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Development is a dynamic process shaped by the interactions between genes and environments. Within the field of developmental biology, the complex interactions between genes and their products that create the foundation for cellular differentiation and the formation of the nervous system have been well described. Advances in molecular biology have permitted increasing precision in the characterization of the cascade of molecular changes that link genes to specific developmental endpoints. However, beyond addressing questions regarding the processes linking a gene to a phenotypic outcome, description of these molecular changes has also provided insight into the ways in which the environment induces lasting biological effects. Though environments – particularly characteristics of the social world around us – are typically viewed as a separate and distinct influence from genes, there is increasing understanding of the interplay between genes and environments.
Cognitive functions are highly heritable and polygenic, determined by many different genes. This chapter summarizes current knowledge regarding the genetic basis of cognitive abilities based on evidence from twin studies and behavioral genetic studies, focusing on single genes or polygenic scores. Given the focus of this book on aging, we also highlight differences of genetic influences on cognition across the adult life span, which contribute to the large interindividual differences in the decline of cognition in old age. In addition, we discuss the complex interplay between genetic and environmental factors in influencing cognition in adulthood and aging. Here, we focus on gene-environment interactions, gene-environment correlations, and epigenetic mechanisms, which likely account for some of the differential patterns in cognitive aging trajectories.
Major depressive disorder (MDD) is a common and disabling condition with well-established heritability and environmental risk factors. Gene–environment interaction studies in MDD have typically investigated candidate genes, though the disorder is known to be highly polygenic. This study aims to test for interaction between polygenic risk and stressful life events (SLEs) or childhood trauma (CT) in the aetiology of MDD.
Method
The RADIANT UK sample consists of 1605 MDD cases and 1064 controls with SLE data, and a subset of 240 cases and 272 controls with CT data. Polygenic risk scores (PRS) were constructed using results from a mega-analysis on MDD by the Psychiatric Genomics Consortium. PRS and environmental factors were tested for association with case/control status and for interaction between them.
Results
PRS significantly predicted depression, explaining 1.1% of variance in phenotype (p = 1.9 × 10−6). SLEs and CT were also associated with MDD status (p = 2.19 × 10−4 and p = 5.12 × 10−20, respectively). No interactions were found between PRS and SLEs. Significant PRSxCT interactions were found (p = 0.002), but showed an inverse association with MDD status, as cases who experienced more severe CT tended to have a lower PRS than other cases or controls. This relationship between PRS and CT was not observed in independent replication samples.
Conclusions
CT is a strong risk factor for MDD but may have greater effect in individuals with lower genetic liability for the disorder. Including environmental risk along with genetics is important in studying the aetiology of MDD and PRS provide a useful approach to investigating gene–environment interactions in complex traits.
Sleep apnea is an important cause in sleep medicine for a direct sustained assault on the brain, and also indirectly through a range of mechanisms. This chapter summarizes some of these mechanisms, as they are all active in the obstructive sleep apnea (OSA) patient. Sleep deprivation/fragmentation, and sleep apnea, alters task-related activation in the executive network. Brain-derived neurotrophic factor (BDNF) may be a special link between sleep, cognition, and brain health. Supportive evidence of a central role for BDNF in sleep homeostasis has emerged, particularly providing a link between wake and use-dependent synaptic plasticity and subsequent slow-wave sleep. Structural and functional imaging can establish if genomic modifiers such as these modify the impact of sleep hypoxia or sleep fragmentation on the adult or pediatric brain structure and function, including cognition. Such imaging genomics has shown some utility in assessing the effects of stimulants in relation to catecholamine metabolizing pathway polymorphisms.
There has been much debate as to whether the association between cannabis and subclinical expression of psychosis is causal, or whether psychotic experiences may prompt cannabis use in individuals at genetic risk for psychosis as a means of self-medication. The Genetic Risk and Outcome in Psychosis (GROUP) study investigated the association between familial liability for psychosis and sensitivity to cannabis, using patient sibling and sibling-control pairs analyses. This study focused on gene-environment interactions relevant to psychotic disorders, and included patients with psychotic disorder, their siblings and community controls. Caspi and colleagues highlighted the importance of individual genetic vulnerability when they reported an interaction between cannabis use and variation in the gene that encodes catecholamine-O-methyl transferase (COMT). Given that different types of cannabis clearly affect mental health differentially, more research is needed to understand how genetic liability may increase sensitivity to, or preference for, the specific constituents of cannabis.
from
Part I
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Theoretical and conceptual foundations
By
Jennifer H. Barnett, Department of Psychiatry, University of Cambridge, Box 189, Addenbrooke's Hospital, Cambridge, UK,
Peter B. Jones, Department of Psychiatry, University of Cambridge, Box 189, Addenbrooke's Hospital, Cambridge, UK
This chapter considers how genes might interact with aspects of the social environment in the genesis of psychiatric disorders. Psychiatric research has been revolutionised by molecular genetics, such as the hunt for candidate genes for schizophrenia. The search for gene-environment interactions depends crucially on the development of similarly sophisticated means of measuring environmental risk. The chapter describes evidence for such interactions from early adoption studies to recent investigations using modern molecular genetic techniques. It discusses the principal methodological issues of such research, and the need for clarification of the mechanisms of gene-environment interaction. Differences in environmental and phenotypic measures are understandable given that many gene-environment studies will necessarily be opportunistic, taking advantage of large datasets such as birth cohorts where much of the phenotypic and environmental exposure data have been collected. Finally, the chapter also considers the challenges that increasing knowledge of epigenetics will bring to the field.
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