupting delicate balances.

2. Gene flow: Genetically engineered organisms can interbreed with wild relatives, potentially altering native species' genetics.

3. Loss of biodiversity: Over-reliance on engineered organisms could lead to reduced genetic diversity within species.

4. Regulatory frameworks: Existing regulations may be inadequate to address the unique challenges of genetic engineering in conservation.

5. Public acceptance: Genetic engineering in conservation can be controversial, requiring careful consideration of societal values and concerns.

To maximize benefits and minimize risks, it's essential to:

1. Conduct thorough risk assessments

2. Develop robust regulatory frameworks

3. Encourage transparent communication and public engagement

4. Foster interdisciplinary collaboration

5. Monitor and adapt to emerging challenges and opportunities

By acknowledging both the potential benefits and concerns, we can harness genetic engineering as a tool to support environmental conservation and biodiversity while minimizing its risks.

Yes, cryopreservation at minus 18 C with low oxygen :)

Horizontal gene transfer (HGT) is a process by which genetic material is transferred between organisms that are not parent and offspring. It plays a significant role in shaping the genetic diversity of microorganisms in several ways:Introduction of Novel Genes: HGT allows microorganisms to acquire novel genes from other organisms or environments. These genes may confer new functionalities, such as antibiotic resistance, virulence factors, or metabolic capabilities, enhancing the adaptive potential of the recipient microorganism.and Rapid Evolution: HGT enables microorganisms to rapidly acquire genetic variation without relying solely on traditional vertical inheritance from parent to offspring. This rapid exchange of genetic material facilitates the adaptation of microorganisms to changing environments, including the emergence of new pathogens or the development of resistance to environmental stresses.and Genetic Recombination,Dissemination of Adaptive Traits,Facilitation of Evolutionary Innovation

For genetic diversity analysis, several statistical methods can be used, depending on the specific goals and data available. Some commonly used methods include: 1. Principal Component Analysis (PCA): PCA is used to reduce the dimensionality of genetic data while preserving important variation. It can help visualize genetic relationships between individuals or populations. 2. Cluster Analysis: Methods like hierarchical clustering or model-based clustering (e.g., STRUCTURE) can group individuals or populations based on genetic similarity, providing insights into population structure and admixture. 3. F-statistics (FST, FIS, FIT): These statistics quantify genetic differentiation, inbreeding, and total genetic diversity, respectively. They are often used in conjunction with other methods to assess genetic diversity and population structure.

Hii

Which method is used for SSR genotyping?

yes,

But sometimes matter experiment conditions so you check your experiment setup and regents and use a higher annealing temperature.

Holistic healing approach is the most beneficial for all health issues.

I am unsure what is the broad intellectual reason for this inquiry? This kind of comparison of alleged separation of ethnic groups is belied by both the past bottleneck in hominin population sometime between ~800k-900k years ago and the apparent recent interbreeding among all extant human lineages so that we are all very closely related and there are only 5-15% of differences between any two individuals on earth. Those minor differences do not support the old 20th century concept of "race" as purported meaningful geographic separation of human populations. Instead, it underscores the how similar all living humans are to each other, and those minor variations are literally only skin deep differences between lineages exposed to different UV, temperature, and humidity regimes, etc. All of us, regardless of our ethnicity, are more related to every other human on earth than chimpanzees are related to all the other chimpanzees, despite their smaller population size and geographic distribution. What is the importance of your research question? Are these assumptions of more discrete past ethnic groups not reiterating some of the baggage from the old, and fortunately outdated, concept of "race" that alleged discreteness of breeding interactions within nd between particular geographic regions? Whatever past separations did exist between populations, clearly it was no barrier to significant interbreeding that makes all living humans essentially the same.

Muhammad Umar More specifically, I plan to leverage my white privilege to create a more equitable healthcare system.

Hi

It seems that PopART masks all N or gappy sites. I also had the same issue.

DEV

Take a look at this: https://www.biostars.org/p/80014/

People usually suggest excluding monomorphic loci from STRUCTURE analysis because there is no differentiation or any variation in these loci and they bring no information on ancestry. This means that using a dataset with some monomorphic loci versus excluding them would lead to the same results (or with negligible differences?). So, it just saves time.

However, if we are interested in the real state of the populations/localities, it seems reasonable not to exclude them because we have always only some random(?) proportion of a genome. I think it is the same as with sampling individuals from a population - different sets lead to different estimates of an average, for example, and the point is to maximise the sample sizes to improve the accuracy of estimates. Still, not sure...

By computer modelling those parameters and observing yield in response to varying combinations of traits :)

Gbolabo Olaitan Onasanya thank you very much for this answer, this will be considered. again, thank you.

Pre-breeding aims to isolate desired genetic traits (e.g. disease resistance) from unadapted material like CWR and introduce them into breeding lines that are more readily crossable with modern, elite varieties. Pre-breeding broadens the elite genepool by re-capturing lost beneficial genetic diversity. Pre-breeding provides a unique opportunity, through the introgression of desirable genes from wild germplasm into genetic backgrounds readily used by the breeders with minimum linkage drag, to overcome this.

Prebreeding has become quintessential in the present world of breeders. The improved lines have either been utilised to full extent or exhausted due to non conservation of the germplasm. Morphological diversity is to be used for developing mitigating new challenges.

The concept of inbreeding depression refers to the decline in the overall fitness and performance of offspring when closely related individuals, such as siblings or parent-offspring, are bred together over multiple generations. Inbreeding depression occurs due to the increased expression of harmful or deleterious recessive alleles that are normally masked in genetically diverse populations.

In plant breeding, inbreeding depression has significant implications. When plants are bred within a small, genetically similar population, the frequency of homozygous genotypes increases. Homozygosity can expose recessive alleles that may be associated with reduced vigor, impaired growth, lower fertility, susceptibility to diseases, or other undesirable traits. As a result, inbred plants often exhibit decreased overall fitness and performance compared to their genetically diverse counterparts.

Dear Professor,

I think it is not available as open access. I can suggest another application if you, please reveal the purpose.

Thank You

It looks like you have many clearly differentiated populations. I'm not sure what you'd like interpreted and I'd need more context to comment properly. What is the data and what is your question?

For RAPD markers, the genetic background is not always known, so if you want to calculate some similarity index from gel images, I suggest scoring the bands as present or absent. The resulting data can be used to calculate e.g. a Jaccard index. If you need software support, I recommend MolMarker.

Reference: https://www.mdpi.com/1424-2818/14/6/497

Avaiable at: https://sourceforge.net/projects/molmarker/

Crop wild relatives are a critical source of adaptive traits/genes, including resistance to diseases, pests and stresses such as drought and extreme temperatures that can be used in plant breeding, with the potential to enhance sustainable food security in the face of challenges such as climate change and population growth.

Including an outgroup in phylogenetic tree construction is important because it helps to root the tree and determine the direction of evolution. The outgroup is chosen based on its evolutionary distance from the ingroup, meaning it is a taxon that is closely related to the ingroup but not a member of it. The inclusion of the outgroup can affect the placement of the other taxa in the tree, including its position within or outside of the ingroup.

The difference in results between the two methods you used, neighborhood joining and maximum likelihood, could be due to the different algorithms and assumptions used in each method. It is not uncommon for the placement of the outgroup to affect the position of the ingroup in the tree, and the results from each method should be evaluated and compared to each other and to the available literature to determine the most reasonable placement of the taxa.

If you are concerned about the placement of the outgroup and its effect on the results, you could try using a different outgroup that is more distantly related to the ingroup or choose a different method that may be less affected by the outgroup. However, it is important to keep in mind that the choice of outgroup and method should be based on sound evolutionary and statistical principles.

Absolutely delighted to do so. Let me know if you have any other questions.

You can't do much with two haplotypes and another species as an outgroup; you should consider looking up old museum and archaeological materials on your animal of study to genotype them and learn how variability has changed over time.

please let me know if u solved the issue

It depends on the level and what you mean by genetic diversity. Within a single organism? No. Within a population? No. Within a species? Normally not. Between species - yes, but that would be genetic diversity in the sense of species diversity.

Cheers

Konrad

It surely depends on what type and range of organisms you are studying. The diversity we observe in HIV-1 in one single infected individual over ten years is greater than the diversity we observe between different species of insects or mammals over millions of years. The diversity between viruses today in the global SARS-CoV-2 pandemic varies with time, depending on whether two major lineages such as Delta and Omicron are co-circulating or Omicron has taken over and delta died out.

The diversity in insect or birds or shellfish is different than the diversity in mammals. You should compare the values you have in your organism to the values from other similar organisms.

hello Samaun Parvez

don't get confused with ploidy level and multiple allele. traits exhibition depends on allelomorph of candidate gene not on its ploidy level.

solution for your confusion is go for binary coding (presence or absence) in Darwin.

ex. for scoring

sample Primer 1 Primer 2

210 220 250 310 320 350

1 1 0 1 1 1 1

2 1 1 1 1 1 1

3 1 1 1 0 1 1

similarly in case GenAlEx (based on band weightage)

Dear Brandon,

I hope this article will be of help for you. Best regards,

Noemi

Have you tried directly from this link?: http://www.ucm.es/info/prodanim/html/JP_Web_archivos/Molkin30.zip

Thank you so much for your answer Professor

See if the attached screenshot paper is of help to you. Best wishes, David Booth

Dear shi

You should be using Arlequin program Pop gene program to calculate PIC

Heterozygosity is a measure of variability (diversity). Under no dominance situation, a population has maximum variability for a biallelic gene if the gene frequency is 0.5 (heterozygosity=0.5). In your case, heterozygosity is extremely low, and therefore, the diversity is very low.

Anoop Singh This is a method that we used for 20 years. Sorry, there is no reference for it.

All of what colleagues wrote from the programs are effective and good at parsing SSR

And I think the best modern software is STRUCTURE

Mega and Power Marker

John Paul Manamtam Payopay : Yes, if you prepare data.

Hi Sheila

Please contact us

Dear Lidia Samarina

Thank you very much, I have sent you some queries.

Whether this gene (or any gene) is appropriate for your study must be determined empirically and tends to be taxon-specific: for example, there is little variation within populations in the North American darters (Percidae), but fixed differences are common between populations; conversely, there is enough variation in many of the cyprinid species I have worked with. Note that Cytochrome b was (and is still) used intensively in North American Ichthyology since the early 1990s - and this literature would provide a very useful set of references to put your data in context. The more prominent studies using cytb were phylogeographic in nature (differences among conspecific populations), although some (including myself) have used cytb for intrapopulational work when appropriate. Note that much of the variation within species will be at the third codon position.

Genotyping and Quality Control

ORIGINAL RESEARCH article

Front. Genet., 10 October 2018 | https://doi.org/10.3389/fgene.2018.00438

Samples were genotyped at Geneseek (Neogen Corporation, Nebraska, United States) using the Geneseek Genomic Profiler (GGP) High Density (HD) SNP array consisting of 150,000 SNPs, while SNPs for the reference breeds had been genotyped with the Illumina HD Bovine (777K SNPs) array. The SNPs in GGP array were optimised for use in dairy cattle having the most informative SNPs from Illumina Bovine 50k and 770k chips and additional variants known to have a large effect on disease susceptibility and performance. Genotype data quality control and cheques were carried out using PLINK v 1.9 (Purcell et al., 2007) and included removal of SNPs with less than 90% call rate, less than 5% minor allele frequency (MAF) and samples with more than 10% missing genotypes. Additional removal of SNPs not mapped to any chromosome left a total of 120,591 SNPs for analysis. Of the 299 animals, 12 failed the above outlined quality cheques and were removed from the analysis. Total genotyping rate in remaining samples was 0.991. The 120,591 SNPs used in the analysis covered 2516.25 Mb with an average distance of 22.67 kb between adjacent SNPs. The mean chromosomal length ranged between 42.8 Mb on BTA 25 and 158.86 Mb on BTA 1. The mean length of adjacent SNPs per chromosome ranged between 18.67 and 23.89 kb on BTA 14 and BTA 29, respectively. The linkage disequilibrium (LD) across the genome averaged 0.41. Private alleles, defined as variants which are segregating in only one population when evaluating multiple populations, were identified using a custom script in R. A total of 143 private variants, most (132) of which originated from the Rwanda cattle population were detected.

Vast literature is available.

Check the following. Good Luck!!

Universal Primers for Amplification of the Complete Mitochondrial

Control Region in Marine Fish Species

(https://link.springer.com/content/pdf/10.1134/S0026893312040024.pdf)

Length variation and sequence divergence in mitochondrial control region of Schizothoracine (Teleostei: Cyperinidae) species

(https://www.tandfonline.com/doi/pdf/10.3109/19401736.2014.945581?needAccess=true)

Genetic diversity of mitochondrial control region (D-Loop) polymorphisms in Coilia ectenes taihuensis inhabiting Taihu Lake, China

(http://www.funpecrp.com.br/gmr/year2017/vol16-1/pdf/gmr-16-01-gmr.16019457.pdf)

You can also choose to study the diversity, evolutionary phylogenetics or domestication of a species. Large stractural varitions on genetic disease is also a choice.

Good, definitely you can see variation exists. But need to check with different Primers as some of the primers do not show any variation.

Probably you might have started screening.

Good Luck!

Mauro Santos

Dear Santos

Many thanks for your help, but I don't how to install lositan.

Hi!

You got the answer.

Simply to show/prove populations are phylogenetically distant from other species three regions of mtDNA viz. cytochrome b, 16S RNA and and D-loop regions will be used.

Dear @Rajasekhar Chowdary Duddukur As suggested by @Suyash Bhimgonda Patil, I also suggest you to access online portal of OP Stat for analysis of genetic diversity, character association, path coefficient analysis and other plant breeding experimental data. It is useful, and I have used it several times.

Best wishes, AKC

Thank you Ashraf M. T. Elewa , your suggestion help me a lot

Hi Marco,

Maybe these approaches are suitable for you:

Genetic landscape shape interpolation analysis (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0128559; https://academic.oup.com/jmammal/article/96/6/1275/1173786?login=true; https://www.hindawi.com/journals/bmri/2016/5792708/)

Spatial network of genetic variation (https://www.nature.com/articles/s41598-017-09154-9)

Genetic networks obtained from POPGRAPH (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0194901; http://dyerlab.github.io/popgraph/)

Barrier analysis (for geneflow patterns)(https://bmcecolevol.biomedcentral.com/articles/10.1186/s12862-019-1510-4)

Spatial GAMM (https://onlinelibrary.wiley.com/doi/abs/10.1111/ele.13394)

for simple interpolation (https://bmcecolevol.biomedcentral.com/articles/10.1186/s12862-016-0810-1; https://onlinelibrary.wiley.com/doi/10.1111/jbi.13574)

Also, maybe useful:

Thank you so much ! Mauro Santos

Thank you Dr Brandon Singhfor this important question about the causes of loss of apple diversity.

In a 2010 report, the FAO estimated that three quarters of crop diversity had been lost between 1900 and 2000, stressing that "the genetic diversity of the plants we grow and consume - and related wild species - could be lost forever, thus compromising future food security ”.

In the specific case of apple and according to Kathrin Strohm (2013),

of the 30,000 apple varieties found all over the world only 30 are used and traded commercially. In the case of United States, it is clear that the biodiversity of apples has declined since the rise of industrial agriculture confirmed Rossi Anastropoulo (2014). Like any commercial industry, the focus in apple production remained fixed on profit. Therefore, in order to increase their crop yields, apple growers began to emphasize traits that would produce a good harvest and make transportation more feasible. These traits include disease resistance, hardiness, and durability, and the high importance placed on these characteristics slowly began to replace any focus on taste. The apple industry did not focus on selling apples that would taste best or even function well in the kitchen, but instead focused on varieties that would make the most money in the marketplace, added Anastropoulo (2014).

The same author mentionned that the apples have unique genetic codes, and once they have disappeared, they are gone forever, if not saved by apple detectives.

Despite the fact thant apple is the favorite fruit of Europeans, the number of commercial varieties collapsed in the 20th century. According to Kathrin Strohm (2013), only 13 apple varieties covered nearly 72 % of the entire European apple acreage in 2007.

In the special case of France, several causes mean that the diversity of apples has decreased. For example, it is said that "all misshapen, dull or stained apples are prohibited for sale". Thus, according to a president of a French association working to save local apples, France imports apples, beautiful and colorful, ruling out a large available local panel, not falling within these standards.

On the other hand, and still in the case of France, it is said that even if local varieties of apples appeal to consumers, tree growers are hardly interested in these old varieties. They want to tonnage, with apples sized, trees planted tightly, treated, suitable for irrigation, chemical thinning, conservation, and which produce every year.

These links could help you to take some informations that you need. Good luck.

You might build a dendrogram/network based on allele shared distances (across codominant loci) for this single population. Maybe check the documentation of the R package "poppr" if this measurement for genetic distance is covered, it's nicely documented by the authors: https://grunwaldlab.github.io/Population_Genetics_in_R/index.html

CNVs are usually detected by techniques like chromosomal microarray and MLPA. But nowadays, advanced NGS technologies enable also CNV detection along with SNV detection. The Read depth and Pair end mapping/End-pair mapping are usual algorithms used for CNV detection using several statistical models or clustering approach. Markov model is also used. CNVs can affect 15% of the human genome and lengths can vary from 50 bp to 1 Mbp. They can also be intragenic. Decrease in copy at particular locus is deletion and increase in copy can be duplication or triplication..

thank you so much

Hi guys, Thanks for your concerns!

Even though this problem seems only happened to me due to my shallow knowledge in this field. But in case someone looking for the answer to a similar question. Below is my solution.

First, I calculated the haplotype diversity (and nucleotide diversity as well) for two of my populations (by using dnaSP). Then I got the mean values of Hd and their standard deviation values. Now, I can compare these two means by using tsum.test function in "BSDA" R package (t-test based on summary data). Below is the R code.

-----

install.packages("BSDA") #install package BSDA from CRAN

library(BSDA) #load package BSDA

tsum.test(mean.x = NULL, s.x = NULL, n.x = NULL, mean.y = NULL, s.y = NULL,

n.y = NULL, alternative = "two.sided", mu = 0, var.equal = FALSE,

conf.level = 0.95)

------

#Now, just put my values into the code line and RUN.

#mean.x = single number representing the mean of x population

# a single number representing the sample standard deviation for x

#n.x = a single number representing the sample size for x

# mean.y= a single number representing the sample mean of y

#s.y = a single number representing the sample standard deviation for y

#n.y = a single number representing the sample size for y

You can also do ANOVA based on summary data using (ind.oneway.second) function in ‘rpsychi’

I'm certain I answered this question yesterday... You can use a GenAlEx genetic distance Tri-matrix file which needs to be converted to a MEGA file and saved as a *.meg file. Otherwise, MEGA will not open it...Try that Aijaz..

So you have a genetic distance file, (tri matrix or sq matrix?). Tick the Tri-matrix option then export as a MEGA file. You need to save it as a '.meg' file? If not, MEGA won't open it.. When selecting which tree to open, tick the genetic distance, Tri matrix option...

Binary data matrix of two different markers has to be combined into one input file. Genelyx software is much easier to analyse. After getting result of Nei's genetic diversity of populations, u can transfer the data to mega for dendrogram construction.

Hi. there,

I can suggest you to calculate the genetic variability within each locality or environmental condition, and then perform CCA. (Canonical correspondence analysis), which gives you an idea that how genetic diversity changes towards environmental conditions. CCA can be done by PAST software.

I might try this: This was developed at my university. It might help.

Hello Sávio Guerreiro , the following article may help to address your question.

Best wishes.

Dear Parmeshwar Sahu

I agree with suggestions given by Jetty Ramadevi. Moreover, you can also access Google; you may find what you desire!

Thank you!

Hi Larissa

I'm having the same problem as you had, with 5 loci.

I tried to change the Data type: Standard, and use the haplotype data, not the sequence, but I'm not sure if it works the same with the sequence.

Thank you so much Jose Alberto Lopez-Aleman for this input. I will surely try those. I was using IBD application. Thanks, again.

Dear Hicham,

I am so happy to see your message. I 'll be very grateful if you could send me some seed of K.balansae. Is it possible? If You interested in the study of the hybrid population, I can send you seeds as vel.

Best wishes!

Erika

SNPhylo

If it worked before I'm asuming your input file is ok, so my recomendation will go in another direction... Maybe you could try setting the folder that will gather the results in a way that its name is not too long. I have experienced problems with STRUCTURE and TESS if I have folders inside folders inside folders... the name of the result files just get too long.

Try creating a folder in your desktop just to run STRUCTURE, then you can move it into wherever you want.

Actually, ITS are not very useful for within species population diversity as it reveals mainly variation between species. It is so called barcoding markers used for species discrimination and identification. For population diversity you need more variable markers. They are selected depending on availability of genomic information for the species and costs and time required. If no genomic data available, arbitrary amplification methods like AFLP will work for diversity, but will not give information of genotypes (they are called dominant markers). Microsatellites (SSR, STR) require big work for their development, but if already discovered can be very convenient. Same thing with SNP, however they are best used in huge amounts, so usually require expensive microchip equipment and expendables. And the ultimate solution is whole genome sequencing, which is very expensive and demanding for time, skills and resources, but giving the most comprehensive variation data possible.