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The role of epigenetics in multi‐generational transmission of asthma: An NIAID workshop report‐based narrative review
Author(s) -
Wheatley Lisa M.,
Holloway John W.,
Svanes Cecilie,
Sears Malcolm R.,
Breton Carrie,
Fedulov Alexey V.,
Nilsson Eric,
Vercelli Donata,
Zhang Hongmei,
Togias Alkis,
Arshad Syed Hasan
Publication year - 2022
Publication title -
clinical and experimental allergy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.462
H-Index - 154
eISSN - 1365-2222
pISSN - 0954-7894
DOI - 10.1111/cea.14223
Subject(s) - epigenetics , dna methylation , offspring , recall , heredity , developmental psychology , mechanism (biology) , genetics , disease , psychology , biology , inheritance (genetic algorithm) , bioinformatics , medicine , cognitive psychology , pregnancy , gene , philosophy , gene expression , epistemology
Abstract There 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.