Abstract

Until recently, advances in understanding the genetic architecture of psychiatric disorders have been impeded by a historic, and often mandated, commitment to the use of traditional, and unvalidated, categorical diagnoses in isolation as the relevant phenotype. Such studies typically required lengthy structured interviews to delineate differences in the character and duration of behavioral symptomatology amongst disorders that were thought to be etiologic, and they were often underpowered as a result. Increasing acceptance of the fact that co-morbidity in psychiatric disorders is the rule rather than the exception has led to alternative designs in which shared dimensional symptomatology is analyzed as a quantitative trait and to association analyses in which combined polygenic risk scores are computationally compared across multiple traditional categorical diagnoses to identify both distinct and unique genetic and environmental elements. Increasing evidence that most mental disorders share many common genetic risk variants and environmental risk modifiers suggests that the broad spectrum of psychiatric pathology represents the pleiotropic display of a more limited series of pathologic events in neuronal development than was originally believed, regulated by many common risk variants and a smaller number of rare ones.

Highlights

  • Investigation of the underlying genetic risk of individual mental disorders has been hampered by a number of factors, including a lack of validity for current categorical diagnoses and a high rate of co-morbidity amongst disorders, as well as the operational difficulty in accruing a sufficient number of subjects satisfying rigidly defined diagnostic requirements

  • Plana-Ripoll et al.[7], in a large-scale study encompassing almost 84 million person years, have convincingly shown that co-morbidity is present across all psychiatric disorders and is bi-directional, meaning that the risk for any given additional mental disorder is increased by the presence of the first

  • As an example of these methods, one genomewide association study (GWAS) on alcohol dependence found genetic correlations based on linkage disequilibrium score regression (LDSR) with depressive symptoms, a diagnosis of major depressive disorder (MDD), attention deficit hyperactivity disorder (ADHD), SCZ, neuroticism, and subjective well-being at the p = 10-5 level or better[28]

Read more

Summary

Introduction

Investigation of the underlying genetic risk of individual mental disorders has been hampered by a number of factors, including a lack of validity for current categorical diagnoses and a high rate of co-morbidity amongst disorders, as well as the operational difficulty in accruing a sufficient number of subjects satisfying rigidly defined diagnostic requirements. As an example of these methods, one GWAS on alcohol dependence found genetic correlations based on LDSR with depressive symptoms, a diagnosis of MDD, attention deficit hyperactivity disorder (ADHD), SCZ, neuroticism, and subjective well-being at the p = 10-5 level or better[28]. A study using a summary statistic to determine polygenic risk correlated among 24 disorders found a strong overlap between SCZ and BPD, both of which were associated with depression[29]. The benefit of using such a method is a dimensional reduction analysis; rather than looking at single genes, the module “eigengene”, which roughly approximates the first principal component, is used for a group of genes hypothesized to be functioning in concert based on a pattern of up- or down-regulation together Such gene networking approaches have demonstrated shared and differential module expression in BPD, ASD, ADHD, alcoholism, depression, and SCZ39,42–44. Two in particular were highly expressed in the brain, and a statistic, k-within, was used to find hub genes in one module related to SCZ45

Conclusions
Findings
Davis LK
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call