Critical Issues in the Inclusion of Genetic and Epigenetic Information in Prevention and Intervention Trials

Abstract

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).

Epigenetic Modifications in Stress Response Genes Associated With Childhood Trauma

“…Targeted assays with high sensitivity covering functionally relevant regions could be an interesting complement here (297). 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). Consequently, multiple validations via more than one method might bring more reliability.…”

Testing an Attachment-Based Parenting Intervention-VIPP-FC/A in Adoptive Families with Post-institutionalized Children: Do Maternal Sensitivity and Genetic Markers Count?

“…The randomization of participants to trial groups allows for careful evaluation of the role of genetics as well as the role of environment. This appears particularly relevant because of the manner in which the environment is controlled, thus limiting potential gene–environment correlation, one of the main pitfall of this kind of research ( Latendresse et al, 2017 ). Second, in order to manipulate the environmental variable we compared two conditions (i.e., control vs. intervention) by conducting an evidence-based intervention with a possibly challenging population such as it is that of adoption mothers in their first year after adoption placement.…”

The Implications of Genetics for Prevention and Intervention Programming

“…The authors present a useful set of “how to” guidelines for working with genomic data, including quality control, population structure/stratification, genotype imputation, and statistical approaches designed to maximize power. In doing so, the authors provide a bit of history regarding research with candidate genes and GWAS, including successes and failures of both and possible solutions (Latendresse, Musci, & Maher, 2017). In addition, the authors include sections on methylation, gene-environment interplay, and genetics in randomized controlled trials that describe exciting opportunities for prevention researchers.…”