Critical issues in the inclusion of genetic and epigenetic information in prevention and intervention trials
Open accessAbstract
Human genetic research in the past decade has generated a wealth of data from the genome-wide association scan era, much of which is catalogued and freely available. These data will typically test the relationship between a single nucleotide variant or polymorphism (SNP) and some outcome, disease, or trait. Ongoing investigations will yield a similar wealth of data regarding epigenetic phenomena. These data will typically test the relationship between DNA methylation at a single genomic location/region and some outcome. Most of these findings will be the result of cross-sectional investigations typically using ascertained cases and controls. Consequently, most methodological consideration focuses on methods appropriate for simple case-control comparisons. It is expected that a growing number of investigators with longitudinal experimental prevention or intervention cohorts will also measure genetic and epigenetic indicators as part of their investigations, harvesting the wealth of information generated by the genome-wide association study (GWAS) era to allow for targeted hypothesis testing in the next generation of prevention and intervention trials. Herein, we discuss appropriate quality control and statistical modelling of genetic, polygenic, and epigenetic measures in longitudinal models. We specifically discuss quality control, population stratification, genotype imputation, pathway approaches, and proper modelling of an interaction between a specific genetic variant and an environment variable (GxE interaction).
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Issues such as cost, speed of delivery, errors of variant annotation, logical and methodological issues remain in human genomic and epigenomic studies.
“Nonetheless, issues such as cost, speed of delivery, errors of variant annotation, logical and methodological issues (e.g., the appropriate selection of the cohort, population stratification and statistical approaches) remain in human genomic and epigenomic studies (298, 299).”
From Epigenetic Modifications in Stress Response Genes Associated With Childhood Trauma (2019) · cites this paper as [298]
The Abstract and Population Stratification section (page numbers unavailable in our copy) discuss methodological and statistical pitfalls, including population stratification. The paper describes genomic arrays as relatively inexpensive, but the cited passages do not establish cost or speed of delivery as remaining problems, or discuss errors of variant annotation as such. (LLM, conf=0.88)
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Keller, F. (2019) · Advances in prevention science · doi:10.1007/978-3-030-00627-3_16 · Cited by 6 · see citing works
Yang, Z., Liu, Y., & Yang, H. (2026) · Chinese Medical Journal · doi:10.1097/cm9.0000000000004201
- Authors
- Latendresse, S. J., Musci, R., & Maher, B. S.
- Affiliations
- Baylor University (US)
- Johns Hopkins University (US)
- Corresponding author
- Latendresse, Shawn J.
- Maher, Brion S.
- Venue
- Prevention Science
- Published
- 2017-04-13
- Volume
- 19
- Issue
- 1
- Pages
- 58-67
- ISSN
- 1389-4986 (ISSN-L)
- 1573-6695
- PMC
- PMC5640466
- OpenAlex
- W2605722624
- Cited by
- 12
- References
- 84
- Citations by year
- 2026: 1 · 2023: 1 · 2020: 4 · 2019: 3 · 2018: 1 · 2017: 2
- FWCI
- 1.82
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Prevention Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Drug Abuse (R01DA036525, R01DA039408)
- National Institute on Alcohol Abuse and Alcoholism (K01AA020333)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Keywords
- genetic data, epigenetic information, DNA methylation, single nucleotide polymorphisms, prevention trials, longitudinal data, quality control, population stratification, genotype imputation, pathway analysis, gene-environment interaction, polygenic risk score, statistical modeling
- MeSH terms
- Cross-Sectional Studies, Genotype, Humans, Preventive Medicine, DNA Methylation, Polymorphism, Single Nucleotide, Genetic Research, Genome-Wide Association Study, Epigenomics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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