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dc.contributor.authorWheatley, Lisa M.
dc.contributor.authorHolloway, John W.
dc.contributor.authorSvanes, Cecilie
dc.contributor.authorSears, Malcolm R.
dc.contributor.authorBreton, Carrie
dc.contributor.authorFedulov, Alexey V.
dc.contributor.authorNilsson, Eric
dc.contributor.authorVercelli, Donata
dc.contributor.authorZhang, Hongmei
dc.contributor.authorTogias, Alkis
dc.contributor.authorArshad, Syed Hasan
dc.date.accessioned2022-12-30T12:02:57Z
dc.date.available2022-12-30T12:02:57Z
dc.date.created2022-11-15T13:19:16Z
dc.date.issued2022
dc.identifier.issn0954-7894
dc.identifier.urihttps://hdl.handle.net/11250/3040078
dc.description.abstractThere is mounting evidence that environmental exposures can result in effects on health that can be transmitted across generations, without the need for a direct exposure to the original factor, for example, the effect of grandparental smoking on grandchildren. Hence, an individual's health should be investigated with the knowledge of cross-generational influences. Epigenetic factors are molecular factors or processes that regulate genome activity and may impact cross-generational effects. Epigenetic transgenerational inheritance has been demonstrated in plants and animals, but the presence and extent of this process in humans are currently being investigated. Experimental data in animals support transmission of asthma risk across generations from a single exposure to the deleterious factor and suggest that the nature of this transmission is in part due to changes in DNA methylation, the most studied epigenetic process. The association of father's prepuberty exposure with offspring risk of asthma and lung function deficit may also be mediated by epigenetic processes. Multi-generational birth cohorts are ideal to investigate the presence and impact of transfer of disease susceptibility across generations and underlying mechanisms. However, multi-generational studies require recruitment and assessment of participants over several decades. Investigation of adult multi-generation cohorts is less resource intensive but run the risk of recall bias. Statistical analysis is challenging given varying degrees of longitudinal and hierarchical data but path analyses, structural equation modelling and multilevel modelling can be employed, and directed networks addressing longitudinal effects deserve exploration as an effort to study causal pathways.en_US
dc.language.isoengen_US
dc.publisherWileyen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleThe role of epigenetics in multi-generational transmission of asthma: An NIAID workshop report-based narrative reviewen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.doi10.1111/cea.14223
dc.identifier.cristin2074231
dc.source.journalClinical and Experimental Allergyen_US
dc.source.pagenumber1264-1275en_US
dc.relation.projectNorges forskningsråd: 274767en_US
dc.identifier.citationClinical and Experimental Allergy. 2022, 52 (11), 1264-1275.en_US
dc.source.volume52en_US
dc.source.issue11en_US


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal