For example, a group of 10 twins have been pre-selected to have one affected member (of the pair). During the course of the study four other previously non-affected members become affected, giving a pairwise concordance of 4/(4+6) or 4/10 or 40%.


For a group of twins in which at least one member of each pair is affected,  probandwise concordance  is a measure of the proportion of twins who have the illness who have an affected twin and can be calculated with the formula of 2C/(2C+D), in which C is the number of concordant pairs and D is the number of discordant pairs.


For example, consider a group of 10 twins that have been pre-selected to have one affected member. During the course of the study, four other previously non-affected members become affected, giving a probandwise concordance of 8/ (8+6)  or 8/14 or  57% .


Several academic bodies exist to support behavior genetic research, including the  Behavior Genetics Association , the  International Society for Twin Studies , and the  International Behavioural and Neural Genetics Society . Behavior genetic work also features prominently in several more general societies, for instance the  International Society of Psychiatric Genetics .


Prominent specialist journals in the field include  Behavior Genetics ,  Genes, Brain and Behavior , and  Twin Research and Human Genetics .


Varroa sensitive hygiene  ( VSH ) is a behavioral trait of  honey bees   (Apis mellifera)  in which bees detect and remove bee  pupae  that are infested by the  parasitic   mite   Varroa destructor . V. destructor  is considered to be the most dangerous  pest  problem for honey bees worldwide. VSH activity results in significant resistance to the mites.


Some subspecies of  Apis mellifera  show naturally Varroa sensitive hygiene, for example  Apis mellifera lamarckii [ 1 ]  and  Apis mellifera carnica . [ 2 ]


Bees with the VSH trait were initially bred by the  United States Department of Agriculture  Honey Bee Breeding, Genetics and Physiology Laboratory in  Baton Rouge, Louisiana , from colonies in which mite populations grew only slowly. [ 3 ]


The factor causing slow mite  population growth  was found to be  heritable . [ 4 ]  The rate of mite population growth was found to be correlated with the reproductive rates of mites, [ 3 ]  resulting in naming the factor "suppressed mite reproduction" (SMR). [ 5 ]  It was later discovered that the factor is founded on  hygienic activity  of adult bees, [ 6 ] [ 7 ]  so SMR was renamed VSH. The behavior involves nest cleaning bees recognizing infested brood aged 15–18 days old (emergence occurs at 21 days old). Mite-infested bee pupae are removed from their brood cells, which kills any immature varroa mites present. [ 8 ]


VSH activity results in (1) an abnormally low proportion of mites that produce offspring within the population that remains in capped brood and (2) reducing the brood infestation rate by greater than 70%.  The specifics of how hygienic bees detect mite infested  brood  currently are unknown.


Bees bred to have high levels of VSH can keep mite populations below thresholds recommended for  Varroa  treatment including  miticides . [ 9 ]   Queens  from such VSH breeding sources can be allowed to mate freely with non-VSH  drones , and the resulting  hybrid  colonies from these  outcrosses  will retain lower and variable but generally still useful resistance to  V. destructor  while retaining desirable  beekeeping  traits such as  honey  production. [ 10 ]


VSH outcrossed to commercial  Italian bees  recently have been shown to perform well in migratory  crop   pollination . [ 11 ] [ 12 ]


VSH thus is a trait that can be used by  breeders  to mix with any type of desirable honey bee and is expanding resistance to  V. destructor  among diverse bee strains. In 2018 it was announced that VSH  Buckfast  bees would be made available for sale in 2019. [ 13 ]


The  Virginia Institute for Psychiatric and Behavioral Genetics  (abbreviated  VIPBG ) is a  human genetics   research center , located at  Virginia Commonwealth University  (VCU), that aims to study the role played by genetic factors in the etiology of  psychiatric conditions  and  substance abuse . [ 1 ]  It was co-founded in 1996 by VCU psychiatry professors  Kenneth Kendler  and  Lindon Eaves . [ 2 ]  Kendler serves as the Institute's director and the director of its Psychiatric Genetics Research Program (abbreviated PGRP), while Eaves is the director of the Institute's Genetic Epidemiology Research Group (abbreviated GERG). The PGRP and GERG are subsidiaries of the Department of Psychiatry and the Department of Human and Molecular Genetics, respectively, at the  VCU School of Medicine . The Institute's associate director is  Michael Neale . [ 3 ]  In 2017, the  Virginia General Assembly  passed a bill commending Kendler and the VIPBG, describing the latter as "...an exciting, highly collaborative research environment with a strong record of funding, research, and training, including more than 140 predoctoral and postdoctoral students over the past decade". [ 4 ]


The Welfare Trait: How State Benefits Affect Personality  is a 2015 book by Adam Perkins, Lecturer in the Neurobiology of Personality at  King's College London . [ 1 ]


Perkins claims that individuals with aggressive, rule-breaking and anti-social tendencies are over-represented among long-term  welfare  recipients. He calls this an "employment–resistant personality profile" and finds that it is heritable. [ 2 ]


The book was controversial. [ 3 ]  It initially attracted little attention, with the journal  Nature  refusing to review it. [ 2 ]  In 2016, a talk by Perkins was cancelled for fear of disruption. [ 4 ]  Perkins later wrote "I was no-platformed by student 'radicals' for telling the truth about welfare". [ 5 ]  That year, Perkins secretly gave a presentation on the book at the  London Conference on Intelligence . [ 6 ]


The Adam Smith Institute commended the book's "praiseworthy boldness". [ 7 ]  However the argument was criticised in  The Guardian  for cherry-picking the data, relying too heavily on mice studies, and resembling  eugenics . [ 8 ]  A professor at  University College London  reviewed the book negatively, claiming Perkins failed to prove causal links for his assertions, and that "his proposals are more likely to harm, then help, children." [ 8 ]


A 2017 review in the  British Journal of Psychiatry  wrote "it is true that there is good-quality evidence for the transmission of dysfunctional personality traits by epigenetic means across generations". [ 9 ]


In 2018, a correction to one of Perkins' papers underlying the book identified seven errors. [ 10 ]


This article about a book on  poverty  is a  stub . You can help Wikipedia by  expanding it .


The  University of Maryland  hosted a 1995 conference to examine and discuss the relationship between  genetics and crime . It took place from September 22 to 24 at the  Aspen Institute  in  Queenstown ,  Maryland . It was originally planned for 1992, but was effectively cancelled after the original proposal was met with fierce backlash and accusations of  racism .


In 1992,  UMD  professor David Wasserman set out to organize a meeting to discuss the potential  genetic  bases for  criminal behavior . The originally planned conference was entitled "Genetic Factors in Crime: Findings, Uses and Implications", and was scheduled to be held on the University of Maryland's College Park campus on October 9, 1992. [ 1 ] [ 2 ]  It was to be funded by a $78,000 grant from the  National Institutes of Health  (NIH). [ 3 ]


When the planned conference became known to the public, it faced swift backlash and condemnation from  civil rights  activists, as well as from  Peter Breggin , a prominent critic of  psychiatry . Breggin's claim that the conference was racist had attracted the attention of the  Congressional Black Caucus , which subsequently joined Breggin in protesting the conference, [ 4 ]  as did the  Association of Black Psychologists  and the  NAACP 's  Baltimore  chapter. [ 5 ]  Wasserman responded to the conference's critics by saying that the conference would aim to critically examine, not promote, research linking genes to crime, but this did not satisfy its critics. [ 6 ]  The backlash to  Alcohol, Drug Abuse, and Mental Health Administration  then-director  Frederick K. Goodwin 's controversial statement earlier that year, which compared inner cities in the United States to " jungles ", inspired further opposition. [ 7 ]  Breggin had publicly linked the planned conference both to Goodwin's remarks and to an anti- violence  initiative that had recently been proposed by then- Secretary of Health and Human Services   Louis Wade Sullivan . [ 6 ]


In July 1992, the NIH's  National Center for Human Genome Research  revoked the $78,000 grant that it had already given in response to the opposition, effectively cancelling it. That September, the NIH announced that it would be postponing the conference indefinitely, putting an end to the organizers' attempts to convince the NIH to reinstate the grant. [ 7 ] [ 8 ] [ 9 ]  In defending their decision to revoke the conference grant, it also cited a brochure promoting the conference and claimed that the brochure implied, falsely, that the NIH itself believed that there was a definitive link between genetics and crime. [ 10 ]  John W. Diggs, the NIH's then-deputy director for outside research, told  the New York Times  that the brochure suggested as scientific facts the link between genetics and crime and the potential for genetic tests to identify people predisposed to criminal behavior. Diggs further said that these statements "inflamed public opinion" and deviated significantly from the scope of the original grant proposal. [ 2 ]


The University of Maryland responded to the cancellation by claiming that the NIH's withdrawal of the grant was a violation of the University's  academic freedom [ 2 ]  and appealed the NIH's decision to cancel the conference. The appeal succeeded, and NIH funded the conference again, this time with a $133,000 grant. [ 11 ]  The NIH's new grant was awarded after Wasserman broadened the scope of topics to be covered in the conference, as well as allowing for more discussion on the desirability and potential implications of the research. This broader scope was reflected in the conference's new name, "The Meaning and Significance of Research on Genetics and Criminal Behavior", and also persuaded some critics of the original 1992 conference to attend the new one. [ 1 ]  After the change in scope, the conference ended up focusing "...far more on the significance of the research into the biology of violent behavior than it did on the research itself", as  Natalie Angier  noted. [ 12 ]  Despite the changed scope, however, Goodwin criticized the 1995 conference for being designed to attract controversy, rather than to address the topic from a scientific perspective. [ 7 ]


The conference took place from Friday, September 22 to Sunday, September 24, 1995, at the Aspen Institute, a liberal  think tank  in Queenstown, Maryland. In total, about 70 "biologists, criminologists, historians, and philosophers" attended the conference, according to  Science . [ 13 ]  Panelists included Diana Fishbein,  Dorothy Nelkin , Evan Balaban,  David Comings , [ 1 ]   Irving Gottesman , [ 14 ]   Adrian Raine , [ 3 ]  and  Adrienne Asch . [ 12 ]


On September 23, 1995, the second day of the conference, over 30 protesters with signs and  red flags  marched into the meeting. The protesters, who were affiliated with the  Progressive Labor Party , [ 12 ]  accused the conference's organizers of "pushing  genocide ". They went on to invade one of the conference rooms at the institute, where they grabbed a microphone and denounced the meeting. The protest was ended peacefully two hours later. [ 9 ]  Some of the protesters also compared the topics discussed at the conference to  eugenics , a sentiment echoed by panelist  Paul Billings . In the opening discussion, Billings asked, "How do we know the bad old days are over? I would argue that the evidence is that the bad old days aren't over." [ 9 ] [ 15 ]


On September 24, the third and last day of the conference,  Howard University  professor Rodney Green began addressing the audience just as they were leaving for a coffee break. Green told them, "We have to consider whether this conference should continue. What we heard this morning...was a racist presentation under the guise of genetics." [ 16 ]


After the conference ended, a report based on the talks given there was published in  Politics and the Life Sciences . [ 17 ]  Some of the conference talks were also published in the 2001 book  Genes and Criminal Behavior , published as part of  Cambridge University Press 's  Cambridge Studies in Philosophy and Public Policy  series. [ 18 ]


In  genetics ,  concordance  is the  probability  that a pair of individuals will both have a certain characteristic ( phenotypic trait ) given that one of the pair has the characteristic. Concordance can be measured with  concordance rates , reflecting the  odds  of one person having the trait if the other does. Important  clinical  examples include the chance of offspring having a certain disease if the mother has it, if the father has it, or if both parents have it. Concordance among  siblings  is similarly of interest: what are the odds of a subsequent offspring having the disease if an older child does? In  research , concordance is often discussed in the context of both members of a pair of  twins . Twins are concordant when both have or both lack a given trait.  [ 1 ]  The ideal example of concordance is that of  identical twins , because the genome is the same, an equivalence that helps in discovering causation via  deconfounding , regarding genetic effects versus  epigenetic  and environmental effects ( nature versus nurture ).


In contrast,  discordance  occurs when a similar trait is not shared by the persons.  Studies of twins have shown that genetic traits of  monozygotic twins  are fully concordant, whereas in  dizygotic twins , half of genetic traits are concordant, while the other half are discordant.  Discordant rates that are higher than concordant rates express the influence of the environment on twin traits. [ 2 ]


A  twin study  compares the concordance rate of identical twins to that of fraternal twins. This can help suggest whether a disease or a certain trait has a genetic cause. [ 3 ]  Controversial uses of twin data have looked at concordance rates for  homosexuality  and  intelligence . Other studies have involved looking at the genetic and environmental factors that can lead to increased LDL in women twins. [ 4 ]


Because identical twins are genetically virtually identical, it follows that a genetic pattern carried by one would very likely also be carried by the other. If a characteristic identified in one twin is caused by a certain gene, then it would also very likely be present in the other twin. Thus, the concordance rate of a given characteristic helps suggest whether or to what extent a characteristic is related to genetics.


There are several problems with this assumption:


In  genotyping  studies where DNA is directly assayed for positions of variance (see  SNP ), concordance is a measure of the percentage of  SNPs  that are measured as identical.  Samples from the same individual or identical twins theoretically have a concordance of 100%, but due to assaying errors and  somatic mutations , they are usually found in the range of 99% to 99.95%.  Concordance can therefore be used as a method of assessing the accuracy of a  genotyping   assay  platform.


Because a child inherits half of his or her  DNA  from each parent, parents and children, siblings, and  fraternal  (dizygotic) twins have a concordance that averages 50% using this measure.


DecodeME  is an ongoing  genome-wide association study  searching for genetic risk factors for  Myalgic encephalomyelitis/chronic fatigue syndrome  (ME/CFS). With a planned recruitment of 25,000 patients, it is expected to be the largest such study to date. [ 4 ] [ 5 ]  Recruitment closed on 15 November 2023 and results are expected in 2025. [ 6 ]


ME/CFS is a chronic medical condition that often causes significant disability, and whose cause is unknown. [ 7 ]  Genetic studies of ME/CFS have been done before, but without significant findings. The authors of a 2022 study suggested that research with more participants is needed to discover  statistically significant  differences. [ 8 ]


DecodeME aims to perform such a large study. It is being run as a partnership between  Action for ME  and the  University of Edinburgh 's MRC Human Genetics Unit, with  Chris Ponting  as chief investigator, and with £3.2 million in funding from the UK's  Medical Research Council  and the  National Institute for Health Research . [ 4 ]  The researchers have also worked with Forward ME and a group of patient advocates, the latter of which contributed to the design of the study. [ 9 ] [ 10 ]


The investigators hope the study's findings will inform further research into the pathology of ME/CFS and potential treatments. They also hope that discovering a genetic connection will help dispel some of the stigma around ME/CFS. [ 11 ]


The study announced receipt of funding in June 2020, and recruitment was opened on 12 September 2022. [ 4 ] [ 10 ] [ 12 ]  In January 2023, the team wrote that over 17,000 patients had completed the survey, of which almost 9,000 were sent collection kits. [ 13 ]  On 2 May 2023 they announced that over 10,000 people had been asked to provide samples. [ 14 ]


In June 2023, DecodeME made changes to their data analysis methodology that allowed them to invite additional participants to give DNA. [ 15 ]  Recruitment is expected to close on 15 November. [ 16 ]  Recruitment for the study closed on 15 November 2023. [ 17 ]


DecodeME is a genome-wide association study with a case-control design. Expected recruitment is at least 20,000 patients whose onset was not associated with COVID-19, and 5,000 people with  long COVID  who were diagnosed with ME/CFS after  COVID-19 .  DNA  will be collected by sending patients kits to collect  saliva  at home and control samples will be obtained from the  UK Biobank . [ 5 ] [ 18 ]  There will also be a survey to collect data on symptoms. [ 12 ]  Results are expected to be published by September 2024. [ 19 ]  If new risk factors are identified, it may enable further research into potential causes, tests, or treatments. [ 4 ] [ 20 ]


Digital sequence information  (DSI) is a placeholder term used in international policy fora, particularly the  Convention on Biological Diversity  (CBD), to refer to data derived from dematerialized  genetic resources  (GR).


The 2018 Ad Hoc Technical Expert Group on DSI reached consensus that the term was "not appropriate". [ 1 ]  Nevertheless, the term is generally agreed to include  nucleic acid sequence  data, and may be construed to include other data types derived from or linked to dematerialized genetic resources, including, for example,  protein  sequence data. [ 2 ] [ 3 ]  The appropriateness and meaning of this term remain controversial as evidenced by its continued placeholder status, post the 15th Conference of the Parties to the CBD. [ 4 ] [ 5 ] [ 6 ]  DSI is crucial to research in a wide range of contexts, including  public health ,  medicine ,  biodiversity ,  plant  and  animal breeding , and  evolution  research.


The  Nagoya Protocol , a component of the Convention on Biological Diversity, establishes a right for countries to regulate, and to share in benefits derived from, their nation's genetic resources by arranging  Access and Benefit Sharing Agreements  with users. Academic researchers, however, generally share DSI freely and openly online, following a set of principles that align with the  open science  movement. [ 5 ]  Open sharing of DSI is recognized to have broad benefits, and open science is a major and growing focus of international science policy. [ 7 ] [ 8 ]  This creates a perceived conflict with benefit sharing obligations, as individuals can access and use these  open data  without entering into benefit-sharing agreements.


Parties to the Convention on Biological Diversity have considered a range of policy options that strike different balances between these two important international policy goals. [ 4 ]  At COP 15, parties agreed to establish a multilateral mechanism for benefit-sharing from the use of DSI. [ 9 ]  At COP 16, parties agreed to implement this by establishing a fund to which commercial users of DSI would be expected to contribute a percent of their revenue (see  COP 16  below).


In May 2024, [ 10 ]  a  WIPO  Diplomatic Conference concluded the  International Treaty on Intellectual Property, Genetic Resources and Associated Traditional Knowledge (GRATK) [ 11 ] ,  which mandates disclosure requirements for  patents  based on genetic resources (GR). [ 12 ] [ 13 ]


Some observers to the negotiations claim that, under the final wording of the treaty, the disclosure requirements apply to patents based on DSI, as long as the DSI was necessary to the patented invention, and/or the invention depends on the specific properties of the DSI:


The draft versions of the Treaty previously contemplated mentions of the qualifier “ direct ” in the trigger. Drafts also contemplated mentions of the qualifier “ material ,” both in the trigger itself and in its definition. These qualifiers were deleted by the drafters, leaving  only the criteria of necessity  and  dependence on specific properties . If a GR was necessary to create a claimed invention, and the invention depends on such GR,  even if indirectly and/or immaterially , it falls under the scope of this Treaty. A claimed invention relying on DSI obtained from a GR will therefore have to disclose the GR from which the DSI derives. [ 14 ]


At the 2024 United Nations Biodiversity Conference of the Parties to the CBD in Cali (COP16), a decision was made to establish the Cali Fund, to which industries that use DSI, like pharma, cosmetics, nutraceuticals, and biotech, will be expected to contribute a small portion of their profits to support global biodiversity conservation. [ 15 ]  A methodology for implementation, including thresholds and contribution rates, is scheduled to be agreed at the seventeenth conference of the parties to the CBD. The fund will support biodiversity protection and reward Indigenous and forest communities, with 50% allocated to local and Indigenous groups. [ 16 ]


DSI is also an important concept in other international legally binding instruments with access and benefit-sharing obligations, including:


In  genomics , a  genome-wide association study  ( GWA study , or  GWAS ), is an  observational study  of a genome-wide set of  genetic variants  in different individuals to see if any variant is associated with a trait. GWA studies typically focus on associations between  single-nucleotide polymorphisms  (SNPs) and traits like major human diseases, but can equally be applied to any other genetic variants and any other organisms.


When applied to human data, GWA studies compare the DNA of participants having varying  phenotypes  for a particular trait or disease. These participants may be people with a disease (cases) and similar people without the disease (controls), or they may be people with different phenotypes for a particular trait, for example blood pressure. This approach is known as phenotype-first, in which the participants are classified first by their clinical manifestation(s), as opposed to  genotype-first . Each person gives a sample of DNA, from which millions of  genetic variants  are read using  SNP arrays . If there is significant statistical evidence that one type of the variant (one  allele ) is more frequent in people with the disease, the variant is said to be  associated  with the disease. The associated SNPs are then considered to mark a region of the human genome that may influence the risk of disease.


GWA studies investigate the entire genome, in contrast to methods that specifically test a small number of pre-specified genetic regions. Hence, GWAS is a  non-candidate-driven  approach, in contrast to  gene-specific candidate-driven studies . GWA studies identify SNPs and other variants in DNA associated with a disease, but they cannot on their own specify which genes are causal. [ 1 ] [ 2 ] [ 3 ]


The first successful GWAS published in 2002 studied myocardial infarction. [ 4 ]  This study design was then implemented in the landmark GWA 2005 study investigating patients with  age-related macular degeneration , and found two SNPs with significantly altered  allele frequency  compared to healthy controls. [ 5 ]  As of 2017 [update] , over 3,000 human GWA studies have examined over 1,800 diseases and traits, and thousands of SNP associations have been found. [ 6 ]  Except in the case of rare  genetic diseases , these associations are very weak, but while each individual association may not explain much of the risk, they provide insight into critical genes and pathways and can be important when considered  in aggregate .


Any two  human genomes  differ in millions of different ways.  There are small variations in the individual nucleotides of the genomes ( SNPs ) as well as many larger variations, such as  deletions ,  insertions  and  copy number variations . Any of these may cause alterations in an individual's traits, or  phenotype , which can be anything from disease risk to physical properties such as height. [ 8 ]  Around the year 2000, prior to the introduction of GWA studies, the primary method of investigation was through inheritance studies of  genetic linkage  in families. This approach had proven highly useful towards  single gene disorders . [ 9 ] [ 8 ] [ 10 ]  However, for common and complex diseases the results of genetic linkage studies proved hard to reproduce. [ 8 ] [ 10 ]  A suggested alternative to linkage studies was the  genetic association  study. This study type asks if the  allele  of a  genetic variant  is found more often than expected in individuals with the phenotype of interest (e.g. with the disease being studied). Early calculations on statistical power indicated that this approach could be better than linkage studies at detecting weak genetic effects. [ 11 ]


In addition to the conceptual framework several additional factors enabled the GWA studies. One was the advent of  biobanks , which are repositories of human genetic material that greatly reduced the cost and difficulty of collecting sufficient numbers of biological specimens for study. [ 12 ]  Another was the  International HapMap Project , which, from 2003 identified a majority of the common SNPs interrogated in a GWA study. [ 13 ]  The  haploblock structure  identified by HapMap project also allowed the focus on the subset of SNPs that would describe most of the variation. Also the development of the methods to genotype all these SNPs using  genotyping arrays  was an important prerequisite. [ 14 ]


The most common approach of GWA studies is the  case-control  setup, which compares two large groups of individuals, one healthy control group and one case group affected by a disease. All individuals in each group are typically genotyped at common known SNPs. The exact number of SNPs depends on the genotyping technology, but are typically one million or more. [ 7 ]  For each of these SNPs it is then investigated if the  allele frequency  is significantly altered between the case and the control group. [ 16 ]  In such setups, the fundamental unit for reporting effect sizes is the  odds ratio . The odds ratio is the ratio of two odds, which in the context of GWA studies are the odds of case for individuals having a specific allele and the odds of case for individuals who do not have that same allele.


Example : suppose that there are two alleles, T and C. The number of individuals in the case group having allele T is represented by 'A' and the number of individuals in the control group having allele T is represented by 'B'. Similarly, the number of individuals in the case group having allele C is represented by 'X' and the number of individuals in the control group having allele C is represented by 'Y'. In this case the odds ratio for allele T is A:B (meaning 'A to B', in standard odds terminology) divided by X:Y, which in mathematical notation is simply (A/B)/(X/Y).


When the allele frequency in the case group is much higher than in the control group, the odds ratio is higher than 1, and vice versa for lower allele frequency. Additionally, a  P-value  for the significance of the odds ratio is typically calculated using a simple  chi-squared test . Finding odds ratios that are significantly different from 1 is the objective of the GWA study because this shows that a SNP is associated with disease. [ 16 ]  Because so many variants are tested, it is standard practice to require the p-value to be lower than  5 × 10 −8  to consider a variant significant.


Variations on the case-control approach .  A common alternative to case-control GWA studies is the analysis of quantitative phenotypic data, e.g. height or  biomarker  concentrations or even  gene expression . Likewise, alternative statistics designed for  dominance  or  recessive  penetrance patterns can be used. [ 16 ]  Calculations are typically done using  bioinformatics software  such as SNPTEST and PLINK, which also include support for many of these alternative statistics. [ 15 ] [ 17 ]  GWAS focuses on the effect of individual SNPs. However, it is also possible that complex interactions among two or more SNPs ( epistasis ) might contribute to complex diseases. Due to the potentially exponential number of interactions, detecting statistically significant interactions in GWAS data is both computationally and statistically challenging. This task has been tackled in existing publications that use algorithms inspired from data mining. [ 18 ]  Moreover, the researchers try to integrate GWA data with other biological data such as  protein-protein interaction network  to extract more informative results. [ 19 ] [ 20 ]  Despite the previously perceived challenge posed by the vast number of SNP combinations, a recent study has successfully unveiled complete epistatic maps at a gene-level resolution in plants/Arabidopsis thaliana [ 21 ]


A key step in the majority of GWA studies is the  imputation  of genotypes at SNPs not on the genotype chip used in the study. [ 23 ]  This process greatly increases the number of SNPs that can be tested for association, increases the power of the study, and facilitates meta-analysis of GWAS across distinct cohorts. Genotype imputation is carried out by statistical methods that impute genotypic data to a set of reference panel of haplotypes, which typically have been densely genotyped using whole-genome sequencing. These methods take advantage of sharing of haplotypes between individuals over short stretches of sequence to impute alleles. Existing software packages for genotype imputation include IMPUTE2, [ 24 ]  Minimac, Beagle [ 25 ]  and MaCH. [ 26 ]


In addition to the calculation of association, it is common to take into account any variables that could potentially  confound  the results. Sex, age, and ancestry are common examples of confounding variables. Moreover, it is also known that many genetic variations are associated with the geographical and historical populations in which the mutations first arose. [ 27 ]  Because of this association, studies must take account of the geographic and ethnic background of participants by controlling for what is called  population stratification . If they did not do so, the studies could produce false positive results. [ 28 ]


After odds ratios and  P-values  have been calculated for all SNPs, a common approach is to create a  Manhattan plot . In the context of GWA studies, this plot shows the negative logarithm of the  P-value  as a function of genomic location. Thus the SNPs with the most significant association stand out on the plot, usually as stacks of points because of haploblock structure. Importantly, the P-value threshold for significance is corrected for  multiple testing  issues. The exact threshold varies by study, [ 29 ]  but the conventional  genome-wide significance  threshold is  5 × 10 −8  to be significant in the face of hundreds of thousands to millions of tested SNPs. [ 7 ] [ 16 ] [ 30 ]  GWA studies typically perform the first analysis in a discovery cohort, followed by validation of the most significant SNPs in an independent validation cohort. [ 31 ]


Attempts have been made at creating comprehensive catalogues of SNPs that have been identified from GWA studies. [ 33 ]  As of 2009, SNPs associated with diseases are numbered in the thousands. [ 34 ]


The first GWA study, conducted in 2005, compared 96 patients with  age-related macular degeneration  (ARMD) with 50 healthy controls. [ 35 ]  It identified two SNPs with significantly altered allele frequency between the two groups. These SNPs were located in the gene encoding  complement factor H , which was an unexpected finding in the research of ARMD. The findings from these first GWA studies have subsequently prompted further functional research towards therapeutical manipulation of the complement system in ARMD. [ 36 ]


Another landmark publication in the history of GWA studies was the  Wellcome Trust Case Control Consortium  (WTCCC) study, the largest GWA study ever conducted at the time of its publication in 2007. The WTCCC included 14,000 cases of seven common diseases (~2,000 individuals for each of  coronary heart disease ,  type 1 diabetes ,  type 2 diabetes ,  rheumatoid arthritis ,  Crohn's disease ,  bipolar disorder , and  hypertension ) and 3,000 shared controls. [ 15 ]  This study was successful in uncovering many genes associated with these diseases. [ 15 ] [ 37 ]


Since these first landmark GWA studies, there have been two general trends. [ 38 ]  One has been towards larger and larger sample sizes. In 2018, several genome-wide association studies are reaching a total sample size of over 1 million participants, including 1.1 million in a genome-wide study of  educational attainment [ 39 ]  follow by another in 2022 with 3 million individuals [ 40 ]  and a study of  insomnia  containing 1.3 million individuals. [ 41 ]  The reason is the drive towards reliably detecting risk-SNPs that have smaller  effect sizes  and lower allele frequency. Another trend has been towards the use of more narrowly defined phenotypes, such as  blood lipids ,  proinsulin  or similar biomarkers. [ 42 ] [ 43 ]  These are called  intermediate phenotypes , and their analyses may be of value to functional research into biomarkers. [ 44 ]


A variation of GWAS uses participants that are first-degree  relatives  of people with a disease. This type of study has been named  genome-wide association study by proxy  ( GWAX ). [ 45 ]


A central point of debate on GWA studies has been that most of the SNP variations found by GWA studies are associated with only a small increased risk of the disease, and have only a small predictive value. The median odds ratio is 1.33 per risk-SNP, with only a few showing odds ratios above 3.0. [ 1 ] [ 46 ]  These magnitudes are considered small because they do not explain much of the heritable variation. This  heritable  variation is estimated from heritability studies based on  monozygotic  twins. [ 47 ]  For example, it is known that 40% of variance in depression can be explained by hereditary differences, but GWA studies only account for a minority of this variance. [ 47 ]


A challenge for future successful GWA study is to apply the findings in a way that accelerates  drug  and diagnostics development, including better integration of genetic studies into the drug-development process and a focus on the role of genetic variation in maintaining health as a blueprint for designing new  drugs  and  diagnostics . [ 48 ]  Several studies have looked into the use of risk-SNP markers as a means of directly improving the accuracy of  prognosis . Some have found that the accuracy of prognosis improves, [ 49 ]  while others report only minor benefits from this use. [ 50 ]  Generally, a problem with this direct approach is the small magnitudes of the effects observed. A small effect ultimately translates into a poor separation of cases and controls and thus only a small improvement of prognosis accuracy. An alternative application is therefore the potential for GWA studies to elucidate  pathophysiology . [ 51 ]


One such success is related to identifying the genetic variant associated with response to anti- hepatitis C  virus treatment. For genotype 1 hepatitis C treated with  Pegylated interferon-alpha-2a  or  Pegylated interferon-alpha-2b  combined with  ribavirin , a GWA study [ 52 ]  has shown that SNPs near the human  IL28B  gene, encoding interferon lambda 3, are associated with significant differences in response to the treatment. A later report demonstrated that the same genetic variants are also associated with the natural clearance of the genotype 1 hepatitis C virus. [ 53 ]  These major findings facilitated the development of personalized medicine and allowed physicians to customize medical decisions based on the patient's genotype. [ 54 ]


The goal of elucidating pathophysiology has also led to increased interest in the association between risk-SNPs and the  gene expression  of nearby genes, the so-called  expression quantitative trait loci  (eQTL) studies. [ 55 ]  The reason is that GWAS studies identify risk-SNPs, but not risk-genes, and specification of genes is one step closer towards actionable  drug targets . As a result, major GWA studies by 2011 typically included extensive eQTL analysis. [ 56 ] [ 57 ] [ 58 ]  One of the strongest eQTL effects observed for a GWA-identified risk SNP is the SORT1 locus. [ 42 ]  Functional follow up studies of this locus using  small interfering RNA  and  gene knock-out mice  have shed light on the metabolism of  low-density lipoproteins , which have important clinical implications for  cardiovascular disease . [ 42 ] [ 59 ] [ 60 ]


For example, a  meta-analysis  accomplished in 2018 revealed the discovery of 70 new loci associated with  atrial fibrillation . It has been identified different variants associated with  transcription factor  coding-genes, such as  TBX3  and  TBX5 ,  NKX2-5  o  PITX2 , which are involved in cardiac conduction regulation, in  ionic channel  modulation and cardiac development. It was also identified new genes involved in  tachycardia  ( CASQ2 ) or associated with alteration of  cardiac muscle cell  communication ( PKP2 ). [ 61 ]


Research using a High-Precision Protein Interaction Prediction (HiPPIP) computational model that discovered 504 new  protein-protein interactions  (PPIs) associated with genes linked to  schizophrenia . [ 62 ] [ 63 ] [ 64 ]  While the evidence supporting the genetic basis of schizophrenia is not controversial, one study found that 25 candidate schizophrenia genes discovered from GWAS had little association with schizophrenia, demonstrating that GWAS alone may be insufficient to identify candidate genes. [ 65 ]


Another group of researchers conducted a joint analysis of GWAS summary statistics from seventeen  pain  susceptibility traits in the  UK Biobank  and revealed 99 genome-wide significant risk loci, among which 34 loci were new. Also, with leave-one-trait-out meta-analyses these loci were grouped in four categories: Loci associated with nearly all pain-related traits, associated with a single trait, associated with multiple forms of skeletomuscular pain and with headache-related pain.


Moreover, 664 genes were mapped to the 99 loci by genomic proximity,  eQTLs  and chromatin interaction, where 15% of these genes showed differential expression in individuals with acute or  chronic pain  compared to healthy individuals. [ 66 ]


Population  level GWA studies may be used to identify  adaptive  genes to help evaluate ability of species to adapt to changing environmental conditions as the global  climate becomes warmer . [ 67 ]  This could help determine  extirpation  risk for species and could therefore be an important tool for  conservation  planning. Utilizing GWA studies to determine adaptive genes could help elucidate the relationship between neutral and adaptive  genetic diversity .


GWA studies act as an important tool in plant breeding. With large genotyping and phenotyping data, GWAS are powerful in analyzing complex inheritance modes of traits that are important yield components such as number of grains per spike, weight of each grain and plant structure. In a study on GWAS in spring wheat, GWAS have revealed a strong correlation of grain production with booting data, biomass and number of grains per spike. [ 68 ]  GWA study is also a success in study genetic architecture of complex traits in rice. [ 69 ]


The emergences of  plant pathogens  have posed serious threats to plant health and biodiversity. Under this consideration, identification of wild types that have the natural resistance to certain pathogens could be of vital importance. Furthermore, we need to predict which  alleles  are associated with the resistance. GWA studies is a powerful tool to detect the relationships of certain variants and the resistance to the  plant pathogen , which is beneficial for developing new pathogen-resisted cultivars. [ 70 ]


The first GWA study in chickens was done by Abasht and Lamont  [ 71 ]  in 2007. This GWA was used to study the fatness trait in F2 population found previously. Significantly related SNPs were found are on 10 chromosomes (1, 2, 3, 4, 7, 8, 10, 12, 15 and 27).


GWA studies have several issues and limitations that can be taken care of through proper quality control and study setup. Lack of well defined case and control groups, insufficient sample size, control for  population stratification  are common problems. [ 2 ]  On the statistical issue of multiple testing, it has been noted that "the GWA approach can be problematic because the massive number of statistical tests performed presents an unprecedented potential for  false-positive  results". [ 2 ]  This is why all modern GWAS use a  very low p-value threshold.  In addition to easily correctible problems such as these, some more subtle but important issues have surfaced. A high-profile GWA study that investigated individuals with very long life spans to identify SNPs associated with longevity is an example of this. [ 72 ]  The publication came under scrutiny because of a discrepancy between the type of  genotyping array  in the case and control group, which caused several SNPs to be falsely highlighted as associated with longevity. [ 73 ]  The study was subsequently  retracted , [ 74 ]  but a modified manuscript was later published. [ 75 ]  Now, many GWAS control for genotyping array. If there are substantial differences between groups on the type of genotyping array, as with any confounder, GWA studies could result in a false positive. Another consequence is that such studies are unable to detect the contribution of very rare mutations not included in the array or able to be imputed. [ 76 ]


Additionally, GWA studies identify candidate risk variants for the population from which their analysis is performed, and with most GWA studies historically stemming from European databases, there is a lack of translation of the identified risk variants to other non-European populations. [ 77 ]  For instance, GWA studies for diseases like  Alzheimer's disease  have been conducted primarily in Caucasian populations, which does not give adequate insight in other ethnic populations, including African Americans or  East Asians . Alternative strategies suggested involve  linkage analysis . [ 78 ] [ 79 ]  More recently, the rapidly decreasing price of complete genome  sequencing  have also provided a realistic alternative to  genotyping array -based GWA studies. High-throughput sequencing does have potential to side-step some of the shortcomings of non-sequencing GWA. [ 80 ]  Cross-trait  assortative mating  can inflate estimates of genetic phenotype similarity. [ 81 ]


Genotyping arrays designed for GWAS rely on  linkage disequilibrium  to provide coverage of the entire genome by genotyping a subset of variants. Because of this, the reported associated variants are unlikely to be the actual causal variants. Associated regions can contain hundreds of variants spanning large regions and encompassing many different genes, making the biological interpretation of GWAS loci more difficult. Fine-mapping is a process to refine these lists of associated variants to a credible set most likely to include the causal variant.


Fine-mapping requires all variants in the associated region to have been genotyped or imputed (dense coverage), very stringent quality control resulting in high-quality genotypes, and large sample sizes sufficient in separating out highly correlated signals. There are several different methods to perform fine-mapping, and all methods produce a posterior probability that a variant in that locus is causal. Because the requirements are often difficult to satisfy, there are still limited examples of these methods being more generally applied.


The  Maudsley Bipolar Twin Study  is an ongoing  twin study  of  bipolar disorder  running at the  Institute of Psychiatry ,  King's College London  since 2003.  The study is investigating possible differences between people with a  diagnosis  of bipolar disorder and people without the diagnosis.  In particular it is investigating difference in  cognition  and  brain structure/function .


The Maudsley Study of bipolar disorder investigates different aspects of thinking, such as memory and attention, in twins with and without bipolar disorder.  The tasks participants complete involve defining words and solving different kinds of problems. With adequate numbers of twins participating in the study, the hope is to understand any differences between these two groups. The eventual aim is to increase understanding of this complex mood disorder and to enhance future therapies for it.


This  psychiatry -related article is a  stub . You can help Wikipedia by  expanding it .


The  Överkalix study  ( Swedish :  Överkalixstudien ) was a study conducted on the physiological  effects of various environmental factors on  transgenerational epigenetic  inheritance. The study was called " Cardiovascular and diabetes mortality determined by nutrition during parents’ and grandparents’ slow growth period " and was published in the journal  European Journal of Human Genetics  in 2002. [ 1 ]  The study was conducted utilizing historical records, including  harvests  and  food prices , in  Överkalix , a small isolated  municipality  in northeast  Sweden .  Mortality  risk ratios (RR) on children and grandchildren were determined based on available  food supply , as indicated by these historical data.


The study started with 320  probands , 164 men and 139 women, born in 1890, 1905, or 1920, and their 1,818 children and grandchildren. 44 were still alive in 1995 when mortality follow-up stopped and other issues (eg. missing birthyear, cause of death unknown) reduced the final analysis to 239 probands. Between 7-22% of these were further removed from each ancestor-specific analysis due to the ancestor experiencing both "good" and "bad" years during the sample period.


The study found that a severe increase/decrease in food supply in male ancestors shortly before puberty was linked to a change in death rates from cardiovascular disease (CVD) and  diabetes  rates in their descendants. In particular children of a father who suffered a deficit of food had lower rates of deaths from CVD, and a paternal grandfather who suffered a similar deficit saw a reduction in deaths from diabetes in their grandchildren. Conversely a surplus in food supply for a paternal grandfather saw a 4-fold increase in the number of diabetes-related deaths in their grandchildren.


These effects were observed correlating with food surplus/deficit during the  slow growth period  (SGP). The SGP is the time before the start of  puberty , when environmental factors have a larger impact on the body. The ancestors' SGP in this study, was set between the ages of 9-12 for boys and 8–10 years for girls. This occurred in the SGP of both grandparents, or during the  gestation  period/infant life of the grandmothers, but not during either grandparent's puberty.


The study notes a number of potential issues, including small sample sizes, borderline  statistical significance , progeny size effects, puberty estimation, regional food access and  environmental  effects. [ 1 ]  A 2018 study attempted to  replicate  these findings using a much larger sample of over 11,500 grandchildren using data from the Uppsala Multigeneration Study. It failed to replicate the association with either cardiovascular or diabetes mortality, but did report an increased all-cause mortality correlated to SGP food intake, mostly due to  cancer . [ 2 ]


Sex-specific effects can be due to parental imprinting, a process that results in allele-specific differences in transcription,  DNA methylation , and  DNA   replication timing . Imprinting is an important process in human development, and its deregulation can cause certain defined disease states of other imprinted human disease loci. The establishment of parental imprints occurs during  gametogenesis  as  homologous   DNA  passes through  sperm  or  egg ; subsequently during  embryogenesis  and into adulthood,  alleles  of imprinted genes are maintained in two "conformational"/epigenetic states:  paternal  or  maternal . Thus, genomic imprints template their own replication, are heritable, can be identified by molecular analysis, and serve as markers of the parental origin of genomic regions.


The estimation of percentage of human genes subject to parental imprinting is approximately one to two percent, currently parental imprinting has been identified in fewer than 100 distinct named genes. [ 3 ]


In  genetics , a  polygenic score  ( PGS ) is a number that summarizes the estimated effect of many genetic variants on an individual's  phenotype . The PGS is also called the  polygenic index  ( PGI ) or  genome-wide score ; in the context of disease risk, it is called a  polygenic risk score  ( PRS  or  PR score [ 1 ] ) or  genetic risk score . The score reflects an individual's estimated genetic predisposition for a given trait and can be used as a predictor for that trait. [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 6 ]  It gives an estimate of how likely an individual is to have a given trait based only on genetics, without taking environmental factors into account; and it is typically calculated as a weighted sum of trait-associated  alleles . [ 7 ] [ 8 ] [ 9 ]


Recent progress in genetics has developed polygenic predictors of complex human traits, including risk for many important  complex diseases [ 10 ] [ 11 ]  that are typically affected by many genetic variants, each of which confers a small effect on overall risk. [ 12 ] [ 13 ]  In a polygenic risk predictor the lifetime (or age-range) risk for the disease is a numerical function captured by the score which depends on the states of thousands of individual genetic variants (i.e.,  single-nucleotide polymorphisms , or SNPs).


Polygenic scores are widely used in  animal breeding  and  plant breeding  due to their efficacy in improving livestock breeding and crops. [ 14 ]  In humans, polygenic scores are typically generated from data of  genome-wide association study  (GWAS). They are an active area of research spanning topics such as learning algorithms for genomic prediction; new predictor training; validation testing of predictors; and clinical application of PRS. [ 15 ] [ 16 ] [ 17 ] [ 4 ] [ 11 ]  In 2018, the American Heart Association named polygenic risk scores as one of the major breakthroughs in research in heart disease and stroke. [ 18 ]


DNA  in living organisms is the molecular  genetic code  for life. Although polygenic risk scores from study in humans have gained the most attention, the basic idea was first introduced for selective plant and animal breeding. [ 20 ]  Similar to the latter-day approaches of constructing a polygenic risk score, an individual's—animal or plant—breeding value was calculated to be the combined weight of several single-nucleotide polymorphisms (SNPs) by their individual effects on a trait. [ 21 ]


Human DNA contains about 3 billion bases. The human genome can be broadly separated into coding and non-coding sequences, where the coding genome  encodes instructions for  genes , including some of the sequence that codes for  proteins . Genome-wide association studies enable mapping  phenotypes  to the variations in nucleotide bases in human populations. Improvements in methodology and studies with large cohorts have enabled the mapping of many traits—some of which are diseases—to the human genome. Learning which variations influence which specific traits and how strongly they do so, are the key targets for constructing polygenic scores in humans.


The methods were first considered for humans after the year 2000, and specifically by a proposal in 2007 that such scores could be used in human genetics to identify individuals at high risk for disease. [ 22 ]  The concept was successfully applied in 2009 by researchers who organized a  genome-wide association study  (GWAS) regarding  schizophrenia  with the objective of constructing scores of risk propensity. That study was the first to use the term  polygenic score  for a prediction drawn from a linear combination of  single-nucleotide polymorphism  (SNP) genotypes—which was able to explain 3% of the variance in schizophrenia. [ 23 ]


A PRS is constructed from the estimated effect sizes derived from a  genome-wide association study  (GWAS). In a GWAS, single-nucleotide polymorphisms (SNPs) are tested for an association between  cases  and  controls , (see top graphic). The results from a GWAS estimate the strength of the association at each SNP, i.e., the effect size at the SNP, as well as a p-value for statistical significance. A typical score is then calculated by adding the number of risk-modifying alleles across a large number of SNPs, where the number of alleles for each SNP is multiplied by the weight for the SNP. [ 24 ]


In mathematical form, the estimated polygenic score

 S
 ^

 {\displaystyle {\hat {S}}}

  is obtained as the sum across  m  number of SNPs with risk-increasing alleles weighted by their weights, i.e.,

 β
 ^

 j

 {\displaystyle {\hat {\beta }}_{j}}

 .


S
 ^

 =

 ∑

 j
 =
 1

 m

 X

 j

 β
 ^

 j

 {\displaystyle {\hat {S}}=\sum _{j=1}^{m}X_{j}{\hat {\beta }}_{j}}


This idea can be generalized to the study of any trait, and is an example of the more general mathematical term  regression analysis . [ citation needed ]


Methods for generating polygenic scores in humans are an active area of research. [ 25 ] [ 8 ]  Two key considerations in developing polygenic scores are  which SNPs  and  the number of SNPs  to include. The simplest, the so-called "pruning and thresholding" method, sets weights equal to the coefficient estimates from a  regression  of the trait on each genetic variant. The included SNPs may be selected using an algorithm that attempts to ensure that each marker is approximately independent.


Independence of each SNP is important for the score's predictive accuracy. SNPs that are physically close to each other are more likely to be in  linkage disequilibrium , meaning they typically are inherited together and therefore don't provide independent predictive power.  That's what's referred to as 'pruning'. The 'thresholding' refers to using only SNPs that meet a specific p-value threshold. [ 26 ]


Penalized regression  can also be used to construct polygenic scores. [ 2 ] [ 27 ]  From prior information penalized regression assigns probabilities on: 1) how many genetic variants are expected to affect a trait, and 2) the distribution of their effect sizes. These methods in effect "penalize" the large coefficients in a regression model and shrink them conservatively. One popular tool for this approach is "PRS-CS". [ 28 ]  Another is to use certain  Bayesian methods , first proposed in 2001 that directly incorporate genetic features of a given trait as well as genomic features like linkage disequilibrium. [ 29 ]  (One Bayesian method uses "linkage disequilibrium prediction" or  LDpred .) [ 6 ] [ 30 ]


More approaches for developing polygenic risk scores continue to be described. For example, by incorporating effect sizes from populations of different ancestry, the predictive ability of scores can be improved. [ 31 ]  Incorporating knowledge of the functional roles of specific genomic chunks can improve the utility of scores. [ 32 ]  Studies have examined the performances of these methods on standardized dataset. [ 33 ]


As the number of genome-wide association studies has exploded, along with rapid advances in methods for calculating polygenic scores, its most obvious application is in clinical settings for disease prediction or risk stratification. It is important not to over- or under-state the value of polygenic scores. A key advantage of quantifying polygenic contribution for each individual is that the genetic liability does not change over an individual's lifespan. However, while a disease may have strong genetic contributions, the risk arising from one's genetics has to be interpreted in the context of environmental factors. For example, even if an individual has a high genetic risk for alcoholism, that risk is lessened if that individual is never exposed to alcohol. [ 26 ]


For humans, while most polygenic scores are not predictive enough to diagnose disease, they could be used in addition to other covariates (such as age, BMI, smoking status) to improve estimates of disease susceptibility. [ 26 ] [ 8 ] [ 13 ]  However, even if a polygenic score might not make reliable diagnostic predictions across an entire population, it may still make very accurate predictions for outliers at extreme high or low risk. The clinical utility may therefore still be large even if average measures of prediction performance are moderate. [ 11 ]


Although issues such as poorer predictive performance in individuals of non-European ancestry limit widespread use, [ 34 ]  several authors have noted that some causal variants for some conditions, but not others, are shared between Europeans and other groups across different continents for (e.g.) BMI and type 2 diabetes in African populations [ 35 ]  as well as schizophrenia in Chinese populations. [ 36 ]  Other researchers recognize that polygenic under-prediction in non-European population should galvanize new GWAS that prioritize greater genetic diversity in order to maximize the potential health benefits brought about by predictive polygenic scores. [ 37 ]  Significant scientific efforts are being made to this end. [ 38 ]


Embryo genetic screening  is common with millions biopsied and tested each year worldwide. Genotyping methods have been developed so that the embryo genotype can be determined to high precision. [ 39 ] [ 40 ]  Testing for  aneuploidy  and  monogenetic diseases  has increasingly become established over decades, whereas tests for polygenic diseases have begun to be employed more recently, having been first used in embryo selection in 2019. [ 41 ]
The use of polygenic scores for  embryo selection  has been criticised due to alleged ethical and safety issues as well as limited practical utility. [ 42 ] [ 43 ] [ 44 ]  However, trait-specific evaluations claiming the contrary have been put forth [ 45 ] [ 46 ]  and ethical arguments for PGS-based embryo selection have also been made. [ 47 ] [ 48 ] [ 49 ]  The topic continues to be an active area of research not only within genomics but also within clinical applications and ethics.


As of 2019, polygenic scores from well over a hundred phenotypes have been developed from genome-wide association statistics. [ 50 ]  These include scores that can be categorized as anthropometric, behavioural, cardiovascular, non-cancer illness, psychiatric/neurological, and response to treatment/medication. [ 51 ]


When predicting disease risk, a PGS gives a continuous score that estimates the risk of having or getting the disease, within some pre-defined time span. A common metric for evaluating such continuous estimates of yes/no questions (see  Binary classification ) is the  area under the ROC curve  (AUC). Some example results of PGS performance, as measured in AUC (0 ≤ AUC ≤ 1 where a larger number implies better prediction), include:


The performance of a polygenic predictor is highly dependent on the size of the dataset that is available for analysis and ML training. Recent scientific progress in prediction power relies heavily on the creation and expansion of large  biobanks  containing data for both genotypes and phenotypes of very many individuals. As of 2021, there exist several biobanks with hundreds of thousands samples, i.e., data entries with both genetic and trait information for each individual (see for instance the incomplete  list of biobanks ).


With the use of these growing biobanks, data from many thousands of individuals are used to detect the relevant variants for a specific trait. Exactly how many are required depends very much on the trait in question. Typically, increasing levels of prediction are observed until a plateau phase where the performance levels off and does not change much when increasing the sample size even further. This is the limit of how accurate a polygenic predictor that only uses genetic information can be and is set by the  heritability  of the specific trait. The sample size required to reach this performance level for a certain trait is determined by the complexity of the underlying genetic architecture and the distribution of genetic variance in the sampled population. This sample size dependence is illustrated in the figure for hypothyroidism, hypertension and type 2 diabetes.


Note again, that current methods to construct polygenic predictors are sensitive to the ancestries present in the data. As of 2021, most available data have been primarily of populations with European ancestry, which is the reason why PGS generally perform better within this ancestry. The construction of more diverse biobanks with successful recruitment from all ancestries is required to rectify this skewed access to and benefits from PGS-based medicine. [ 37 ]


A landmark study examining the role of polygenic risk scores in cardiovascular disease invigorated interest the clinical potential of polygenic scores. [ 4 ]  This study demonstrated that an individual with the highest polygenic risk score (top 1%) had a lifetime cardiovascular risk >10% which was comparable to those with rare genetic variants. This comparison is important because clinical practice can be influenced by knowing which individuals have this rare genetic cause of cardiovascular disease. [ 55 ]  Since this study, polygenic risk scores have shown promise for disease prediction across other traits. [ 8 ]  Polygenic risk scores have been studied heavily in obesity, coronary artery disease, diabetes, breast cancer, prostate cancer, Alzheimer's disease and psychiatric diseases. [ 9 ]


As of January 2021 providing PRS directly to individuals was undergoing research trials in health systems around the world, but is not yet offered as standard of care. [ 56 ]  Most use is therefore through  consumer genetic testing , where a number of private companies report PRS for a number of diseases and traits. Consumers download their genotype (genetic variant) data and upload them into online PRS calculators, e.g.  Scripps Health ,  Impute.me  or  Color Genomics . [ 57 ]  The most frequently reported motivation for individuals to seek out PRS reports is general curiosity (98.2%), and the reactions are generally mixed with common misinterpretations. [ 58 ]  It is speculated that personal use of PRS could contribute to treatment choices, but that more data is needed. [ 56 ]  As of 2020 a more typical use was that clinicians face individuals with commercially derived disease-specific PRS in the expectation that the clinician will interpret them, something that may create extra burdens for the clinical care system. [ 56 ] [ 59 ]


At a fundamental level, the use of polygenic scores in clinical context will have similar technical issues as existing tools. For example, if a tool is not validated in a diverse population, then it may exacerbate disparities with unequal efficacy across populations. This is especially important in genetics where, as of 2018, a majority of the studies to date have been done in Europeans. [ 60 ]  Other challenges that can arise include how precisely the polygenic risk score can be calculated and how precise it needs to be for clinical utility. [ 61 ]  Even if a polygenic score is accurately calculated and calibrated for a population, its interpretation must be approached with caution. First, it is important to realize that polygenic traits are different from monogenic traits; the latter stem from fewer genetic loci and can be detected more accurately. Genetic tests are often difficult to interpret and require genetic counseling. Currently, polygenic-score results are being shared with clinicians. [ 62 ]  Since monogenic genetic testing is far more mature than polygenic scores, we can look there for approximating the clinical impact of polygenic scores. While some studies have found negative effects of returning monogenic genetic results to patients, [ 63 ]  the majority of studies have that negative consequences are minor. [ 64 ]


Unlike many other clinical laboratory or imaging methods, an individual's germ-line genetic risk can be calculated at birth for a variety of diseases after sequencing their DNA once. [ 61 ] [ 8 ]  Thus, polygenic scores may ultimately be a cost-effective measure that can be informative for clinical management. Moreover, the polygenic risk score may be informative across an individual's lifespan helping to quantify the genetic lifelong risk for certain diseases. For many diseases, having a strong genetic risk can results in an earlier onset of presentation (e.g. Familial Hypercholesterolemia). [ 65 ]  Recognizing an increased genetic burden earlier can allow clinicians to intervene earlier and avoid delayed diagnoses. Polygenic score can be combined with traditional risk factors to increase clinical utility. [ 66 ] [ 67 ] [ 68 ] [ 69 ]   For example, polygenic risk scores may help improve diagnosis of diseases. This is especially evident in distinguishing Type 1 from Type 2 Diabetes. [ 70 ]  Likewise, a polygenic risk score based approach may reduce invasive diagnostic procedures as demonstrated in Celiac disease. [ 71 ]  Polygenic scores may also empower individuals to alter their lifestyles to reduce risk for diseases. While there is some evidence for behavior modification as a result of knowing one's genetic predisposition, [ 72 ] [ 73 ] [ 74 ]  more work is required to evaluate risk-modifying behaviors across a variety of different disease states. [ 61 ]  Population level screening is another use case for polygenic scores. The goal of population-level screening is to identify patients at high risk for a disease who would benefit from an existing treatment. [ 75 ]  Polygenic scores can identify a subset of the population at high risk that could benefit from screening. Several clinical studies are being done in breast cancer [ 76 ] [ 77 ]  and heart disease is another area that could benefit from a polygenic score based screening program. [ 61 ]


A variety of applications exists for polygenic scores. In humans, polygenic scores were originally computed in an effort to predict the prevalence and etiology of complex,  heritable  diseases, which are typically affected by many genetic variants that individually confer a small effect to overall risk.  Additionally, a polygenic score can be used in several different ways: as a lower bound to test whether heritability estimates may be biased; as a measure of genetic overlap of traits ( genetic correlation ), which might indicate e.g. shared genetic bases for groups of mental disorders; as a means to assess group differences in a trait such as height, or to examine changes in a trait over time due to  natural selection  indicative of a  soft selective sweep  (as e.g. for intelligence where the changes in frequency would be too small to detect on each individual hit but not on the overall polygenic score); in  Mendelian randomization  (assuming no  pleiotropy  with relevant traits); to detect & control for the presence of genetic confounds in outcomes (e.g. the correlation of schizophrenia with poverty); or to investigate  gene–environment interactions  and  correlations . Polygenic scores also have useful statistical properties in (genomic) association testing, for instance to account for outcome-specific background effects and/or improve  statistical power . [ 78 ] [ 79 ] [ 80 ]


The benefit of polygenic scores is that they can be used to predict the future for crops,  animal breeding , and humans alike. Although the same basic concepts underlie these areas of prediction, they face different challenges that require different methodologies. The ability to produce very large family size in nonhuman species, accompanied by deliberate selection, leads to a smaller effective population, higher degrees of linkage disequilibrium among individuals, and a higher average genetic relatedness among individuals within a population. For example, members of plant and animal breeds that humans have effectively created, such as modern  maize  or domestic  cattle , are all technically "related". In human genomic prediction, by contrast, unrelated individuals in large populations are selected to estimate the effects of common SNPs. Because of smaller effective population in livestock, the mean coefficient of relationship between any two individuals is likely high, and common SNPs will tag causal variants at greater physical distance than for humans; this is the major reason for lower SNP-based heritability estimates for humans compared to livestock. In both cases, however, sample size is key for maximizing the accuracy of genomic prediction. [ 81 ]


While modern genomic prediction scoring in humans is generally referred to as a "polygenic score" (PGS) or a "polygenic risk score" (PRS), in livestock the more common term is "genomic estimated breeding value", or GEBV (similar to the more familiar "EBV", but with genotypic data). Conceptually, a GEBV is the same as a PGS: a linear function of genetic variants that are each weighted by the apparent effect of the variant. Despite this, polygenic prediction in livestock is useful for a fundamentally different reason than for humans. In humans, a PRS is used for the prediction of individual phenotype, while in livestock a GEBV is typically used to predict the offspring's average value of a phenotype of interest in terms of the genetic material it inherited from a parent. In this way, a GEBV can be understood as the average of the offspring of an individual or pair of individual animals. GEBVs are also typically communicated in the units of the trait of interest. For example, the expected increase in milk production of the offspring of a specific parent compared to the offspring from a reference population might be a typical way of using a GEBV in dairy cow breeding and selection. [ 81 ]


The  Twins Early Development Study  (TEDS) is an ongoing longitudinal  twin study  based at  King's College London . The main goal of TEDS is to use  behavioural genetic  methods to find out how nature ( genes ) and nurture (environments) can explain why people differ with respect to their cognitive abilities, learning abilities and behaviours. The study was founded by  Robert Plomin  in 1994, and it includes participants from over 15,000 families. [ 1 ]  The original participants were identified from birth records among the set of twins born in  the United Kingdom  from 1994 to 1996. They were first assessed at the age of two and regularly re-assessed thereafter. [ 2 ]


In 2021,  Thalia Eley  took over the study with Plomin stepping down as director. The goals of the next phase of the TEDS study will focus on the Children of the TEDS Group (CoTEDS) and will explore Eley's own research area on  mental health .


Mobile genetic elements  ( MGEs ), sometimes called  selfish genetic elements , [ 1 ]  are a type of  genetic material  that can move around within a genome, or that can be transferred from one species or replicon to another. MGEs are found in all organisms. In humans, approximately 50% of the genome are thought to be MGEs. [ 2 ]  MGEs play a distinct role in evolution. Gene duplication events can also happen through the mechanism of MGEs. MGEs can also cause mutations in protein coding regions, which alters the protein functions. These mechanisms can also rearrange genes in the host genome generating variation. These mechanism can increase fitness by gaining new or additional functions. An example of MGEs in evolutionary context are that virulence factors and  antibiotic resistance  genes of MGEs can be transported to share genetic code with neighboring bacteria. However, MGEs can also decrease fitness by introducing disease-causing alleles or mutations. [ 3 ]  The set of MGEs in an organism is called a  mobilome , which is composed of a large number of  plasmids ,  transposons  and  viruses . [ 4 ]


CRISPR-Cas  systems in bacteria and  archaea  are adaptive immune systems to protect against deadly consequences from MGEs. Using comparative genomic and phylogenetic analysis, researchers found that CRISPR-Cas variants are associated with distinct types of MGEs such as transposable elements. In  CRISPR-associated transposons , CRISPR-Cas controls transposable elements for their propagation. [ 28 ]


MGEs such as plasmids by a horizontal transmission are generally beneficial to an organism. The ability of transferring plasmids (sharing) is important in an evolutionary perspective. Tazzyman and Bonhoeffer found that fixation (receiving) of the transferred plasmids in a new organism is just as important as the ability to transfer them. [ 29 ]  Beneficial rare and transferable plasmids have a higher fixation probability, whereas deleterious transferable genetic elements have a lower fixation probability because they are lethal to the host organisms.


One type of MGEs, namely the Integrative Conjugative Elements (ICEs) are central to horizontal gene transfer shaping the genomes of prokaryotes enabling rapid acquisition of novel adaptive traits. [ 30 ] [ 31 ]


As a representative example of ICEs, the ICE Bs1  is well-characterized for its role in the global DNA damage SOS response of  Bacillus subtilis [ 32 ]  and also its potential link to the radiation and desiccation resistance of  Bacillus pumilus  SAFR-032 spores, [ 33 ]  isolated from spacecraft cleanroom facilities. [ 34 ] [ 35 ] [ 36 ]


Transposition by transposable elements is mutagenic. Thus, organisms have evolved to repress the transposition events, and failure to repress the events causes cancers in somatic cells. Cecco et al. found that during early age transcription of retrotransposable elements are minimal in mice, but in advanced age the transcription level increases. [ 37 ]  This age-dependent expression level of transposable elements is reduced by calorie restriction diet. Replication of transposable elements often results in repeated sequences being added into the genome. These sequences are often non coding but can interfere with coding sequences of DNA. Though mutagenetic by nature, transposons increase the genome of an organism that they transpose into. More research should be conducted into how these elements may serve as a rapid adaptation tool employed by organisms to generate variability. Many transposition elements are dormant or require activation. should also be noted that current values for coding sequences of DNA would be higher if transposition elements that code for their own transposition machinery were considered  as coding sequences.


Some others researched examples include  Mavericks , [ 38 ] [ 39 ] [ 40 ]   Starships [ 41 ] [ 40 ]  and Space invaders (or SPINs) [ 42 ] [ 43 ]


The consequence of mobile genetic elements can alter the transcriptional patterns, which frequently leads to genetic disorders such as immune disorders, breast cancer, multiple sclerosis, and amyotrophic lateral sclerosis. In humans, stress can lead to transactional activation of MGEs such as  endogenous retroviruses , and this activation has been linked to  neurodegeneration . [ 44 ]


The total of all mobile genetic elements in a genome may be referred to as the  mobilome .


Barbara McClintock  was awarded the  1983   Nobel Prize in Physiology or Medicine  "for her discovery of mobile genetic elements" ( transposable elements ). [ 45 ]


Mobile genetic elements play a critical role in the spread of virulence factors, such as  exotoxins  and  exoenzymes , among bacteria. Strategies to combat certain bacterial infections by targeting these specific virulence factors and mobile genetic elements have been proposed. [ 46 ]


ACLAME  (The  CLAssification of Mobile genetic Elements ) is a database of sequenced  mobile genetic elements . [ 1 ]


The  arginine catabolic mobile element  (ACME) is a  mobile genetic element  of  Staphylococcus   bacterial  species.  This genetic element provides for several immune modulating functions, including resistance to  polyamines  which serve as a non-specific immune response both on intact skin and following the inflammatory response in wound healing. [ 1 ] [ 2 ]   Diverse ACME are present in several species of  Staphylococcus , including  Staphylococcus epidermidis . [ 3 ] [ 4 ]


ACME are not common among antibiotic sensitive  S. aureus  (MSSA). [ 5 ]   The elements for the most prominent MRSA ACME appear to have assembled recently in  S. epidermidis  into the speG-positive ACME which was transferred to virulent  S. aureus  during the evolution of the epidemic USA300  MRSA  strain. [ 6 ] [ 7 ]   This broadened the ability of  S. aureus  to colonize sites beyond the nose. [ 8 ]   This strain is able to persist on intact skin and is spread rapidly person-to-person.  As a result, the speG-positive ACME is a particularly important element of MRSA pathogenesis.


Cer6  is a  LTR retrotransposon  that is described from  sequencing  data in the  chromosome  III of  C. elegans . [ 1 ] [ 2 ] [ 3 ]


LTR retrotransposons are class I  transposable element  characterized by the presence of  long terminal repeats  (LTRs) directly flanking an internal coding region. As retrotransposons, they mobilize through  reverse transcription  of their  mRNA  and integration of the newly created  cDNA  into another location. Their mechanism of retrotransposition is shared with  retroviruses , with the difference that most LTR-retrotransposons do not form infectious particles that leave the cells and therefore only replicate inside their genome of origin.


ColE1  is a  plasmid  found in  bacteria .  Its name derives from the fact that it carries a gene for  colicin  E1 (the  cea  gene).  It also codes for immunity from this product with the  imm  gene.  In addition, the plasmid has a series of mobility ( mob ) genes. Its size and the presence of a single EcoRI  recognition site  caused it to be considered as a  vector  candidate. [ 1 ]


ColE1 replication begins at the  origin .  555 bp  upstream from this point, RNA polymerase initiates transcription of RNAII which acts as a pre-primer and begins the synthesis of the  leader strand .  The transcript folds into a secondary structure which stabilises the interaction between the nascent RNA and the origin's DNA.  This hybrid is attacked by  RNase H , which cleaves the RNA strand, exposing a 3' hydroxyl group.  This allows the extension of the leading strand by  DNA polymerase I .  Lagging strand synthesis is later initiated by a primase encoded by the host cell.
Replication is carried out entirely by host proteins (RNA polymerase, DNA polymerase I and RNase H) so that inhibition of translation will stop the growth of the cells, but not the replication of ColE1. Since the translation of Rop protein will also be inhibited, a higher than normal copy number will result in these cells.


RNAI  is a counter-transcript to a section of RNAII and so binds to its 5' end.  This alters the folding of RNAII so that the DNA-RNA hybrid is not stabilized and cleavage does not occur.  This ensures that at high  copy numbers , replication is slowed down due to increased RNAI concentration.   Rop  is a secondary replication repressor, it stabilizes the RNAI-RNAII hybrid.   Rop may be especially important at preventing runaway replication at slow growth rates.


ColE1 has a copy number of 25-30 according to its original publication. [ 2 ]


A  composite transposon  is similar in function to  simple transposons  and  insertion sequence (IS) elements  in that it has protein coding  DNA  segments flanked by inverted, repeated sequences that can be recognized by transposase enzymes.  A composite  transposon , however, is flanked by two separate IS elements which may or may not be exact replicas.  Instead of each IS element moving separately, the entire length of DNA spanning from one IS element to the other is transposed as one complete unit.  Composite transposons will also often carry one or more genes conferring  antibiotic resistance .


Two SINEs may act in concert to flank and mobilize an intervening single copy DNA sequence. This was reported for a 710 bp DNA sequence upstream of the bovine beta globin gene. [ 1 ]  The DNA arrangement forms a composite transposon whose presence has been confirmed by the complete  bovine genomic sequence  where the mobilized sequence may be found on bovine chromosome 15 in contig  NW_001493315.1  nucleotides #1085432–1086142 and the originating sequence may be found on bovine chromosome 2 in contig  NW_001501789.2  nucleotides #1096679–1097389. It is likely that similar composite transposons exist in other bovine genomic regions and other mammalian genomes. They could be detected with suitable algorithms.


In  molecular biology , a  cryptic plasmid  is a  plasmid  that doesn't appear to provide any clear advantage to its host, yet still persists in  bacterial  populations. [ 1 ]  These plasmids appear to lack any genetic functions of interest and do not seem to contain genes that could provide beneficial functions to their hosts. Given the lack of a clear advantage to their hosts and the likely cost of maintaining them, these plasmids are often referred to as selfish elements or genetic parasites. [ 1 ] The maintenance of cryptic plasmids might be explained by mechanisms like  horizontal gene transfer  (e.g.,  conjugation ,  transduction ) that balance their loss due to segregation. [ 2 ]  However, cryptic plasmids could potentially be important in antibiotic resistance, by contributing to heteroresistance in bacterial populations.  [ 3 ]


They have been found to be highly abundant, as seen in Lactobacillus where most plasmids are cryptic.  [ 4 ]


Diversity-generating retroelements (DGRs)  are a family of  retroelements  that were first found in  Bordetella phage (BPP-1) , [ 1 ]  and since   been found in bacteria (e.g. Treponema denticola [ 2 ]   and  Legionella pneumophila [ 3 ] ),  Archaea , Archaean viruses (e.g. ANMV-1), [ 4 ]  temperate  phages  (e.g. Hankyphage [ 5 ]  and  CrAss-like phage [ 6 ] ), and lytic phages. [ 7 ]  DGRs benefit their host by mutating particular regions of specific target proteins, for instance, phage tail fiber in BPP-1, lipoprotein in legionella  pneumophila  ( the pathogen behind Legionnaires disease), and TvpA in  Treponema denticola (oral-associated periopathogen) [ 8 ] .   An error-prone  reverse transcriptase  is responsible for generating these  hypervariable regions  in target proteins (Mutagenic retrohoming). In mutagenic retrohoming, a mutagenized cDNA (containing substantial A to N mutations) is reverse transcribed from a template region (TR), and is replaced with a segment similar to the template region called variable region (VR). Accessory variability determinant (Avd) protein is another component of DGRs, and its complex formation with the error-prone RT is of importance to mutagenic rehoming. [ 9 ] [ 10 ]


DGRs are beneficial to the evolution and survival of their host. A large fraction of  Faecalibacterium prausnitzii  phages contain DGRs that are believed to have a role in phage adaptability to the digestive system, as patients with inflammatory bowel disease (IBD), have more phages, but less F.prausnitzii in their stool samples compared to healthy individuals, suggesting that these phages activate during the illness, and that they may trigger F.prausnitzii depletion. [ 11 ]  Several tools have been implemented to identify DGRs, such as DiGReF, [ 12 ]  DGRscan, [ 13 ]  MetaCSST, [ 14 ]  and  myDGR [ 15 ]


DNA transposons  are DNA sequences, sometimes referred to "jumping genes", that can move and integrate to different locations within the  genome . [ 1 ]  They are  class II  transposable elements  (TEs) that move through a  DNA  intermediate, as opposed to class I TEs,  retrotransposons , that move through an  RNA  intermediate. [ 2 ]  DNA transposons can move in the DNA of an organism via a single-or double-stranded DNA intermediate. [ 3 ]  DNA transposons have been found in both  prokaryotic  and  eukaryotic  organisms. They can make up a significant portion of an organism's genome, particularly in eukaryotes. In prokaryotes, TE's can facilitate the  horizontal transfer  of  antibiotic resistance  or other  genes  associated with  virulence . After replicating and propagating in a host, all transposon copies become inactivated and are lost unless the transposon passes to a genome by starting a new life cycle with horizontal transfer. [ 4 ]  DNA transposons do not randomly insert themselves into the genome, but rather show preference for specific sites.


With regard to movement, DNA transposons can be categorized as autonomous and nonautonomous. [ 5 ]  Autonomous ones can move on their own, while nonautonomous ones require the presence of another transposable element's gene,  transposase , to move. There are three main classifications for movement for DNA transposons: "cut and paste," [ 6 ]  " rolling circle " (Helitrons), [ 7 ]  and "self-synthesizing" (Polintons). [ 8 ]  These distinct mechanisms of movement allow them to move around the genome of an organism. Since DNA transposons cannot synthesize DNA, they replicate using the host replication machinery. These three main classes are then further broken down into 23 different superfamilies characterized by their structure, sequence, and mechanism of action. [ 9 ]


DNA transposons are a cause of  gene expression  alterations. As newly inserted DNA into active  coding sequences , they can disrupt normal protein functions and cause mutations. Class II TEs make up about 3% of the human genome. Today, there are no active DNA transposons in the human genome. Therefore, the elements found in the human genome are called fossils.


Traditionally, DNA transposons move around in the genome by a cut and paste method. The system requires a  transposase  enzyme that catalyzes the movement of the DNA from its current location in the genome and inserts it in a new location. Transposition requires three  DNA  sites on the  transposon : two at each end of the transposon called  terminal inverted repeats  and one at the target site. The transposase will bind to the  terminal inverted repeats  of the transposon and mediate  synapsis  of the transposon ends. The transposase enzyme then disconnects the element from the flanking DNA of the original donor site and mediates the joining reaction that links the transposon to the new insertion site. The addition of the new DNA into the target site causes short gaps on either side of the inserted segment. [ 10 ]  Host systems repair these gaps resulting in the target sequence  duplication  (TSD) that are characteristic of transposition. In many reactions, the transposon is completely excised from the donor site in what is called a "cut and paste" [ 11 ]  transposition and inserted into the target DNA to form a simple insertion. Occasionally, genetic material not originally in the transposable element gets copied and moved as well.


Helitrons  are also a group of eukaryotic class II TEs. Helitrons do not follow the classical "cut and paste" mechanism. Instead, they are hypothesized to move around the genome via a rolling circle like mechanism. This process involves making a nick to a circular strand by an enzyme, which separates the DNA into two single strands. The initiation protein then remains attached to the 5' Phosphate on the nicked strand, exposing the 3' hydroxyl of the complementary strand. This allows a  polymerase  enzyme to begin replication on the un-nicked strand. Eventually the entire strand is replicated at which point the newly synthesized DNA disassociates and is replicated in parallel with the original template strand. [ 12 ]   Helitrons encode an unknown protein which is thought to have HUH  endonuclease  function as well as 5' to 3'  helicase  activity. This enzyme would make a single stranded cut in the DNA which explains the lack of Target Site Duplications found in Helitrons. Helitrons were also the first class of transposable elements to be discovered computationally and marked a paradigm shift in the way that whole genomes were studied. [ 13 ]


Polintons  are also a group of eukaryotic class II TEs. As one of the most complex known DNA  transposons  in eukaryotes, they make up the genomes of  protists ,  fungi , and  animals , such as the  entamoeba ,  soybean rust , and  chicken , respectively. They contain  genes  with  homology  to  viral   proteins  and which are often found in  eukaryotic   genomes , like  polymerase  and  retroviral   integrase . However, there is no known protein functionally similarly to the viral  capsid  or  envelope  proteins. They share their many structural characteristics with linear  plasmids ,  bacteriophages  and  adenoviruses , which replicate using protein-primed DNA polymerases. Polintons have been proposed to go through a similar self-synthesis by their polymerase. Polintons, 15–20 kb long, encode up to 10 individual proteins. For  replication , they utilize a protein-primed  DNA polymerase B , retroviral  integrase ,  cysteine   protease , and  ATPase . First, during host genome replication, a single-stranded extra-chromosomal Polinton element is excised from the host DNA using the integrase, forming a racket-like structure. Second, the Polinton undergoes replication using the DNA polymerase B, with initiation started by a terminal protein, which may encoded in some linear plasmids. Once the double stranded Polinton is generated, the integrase serves to insert it into the host genome. Polintons exhibit high variability between difference species and may tightly regulated, resulting in a low frequency rate in many genomes. [ 14 ]


As of the most recent update in 2023, 31 superfamilies of DNA transposons were recognized and annotated in
Repbase, a database of repetitive DNA elements maintained by the  Genetic Information Research Institute : [ 15 ] [ 16 ]


DNA   transposons , like all transposons, are quite impactful with respect to gene expression. A sequence of DNA may insert itself into a previously functional gene and create a  mutation . This can happen in three distinct ways: 1. alteration of function, 2.  chromosomal rearrangement , and 3. a source of novel genetic material. [ 17 ]  Since DNA transposons may happen to take parts of genomic sequences with them, exon shuffling may occur. Exon shuffling is the creation of novel gene products due to the new placement of two previously unrelated exons through transposition. [ 18 ]  Because of their ability to alter DNA  expression , transposons have become an important target of research in  genetic engineering .


Barbara McClintock  first discovered and described DNA  transposons  in  Zea mays , [ 19 ]  during the 1940s; this is an achievement that would earn her the  Nobel Prize  in 1983. She described the  Ac/Ds  system where the Ac unit (activator) was autonomous but the Ds genomic unit required the presence of the activator in order to move. This TE is one of the most visually obvious as it was able to cause the maize to change color from yellow to brown/spotted on individual kernels.


The  Mariner/Tc1   transposon , found in many animals but studied in  Drosophila  was first described by Jacobson and Hartl. [ 20 ]  Mariner is well known for being able to excise and insert horizontally in to a new organism. [ 21 ]  Thousands of copies of the TE have been found interspersed in the human genome as well as other animals.


The Hobo transposons in Drosophila have been extensively studied due to their ability to cause  gonadal dysgenesis . [ 22 ]  The insertion and subsequent expression of hobo-like sequences results in the loss of  germ cells  in the  gonads  of developing flies.


Bacterial  transposons  are especially good at facilitating  horizontal gene transfer  between microbes. Transposition facilitates the transfer and accumulation of  antibiotic resistance   genes . In bacteria, transposable elements can easily jump between the chromosomal genome and  plasmids . In a 1982 study by Devaud et al., a multi-drug resistant strain of  Acinetobacter  was isolated and examined. Evidence pointed to the transfer of a plasmid in to the bacterium, where the resistance genes were transposed in to the chromosomal genome. [ 23 ]


Transposons may have an effect on the promotion of genetic diversity of many organisms. DNA transposons can drive the evolution of genomes by promoting the relocation of sections of DNA sequences. As a result, this can alter gene regulatory regions and phenotypes. [ 24 ]  The discovery of transposons was made by  Barbara McClintock  who noticed that these elements could actually change the color of the  maize  plants she was studying, providing quick evidence of one outcome from transposon movement. [ 25 ]  Another example is the Tol2 DNA transposon in medaka fish that is said to be the result of their variety in pigmentation patterns. [ 26 ]  These examples show that transposons can greatly influence the process of  evolution  by rapidly inducing changes in the genome.


All DNA  transposons  are inactive in the human  genome . [ 27 ]  Inactivated, or  silenced , transposons do not result in a  phenotypic  outcome and do not move around in the genome. Some are inactive because they have  mutations  that affect their ability to move between chromosomes, while others are capable of moving but remain inactive due to epigenetic defenses, like DNA  methylation  and  chromatin remodeling . For example, chemical modifications of DNA can constrict certain areas of the genome such that  transcription   enzymes  are unable to reach them.  RNAi , specifically  siRNA  and  miRNA  silencing, is a naturally occurring mechanisms that, in addition to regulating eukaryotic gene expression, prevents transcription of DNA transposons. Another mode of inactivation is overproduction inhibition. When  transposase  exceeds a threshold concentration, transposon activity is decreased. [ 28 ]  Since transposase can form inactive or less active monomers that will decrease transposition activity overall, a decrease in the production of transposase will also occur when large copies of those less active elements increase in the host genome.


Horizontal transfer  refers to the movement of DNA information between cells of different organisms. Horizontal transfer can involve the movement of TEs from one organism into the genome of another. The insertion itself allows the TE to become an activated gene in the new host. Horizontal transfer is used by DNA transposons to prevent inactivation and complete loss of the transposon. This inactivation is termed vertical inactivation, meaning that the DNA transposon is inactive and remains as a fossil. This type of transfer is not the most common, but has been seen in the case of the wheat virulence protein ToxA, which was transferred between the different fungal pathogens  Parastagonospora nodorum ,  Pyrenophora tritici-repentis , and  Bipolaris sorokiniana. [ 29 ]  Other examples include transfer between marine  crustaceans , insects of different  orders , and organisms of different  phyla , such as humans and  nematodes . [ 30 ]


Eukaryotic genomes differ in TE content. Recently, a study of the different superfamilies of TEs reveals that there are striking similarities between the groups. It has been hypothesized that many of them are represented in two or more Eukaryotic supergroups. This means that divergence of the transposon superfamilies could even predate the divergence of Eukaryotic supergroups. [ 31 ]


V(D)J recombination , although not a DNA TE, is remarkably similar to  transposons . V(D)J recombination is the process by which the large variation in antibody binding sites is created. In this mechanism, DNA is recombined in order to create genetic diversity. [ 32 ]  Because of this, it has been hypothesized that these proteins, particularly  Rag1  and  Rag2 [ 33 ]  are derived from transposable elements. [ 34 ]


There is evidence suggesting that at least 40 human DNA transposon families were active during mammalian radiation and early primate lineage. Then, there was a pause in transpositional activity during the later portion of primate radiation, with a complete halt in transposon movement in an anthropoid primate ancestor. There is no evidence of any transposable element younger than about 37 million years. [ 35 ]


Transposable elements  (transposons, TEs, 'jumping genes') are short strands of repetitive  DNA  that can self-replicate and  translocate  within  genomes  of plants, animals, and  prokaryotes , and they are generally perceived as parasitic in nature. Their  transcription  can lead to the production of  dsRNAs  (double-stranded RNAs), which resemble  retrovirus  transcripts. While most host cellular RNA has a singular, unpaired  sense strand , dsRNA possesses sense and anti-sense transcripts paired together, and this difference in structure allows a host organism to detect dsRNA production, and thereby the presence of transposons. Plants lack distinct divisions between  somatic cells  and reproductive cells, and also have, generally, larger genomes than animals and prokaryotes, making plants an intriguing case-study for better understanding the  epigenetic  regulation and function of transposable elements. [ 1 ]


Transposons vary in their structure and manner of proliferation, both of which help to define their classification. Each class contains autonomous elements, a sub-variety distinguished by the ability to self-proliferate, and also non-autonomous elements, which lack that ability.