As with any potentially powerful technique, gene drives can be misused in a variety of ways or induce  unintended consequences . For example, a gene drive intended to affect only a local population might spread across an entire species. Gene drives that eradicate populations of invasive species in their non-native habitats may have consequences for the population of the species as a whole, even in its native habitat. Any accidental return of individuals of the species to its original habitats, through natural migration, environmental disruption (storms, floods, etc.), accidental human transportation, or purposeful relocation, could unintentionally drive the species to extinction if the relocated individuals carried harmful gene drives. [ 10 ]


Gene drives can be built from many naturally occurring  selfish genetic elements  that use a variety of molecular mechanisms. [ 3 ]  These naturally occurring mechanisms induce similar  segregation distortion  in the wild, arising when  alleles  evolve molecular mechanisms that give them a transmission chance greater than the normal 50%.


Most gene drives have been developed in insects, notably mosquitoes, as a way to control insect-borne pathogens. Recent developments designed gene drives directly in viruses, notably  herpesviruses . These viral gene drives can propagate a modification into the population of viruses, and aim to reduce the infectivity of the virus. [ 11 ] [ 12 ]


In  sexually-reproducing  species, most genes are present in two copies (which can be the same or different  alleles ), either one of which has a 50% chance of passing to a descendant. By biasing the inheritance of particular altered genes, synthetic gene drives could more effectively spread alterations through a population. [ 5 ] [ 6 ]


Typically, scientists insert the gene drive into an organism's DNA along with the CRISPR-Cas9 machinery. When the modified organism mates and its DNA mixes with that of its mate, the CRISPR-Cas9 tool cuts the partner's DNA at the same spot where the gene drive is located in the first organism. The cell repairs the cut DNA by copying the gene drive from the first organism into the corresponding spot in the DNA of the offspring. This means both copies of the gene (one from each parent) now contain the gene drive.


At the molecular level, an endonuclease gene drive works by cutting a  chromosome  at a specific site that does not encode the drive, inducing the cell to  repair the damage  by copying the drive sequence onto the damaged chromosome. The cell then has two copies of the drive sequence. The method derives from  genome editing  techniques and relies on  homologous recombination . To achieve this behavior, endonuclease gene drives consist of two nested elements:


As a result, the gene drive insertion in the genome will re-occur in each organism that inherits one copy of the modification and one copy of the wild-type gene. If the gene drive is already present in the egg cell (e.g. when received from one parent), all the gametes of the individual will carry the gene drive (instead of 50% in the case of a normal gene). [ 5 ]


Since it can never more than double in frequency with each generation, a gene drive introduced in a single individual typically requires dozens of generations to affect a substantial fraction of a population. Alternatively, releasing drive-containing organisms in sufficient numbers can affect the rest within a few generations; for instance, by introducing it in every thousandth individual, it takes only 12–15 generations to be present in all individuals. [ 16 ]  Whether a gene drive will ultimately become fixed in a population and at which speed depends on its effect on individual fitness, on the rate of allele conversion, and on the population structure. In a well mixed population and with realistic allele conversion frequencies (≈90%), population genetics predicts that gene drives get fixed for a selection coefficient smaller than 0.3; [ 16 ]  in other words, gene drives can be used to spread modifications as long as reproductive success is not reduced by more than 30%. This is in contrast with normal genes, which can only spread across large populations if they increase fitness.


Because the strategy usually relies on the simultaneous presence of an unmodified and a gene drive allele in the same  cell nucleus , it had generally been assumed that a gene drive could only be engineered in sexually reproducing organisms, excluding  bacteria  and  viruses . However, during a  viral infection , viruses can accumulate hundreds or thousands of genome copies in infected cells. Cells are frequently co-infected by multiple virions and  recombination  between viral genomes is a well-known and widespread source of diversity for many viruses. In particular,  herpesviruses  are nuclear-replicating  DNA viruses  with large double-stranded DNA genomes and frequently undergo homologous recombination during their replication cycle.


These properties have enabled the design of a gene drive strategy that doesn't involve sexual reproduction, instead relying on  co-infection  of a given cell by a naturally occurring and an engineered virus. Upon co-infection, the unmodified genome is cut and repaired by homologous recombination, producing new gene drive viruses that can progressively replace the naturally occurring population. In  cell culture  experiments, it was shown that a viral gene drive can spread into the viral population and strongly reduce the infectivity of the virus, which opens novel therapeutic strategies against herpesviruses. [ 11 ]


Because gene drives propagate by replacing other alleles that contain a cutting site and the corresponding homologies, their application has been mostly limited to sexually reproducing species (because they are  diploid  or  polyploid  and alleles are mixed at each generation). As a side effect, inbreeding could in principle be an escape mechanism, but the extent to which this can happen in practice is difficult to evaluate. [ 17 ]


Due to the number of generations required for a gene drive to affect an entire population, the time to universality varies according to the reproductive cycle of each species: it may require under a year for some invertebrates, but centuries for organisms with years-long intervals between birth and  sexual maturity , such as humans. [ 18 ]  Hence this technology is of most use in fast-reproducing species.


Effectiveness in real practice varies between techniques, especially by choice of  germline  promoter. Lin and Potter 2016 (a) discloses the promoter technology  homology assisted CRISPR knockin  (HACK) and Lin and Potter 2016 (b) demonstrates actual use, achieving a high proportion of altered progeny from each altered  Drosophila  mother. [ 19 ]


Issues highlighted by researchers include: [ 20 ]


The  Broad Institute  of MIT and Harvard added gene drives to a list of uses of gene-editing technology it doesn't think companies should pursue. [ 21 ] [ better source needed ]


Gene drives affect all future generations and represent the possibility of a larger change in a living species than has been possible before. [ 22 ]


In December 2015, scientists of major world academies called for a moratorium on inheritable  human genome  edits that would affect the germline, including those related to  CRISPR-Cas9  technologies, [ 23 ]  but supported continued basic research and gene editing that would not affect future generations. [ 24 ]  In February 2016, British scientists were given permission by regulators to genetically modify  human embryos  by using CRISPR-Cas9 and related techniques on condition that the embryos were destroyed in seven days. [ 25 ] [ 26 ]  In June 2016, the US  National Academies of Sciences, Engineering, and Medicine  released a report on their "Recommendations for Responsible Conduct" of gene drives. [ 27 ]


A 2018 mathematical modelling studies suggest that despite preexisting and evolving gene drive resistance (caused by mutations at the cutting site), even an inefficient CRISPR "alteration-type" gene drive can achieve  fixation  in small populations. With a small but non-zero amount of gene flow among many local populations, the gene drive can escape and convert outside populations as well. [ 28 ]


Kevin M. Esvelt  stated that an open conversation was needed around the safety of gene drives: "In our view, it is wise to assume that invasive and self-propagating gene drive systems are likely to spread to every population of the target species throughout the world. Accordingly, they should only be built to combat true plagues such as malaria, for which we have few adequate countermeasures and that offer a realistic path towards an international agreement to deploy among all affected nations.". [ 29 ]  He moved to an open model for his own research on using gene drives to eradicate  Lyme disease  in  Nantucket  and  Martha's Vineyard . [ 30 ]  Esvelt and colleagues suggested that CRISPR could be used to save endangered wildlife from extinction. Esvelt later retracted his support for the idea, except for extremely hazardous populations such as malaria-carrying mosquitoes, and isolated islands that would prevent the drive from spreading beyond the target area. [ 31 ]


Austin Burt, an  evolutionary geneticist  at  Imperial College London , introduced the possibility of conducting gene drives based on natural homing endonuclease  selfish genetic elements  in 2003. [ 6 ]


Researchers had already shown that such genes could  act selfishly  to spread rapidly over successive generations. Burt suggested that gene drives might be used to prevent a mosquito population from transmitting the  malaria parasite  or to crash a mosquito population. Gene drives based on homing endonucleases have been demonstrated in the laboratory in  transgenic  populations of mosquitoes [ 32 ]  and fruit flies. [ 33 ] [ 34 ]  However, homing endonucleases are sequence-specific. Altering their specificity to target other sequences of interest remains a major challenge. [ 3 ]  The possible applications of gene drive remained limited until the discovery of CRISPR and associated RNA-guided endonucleases such as  Cas9  and  Cas12a .


In June 2014, the  World Health Organization  (WHO) Special Programme for Research and Training in Tropical Diseases [ 35 ]  issued guidelines [ 36 ]  for evaluating genetically modified mosquitoes. In 2013 the  European Food Safety Authority  issued a protocol [ 37 ]  for  environmental assessments  of all  genetically modified organisms .


Target Malaria , a project funded by the  Bill and Melinda Gates Foundation , invested $75 million in gene drive technology. The foundation originally estimated the technology to be ready for field use by 2029 somewhere in Africa. However, in 2016 Gates changed this estimate to some time within the following two years. [ 38 ]  In December 2017, documents released under the  Freedom of Information Act  showed that  DARPA  had invested $100 million in gene drive research. [ 39 ]


Scientists have designed multiple strategies to maintain control over gene drives. [ citation needed ]


In 2020, researchers reported the development of two active  guide RNA -only elements that, according to their study, may enable halting or deleting gene drives introduced into populations in the wild with  CRISPR-Cas9 gene editing . The paper's senior author cautions that the two neutralizing systems they demonstrated in cage trials "should not be used with a  false sense of security  for field-implemented gene drives". [ 40 ] [ 41 ]


If elimination is not necessary, it may be desirable to intentionally preserve the target population at a lower level by using a less severe gene drive technology. This works by maintaining the semi-defective population indefinitely in the target area, thereby crowding out potential nearby, wild populations that would otherwise move back in to fill a void. [ 42 ]


CRISPR [ 43 ]  is the leading  genetic engineering  method. [ 44 ]  In 2014, Esvelt and coworkers first suggested that CRISPR/Cas9 might be used to build gene drives. [ 5 ]  In 2015, researchers reported successful engineering of CRISPR-based gene drives in  Saccharomyces [ 45 ] ,   Drosophila , [ 46 ]  and  mosquitoes . [ 47 ] [ 48 ]  They reported efficient inheritance distortion over successive generations, with one study demonstrating the spread of a gene into laboratory populations. [ 48 ]  Drive-resistant alleles were expected to arise for each of the described gene drives; however, this could be delayed or prevented by targeting highly conserved sites at which resistance was expected to have a severe fitness cost.


Because of CRISPR's targeting flexibility, gene drives could theoretically be used to engineer almost any trait. Unlike previous approaches, they could be tailored to block the evolution of drive resistance by targeting multiple sequences. CRISPR could also enable gene drive architectures that control rather than eliminate populations. [ citation needed ]


In 2022, t-CRISPR, was used to pass the “t haplotype” gene to about 95% of offspring. The approach spreads faulty copies of a female fertility gene to offspring, rendering them infertile. The researchers reported that their models suggested that adding 256 altered animals to an island with a population of 200,000 mice would eliminate the population in about 25 years. The traditional approaches of poison and traps were not needed. [ 49 ]


Gene drives have two main classes of application, which have implications of different significance:


Because of their unprecedented potential risk, safeguard mechanisms have been proposed and tested. [ 45 ] [ 50 ]


One possible application is to genetically modify  mosquitoes ,  mice , and other disease vectors so that they cannot transmit diseases, such as  malaria  and  dengue fever  in the case of mosquitoes, and  tick-borne disease  in the case of mice. [ 51 ]  Researchers have claimed that by applying the technique to 1% of the wild population of mosquitoes, that they could eradicate malaria within a year. [ 52 ]


A gene drive could be used to eliminate  invasive species  and has, for example, been proposed as a way to eliminate  invasive species in New Zealand . [ 53 ]  Gene drives for biodiversity conservation purposes are being explored as part of The Genetic Biocontrol of Invasive Rodents (GBIRd) program because they offer the potential for reduced risk to non-target species and reduced costs when compared to traditional invasive species removal techniques. Given the risks of such an approach described below, the GBIRd partnership is committed to a deliberate, step-wise process that will only proceed with public alignment, as recommended by the world's leading gene drive researchers from the Australian and US National Academy of
Sciences and many others. [ 54 ]  A wider outreach network for gene drive research exists to raise awareness of the value of gene drive research for the public good. [ 55 ]


Some scientists are concerned about the technique, fearing it could spread and wipe out species in native habitats. [ 56 ]  The gene could mutate, potentially causing unforeseen problems (as could any gene). [ 57 ]  Many non-native species can hybridize with native species, such that a gene drive afflicting a non-native plant or animal that hybridizes with a native species could doom the native species. Many non-native species have naturalized into their new environment so well that crops and/or native species have adapted to depend on them. [ 58 ]


The Predator Free 2050 project is a New Zealand government program to eliminate eight invasive mammalian predator species (including rats, short-tailed weasels, and possums) from the country by 2050. [ 59 ] [ 60 ]  The project was first announced in 2016 by New Zealand's prime minister  John Key  and in January 2017 it was announced that gene drives would be considered in the effort, but this has not yet been actualised. [ 60 ]  In 2017, one group in Australia and another in Texas released preliminary research into creating 'daughterless mice' using gene drives in mammals. [ 61 ]


In 2017, scientists at the University of California, Riverside developed a gene drive to attack the  invasive   spotted-wing drosophila , a type of fruit fly native to Asia that is costing California's cherry farms $700 million per year because of its tail's razor-edged  ovipositor  that destroys unblemished fruit. The primary alternative control strategy involves the use of  insecticides  called  pyrethroids  that kill almost all insects that it contacts. [ 21 ]


The transhumanist philosopher  David Pearce  has advocated for using CRISPR-based gene drives to reduce the  suffering of wild animals . [ 62 ]   Kevin M. Esvelt , an American biologist who has helped develop gene drive technology, has argued that there is a moral case for the elimination of the  New World screwworm  through such technologies because of the immense suffering that infested wild animals experience when they are eaten alive. [ 63 ]


In the field of  molecular biology ,  gene expression profiling  is the measurement of the activity (the  expression ) of thousands of genes at once, to create a global picture of cellular function. These profiles can, for example, distinguish between cells that are actively dividing, or show how the cells react to a particular treatment.  Many experiments of this sort measure an entire  genome  simultaneously, that is, every gene present in a particular cell.


Several  transcriptomics technologies  can be used to generate the necessary data to analyse.  DNA microarrays [ 1 ]  measure the relative activity of previously identified target genes. Sequence based techniques, like  RNA-Seq , provide information on the sequences of genes in addition to their expression level.


Expression profiling is a logical next step after  sequencing a genome : the sequence tells us what the cell could possibly do, while the expression profile tells us what it is actually doing at a point in time. Genes contain the instructions for making messenger RNA ( mRNA ), but at any moment each cell makes mRNA from only a fraction of the genes it carries.  If a gene is used to produce mRNA, it is considered "on", otherwise "off".  Many factors determine whether a gene is on or off, such as the time of day, whether or not the cell is actively dividing, its local environment, and chemical signals from other cells. For instance,  skin  cells,  liver  cells and nerve cells turn on (express) somewhat different genes and that is in large part what makes them different.  Therefore, an expression profile allows one to deduce a cell's type, state, environment, and so forth.


Expression profiling experiments often involve measuring the relative amount of mRNA expressed in two or more experimental conditions. This is because altered levels of a specific sequence of mRNA suggest a changed need for the protein coded by the mRNA, perhaps indicating a  homeostatic response  or a pathological condition. For example, higher levels of mRNA coding for  alcohol dehydrogenase  suggest that the cells or tissues under study are responding to increased levels of ethanol in their environment. Similarly, if breast cancer cells express higher levels of mRNA associated with a particular  transmembrane receptor  than normal cells do, it might be that this receptor plays a role in breast cancer. A drug that interferes with this receptor may prevent or treat breast cancer. In developing a drug, one may perform gene expression profiling experiments to help assess the drug's toxicity, perhaps by looking for changing levels in the expression of  cytochrome P450  genes, which may be a  biomarker  of drug metabolism. [ 2 ]  Gene expression profiling may become an important diagnostic test. [ 3 ] [ 4 ]


The human genome contains on the order of 20,000 genes which work in concert to produce roughly 1,000,000 distinct proteins.  This is due to  alternative splicing , and also because cells make important changes to proteins through  posttranslational modification  after they first construct them, so a given gene serves as the basis for many possible versions of a particular protein. In any case, a single mass spectrometry experiment can identify about
2,000 proteins [ 5 ]  or 0.2% of the total.  While knowledge of the precise proteins a cell makes ( proteomics ) is more relevant than knowing how much messenger RNA is made from each gene, [ why? ]  gene expression profiling provides the most global picture possible in a single experiment.  However, proteomics methodology is improving.  In other species, such as yeast, it is possible to identify over 4,000 proteins in just over one hour. [ 6 ]


Sometimes, a scientist already has an idea of what is going on, a  hypothesis , and he or she performs an expression profiling experiment with the idea of potentially disproving this hypothesis. In other words, the scientist is making a specific prediction about levels of expression that could turn out to be false.


More commonly, expression profiling takes place before enough is known about how genes interact with experimental conditions for a testable hypothesis to exist. With no hypothesis, there is nothing to disprove, but expression profiling can help to identify a candidate hypothesis for future experiments.  Most early expression profiling experiments, and many current ones, have this form [ 7 ]  which is known as class discovery.  A popular approach to class discovery involves grouping similar genes or samples together using one of the many existing clustering methods such the traditional  k-means  or  hierarchical clustering , or the more recent  MCL . [ 8 ]  Apart from selecting a clustering algorithm, user usually has to choose an appropriate proximity measure (distance or similarity) between data objects. [ 9 ]  The figure above represents the output of a two dimensional cluster, in which similar samples (rows, above) and similar gene probes (columns) were organized so that they would lie close together.  The simplest form of class discovery would be to list all the genes that changed by more than a certain amount between two experimental conditions.


Class prediction is more difficult than class discovery, but it allows one to answer questions of direct clinical significance such as, given this profile, what is the probability that this patient will respond to this drug? This requires many examples of profiles that responded and did not respond, as well as  cross-validation  techniques to discriminate between them.


In general, expression profiling studies report those genes that showed statistically significant differences under changed experimental conditions. This is typically a small fraction of the genome  for several reasons. First, different cells and tissues express a subset of genes as a direct consequence of  cellular differentiation  so many genes are turned off. Second, many of the genes code for proteins that are required for survival in very specific amounts so many genes do not change. Third, cells use many other mechanisms to regulate proteins in addition to altering the amount of  mRNA , so these genes may stay consistently expressed even when protein concentrations are rising and falling. Fourth, financial constraints limit expression profiling experiments to a small number of observations of the same gene under identical conditions, reducing the  statistical power  of the experiment, making it impossible for the experiment to identify important but subtle changes. Finally, it takes a great amount of effort to discuss the biological significance of each regulated gene, so scientists often limit their discussion to a subset.  Newer  microarray analysis techniques  automate certain aspects of attaching biological significance to expression profiling results, but this remains a very difficult problem.


The relatively short length of gene lists published from expression profiling experiments limits the extent to which experiments performed in different laboratories appear to agree. Placing expression profiling results in a publicly accessible  microarray database  makes it possible for researchers to assess expression patterns beyond the scope of published results, perhaps identifying similarity with their own work.


Both  DNA microarrays   and  quantitative PCR  exploit the preferential binding or " base pairing " of complementary nucleic acid sequences, and both are used in gene expression profiling, often in a serial fashion. While high throughput DNA microarrays lack the quantitative accuracy of qPCR, it takes about the same time to measure the gene expression of a few dozen genes via qPCR as it would to measure an entire genome using DNA microarrays. So it often makes sense to perform semi-quantitative DNA microarray analysis experiments to identify candidate genes, then perform qPCR on some of the most interesting candidate genes to validate the microarray results. Other experiments, such as a  Western blot  of some of the protein products of differentially expressed genes, make conclusions based on the expression profile more persuasive, since the mRNA levels do not necessarily correlate to the amount of expressed protein.


Data analysis of microarrays has become an area of intense research. [ 10 ]  Simply stating that a group of genes were regulated by at least twofold, once a common practice, lacks a solid statistical footing. With five or fewer replicates in each group, typical for microarrays, a single  outlier  observation can create an apparent difference greater than two-fold. In addition, arbitrarily setting the bar at two-fold is not biologically sound, as it eliminates from consideration many genes with obvious biological significance.


Rather than identify differentially expressed genes using a fold change cutoff, one can use a variety of  statistical tests  or  omnibus tests  such as  ANOVA , all of which consider both fold change and variability to create a  p-value , an estimate of how often we would observe the data by chance alone. Applying p-values to microarrays is complicated by the large number of  multiple comparisons  (genes) involved. For example, a p-value of 0.05 is typically thought to indicate significance, since it estimates a 5% probability of observing the data by chance. But with 10,000 genes on a microarray, 500 genes would be identified as significant at p < 0.05 even if there were no difference between the experimental groups. One obvious solution is to consider significant only those genes meeting a much more stringent p value criterion, e.g., one could perform a  Bonferroni correction  on the p-values, or use a  false discovery rate  calculation to adjust p-values in proportion to the number of parallel tests involved. Unfortunately, these approaches may reduce the number of significant genes to zero, even when genes are in fact differentially expressed. Current statistics such as  Rank products  aim to strike a balance between false discovery of genes due to chance variation and non-discovery of differentially expressed genes. Commonly cited methods include the Significance Analysis of Microarrays (SAM) [ 11 ]  and a wide variety of methods are available from  Bioconductor  and a variety of analysis packages from  bioinformatics companies .


Selecting a different test usually identifies a different list of significant genes [ 12 ]  since each test operates under a specific set of assumptions, and places a different emphasis on certain features in the data. Many tests begin with the assumption of a  normal distribution  in the data, because that seems like a sensible starting point and often produces results that appear more significant. Some tests consider the  joint distribution  of all gene observations to estimate general variability in measurements, [ 13 ]  while others look at each gene in isolation. Many modern microarray analysis techniques involve  bootstrapping (statistics) ,  machine learning  or  Monte Carlo methods . [ 14 ]


As the number of replicate measurements in a microarray experiment increases, various statistical approaches yield increasingly similar results, but lack of concordance between different statistical methods makes array results appear less trustworthy. The MAQC Project [ 15 ]  makes recommendations to guide researchers in selecting more standard methods (e.g. using p-value and fold-change together for selecting the differentially expressed genes) so that experiments performed in different laboratories will agree better.


Different from the analysis on differentially expressed individual genes, another type of analysis focuses on differential expression or perturbation of pre-defined gene sets and is called gene set analysis. [ 16 ] [ 17 ]  Gene set analysis demonstrated several major advantages over individual gene differential expression analysis. [ 16 ] [ 17 ]  Gene sets are groups of genes that are functionally related according to current knowledge. Therefore, gene set analysis is considered a knowledge based analysis approach. [ 16 ]  Commonly used gene sets include those derived from  KEGG  pathways,  Gene Ontology  terms, gene groups that share some other functional annotations, such as common transcriptional regulators etc. Representative gene set analysis methods include  Gene Set Enrichment Analysis  (GSEA), [ 16 ]  which estimates significance of gene sets based on permutation of sample labels, and Generally Applicable Gene-set Enrichment (GAGE), [ 17 ]  which tests the significance of gene sets based on permutation of gene labels or a parametric distribution.


While the statistics may  identify which gene products change under experimental conditions, making biological sense of expression profiling rests on knowing which protein each gene product makes and what function this protein performs. Gene annotation provides functional and other information, for example the location of each gene within a particular chromosome.  Some functional annotations are more reliable than others; some are absent. Gene annotation databases change regularly, and various databases refer to the same protein by different names, reflecting a changing understanding of protein function. Use of standardized  gene nomenclature  helps address the naming aspect of the problem, but exact matching of transcripts to genes [ 18 ] [ 19 ]  remains an important consideration.


Having identified some set of regulated genes, the next step in expression profiling involves looking for patterns within the regulated set. Do the proteins made from these genes perform similar functions? Are they chemically similar? Do they reside in similar parts of the cell?   Gene ontology  analysis provides a standard way to define these relationships. Gene ontologies start with very broad categories, e.g., "metabolic process" and break them down into smaller categories, e.g., "carbohydrate metabolic process" and finally into quite restrictive categories like "inositol and derivative phosphorylation".


Genes have other attributes beside biological function, chemical properties and cellular location. One can compose sets of genes based on proximity to other genes, association with a disease, and relationships with drugs or toxins. The Molecular Signatures Database [ 20 ]  and the  Comparative Toxicogenomics Database [ 21 ]  are examples of resources to categorize genes in numerous ways.


Regulated genes are categorized in terms of what they are and what they do, important relationships between genes may emerge. [ 23 ]  For example, we might see evidence that a certain gene creates a protein to make an enzyme that activates a protein  to turn on a second gene on our list. This second gene may  be a  transcription factor  that regulates yet another gene from our list. Observing these links we may begin to suspect that they represent much more than chance associations in the results, and that they are all on our list because of an underlying biological process. On the other hand, it could be that if one selected genes at random, one might find many that seem to have something in common. In this sense, we need rigorous statistical procedures to test whether the emerging biological themes is significant or not. That is where gene set analysis [ 16 ] [ 17 ]  comes in.


Fairly straightforward statistics provide estimates of whether associations between genes on lists are greater than what one would expect by chance. These statistics are interesting, even if they represent a substantial oversimplification of what is really going on. Here is an example. Suppose there are 10,000 genes in an experiment, only 50 (0.5%) of which play a known role in making  cholesterol . The experiment identifies 200 regulated genes. Of those, 40 (20%) turn out to be on a list of cholesterol genes as well. Based on the overall prevalence of the cholesterol genes (0.5%) one expects an average of 1 cholesterol gene for every 200 regulated genes, that is, 0.005 times 200. This expectation is an average, so one expects to see more than one some of the time. The question becomes how often we would see 40 instead of 1 due to pure chance.


According to the  hypergeometric distribution , one would expect to try about 10^57 times (10 followed by 56 zeroes) before picking 39 or more of the cholesterol genes from a pool of 10,000 by drawing 200 genes at random. Whether one pays much attention to how infinitesimally small the probability of observing this by chance is, one would conclude that the regulated gene list is enriched [ 24 ]  in genes with a known cholesterol association.


One might further hypothesize that the experimental treatment regulates cholesterol, because the treatment seems to selectively regulate genes associated with cholesterol. While this may be true, there are a number of reasons why making this a firm conclusion based on enrichment alone represents an unwarranted leap of faith. One previously mentioned issue has to do with the observation that gene regulation may have no direct impact on protein regulation: even if the proteins coded for by these genes do nothing other than make cholesterol, showing that their mRNA is altered does not directly tell us what is happening at the protein level. It is quite possible that the amount of these cholesterol-related proteins remains constant under the experimental conditions. Second, even if protein levels do change, perhaps there is always enough of them around to make cholesterol as fast as it can be possibly made, that is, another protein, not on our list, is the  rate determining step  in the process of making cholesterol. Finally, proteins typically play many roles, so these genes may be regulated not because of their shared association with making cholesterol but because of a shared role in a completely independent process.


Bearing the foregoing caveats in mind, while gene profiles do not in themselves prove causal relationships between treatments and biological effects, they do offer unique biological insights that would often be very difficult to arrive at in other ways.


As described above, one can identify significantly regulated genes first and then find patterns by comparing the list of significant genes to sets of genes known to share certain associations. One can also work the problem in reverse order. Here is a very simple example. Suppose there are 40 genes associated with a known process, for example, a predisposition to diabetes. Looking at two groups of expression profiles, one for mice fed a high carbohydrate diet and one for mice fed a low carbohydrate diet, one observes that all 40 diabetes genes are expressed at a higher level in the high carbohydrate group than the low carbohydrate group. Regardless of whether any of these genes would have made it to a list of significantly altered genes, observing all 40 up, and none down appears unlikely to be the result of pure chance: flipping 40 heads in a row is predicted to occur about one time in a trillion attempts using a fair coin.


For a type of cell, the group of genes whose combined expression pattern is uniquely characteristic to a given condition constitutes the  gene signature  of this condition. Ideally, the gene signature can be used to select a group of patients at a specific state of a disease with accuracy that facilitates selection of treatments. [ 25 ] [ 26 ]
Gene Set Enrichment Analysis (GSEA) [ 16 ]  and similar methods [ 17 ]  take advantage of this kind of logic but uses more sophisticated statistics, because component genes in real processes display more complex behavior than simply moving up or down as a group, and the amount the genes move up and down is meaningful, not just the direction. In any case, these statistics measure how different the behavior of some small set of genes is compared to genes not in that small set.


GSEA uses a  Kolmogorov Smirnov  style statistic to see whether any previously defined gene sets exhibited unusual behavior in the current expression profile. This leads to a multiple hypothesis testing challenge, but reasonable methods exist to address it. [ 27 ]


Expression profiling provides new information about what genes do under various conditions. Overall, microarray technology produces reliable expression profiles. [ 28 ]  From this information one can generate new hypotheses about biology or test existing ones. However, the size and complexity of these experiments often results in a wide variety of possible interpretations. In many cases, analyzing expression profiling results takes far more effort than performing the initial experiments.


Most researchers use multiple statistical methods and exploratory data analysis before publishing their expression profiling results, coordinating their efforts with a  bioinformatician  or other expert in  DNA microarrays . Good experimental design, adequate biological replication and follow up experiments play key roles in successful expression profiling experiments.


In  molecular cloning  and  biology , a  gene knock-in  (abbreviation:  KI ) refers to a  genetic engineering  method that involves the one-for-one substitution of DNA sequence information in a  genetic locus  or the  insertion  of sequence information not found within the locus. [ 1 ]  Typically, this is done in mice since the technology for this process is more refined and there is a high degree of shared sequence complexity between mice and humans. [ 2 ]  The difference between knock-in technology and traditional  transgenic  techniques is that a knock-in involves a  gene  inserted into a specific locus, and is thus a "targeted" insertion. It is the opposite of  gene knockout .


A common use of knock-in technology is for the creation of disease models. It is a technique by which scientific investigators may study the function of the regulatory machinery (e.g.  promoters ) that governs the expression of the natural gene being replaced. This is accomplished by observing the new  phenotype  of the organism in question. The  BACs  and  YACs  are used in this case so that large fragments can be transferred.


Gene knock-in originated as a slight modification of the original knockout technique developed by  Martin Evans ,  Oliver Smithies , and  Mario Capecchi . Traditionally, knock-in techniques have relied on  homologous recombination  to drive targeted gene replacement, although other methods using a  transposon -mediated system to insert the target gene have been developed. [ 3 ]  The use of  loxP  flanking sites that become excised upon expression of  Cre recombinase  with gene vectors is an example of this.  Embryonic stem cells  with the modification of interest are then implanted into a viable  blastocyst , which will grow into a mature  chimeric  mouse with some cells having the original blastocyst cell genetic information and other cells having the modifications introduced to the embryonic stem cells. Subsequent offspring of the chimeric mouse will then have the gene knock-in. [ 4 ]


Gene knock-in has allowed, for the first time, hypothesis-driven studies on gene modifications and resultant phenotypes. Mutations in the human  p53  gene, for example, can be induced by exposure to  benzo(a)pyrene  (BaP) and the mutated copy of the p53 gene can be inserted into mouse genomes.  Lung tumors  observed in the knock-in mice offer support for the hypothesis of BaP’s  carcinogenicity . [ 5 ]  More recent developments in knock-in technique have allowed for pigs to have a gene for  green fluorescent protein  inserted with a  CRISPR/Cas9  system, which allows for much more accurate and successful gene insertions. [ 6 ]  The speed of CRISPR/Cas9-mediated gene knock-in also allows for  biallelic  modifications to some genes to be generated and the phenotype in mice observed in a single generation, an unprecedented timeframe. [ 7 ]


Knock-in technology is different from  knockout  technology in that knockout technology aims to either  delete  part of the DNA sequence or  insert  irrelevant DNA sequence information to disrupt the expression of a specific genetic locus. Gene knock-in technology, on the other hand, alters the genetic locus of interest via a one-for-one substitution of DNA sequence information or by the addition of sequence information that is not found on said genetic locus. A gene knock-in therefore can be seen as a  gain-of-function mutation  and a gene knockout a  loss-of-function mutation , but a gene knock-in may also involve the substitution of a functional gene locus for a mutant phenotype that results in some loss of function. [ 8 ]


Because of the success of gene knock-in methods thus far, many clinical applications can be envisioned. Knock-in of sections of the human  immunoglobulin gene  into mice has already been shown to allow them to produce humanized antibodies that are therapeutically useful. [ 9 ]  It should be possible to modify  stem cells  in humans to restore targeted gene function in certain tissues, for example possibly correcting the mutant gamma-chain gene of the  IL-2 receptor  in  hematopoietic stem cells  to restore  lymphocyte  development in people with X-linked  severe combined immunodeficiency . [ 4 ]


While gene knock-in technology has proven to be a powerful technique for the generation of models of human disease and insight into proteins  in vivo , numerous limitations still exist. Many of these are shared with the limitations of knockout technology. First, combinations of knock-in genes lead to growing complexity in the interactions that inserted genes and their products have with other sections of the genome and can therefore lead to more side effects and difficult-to-explain  phenotypes . Also, only a few loci, such as the  ROSA26  locus have been characterized well enough where they can be used for conditional gene knock-ins; making combinations of  reporter  and transgenes in the same locus problematic. The biggest disadvantage of using gene knock-in for human disease model generation is that mouse physiology is not identical to that of humans and human  orthologs  of proteins expressed in mice will often not wholly reflect the role of a gene in human pathology. [ 10 ]  This can be seen in mice produced with the  ΔF508  fibrosis mutation in the  CFTR gene , which accounts for more than 70% of the mutations in this gene for the human population and leads to  cystic fibrosis . While ΔF508 CF mice do exhibit the processing defects characteristic of the human mutation, they do not display the pulmonary pathophysiological changes seen in humans and carry virtually no lung phenotype. [ 11 ]  Such problems could be ameliorated by the use of a variety of animal models, and pig models (pig lungs share many biochemical and physiological similarities with human lungs) have been generated in an attempt to better explain the activity of the ΔF508 mutation. [ 12 ]


Gene knockdown  is an experimental technique by which the  expression  of one or more of an  organism 's  genes  is reduced. The reduction can occur either through  genetic modification  or by treatment with a  reagent  such as a short DNA or RNA  oligonucleotide  that has a sequence complementary to either gene or an mRNA transcript. [ 1 ]


If a  DNA  of an organism is genetically modified, the resulting organism is called a "knockdown organism." If the change in  gene expression  is caused by an  oligonucleotide  binding to an  mRNA  or temporarily binding to a  gene , this leads to a temporary change in gene expression that does not modify the chromosomal DNA, and the result is referred to as a "transient knockdown". [ 1 ]


In a transient knockdown, the binding of this oligonucleotide to the active gene or its transcripts causes decreased expression through a variety of processes. Binding can occur either through the blocking of  transcription  (in the case of gene-binding), the degradation of the  mRNA  transcript (e.g. by small interfering RNA ( siRNA )) or  RNase -H dependent antisense, or through the blocking of either  mRNA   translation , pre-m RNA splicing  sites, or  nuclease  cleavage sites used for maturation of other functional RNAs, including  miRNA  (e.g. by  morpholino  oligos or other RNase-H independent antisense). [ 1 ] [ 2 ]


The most direct use of transient knockdowns is for learning about a  gene  that has been  sequenced , but has an unknown or incompletely known function. This experimental approach is known as  reverse genetics . Researchers draw inferences from how the knockdown differs from individuals in which the gene of interest is operational. Transient knockdowns are often used in  developmental biology  because oligos can be injected into single-celled  zygotes  and will be present in the daughter cells of the injected cell through  embryonic  development. [ 3 ]  The term gene knockdown first appeared in the literature in 1994 [ 4 ]


RNA interference  (RNAi) is a means of silencing genes by way of mRNA degradation. [ 5 ]  Gene knockdown by this method is achieved by introducing small double-stranded interfering RNAs (siRNA) into the cytoplasm. Small interfering RNAs can originate from inside the cell or can be exogenously introduced into the cell. Once introduced into the cell, exogenous siRNAs are processed by the RNA-induced silencing complex ( RISC ). [ 6 ]  The siRNA is complementary to the target mRNA to be silenced, and the RISC uses the siRNA as a template for locating the target mRNA. After the RISC localizes to the target mRNA, the RNA is cleaved by a ribonuclease.


RNAi is widely used as a laboratory technique for genetic functional analysis. [ 7 ]  RNAi in organisms such as  C. elegans  and  Drosophila melanogaster  provides a quick and inexpensive means of investigating gene function. In  C. elegans  research, the availability of tools such as the  Ahringer RNAi Library  give laboratories a way of testing many genes in a variety of experimental backgrounds. Insights gained from experimental RNAi use may be useful in identifying potential therapeutic targets,  drug development , or other applications. [ 8 ]  RNA interference is a very useful research tool, allowing investigators to carry out large genetic screens in an effort to identify targets for further research related to a particular pathway, drug, or phenotype. [ 9 ] [ 10 ]


A different means of silencing exogenous DNA that has been discovered in  prokaryotes  is a mechanism involving loci called 'Clustered Regularly Interspaced Short Palindromic Repeats', or  CRISPRs . [ 11 ]  CRISPR-associated (cas) genes encode cellular machinery that cuts exogenous DNA into small fragments and inserts them into a CRISPR repeat locus. When this CRISPR region of DNA is expressed by the cell, the small RNAs produced from the exogenous DNA inserts serve as a template sequence that other Cas proteins use to silence this same exogenous sequence. The transcripts of the short exogenous sequences are used as a guide to silence these foreign DNA when they are present in the cell. This serves as a kind of acquired immunity, and this process is like a prokaryotic RNA interference mechanism. The CRISPR repeats are conserved amongst many species and have been demonstrated to be usable in human cells, [ 12 ]  bacteria, [ 13 ]   C. elegans , [ 14 ]   zebrafish , [ 15 ]  and other organisms for effective genome manipulation. The use of CRISPRs as a versatile research tool can be illustrated [ 16 ]  by many studies making use of it to generate organisms with genome alterations.


Another technology made possible by prokaryotic genome manipulation is the use of transcription activator-like effector nucleases ( TALENs ) to target specific genes. [ 17 ]  TALENs are nucleases that have two important functional components: a DNA binding domain and a DNA cleaving domain. The DNA binding domain is a sequence-specific transcription activator-like effector sequence while the DNA cleaving domain originates from a bacterial endonuclease and is non-specific. TALENs can be designed to cleave a sequence specified by the sequence of the transcription activator-like effector portion of the construct. Once designed, a TALEN is introduced into a cell as a plasmid or mRNA. The TALEN is expressed, localizes to its target sequence, and cleaves a specific site. After cleavage of the target DNA sequence by the TALEN, the cell uses non-homologous end joining as a DNA repair mechanism to correct the cleavage. The cell's attempt at repairing the cleaved sequence can render the encoded protein non-functional, as this repair mechanism introduces insertion or deletion errors at the repaired site.


So far, knockdown organisms with permanent alterations in their DNA have been engineered chiefly for research purposes. Also known simply as  knockdowns , these organisms are most commonly used for reverse genetics, especially in species such as  mice  or  rats  for which transient knockdown technologies cannot easily be applied. [ 3 ] [ 18 ]


There are several companies that offer commercial services related to gene knockdown treatments.


Gene targeting  is a  biotechnological  tool used to change the  DNA sequence  of an organism (hence it is a form of  Genome Editing ). It is based on the natural DNA-repair mechanism of  Homology Directed Repair  (HDR), including  Homologous Recombination . Gene targeting can be used to make a range of sizes of DNA edits, from larger DNA edits such as inserting entire new genes into an organism, through to much smaller changes to the existing DNA such as a single base-pair change. Gene targeting relies on the presence of a repair template to introduce the user-defined edits to the DNA. The user (usually a scientist) will design the repair template to contain the desired edit, flanked by DNA sequence corresponding (homologous) to the region of DNA that the user wants to edit; hence the edit is  targeted  to a particular genomic region. In this way Gene Targeting is distinct from natural homology-directed repair, during which the ‘natural’ DNA repair template of the sister chromatid is used to repair broken DNA (the sister chromatid is the second copy of the gene). The alteration of DNA sequence in an organism can be useful in both a research context – for example to understand the biological role of a gene – and in biotechnology, for example to alter the traits of an organism (e.g. to improve crop plants).


To create a gene-targeted organism, DNA must be introduced into its cells. This DNA must contain all of the parts necessary to complete the gene targeting. At a minimum this is the homology repair template, containing the desired edit flanked by regions of DNA homologous (identical in sequence to) the targeted region (these homologous regions are called “homology arms” ). Often a  reporter gene  and/or a  selectable marker  is also required, to help identify and select for cells (or “events”) where GT has actually occurred. It is also common practice to increase GT rates by causing a double-strand-break (DSB) in the targeted DNA region. [ 2 ]  Hence the genes encoding for the site-specific-nuclease of interest may also be transformed along with the repair template. These genetic elements required for GT may be assembled through conventional  molecular cloning  in bacteria.


Gene targeting methods are established for several  model organisms  and may vary depending on the  species  used. To target genes in  mice , the DNA is inserted into mouse  embryonic stem cells  in culture. Cells with the insertion can contribute to a mouse's tissue via  embryo  injection. Finally,  chimeric  mice where the modified cells make up the reproductive organs are  bred . After this step the entire body of the mouse is based on the selected embryonic stem cell.


To target genes in  moss , the DNA is incubated together with freshly isolated  protoplasts  and with  polyethylene glycol . As mosses are  haploid  organisms, [ 3 ]   moss  filaments ( protonema ) can be directly screened for the target, either by treatment with  antibiotics  or with  PCR . Unique among  plants , this procedure for  reverse genetics  is as efficient as in  yeast . [ 4 ]  Gene targeting has been successfully applied to cattle, sheep, swine and many fungi.


The frequency of gene targeting can be significantly enhanced through the use of site-specific  endonucleases  such as  zinc finger nucleases , [ 5 ]  engineered  homing endonucleases , [ 6 ]   TALENS , or most commonly the  CRISPR -Cas system.  This method has been applied to species including  Drosophila melanogaster , [ 5 ]   tobacco , [ 7 ] [ 8 ]   corn , [ 9 ]   human  cells, [ 10 ]   mice [ 11 ]  and  rats . [ 11 ]


The relationship between gene targeting, gene editing and genetic modification is outlined in the Venn diagram below. It displays how 'Genetic engineering' encompasses all 3 of these techniques. Genome editing is characterised by making small edits to the genome at a specific location, often following cutting of the target DNA region by a site-specific-nuclease such as CRISPR. [ 12 ]  Genetic modification usually describes the insertion of a transgene (foreign DNA, i.e. a gene from another species) into a random location within the genome. [ 13 ] [ 14 ]  Gene-targeting is a specific biotechnological tool that can lead to small changes to the genome at a specific site [ 2 ]  - in which case the edits caused by gene-targeting would count as genome editing. However gene targeting is also capable of inserting entire genes (such as transgenes) at the target site if the transgene is incorporated into the homology repair template that is used during gene-targeting. [ 15 ] [ 16 ]  In such cases the edits caused by gene-targeting would, in some jurisdictions, be considered as equivalent to Genetic Modification as insertion of foreign DNA has occurred. [ 16 ]


Gene targeting is one specific form of  genome editing  tool. Other genome editing tools include targeted mutagenesis, base editing and  prime editing , all of which create edits to the endogenous DNA (DNA already present in the organism) at a specific genomic location. [ 17 ] [ 18 ]  This site-specific or ‘targeted’ nature of genome editing is typically what makes genome-editing different to traditional ‘genetic modification’ which inserts a  transgene  at a non-specific location in the organisms' genome, as well as gene-editing making small edits to the DNA already present in the organisms, verses genetic modification insertion 'foreign' DNA from another species. [ 19 ] [ 20 ]


Because gene editing makes smaller changes to endogenous DNA, many mutations created through genome-editing could in theory occur through natural mutagenesis or, in the context of plants, through  mutation breeding  which is part of conventional breeding (in contrast the insertion of a transgene to create a Genetically Modified Organism (GMO) could not occur naturally). However, there are exceptions to this general rule; as explained in the introduction, GT can introduce a range of possible size of edits to DNA; from very small edits such as changing, inserting or deleting 1 base-pair, through to inserting much longer DNA sequences, which could in theory include insertion of an entire transgene. [ 16 ]  However, in practice GT is more commonly used to insert smaller sequences. The range of edits possible through GT can make it challenging to regulate (see  Regulation ).


The two most established forms of gene editing are  gene-targeting  and  targeted-mutagenesis . While gene targeting relies on the  Homology Directed Repair  (HDR) (also called  Homologous Recombination , HR) DNA repair pathway, targeted-mutagenesis uses  Non-Homologous-End-Joining  (NHEJ) of broken DNA. NHEJ is an error-prone DNA repair pathway, meaning that when it repairs the broken DNA it can insert or delete DNA bases, creating insertions or deletions (indels). The user cannot specify what these random indels will be, hence they cannot control exactly what edits are made at the target site. However they can control where these edits will occur (i.e. dictate the target site) through using a site-specific nuclease (previously  Zinc Finger Nucleases  &  TALENs , now commonly  CRISPR ) to break the DNA at the target site. A summary of gene-targeting through HDR (also called Homologous Recombination) and targeted mutagenesis through NHEJ is shown in the figure below.


The more newly developed gene-editing techniques of  prime editing  and  base editing, [ 18 ]  based on CRISPR-Cas methods, are alternatives to gene targeting, which can also create user-defined edits at targeted genomic locations. However each is limited in the length of DNA sequence insertion possible; base editing is limited to single base pair conversions [ 21 ]  while prime editing can only insert sequences of up to ~44bp. [ 22 ] [ 23 ]  Hence GT remains the primary method of targeted (location-specific) insertion of long DNA sequences for genome engineering.


Gene trapping  is based on random insertion of a cassette, while gene targeting manipulates a specific gene. Cassettes can be used for many different things while the flanking homology regions of gene targeting cassettes need to be adapted for each gene. This makes gene trapping more easily amenable for large scale projects than targeting. On the other hand, gene targeting can be used for genes with low transcriptions that would go undetected in a trap screen.  The probability of trapping increases with  intron  size, while for gene targeting, small genes are just as easily altered.


Gene targeting was developed in mammalian cells in the 1980s, [ 24 ] [ 25 ] [ 26 ]  with diverse applications possible as a result of being able to make specific sequence changes at a target genomic site, such as the study of gene function or human disease, particularly in mice models. [ 27 ]  Indeed, gene targeting has been widely used to study human genetic diseases by removing (" knocking out "), or adding (" knocking in "), specific mutations of interest. [ 28 ] [ 29 ]  Previously used to engineer rat cell models, [ 30 ] [ 31 ]  advances in gene targeting technologies enable a new wave of  isogenic human disease models . These models are the most accurate  in vitro  models available to researchers and facilitate the development of personalized drugs and diagnostics, particularly in  oncology . [ 32 ]  Gene targeting has also been investigated for  gene therapy  to correct disease-causing mutations. However the low efficiency of delivery of the gene-targeting machinery into cells has hindered this, with research conducted into viral vectors for gene targeting to try and address these challenges. [ 33 ]


Gene targeting is relatively high efficiency in yeast, bacterial and moss (but is rare in higher eukaryotes). Hence gene targeting has been used in reverse genetics approaches to study gene function in these systems. [ 34 ] [ 35 ] [ 36 ] [ 37 ] [ 38 ]


Gene targeting (GT), or homology-directed repair (HDR), is used routinely in plant genome engineering to insert specific sequences, [ 39 ]   with the first published example of GT in plants in the 1980s. [ 15 ]  However, gene targeting is particularly challenging in higher plants due to the low rates of Homologous Recombination, or Homology Directed Repair, in higher plants and the low rate of transformation (DNA uptake) by many plant species. [ 40 ]  However, there has been much effort to increase the frequencies of gene targeting in plants in the past decades, [ 39 ] [ 40 ] [ 41 ] [ 42 ]  as it is very useful to be able to introduce specific sequences in the plant genome for plant genome engineering. The most significant improvement to gene targeting frequencies in plants was the induction of double-strand-breaks through site specific nucleases such as CRISPR, as described above. Other strategies include  in planta  gene targeting, whereby the homology repair template is embedded within the plant genome and then liberated using CRISPR cutting; [ 43 ]  upregulation of genes involved in the homologous recombination pathway; downregulation of the competing Non-Homologous-End-Joining pathway; [ 39 ]  increasing copy numbers of the homologous repair template; [ 44 ]  and engineering Cas variants to be optimised for plant tissue culture. [ 45 ]  Some of these approaches have also been used to improve gene targeting efficiencies in mammalian cells. [ 46 ]


Plants that have been gene-targeted include  Arabidopsis thaliana  (the most commonly used  model plant ), rice, tomato, maize, tobacco and wheat. [ 40 ]


Gene targeting holds enormous promise to make targeted, user-defined sequence changes or sequence insertions in the genome. However its primary applications - human disease modelling and plant genome engineering - are hindered by the low efficiency of homologous recombination in comparison to the competing non-homologous end joining in mammalian and higher plant cells. [ 47 ]  As described above, there are strategies that can be employed to increase the frequencies of gene targeting in plants and mammalian cells. [ 37 ]  In addition, robust selection methods that allow the selection or specific enrichment of cells where gene targeting has occurred can increase the rates of recovery of gene-targeted cells. [ 48 ]


Mario R. Capecchi ,  Martin J. Evans  and  Oliver Smithies  were awarded the 2007  Nobel Prize in Physiology or Medicine  for their work on "principles for introducing specific gene modifications in mice by the use of embryonic stem cells", or gene targeting. [ 49 ]


As explained above, Gene Targeting is technically capable of creating a range of sizes of genetic changes; from single base-pair mutations through to insertion of longer sequences, including potentially transgenes. This means that products of gene targeting can be indistinguishable from natural mutation, or can be equivalent to GMOs due to their insertion of a transgene (see Venn diagram above). Hence regulating products of Gene Targeting can be challenging and different countries have taken different approaches or are reviewing how to do so as part of broader regulatory reviews into the products of gene-editing. [ 50 ] [ 51 ] [ 52 ]  Broadly adopted classifications split gene-edited organisms into 3 classes of "SDN1-3", referring to Site Directed Nucleases (such as CRISPR-Cas) that are used to generate gene-edited organisms. [ 53 ] [ 16 ]  These SDN classifications can guide national regulations as to which class of SDN they will consider to be ‘GMOs’ and therefore which are subject to potentially strict regulations.


Historically the  European Union  (EU) has broadly been opposed to Genetic Modification technology, on grounds of its  precautionary principle . In 2018 the  European Court of Justice  (ECJ) ruled that gene-edited crops (including gene-targeted crops) should be considered as genetically modified [ 55 ]  and therefore were subject to the GMO Directive, which places significant regulatory burdens on GMO use. However this decision was received negatively by the European scientific community. [ 56 ]  In 2021 the  European Commission  deemed that current EU legislation governing Genetic Modification and Gene-Editing techniques (or NGTs – New Genomic Techniques) was ‘not fit for purpose’ and needed adapting to reflect scientific and technological progress. [ 57 ]  In July 2023 the European Commission published a proposal to change rules for certain products of gene-editing to reduce the regulatory requirements for organisms developed with gene-editing that contained genetic changes that could have occurred naturally. [ 58 ]


A  genealogical DNA test  is a  DNA -based  genetic test  used in  genetic genealogy  that looks at specific locations of a person's  genome  in order to find or verify ancestral  genealogical  relationships, or (with lower reliability) to estimate the  ethnic  mixture of an individual. Since different testing companies use different ethnic reference groups and different matching algorithms, ethnicity estimates for an individual vary between tests, sometimes dramatically.


Three principal types of genealogical DNA tests are available, with each looking at a different part of the  genome  and being useful for different types of genealogical research:  autosomal  (atDNA),  mitochondrial  (mtDNA), and  Y-chromosome  (Y-DNA).


Autosomal tests may result in a large number of DNA matches to both males and females who have also tested with the same company. Each match will typically show an estimated degree of relatedness, i.e., a close family match, 1st-2nd cousins, 3rd-4th cousins, etc. The furthest degree of relationship is usually the "6th-cousin or further" level. However, due to the random nature of which, and how much, DNA is inherited by each tested person from their common ancestors, precise relationship conclusions can only be made for close relations. Traditional  genealogical research , and the sharing of family trees, is typically required for interpretation of the results. Autosomal tests are also used in estimating ethnic mix.


MtDNA and Y-DNA tests are much more objective. However, they give considerably fewer DNA matches, if any (depending on the company doing the testing), since they are limited to relationships along a strict  female line  and a strict  male line  respectively. MtDNA and Y-DNA tests are utilized to identify  archeological cultures  and migration paths of a person's ancestors along a strict mother's line or a strict father's line. Based on MtDNA and Y-DNA, a person's  haplogroup (s) can be identified. The mtDNA test can be taken by both males and females, because everyone inherits their mtDNA from their mother, as the  mitochondrial DNA  is located in the egg cell. However, a Y-DNA test can only be taken by a male, as only males have a  Y-chromosome .


A genealogical DNA test is performed on a DNA sample obtained by cheek-scraping (also known as a  buccal swab ), spit-cups,  mouthwash , or  chewing gum . Typically, the sample collection uses a home test kit supplied by a service provider such as  23andMe ,  AncestryDNA ,  Family Tree DNA , or  MyHeritage . After following the kit instructions on how to collect the sample, it is returned to the supplier for analysis. The sample is then processed using a technology known as  DNA microarray  to obtain the genetic information.


There are three major types of genealogical DNA tests:  Autosomal  (which includes X-DNA), Y-DNA, and mtDNA.


Y-DNA and mtDNA cannot be used for ethnicity estimates, but can be used to find one's  haplogroup , which is unevenly distributed geographically. [ 2 ]  Direct-to-consumer DNA test companies have often labeled  haplogroups  by continent or ethnicity (e.g., an "African haplogroup" or a "Viking haplogroup"), but these labels may be speculative or misleading. [ 2 ] [ 3 ] [ 4 ]


Autosomal DNA is contained in the 22 pairs of chromosomes not involved in determining a person's sex. [ 2 ]  Autosomal DNA recombines in each generation, and new offspring receive one set of chromosomes from each parent. [ 5 ]  These are inherited exactly equally from both parents and roughly equally from grandparents to about 3x great-grandparents. [ 6 ]  Therefore, the number of markers (one of two or more known variants in the  genome  at a particular location – known as  Single-nucleotide polymorphisms  or SNPs) inherited from a specific ancestor decreases by about half with each successive generation; that is, an individual receives half of their markers from each parent, about a quarter of those markers from each grandparent; about an eighth of those markers from each great-grandparent, etc. Inheritance is more random and unequal from more distant ancestors. [ 7 ]  Generally, a genealogical DNA test might test about 700,000 SNPs (specific points in the genome). [ 8 ]


The preparation of a report on the DNA in the sample proceeds in multiple stages:


All major service providers use equipment with chips supplied by  Illumina . [ 9 ]  The chip determines which SNP locations are tested. Different versions of the chip are used by different service providers. In addition, updated versions of the Illumina chip may test different sets of SNP locations. The list of SNP locations and base pairs at that location is usually available to the customer as "raw data". The raw data can be uploaded to some other genealogical service providers to produce an additional interpretation and matches. For additional genealogical analysis the data can also be uploaded to GEDmatch (a third-party web based set of tools that analyzes raw data from the main service providers).  Raw data can also be uploaded to services that provide health risk and trait reports using SNP genotypes.  These reports may be free or inexpensive, in contrast to reports provided by DTC testing companies, who charge about double the cost of their genealogy-only services.  The implications of individual SNP results can be ascertained from raw data results by referring to SNPedia.com.


The major component of an autosomal DNA test is matching other individuals. Where the individual being tested has a number of consecutive SNPs in common with a previously tested individual in the company's database, it can be inferred that they share a segment of DNA at that part of their genomes. [ 10 ]  If the segment is longer than a threshold amount set by the testing company, then these two individuals are considered to be a match. Unlike the identification of base pairs, the data bases against which the new sample is tested, and the algorithms used to determine a match, are proprietary and specific to each company.


The unit for segments of DNA is the  centimorgan  (cM). For comparison, a full human genome is about 6500 cM. The shorter the length of a match, the greater are the chances that a match is spurious. [ 11 ]  An important statistic for subsequent interpretation is the length of the shared DNA (or the percentage of the genome that is shared).


Most companies will show the customers how many cMs they share and across how many segments. From the number of cMs and segments, the relationship between the two individuals can be estimated; however, due to the random nature of DNA inheritance, relationship estimates, especially for distant relatives, are only approximate. Some more distant cousins will not match at all. [ 12 ]  Although information about specific SNPs can be used for some purposes (e.g., suggesting likely eye color), the key information is the percentage of DNA shared by two individuals. This can indicate the closeness of the relationship. However, it does not show the roles of the two individuals, e.g., 50% shared suggests a parent/child relationship, but it does not identify which individual is the parent.


Various advanced techniques and analyses can be done on this data. This includes features such as In-common/Shared Matches, [ 13 ]  Chromosome Browsers, [ 14 ]  and Triangulation. [ 15 ]  This analysis is often required if DNA evidence is being used to prove or disprove a specific relationship.


The X-chromosome SNP results are often included in autosomal DNA tests. Both males and females receive an X-chromosome from their mother, but only females receive a second X-chromosome from their father. [ 16 ]  The X-chromosome has a special path of inheritance patterns and can be useful in significantly narrowing down possible ancestor lines compared to autosomal DNA. For example, an X-chromosome match with a male can only have come from his maternal side. [ 17 ]  Like autosomal DNA, X-chromosome DNA undergoes random recombination at each generation (except for father-to-daughter X-chromosomes, which are passed down unchanged). There are specialized inheritance charts which describe the possible patterns of X-chromosome DNA inheritance for males and females. [ 18 ]


Some genealogical companies offer autosomal STRs (short tandem repeats). [ 19 ]  These are similar to Y-DNA STRs. The number of STRs offered is limited, and results have been used for personal identification, [ 20 ]  paternity cases, and inter-population studies. [ 21 ] [ 22 ]


Law enforcement agencies in the US and Europe use autosomal STR data to identify criminals. [ 19 ] [ 23 ]


The  mitochondrion  is a component of a human cell, and contains its own DNA. Mitochondrial DNA usually has 16,569 base pairs (the number can vary slightly depending on addition or deletion mutations) [ 24 ]  and is much smaller than the human genome DNA which has 3.2 billion base pairs. Mitochondrial DNA is transmitted from mother to child, as it is contained in the egg cell. Thus, a direct maternal ancestor can be traced using  mtDNA . The transmission occurs with relatively rare mutations compared to autosomal DNA. A perfect match found to another person's mtDNA test results indicates shared ancestry of possibly between 1 and 50 generations ago. [ 2 ]  More distant matching to a specific haplogroup or subclade may be linked to a common geographic origin.


The mtDNA, by current conventions, is divided into three regions. They are the coding region (00577-16023) and two  Hyper Variable Regions  (HVR1 [16024-16569], and HVR2 [00001-00576]). [ 25 ]


The two most common mtDNA tests are a sequence of HVR1 and HVR2 and a full sequence of the mitochondria. Generally, testing only the HVRs has limited genealogical use so it is increasingly popular and accessible to have a full sequence. The full mtDNA sequence is only offered by Family Tree DNA among the major testing companies [ 26 ]  and is somewhat controversial because the coding region DNA may reveal medical information about the test-taker [ 27 ]


All humans descend in the direct female line from  Mitochondrial Eve , a female who lived probably around 150,000 years ago in Africa. [ 28 ] [ 29 ]  Different branches of her descendants are different haplogroups. Most mtDNA results include a prediction or exact assertion of one's  mtDNA Haplogroup . Mitochrondial haplogroups were greatly popularized by the book  The Seven Daughters of Eve , which explores mitochondrial DNA.


It is not normal for test results to give a base-by-base list of results. Instead, results are normally compared to the  Cambridge Reference Sequence  (CRS), which is the mitochondria of a European who was the first person to have their mtDNA published in 1981 (and revised in 1999). [ 30 ]  Differences between the CRS and testers are usually very few, thus it is more convenient than listing one's raw results for each base pair.


Note that in HVR1, instead of reporting the base pair exactly, for example 16,111, the 16 is often removed to give in this example 111. The letters refer to one of the four bases (A, T, G, C) that make up DNA.


The Y-chromosome is one of the 23rd pair of human chromosomes. Only males have a Y-chromosome, because women have two X chromosomes in their 23rd pair. A man's  patrilineal  ancestry, or male-line ancestry, can be traced using the DNA on his  Y-chromosome  (Y-DNA), because the Y-chromosome is transmitted from a father to son nearly unchanged. [ 31 ]  A man's test results are compared to another man's results to determine the time frame in which the two individuals shared a  most recent common ancestor , or MRCA, in their direct patrilineal lines. If their test results are very close, they are related within a genealogically useful time frame. [ 32 ]  A  surname project  is where many individuals whose Y-chromosomes match collaborate to find their common ancestry.


Women who wish to determine their direct paternal DNA ancestry can ask their father, brother, paternal uncle, paternal grandfather, or a paternal uncle's son (their cousin) to take a test for them.


There are two types of DNA testing: STRs and SNPs. [ 2 ]


Most common is  STRs  (short tandem repeat). A certain section of DNA is examined for a pattern that repeats (e.g. ATCG). The number of times it repeats is the value of the marker. Typical tests test between 12 and 111 STR markers. STRs mutate fairly frequently. The results of two individuals are then compared to see if there is a match. DNA companies will usually provide an estimate of how closely related two people are, in terms of generations or years, based on the difference between their results. [ 33 ]


A person's  haplogroup  can often be inferred from their STR results, but can be proven only with a Y-chromosome SNP test (Y-SNP test).


A  single-nucleotide polymorphism  (SNP) is a change to a single nucleotide in a DNA sequence. Typical Y-DNA SNP tests test about 20,000 to 35,000 SNPs. [ 34 ]  Getting a SNP test allows a much higher resolution than STRs. It can be used to provide additional information about the relationship between two individuals and to confirm haplogroups.


All human men descend in the paternal line from a single man dubbed  Y-chromosomal Adam , who lived probably between 200,000 and 300,000 years ago. [ 35 ] [ 36 ]  A 'family tree' can be drawn showing how men today descend from him. Different branches of this tree are different haplogroups. Most haplogroups can be further subdivided multiple times into sub-clades. Some known sub-clades were founded in the last 1000 years, meaning their timeframe approaches the genealogical era (c.1500 onwards). [ 37 ]


New sub-clades of haplogroups may be discovered when an individual tests, especially if they are non-European. Most significant of these new discoveries was in 2013 when the  haplogroup A00  was discovered, which required theories about Y-chromosomal Adam to be significantly revised. The haplogroup was discovered when an African-American man tested STRs at FamilyTreeDNA and his results were found to be unusual. SNP testing confirmed that he does not descend patrilineally from the "old" Y-chromosomal Adam and so a much older man became Y-Chromosomal Adam.


Many companies offer a percentage breakdown by ethnicity or region. Generally the world is specified into about 20–25 regions, and the approximate percentage of DNA inherited from each is stated. This is usually done by comparing the frequency of each  Autosomal DNA  marker tested to many population groups. [ 2 ]  The reliability of this type of test is dependent on comparative population size, the number of markers tested, the ancestry informative value of the SNPs tested, and the degree of admixture in the person tested. Earlier ethnicity estimates were often wildly inaccurate, but as companies receive more samples over time, ethnicity estimates have become more accurate. Testing companies such as  Ancestry.com  will often regularly update their ethnicity estimates, which has caused some controversy from customers as their results update. [ 38 ] [ 39 ]  Usually the results at the continental level are accurate, but more specific assertions of the test may turn out to be incorrect. [ citation needed ]


The interest in genealogical DNA tests has been linked to both an increase in curiosity about traditional genealogy and to more general personal origins. Those who test for traditional genealogy often utilize a combination of autosomal, mitochondrial, and Y-Chromosome tests. Those with an interest in personal ethnic origins are more likely to use an autosomal test. However, answering specific questions about the ethnic origins of a particular lineage may be best suited to an mtDNA test or a Y-DNA test.


For recent genealogy, exact matching on the mtDNA full sequence is used to confirm a common ancestor on the direct maternal line between two suspected relatives. Because mtDNA mutations are very rare, a  nearly  perfect match is not usually considered relevant to the most recent 1 to 16 generations. [ 40 ]  In cultures lacking  matrilineal surnames  to pass down, neither relative above is likely to have as many generations of ancestors in their matrilineal information table as in the above patrilineal or Y-DNA case: for further information on this difficulty in  traditional genealogy , due to lack of  matrilineal  surnames (or matrinames), see  Matriname . [ 41 ]  However, the foundation of testing is still two suspected descendants of one person. This hypothesize and test DNA pattern is the same one used for autosomal DNA and Y-DNA.


As discussed above, autosomal tests usually report the ethnic proportions of the individual. These attempt to measure an individual's mixed geographic heritage by identifying particular markers, called ancestry informative markers or AIM, that are associated with populations of specific geographical areas. Geneticist  Adam Rutherford  has written that these tests "don’t necessarily show your geographical origins in the past. They show with whom you have common ancestry today." [ 42 ]


The haplogroups determined by Y-DNA and mtDNA tests are often unevenly geographically distributed. Many direct-to-consumer DNA tests described this association to infer the test-taker's ancestral homeland. [ 4 ]  Most tests describe haplogroups according to their most frequently associated continent (e.g., a "European haplogroup"). [ 4 ]  When Leslie Emery and collaborators performed a trial of mtDNA haplogroups as a predictor of continental origin on individuals in the Human Genetic Diversity Panel (HGDP) and 1000 Genomes (1KGP) datasets, they found that only 14 of 23 haplogroups had a success rate above 50% among the HGDP samples, as did "about half" of the haplogroups in the 1KGP. [ 4 ]  The authors concluded that, for most people, "mtDNA-haplogroup membership provides limited information about either continental ancestry or continental region of origin." [ 4 ]


Y-DNA and mtDNA testing may be able to determine with which peoples in present-day  Africa  a person shares a direct line of part of his or her ancestry, but patterns of historic migration and historical events cloud the tracing of ancestral groups. Due to joint long histories in the US, approximately 30% of  African American  males have a European  Y-Chromosome haplogroup [ 43 ]  Approximately 58% of African Americans have at least the equivalent of one great-grandparent (13%) of European ancestry. Only about 5% have the equivalent of one great-grandparent of Native American ancestry. By the early 19th century, substantial families of Free Persons of Color had been established in the  Chesapeake Bay  area who were descended from free people during the colonial period; most of those have been documented as descended from white men and African women (servant, slave or free). Over time various groups married more within mixed-race, black or white communities. [ 44 ]


According to authorities like Salas, nearly three-quarters of the ancestors of African Americans taken in  slavery  came from regions of West Africa. The African-American movement to discover and identify with ancestral tribes has burgeoned since DNA testing became available. African Americans usually cannot easily trace their ancestry during the years of slavery through  surname research ,  census  and property records, and other traditional means. Genealogical DNA testing may provide a tie to regional African heritage.


Melungeons  are one of numerous multiracial groups in the United States with origins wrapped in myth. The historical research of Paul Heinegg has documented that many of the Melungeon groups in the Upper South were descended from mixed-race people who were free in colonial Virginia and the result of unions between the Europeans and Africans. They moved to the frontiers of Virginia, North Carolina, Kentucky and Tennessee to gain some freedom from the racial barriers of the plantation areas. [ 45 ]  Several efforts, including a number of ongoing studies, have examined the genetic makeup of families historically identified as Melungeon. Most results point primarily to a mixture of European and African, which is supported by historical documentation. Some may have Native American heritage as well. Though some companies provide additional Melungeon research materials with Y-DNA and mtDNA tests, any test will allow comparisons with the results of current and past Melungeon DNA studies.


The  pre-columbian  indigenous people of the United States are called "Native Americans" in American English. [ 46 ]  Autosomal testing, Y-DNA, and mtDNA testing can be conducted to determine the ancestry of  Native Americans . A mitochondrial Haplogroup determination test based on mutations in  Hypervariable Region  1 and 2 may establish whether a person's direct female line belongs to one of the canonical Native American Haplogroups,  A ,  B ,  C ,  D  or  X . The vast majority of Native American individuals belong to one of the five identified  mtDNA   Haplogroups . Thus, being in one of those groups provides evidence of potential Native American descent. However, DNA ethnicity results cannot be used as a substitute for legal documentation. [ 47 ]   Native American tribes  have their own requirements for membership, often based on at least one of a person's ancestors having been included on tribal-specific Native American censuses (or final rolls) prepared during  treaty -making, relocation to  reservations  or apportionment of land in the late 19th century and early 20th century. One example is the  Dawes Rolls .


The  Cohanim  (or Kohanim) is a  patrilineal  priestly line of descent in  Judaism . According to the  Bible , the ancestor of the Cohanim is  Aaron , brother of  Moses . Many believe that descent from Aaron is verifiable with a Y-DNA test: the first published study in genealogical Y-Chromosome DNA testing found that a significant percentage of Cohens had distinctively similar DNA, rather more so than general Jewish or Middle Eastern populations. These Cohens tended to belong to  Haplogroup J , with Y-STR values clustered unusually closely around a haplotype known as the  Cohen Modal Haplotype  (CMH). This could be consistent with a shared common ancestor, or with the hereditary priesthood having originally been founded from members of a single closely related clan.


Nevertheless, the original studies tested only six Y-STR markers, which is considered a low-resolution test. In response to the low resolution of the original 6-marker CMH, the testing company FTDNA released a 12-marker CMH signature that was more specific to the large closely related group of Cohens in Haplogroup J1.


A further academic study published in 2009 examined more STR markers and identified a more sharply defined SNP haplogroup,  J1e*  (now J1c3, also called J-P58*) for the J1 lineage. The research found "that 46.1% of Kohanim carry Y chromosomes belonging to a single paternal lineage (J-P58*) that likely originated in the Near East well before the dispersal of Jewish groups in the Diaspora. Support for a Near Eastern origin of this lineage comes from its high frequency in our sample of  Bedouins , Yemenis (67%), and Jordanians (55%) and its precipitous drop in frequency as one moves away from Saudi Arabia and the Near East (Fig. 4). Moreover, there is a striking contrast between the relatively high frequency of J-58* in Jewish populations (»20%) and Kohanim (»46%) and its vanishingly low frequency in our sample of non-Jewish populations that hosted Jewish diaspora communities outside of the Near East." [ 48 ]


Recent phylogenetic research for haplogroup J-M267 placed the "Y-chromosomal Aaron" in a subhaplogroup of J-L862, L147.1 (age estimate 5631-6778yBP yBP): YSC235>PF4847/CTS11741>YSC234>ZS241>ZS227>Z18271 (age estimate 2731yBP). [ 49 ]


Genealogical DNA tests have become popular due to the ease of testing at home and their usefulness in supplementing  genealogical research . Genealogical DNA tests allow for an individual to determine with high accuracy whether he or she is related to another person within a certain time frame, or with certainty that he or she is not related. DNA tests are perceived as more scientific, conclusive and expeditious than searching the civil records. However, they are limited by restrictions on lines that may be studied. The civil records are always only as accurate as the individuals having provided or written the information.


Y-DNA testing  results are normally stated as probabilities: For example, with the same surname a perfect 37/37 marker test match gives a 95% likelihood of the most recent common ancestor (MRCA) being within 8 generations, [ 50 ]  while a 111 of 111 marker match gives the same 95% likelihood of the MRCA being within only 5 generations back. [ 51 ]


As presented above in  mtDNA testing , if a perfect match is found, the mtDNA test results can be helpful. In some cases, research according to traditional genealogy methods encounters difficulties due to the lack of regularly recorded matrilineal surname information in many cultures (see  Matrilineal surname ). [ 41 ]


Autosomal DNA combined with genealogical research has been used by adoptees to find their biological parents, [ 52 ]  has been used to find the name and family of unidentified bodies [ 53 ] [ 54 ]  and by law enforcement agencies to apprehend criminals [ 55 ] [ 56 ]  (for example, the  Contra Costa County  District Attorney's office used the "open-source" genetic genealogy site  GEDmatch  to find relatives of the suspect in the  Golden State Killer  case. [ 57 ] [ 58 ] ).  The Atlantic  magazine commented in 2018 that "Now, the floodgates are open. ..a small, volunteer-run website, GEDmatch.com, has become ... the de facto DNA and genealogy database for all of law enforcement." [ 59 ]  Family Tree DNA announced in February 2019 it was allowing the FBI to access its DNA data for cases of murder and rape. [ 60 ]  However, in May 2019 GEDmatch initiated stricter rules for accessing their autosomal DNA database [ 61 ]  and Family Tree DNA shut down their Y-DNA database ysearch.org, making it more difficult for law enforcement agencies to solve cases. [ 62 ]


Common concerns about genealogical DNA testing are cost and  privacy issues . [ 63 ]  Some testing companies, such as  23andMe  and  Ancestry , [ 64 ]  retain samples and results for their own use without a privacy agreement with subjects. [ 65 ] [ 66 ]


Autosomal DNA tests can identify relationships but they can be misinterpreted. [ 67 ] [ 68 ] [ 69 ]  For example, transplants of stem cell or bone marrow will produce matches with the donor. In addition, identical twins (who have identical DNA) can give unexpected results. [ 70 ]


Testing of the Y-DNA lineage from father to son may reveal complications, due to unusual mutations, secret adoptions, and  non-paternity events  (i.e., that the perceived father in a generation is not the father indicated by written birth records). [ 71 ]  According to the Ancestry and Ancestry Testing Task Force of the  American Society of Human Genetics , autosomal tests cannot detect "large portions" of DNA from distant ancestors because it has not been inherited. [ 72 ]


With the increasing popularity of the use of DNA tests for ethnicity tests, uncertainties and errors in ethnicity estimates are a drawback for Genetic genealogy. While ethnicity estimates at the continental level should be accurate (with the possible exception of East Asia and the Americas), sub-continental estimates, especially in Europe, are often inaccurate. Customers may be misinformed about the uncertainties and errors of the estimates. [ 73 ]


Some have recommended government or other regulation of ancestry testing to ensure its performance to an agreed standard. [ 74 ]


A number of law enforcement agencies took legal action to compel
genetic genealogy companies to release genetic information that could match  cold case  crime victims [ 75 ]  or perpetrators. A number of companies fought the requests. [ 76 ]


The popular consciousness of DNA testing and of DNA generally is subject to a number of misconceptions involving the reliability of testing, the nature of the connections with one's ancestors, the connection between DNA and personal traits, etc. [ 77 ]


Though genealogical DNA tests are not designed mainly for medical purposes, autosomal DNA tests can be used to analyze the probability of hundreds of heritable medical conditions, [ 78 ]  albeit the result is complex to understand and may confuse a non-expert. 23andMe provides medical and trait information from their genealogical DNA test [ 79 ]  and for a fee the  Promethease  web site analyses genealogical DNA test data from Family Tree DNA, 23andMe, or AncestryDNA for medical information. [ 80 ]  Promethease, and its research paper crawling database SNPedia, has received criticism for technical complexity and a poorly defined "magnitude" scale that causes misconceptions, confusion and panic among its users. [ 81 ]


The testing of full MtDNA and YDNA sequences is still somewhat controversial as it may reveal even more medical information. For example, a correlation exists between a lack of  Y-DNA marker DYS464  and  infertility , and between  mtDNA haplogroup H  and protection from  sepsis . Certain haplogroups have been linked to longevity in some population groups. [ 82 ] [ 83 ]  The field of linkage disequilibrium, unequal association of genetic disorders with a certain mitochondrial lineage, is in its infancy, but those mitochondrial mutations that have been linked are searchable in the genome database Mitomap. [ 84 ]  Family Tree DNA's MtFull Sequence test analyses the full MtDNA genome [ 26 ]  and the National Human Genome Research Institute operates the Genetic And Rare Disease Information Center [ 85 ]  that can assist consumers in identifying an appropriate  screening test  and help locate a nearby medical center that offers such a test.


The first company to provide  direct-to-consumer  genealogical DNA tests was the now defunct  GeneTree . However, it did not offer multi-generational genealogy tests. In fall 2001, GeneTree sold its assets to Salt Lake City-based  Sorenson Molecular Genealogy Foundation  (SMGF) which originated in 1999. [ 86 ]
While in operation, SMGF provided free Y-chromosome and mitochondrial DNA tests to thousands. [ 87 ]
Later, GeneTree returned to genetic testing for genealogy in conjunction with the Sorenson parent company and eventually was part of the assets acquired in the  Ancestry.com  buyout of SMGF in 2012. [ 88 ] [ 89 ]


In 2000,  Family Tree DNA , founded by  Bennett Greenspan  and Max Blankfeld, was the first company dedicated to direct-to-consumer testing for genealogy research. They initially offered eleven-marker Y-Chromosome STR tests and HVR1 mitochondrial DNA tests. They originally tested in partnership with the University of Arizona. [ 90 ] [ 91 ]
 [ 92 ] [ 93 ]
 [ 94 ]


In 2007,  23andMe  was the first company to offer a  saliva -based  direct-to-consumer genetic testing . [ 95 ]  It was also the first to implement the use of autosomal DNA for ancestry testing, which other major companies (e.g., Ancestry, Family Tree DNA, and  MyHeritage ) now use. [ 96 ] [ 97 ]


MyHeritage  launched its genetic testing service in 2016, allowing users to use  cheek swabs  to collect samples. [ 98 ]  In 2019, new analysis tools were presented: autoclusters (grouping all matches visually into clusters) [ 99 ]  and family tree theories (suggesting conceivable relations between DNA matches by combining several Myheritage trees as well as the Geni global family tree). [ 100 ]


Living DNA , founded in 2015, also provides a genetic testing service. Living DNA uses  SNP chips  to provide reports on autosomal ancestry, Y, and mtDNA ancestry. [ 101 ] [ 102 ]  Living DNA provides detailed reports on ancestry from the UK as well as detailed Y chromosome and mtDNA reports. [ 103 ] [ 104 ] [ 105 ]


In 2019 it was estimated that large genealogical testing companies had about 26 million DNA profiles. [ 106 ] [ 107 ]  Many transferred their test result for free to multiple testing sites, and also to genealogical services such as  Geni.com  and  GEDmatch . GEDmatch said in 2018 that about half of their one million profiles were from the USA. [ 107 ]


Some  genealogy software  programs – such as Family Tree Maker, Legacy Family Tree (Deluxe Edition) and the Swedish program Genney – allow recording DNA marker test results. This allows for tracking of both Y-chromosome and mtDNA tests, and recording results for relatives. [ 108 ]


A  genetic marker  is a  gene  or  DNA sequence  with a known location on a  chromosome  that can be used to identify individuals or  species . It can be described as a variation (which may arise due to mutation or alteration in the genomic loci) that can be observed. A genetic marker may be a short DNA sequence, such as a sequence surrounding a single base-pair change ( single nucleotide polymorphism , SNP), or a long one, like  minisatellites .


For many years,  gene mapping  was limited to identifying organisms by traditional phenotypes  markers. This included genes that encoded easily observable characteristics, such as blood types or seed shapes. The insufficient number of these types of characteristics in several organisms limited the possible mapping efforts. This prompted the development of gene markers, which could identify genetic characteristics that are not readily observable in organisms (such as protein variation). [ 1 ]


Some commonly used types of genetic markers are:


Molecular genetic markers can be divided into two classes: a) biochemical markers which detect variation at the gene product level such as changes in proteins and amino acids and b) molecular markers which detect variation at the DNA level such as nucleotide changes: deletion, duplication, inversion and/or insertion. Markers can exhibit two modes of inheritance, i.e. dominant/recessive or co-dominant. If the genetic pattern of homo-zygotes can be distinguished from that of hetero-zygotes, then a marker is said to be co-dominant. Generally co-dominant markers are more informative than the dominant markers. [ 3 ]


Genetic markers can be used to study the relationship between an  inherited disease  and its  genetic  cause (for example, a particular  mutation  of a  gene  that results in a defective  protein ). It is known that pieces of DNA that lie near each other on a chromosome tend to be inherited together. This property enables the use of a marker, which can then be used to determine the precise inheritance pattern of the gene that has not yet been exactly localized.