We use polygenic risk scores (PRSs) for schizophrenia (SCZ) and bipolar disorder (BPD) to predict smoking, and addiction to nicotine, alcohol or drugs in individuals not diagnosed with psychotic disorders. Using PRSs for 144 609 subjects, including 10 036 individuals admitted for in-patient addiction treatment and 35 754 smokers, we find that diagnoses of various substance use disorders and smoking associate strongly with PRSs for SCZ (P = 5.3 × 10 -1.4 × 10 ) and BPD (P = 1.7 × 10 -1.9 × 10 ), showing shared genetic etiology between psychosis and addiction. Using standardized scores for SCZ and BPD scaled to a unit increase doubling the risk of the corresponding disorder, the odds ratios for alcohol and substance use disorders range from 1.19 to 1.31 for the SCZ-PRS, and from 1.07 to 1.29 for the BPD-PRS. Furthermore, we show that as regular smoking becomes more stigmatized and less prevalent, these biological risk factors gain importance as determinants of the behavior.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5811785PMC
http://dx.doi.org/10.1111/adb.12496DOI Listing

Publication Analysis

Top Keywords

polygenic risk
8
risk scores
8
bipolar disorder
8
substance disorders
8
scores schizophrenia
4
schizophrenia bipolar
4
disorder associate
4
addiction
4
associate addiction
4
addiction polygenic
4

Similar Publications

Introduction: Traditional multivariate methods for neuroimaging studies overlook the interdependent relationship between brain features. This study addresses this gap by analyzing relative brain volumetric patterns to capture how Alzheimer's disease (AD) and genetics influence brain structure along the disease continuum.

Methods: This study analyzed data from participants across the AD continuum from the Alzheimer's and Families (ALFA) and Alzheimer's Disease Neuroimaging Initiative (ADNI) studies.

View Article and Find Full Text PDF

Background: Osteoporosis is a common age-related disease with disabling consequences, the early diagnosis of which is difficult due to its long and hidden course, which often leads to diagnosis only after a fracture. In this regard, great expectations are placed on advanced developments in machine learning technologies aimed at predicting osteoporosis at an early stage of development, including the use of large data sets containing information on genetic and clinical predictors of the disease. Nevertheless, the inclusion of DNA markers in prediction models is fraught with a number of difficulties due to the complex polygenic and heterogeneous nature of the disease.

View Article and Find Full Text PDF

Background: A significant proportion of individuals maintain healthy cognitive function despite having extensive Alzheimer's disease (AD) pathology, known as cognitive resilience. Understanding the molecular mechanisms that protect these individuals can identify therapeutic targets for AD dementia. This study aims to define molecular and cellular signatures of cognitive resilience, protection and resistance, by integrating genetics, bulk RNA, and single-nucleus RNA sequencing data across multiple brain regions from AD, resilient, and control individuals.

View Article and Find Full Text PDF

Temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) is associated with a complex genetic architecture, but the translation from genetic risk factors to brain vulnerability remains unclear. Here, we examined associations between epilepsy-related polygenic risk scores for HS (PRS-HS) and brain structure in a large sample of neurotypical children, and correlated these signatures with case-control findings in in multicentric cohorts of patients with TLE-HS. Imaging-genetic analyses revealed PRS-related cortical thinning in temporo-parietal and fronto-central regions, strongly anchored to distinct functional and structural network epicentres.

View Article and Find Full Text PDF

Genetic prediction of complex traits, enabled by large-scale genomic studies, has created new measures to understand individual genetic predisposition. Polygenic Risk Scores (PRS) offer a way to aggregate information across the genome, enabling personalized risk prediction for complex traits and diseases. However, conventional PRS calculation methods that rely on linear models are limited in their ability to capture complex patterns and interaction effects in high-dimensional genomic data.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!