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Event: 336

Key Event Title

The KE title should describe a discrete biological change that can be measured. It should generally define the biological object or process being measured and whether it is increased, decreased, or otherwise definably altered relative to a control state. For example “enzyme activity, decreased”, “hormone concentration, increased”, or “growth rate, decreased”, where the specific enzyme or hormone being measured is defined. More help

Increase, Heritable mutations in offspring

Short name
The KE short name should be a reasonable abbreviation of the KE title and is used in labelling this object throughout the AOP-Wiki. The short name should be less than 80 characters in length. More help
Increase, Heritable mutations in offspring

Biological Context

Structured terms, selected from a drop-down menu, are used to identify the level of biological organization for each KE. Note, KEs should be defined within a particular level of biological organization. Only KERs should be used to transition from one level of organization to another. Selection of the level of biological organization defines which structured terms will be available to select when defining the Event Components (below). More help

Key Event Components

Further information on Event Components and Biological Context may be viewed on the attached pdf.Because one of the aims of the AOP-KB is to facilitate de facto construction of AOP networks through the use of shared KE and KER elements, authors are also asked to define their KEs using a set of structured ontology terms (Event Components). In the absence of structured terms, the same KE can readily be defined using a number of synonymous titles (read by a computer as character strings). In order to make these synonymous KEs more machine-readable, KEs should also be defined by one or more “event components” consisting of a biological process, object, and action with each term originating from one of 22 biological ontologies (Ives, et al., 2017; See List). Biological process describes dynamics of the underlying biological system (e.g., receptor signalling). The biological object is the subject of the perturbation (e.g., a specific biological receptor that is activated or inhibited). Action represents the direction of perturbation of this system (generally increased or decreased; e.g., ‘decreased’ in the case of a receptor that is inhibited to indicate a decrease in the signalling by that receptor).Note that when editing Event Components, clicking an existing Event Component from the Suggestions menu will autopopulate these fields, along with their source ID and description. To clear any fields before submitting the event component, use the 'Clear process,' 'Clear object,' or 'Clear action' buttons. If a desired term does not exist, a new term request may be made via Term Requests. Event components may not be edited; to edit an event component, remove the existing event component and create a new one using the terms that you wish to add. More help
Process Object Action
mutation deoxyribonucleic acid increased

Key Event Overview

AOPs Including This Key Event

All of the AOPs that are linked to this KE will automatically be listed in this subsection. This table can be particularly useful for derivation of AOP networks including the KE. Clicking on the name of the AOP will bring you to the individual page for that AOP. More help
AOP Name Role of event in AOP Point of Contact Author Status OECD Status
Alkylation of DNA leading to heritable mutations AdverseOutcome Carole Yauk (send email) Open for citation & comment TFHA/WNT Endorsed


This is a structured field used to identify specific agents (generally chemicals) that can trigger the KE. Stressors identified in this field will be linked to the KE in a machine-readable manner, such that, for example, a stressor search would identify this as an event the stressor can trigger. NOTE: intermediate or downstream KEs in one AOP may function as MIEs in other AOPs, meaning that stressor information may be added to the KE description, even if it is a downstream KE in the pathway currently under development.Information concerning the stressors that may trigger an MIE can be defined using a combination of structured and unstructured (free-text) fields. For example, structured fields may be used to indicate specific chemicals for which there is evidence of an interaction relevant to this MIE. By linking the KE description to a structured chemical name, it will be increasingly possible to link the MIE to other sources of chemical data and information, enhancing searchability and inter-operability among different data-sources and knowledgebases. The free-text section “Evidence for perturbation of this MIE by stressor” can be used both to identify the supporting evidence for specific stressors triggering the MIE as well as to define broad chemical categories or other properties that classify the stressors able to trigger the MIE for which specific structured terms may not exist. More help

Taxonomic Applicability

Latin or common names of a species or broader taxonomic grouping (e.g., class, order, family) can be selected from an ontology. In many cases, individual species identified in these structured fields will be those for which the strongest evidence used in constructing the AOP was available in relation to this KE. More help
Term Scientific Term Evidence Link
Mus musculus Mus musculus High NCBI
medaka Oryzias latipes Moderate NCBI
Drosophila melanogaster Drosophila melanogaster Moderate NCBI

Life Stages

The structured ontology terms for life-stage are more comprehensive than those for taxa, but may still require further description/development and explanation in the free text section. More help

Sex Applicability

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Key Event Description

A description of the biological state being observed or measured, the biological compartment in which it is measured, and its general role in the biology should be provided. For example, the biological state being measured could be the activity of an enzyme, the expression of a gene or abundance of an mRNA transcript, the concentration of a hormone or protein, neuronal activity, heart rate, etc. The biological compartment may be a particular cell type, tissue, organ, fluid (e.g., plasma, cerebrospinal fluid), etc. The role in the biology could describe the reaction that an enzyme catalyses and the role of that reaction within a given metabolic pathway; the protein that a gene or mRNA transcript codes for and the function of that protein; the function of a hormone in a given target tissue, physiological function of an organ, etc. Careful attention should be taken to avoid reference to other KEs, KERs or AOPs. Only describe this KE as a single isolated measurable event/state. This will ensure that the KE is modular and can be used by other AOPs, thereby facilitating construction of AOP networks. More help

Mutations occurring in the offspring are the adverse effect. These mutations may have many eventual consequences including embryonic or fetal death, or genetic disease in the offspring. If mutations are viable, the specific sites and sequence changes of the mutations will govern the phenotypic outcome of the inherited mutation.

DETAILS: Evolutionarily advantageous or beneficial mutations are expected to be rare. Thus, the majority of inherited mutations will be neutral, with a somewhat smaller proportion expected to be harmful. For example, Keightley (2012) used phylogenetic analysis to estimate that approximately 70 new mutations occur per generation, 2.2 of which, on average, are deleterious. These deleterious mutations affect the fitness of the organism (decreasing probability of reproducing) and thus impact the population. Alternatively, one must also consider pathogenic mutations, including those that do not affect fitness (e.g., diseases that may occur later in life and do not affect ability to reproduce). It is currently not possible to fully measure the consequences of pathogenic mutations, because we lack appropriate methods to measure their penetrance (e.g., mutations with low odds ratios, diverse phenotypes, or that contribute to multigenic disorders, etc.). Thus, we currently do not have precise mechanisms to evaluate the full impacts of de novo mutations. However, increasing use of whole genome sequencing is shedding light on the rate, spectrum, and consequences of de novo mutations. Evidence is accumulating on the major role of de novo mutations in rare Mendelian and genetically heterogeneous diseases (e.g., reviewed in Veltman and Brunner, 2012; Geschwind and Flint, 2015; Walsch et al. 2010). The rate and spectrum of human mutations is reviewed in Campbell and Eichler (2013), and potential consequences of mutations explored in Shendure and Akey (2015). Estimates indicate approximately 100 loss-of-function variants in a human genome, with as many as 20 exhibiting complete loss of gene function (McLaughlin et al. 2010). As an example, based on full genome sequencing data, paternal de novo sequence mutations are expected to account for an equal amount of the genetic burden of disease in ageing fathers as maternal aneuploidies due to ageing (Hurles, 2012). It is important to note that although mutations in coding regions are expected to have large effects on fitness, the absolute number of mutations in non-coding sequence that is under selection is actually greater than coding sequence (Green and Ewing, 2013). In general, it is widely accepted that de novo mutations contribute to the overall population genetic disease burden. The application of whole genome sequencing in the clinic is providing new knowledge on the unprecedented extent to which de novo mutations are contributing to a whole host of idiopathic human genetic disorders (e.g., Lupski et al., 2011; Ku et al., 2013; Gilissen et al., 2014).

How It Is Measured or Detected

One of the primary considerations in evaluating AOPs is the relevance and reliability of the methods with which the KEs can be measured. The aim of this section of the KE description is not to provide detailed protocols, but rather to capture, in a sentence or two, per method, the type(s) of measurements that can be employed to evaluate the KE and the relative level of scientific confidence in those measurements. Methods that can be used to detect or measure the biological state represented in the KE should be briefly described and/or cited. These can range from citation of specific validated test guidelines, citation of specific methods published in the peer reviewed literature, or outlines of a general protocol or approach (e.g., a protein may be measured by ELISA).Key considerations regarding scientific confidence in the measurement approach include whether the assay is fit for purpose, whether it provides a direct or indirect measure of the biological state in question, whether it is repeatable and reproducible, and the extent to which it is accepted in the scientific and/or regulatory community. Information can be obtained from the OECD Test Guidelines website and the EURL ECVAM Database Service on Alternative Methods to Animal Experimentation (DB-ALM). ?

A heritable mutation is measured as a mutation occurring in the offspring that is not present in the parents and that is present in every cell type (the latter is not typically measured). Heritable mutations were previously measured using the Mouse Specific Locus Test (SLT) and variations on this assay (in rodents, fish and Drosophila). The Oak Ridge National Laboratory's SLT test, established by William and Lianne Russell, was the gold standard for heritable mutation screening for several decades. Transmission of mutations from exposed males to their offspring can also be measured by analysis of tandem repeat mutations, an accepted though not widely used method. No OECD guideline exists for either assay.

Mouse SLT or variations of this assay: The SLT and dominant cataract methods are no longer used today because they require too many rodents, but there is a fairly large database from the application of these methods. The SLT is based on the use of seven dominant visible trait markers in mice (Davis and Justice, 1998; Russell et al., 1979). Male mice are exposed to the mutagen and mated at varying times post-exposure to evaluate effects on different stages of spermatogenesis. Males are mated with females carrying recessive alleles at the seven loci screened in the assay. Functional mutations at the dominant (male) locus results in expression of the recessive phenotype in the offspring. These phenotypes include changes in coat colour, skeletal malformations, and other traits. Variations on this assay include looking at other visible traits including 34 common skeletal malformations and dominant cataracts. Additional variations include protein electrophoresis to explore protein changes (e.g., Lewis et al., 1991).

Tandem repeat mutation: Tandem repeat mutations can be measured in offspring using a similar approach. Male mice are exposed to the mutagen and mated various times post-exposure to non-exposed females. DNA fingerprinting is used to measure changes in repeat length in offspring relative to their parents. This is currently the only assay that is able to measure the same mutational endpoint in sperm as in offspring, supporting that transmission of mutations from sperm to the offspring occurs. For methodologies please see Vilarino-Guell et al. (2003). A wide range of human genetic disorders are associated with de novo length change mutations in tandem repeat sequences (Mirkin, 2007). However, it should also be noted that the mutations are induced through indirect mechanisms that are likely to be associated with polymerase errors during cell cycle arrest, rather than direct lesions at the locus (Yauk et al., 2002)

Next generation sequencing: With the advent and improvement in sequencing technologies, it is anticipated that heritable mutations will be measured by directly sequencing the offspring of males exposed to mutagenic agents. Current approaches require the exposure of parental gametes to a mutagenic agent, followed by mating and collection of offspring. Whole genome sequencing is applied to compare the genome sequences of parents and offspring to identify and haplotype (i.e., determine the parental origin) of de novo mutations (identified as mutations occurring in offspring but not their parents). Studies such as these have demonstrated that increasing paternal age causes an increase in both single nucleotide variants and tandem repeats in the offspring (Kong et al., 2012; Sun et al., 2012). Proof of principle of the ability of application of genomics tools (array comparative genome hybridization and next generation sequencing) has been published for male mice exposed to radiation (Adewoye et al., 2015). The authors show that the frequency of de novo copy number variants (CNVs) and insertion/deletion events (indels) are significantly elevated in offspring of radiation-exposed fathers. Several papers have described how research in this field should proceed (Beal and Somers, 2011; Yauk et al., 2012; Yauk et al., 2015) and propose that this will be a paradigm-changing technology.

Note: The Dominant Lethal test (OECD TG 478) is used to measure the effects of DNA damage in sperm on dominant lethality in the offspring. The overwhelming majority of dominant lethal mutations are due to chromosomal effects rather than gene mutations (Marchetti et al., 2005). Thus, this TG is not generally suited to the measurement of inherited gene mutations.

Domain of Applicability

This free text section should be used to elaborate on the scientific basis for the indicated domains of applicability and the WoE calls (if provided). While structured terms may be selected to define the taxonomic, life stage and sex applicability (see structured applicability terms, above) of the KE, the structured terms may not adequately reflect or capture the overall biological applicability domain (particularly with regard to taxa). Likewise, the structured terms do not provide an explanation or rationale for the selection. The free-text section on evidence for taxonomic, life stage, and sex applicability can be used to elaborate on why the specific structured terms were selected, and provide supporting references and background information.  More help

Heritable mutations are the basis of evolution and occur in every species.

Regulatory Significance of the Adverse Outcome

An AO is a specialised KE that represents the end (an adverse outcome of regulatory significance) of an AOP. For KEs that are designated as an AO, one additional field of information (regulatory significance of the AO) should be completed, to the extent feasible. If the KE is being described is not an AO, simply indicate “not an AO” in this section.A key criterion for defining an AO is its relevance for regulatory decision-making (i.e., it corresponds to an accepted protection goal or common apical endpoint in an established regulatory guideline study). For example, in humans this may constitute increased risk of disease-related pathology in a particular organ or organ system in an individual or in either the entire or a specified subset of the population. In wildlife, this will most often be an outcome of demographic significance that has meaning in terms of estimates of population sustainability. Given this consideration, in addition to describing the biological state associated with the AO, how it can be measured, and its taxonomic, life stage, and sex applicability, it is useful to describe regulatory examples using this AO. More help

Heritable mutations are an important regulatory endpoint for most agencies around the world (reviewed in Yauk et al. 2015). Strategies and guidelines for regulatory toxicology testing in various national regulatory jurisdictions, including requirements for germ cell mutation assays, have been described extensively by Cimino (2006), and have not changed significantly. While no jurisdiction requires germ cell testing per se in an initial test battery, many regulatory authorities can request germ cell tests for follow-up studies, e.g. in the U.S.A (U.S. EPA), Canada (Health Canada), the United Kingdom (Committee on Mutagenicity: COM), and Europe (Registration, Evaluation, Authorization and Restriction of Chemicals, i.e. REACH). For example, within the REACH strategy a substance that is genotoxic in somatic cells is evaluated from the literature to see if it is a potential germ cell mutagen based on bioavailability to the germ cells and appropriate in vivo data. If such an evaluation shows that the literature is insufficient to determine whether the agent is or is not a potential germ cell mutagen, then that agent can be tested in a suitable germ cell genotoxicity assay. Although germ cell testing is not specifically required under the Canadian Environmental Protection Act (CEPA) New Substances Notification Regulations, germ cell mutation tests are requested and evaluated when necessary. For new chemical assessments under CEPA from 1994 to 2012, a total of 19 chemicals have been evaluated for germ cell mutagenicity (12 for which the test was submitted, plus 7 for which the test was referenced on the MSDS); importantly, this is comparable to the number for which testing in rodent cancer assays was evaluated (i.e. total of 20; 17 for which the test was submitted, plus 3 for which test was referenced on the MSDS) (Personal Communication, New Substances Assessment and Control Bureau, Health Canada). These examples illustrates the regulatory importance of heritable mutations as an adverse outcome.

For pharmaceuticals, the ICH Technical Requirements for Registration of Pharmaceuticals for Human Use does not require germ cell tests and assumes that in vivo somatic tests and carcinogenicity data will provide sufficient predictivity/protection for germ cell effects (ICH, 2011)

The World Health Organization (WHO)/International Programme on Chemical Safety (IPCS) has developed a harmonized scheme for mutagenicity testing. In this document the relationship between somatic cell mutagenicity and germ cell risk is summarized in the following statement. “For substances that give positive results for mutagenic effects in somatic cells in vivo, their potential to affect germ cells should be considered. If there is toxicokinetic or toxicodynamic evidence that germ cells are actually exposed to the somatic mutagen or its bioactive metabolites, it is reasonable to assume that the substance may also pose a mutagenic hazard to germ cells and thus a risk to future generations.” (Eastmond et al. 2009).

The Global Harmonization Scheme (GHS; UN, 2013) is a germ cell mutation classification system developed by the United Nations that identifies them according to the categories noted in the Table below. To date over 60 countries have implemented this programme, and are in the process of integrating it into their relevant regulations. To date its implementation is focussed on product labelling legislation in the respective countries and regulatory jurisdictions.

Categorization of mutagens by GHS Category Description 1A Chemicals known to induce heritable mutations in germ cells of humans 1B Chemicals that should be regarded as if they induce heritable mutations in germ cells of humans 2 Chemicals that cause concern for induction of heritable mutations in germ cells of humans

Finally, we note that mouse data obtained with the specific locus test and other recessive mutation analyses played an important role in estimating radiation dose risk in the human population (BEIR VII 2006).


List of the literature that was cited for this KE description. Ideally, the list of references, should conform, to the extent possible, with the OECD Style Guide ( (OECD, 2015). More help

Adewoye, A.B., S.J. Lindsay, Y.E. Dubrova and M.E. Hurles (2015), "The genome-wide effects of ionizing radiation on mutation induction in the mammalian germline", Nat Commun., 6: 6684.

Beal, M.A., T.C. Glenn and C.M. Somers (2011), "Whole genome sequencing for quantifying germline mutation frequency in humans and model species: cautious optimism", Mutation Research, 750(2): 96-106

Beal, M.A., A. Rowan-Carroll, C. Campbell, A. Williams, C.M. Somers, F. Marchetti and C.L. Yauk (2015), "Single-molecule PCR analysis of an unstable microsatellite for detecting mutations in sperm of mice exposed to chemical mutagens", Mutat. Res., 775: 26-32.

BEIR VII (2006), "Health Risks from Exposure to Low Levels of Ionizing Radiation", Academies NRCotN, editor. Washington, D.C.: National Academies Press.

Campbell, C.D. and E.E. Eichler (2013), "Properties and rates of germline mutations in humans", Trends Genet., 29(10): 575-584.

Cimino, M.C. (2006), "Comparative overview of current international strategies and guidelines for genetic toxicology testing for regulatory purposes", Environ. Mol. Mutagen., 47: 362–390.

Davis, A.P. and M.J. Justice (1998), "An Oak Ridge legacy: the specific locus test and its role in mouse mutagenesis", Genetics, 148(1): 7-12.

Geschwind, D.H. and J. Flint (2015), "Genetics and genomics of psychiatric disease", Science, 349(6255): 1489-1494

Green, P. and B. Ewing (2013), "Comment on “Evidence of abundant purifying selection in humans for recently acquired regulatory functions”", Science, 340(682) discussion 682.

Gilissen, C., J.Y. Hehir-Kwa, D.T. Thung, M. van de Vorst, B.W. van Bon, M.H. Willemsen, M. Kwint, I.M. Janssen, A. Hoischen, A. Schenck, R. Leach, R. Klein, R. Tearle, T. Bo, R. Pfundt, H.G. Yntema, B.B. de Vries, T. Kleefstra, H.G. Brunner, L.E. Vissers and J.A. Veltman (2014), "Genome sequencing identifies major causes of severe intellectual disability", Nature, 511(7509): 344-347.

Eastmond, D.A., A. Hartwig, D. Anderson, W.A. Anwar, M.C. Cimino, I. Dobrev, G.R. Douglas, T. Nohmi, D.H. Phillips and C. Vickers (2009), "Mutagenicity testing for chemical risk assessment: update of the WHO/IPCS Harmonized Scheme", Mutagenesis, 24(4): 341-349.

Kong, A., M.L. Frigge, G. Masson, S. Besenbacher, P. Sulem, G. Magnusson, S.A. Gudjonsson, A. Sigurdsson, A. Jonasdottir, W.S. Wong, G. Sigurdsson, G.B. Walters, S. Steinberg, H. Helgason, G. Thorleifsson, D.F. Gudbjartsson, A. Helgason, O.T. Magnusson, U. Thorsteinsdottir and K. Stefansson K. (2012), "Rate of de novo mutations and the importance of father's age to disease risk", Nature, 488(7412): 471-475.

Hurles, M. (2012), Older males beget more mutations", Nature Genetics, 44(11): 1174-1176.

International Conference on Harmonisation (ICH) (2011), "Guidance On Genotoxicity Testing And Data Interpretation For Pharmaceuticals Intended For Human Use S2(R1)" ICH Harmonised Tripartite Guideline, International Conference on Harmonization, Geneva, Switzerland.

Keightley, P.D. (2012), "Rates and Fitness Consequences of New Mutations in Humans", Genetics, 190(2): 295–304.

Ku, C. S., E.K. Tan and D.N. Cooper (2013), "From the periphery to centre stage: de novo single nucleotide variants play a key role in human genetic disease", J. Med. Genet., 50(4): 203-211.

Lewis, S.E., L.B. Barnett, B.M. Sadler and M.D. Shelby MD (1991), "ENU mutagenesis in the mouse electrophoretic specific-locus test, 1. Dose-response relationship of electrophoretically-detected mutations arising from mouse spermatogonia treated with ethylnitrosourea", Mutat Res., 249(2): 311-5.

Lupski, J.R., J.W. Belmont, E. Boerwinkle and R.A. Gibbs (2011), "Clan genomics and the complex architecture of human disease", Cell, 147(1): 32-43.

Marchetti, F. and A.J. Wyrobek (2005), "Mechanisms and consequences of paternally-transmitted chromosomal abnormalities", Birth Defects Res C Embryo Today, 75(2): 112-129.

Mirkin, S.M. (2007), "Expandable DNA repeats and human disease", Nature, 447(7147): 932-940.

Russell, W.L., E.M. Kelly, P.R. Hunsicker, J.W. Bangham, S.C. Maddux and E.L. Phipps (1979), "Specific-locus test shows ethylnitrosourea to be the most potent mutagen in the mouse" Proceedings of the National Academy of Sciences of the United States of America, 76(11): 5818-5819.

Russel, L.B. (2004), "Effects of male germ-cell stage on the frequency, nature and spectrum of induced specific-locus mutations in the mouse", Genetica, 122: 25-36.

Shendure, J. and J.M. Akey (2015), "The origins, determinants, and consequences of human mutations", Science, 349(6255): 1478-1483.

Sun, J.X., A. Helgason, G. Masson, S.S. Ebenesersdottir, H. Li, S. Mallick, S. Gnerre, N. Patterson, A. Kong, D. Reich and K. Stefansson (2012), "A direct characterization of human mutation based on microsatellites", Nat. Genet., 44(10): 1161-1165.

Veltman, J.A. and H.G. Brunner (2012), "De novo mutations in human genetic disease", Nat. Rev. Genet., 13(8): 565-575.

Vilarino-Guell, C., A.G. Smith and Y.E. Dubrova (2003), "Germline mutation induction at mouse repeat DNA loci by chemical mutagens" Mutation Research, 526(1-2): 63-73.

Walsh, T., M.K. Lee, S. Casadei, A.M. Thornton, S.M. Stray, C. Pennil, A.S. Nord, J.B. Mandell, E.M. Swisher and M.C. King (2010) "Detection of inherited mutations for breast and ovarian cancer using genomic capture and massively parallel sequencing", Proc Natl Acad Sci U S A., 107(28): 12629-12633.

United Nations (UN) (2013), "Globally Harmonized System of Classification and Labelling of Chemicals (GHS)", United Nations, New York, USA.

Yauk, C.L., Y.E. Dubrova, G.R. Grant and A.J. Jeffreys (2002), "A novel single molecule analysis of spontaneous and radiation-induced mutation at a mouse tandem repeat locus" Mutation Research. 500(1-2): 147-56.

Yauk, C.L., L.J. Argueso, S.S. Auerbach, P. Awadalla, S.R. Davis, D.M. Demarini, G.R. Douglas, Y.E. Dubrova, R.K. Elespuru, T.M. Glover, B.F. Hales , M.E. Hurles, C.B. Klein, J.R. Lupski, D.K. Manchester, F. Marchetti, A. Montpetit, J.J. Mulvihill, B. Robaire, W.A. Robbins, G.A. Rouleau, D.T. Shaughnessy, C.M. Somers, J.G. Taylor 6th, J. Trasler, M.D. Waters, T.E. Wilson, K.L. Witt and J.B. Bishop (2013), "Harnessing genomics to identify environmental determinants of heritable disease" Mutation Research, 752(1): 6-9.

Yauk, C.L., M.J. Aardema, J. van Benthem, J.B. Bishop, K.L. Dearfield, D.M. DeMarini, Y.E. Dubrova, M. Honma, J.R. Lupski, F. Marchetti, M.L. Meistrich, F. Pacchierotti, J. Stewart, M.D. Waters and G.R. Douglas (2015), "Approaches for Identifying Germ Cell Mutagens: Report of the 2013 IWGT Workshop on Germ Cell Assays", Mutat. Res. Genet. Toxicol. Environ. Mutagen. 783:36-54.