The predictive capacity of psychiatric and psychological polygenic risk scores for distinguishing cases in a child and adolescent psychiatric sample from controls.

BACKGROUND: Psychiatric traits are heritable, highly comorbid and genetically correlated, suggesting that genetic effects that are shared across disorders are at play. The aim of the present study is to quantify the predictive capacity of common genetic variation of a variety of traits, as captured by their PRS, to predict case-control status in a child and adolescent psychiatric sample including controls to reveal which traits contribute to the shared genetic risk across disorders. METHOD: Polygenic risk scores (PRS) of 14 traits were used as predictor phenotypes to predict case-control status in a clinical sample. Clinical cases (N = 1,402), age 1-21, diagnostic categories: Autism spectrum disorders (N = 492), Attention-deficit/ hyperactivity disorders (N = 471), Anxiety (N = 293), disruptive behaviors (N = 101), eating disorders (N = 97), OCD (N = 43), Tic disorder (N = 50), Disorder of infancy, childhood or adolescence NOS (N = 65), depression (N = 64), motor, learning and communication disorders (N = 59), Anorexia Nervosa (N = 48), somatoform disorders (N = 47), Trauma/stress (N = 39) and controls (N = 1,448, age 17-84) of European ancestry. First, these 14 PRS were tested in univariate regression analyses. The traits that significantly predicted case-control status were included in a multivariable regression model to investigate the gain in explained variance when leveraging the genetic effects of multiple traits simultaneously. RESULTS: In the univariate analyses, we observed significant associations between clinical status and the PRS of educational attainment (EA), smoking initiation (SI), intelligence, neuroticism, alcohol dependence, ADHD, major depression and anti-social behavior. EA (p-value: 3.53E-20, explained variance: 3.99%, OR: 0.66), and SI (p-value: 4.77E-10, explained variance: 1.91%, OR: 1.33) were the most predictive traits. In the multivariable analysis with these eight significant traits, EA and SI, remained significant predictors. The explained variance of the PRS in the model with these eight traits combined was 5.9%. CONCLUSION: Our study provides more insights into the genetic signal that is shared between childhood and adolescent psychiatric disorders. As such, our findings might guide future studies on psychiatric comorbidity and offer insights into shared etiology between psychiatric disorders. The increase in explained variance when leveraging the genetic signal of different predictor traits supports a multivariable approach to optimize precision accuracy for general psychopathology.

Exploring the role of low-frequency and rare exonic variants in alcohol and tobacco use.

BACKGROUND: Alcohol and tobacco use are heritable phenotypes. However, only a small number of common genetic variants have been identified, and common variants account for a modest proportion of the heritability. Therefore, this study aims to investigate the role of low-frequency and rare variants in alcohol and tobacco use. METHODS: We meta-analyzed ExomeChip association results from eight discovery cohorts and included 12,466 subjects and 7432 smokers in the analysis of alcohol consumption and tobacco use, respectively. The ExomeChip interrogates low-frequency and rare exonic variants, and in addition a small pool of common variants. We investigated top variants in an independent sample in which ICD-9 diagnoses of "alcoholism" (N = 25,508) and "tobacco use disorder" (N = 27,068) had been assessed. In addition to the single variant analysis, we performed gene-based, polygenic risk score (PRS), and pathway analyses. RESULTS: The meta-analysis did not yield exome-wide significant results. When we jointly analyzed our top results with the independent sample, no low-frequency or rare variants reached significance for alcohol consumption or tobacco use. However, two common variants that were present on the ExomeChip, rs16969968 (p = 2.39 × 10-7) and rs8034191 (p = 6.31 × 10-7) located in CHRNA5 and AGPHD1 at 15q25.1, showed evidence for association with tobacco use. DISCUSSION: Low-frequency and rare exonic variants with large effects do not play a major role in alcohol and tobacco use, nor does the aggregate effect of ExomeChip variants. However, our results confirmed the role of the CHRNA5-CHRNA3-CHRNB4 cluster of nicotinic acetylcholine receptor subunit genes in tobacco use.

Genetic correlation of antisocial behaviour with alcohol, nicotine, and cannabis use.

BACKGROUND: There is high comorbidity between antisocial behaviour (ASB) and substance use, and twin studies have shown that part of the covariation is due to overlapping genetic influences. Here we used measured genetic effects to estimate the genetic correlations of ASB with nicotine, alcohol, and cannabis use. METHODS: We meta-analysed data from two genome-wide association studies for ASB and used existing summary statistics from the largest genome-wide association studies into substance use (ever smoking, cigarettes smoked per day, weekly alcohol consumption, and lifetime cannabis use). We performed cross-trait LD-score regression to estimate genetic correlations between ASB and substance use phenotypes explained by all single nucleotide polymorphisms (SNPs). When significant, we tested whether the signs of the regression coefficients of SNPs from the ASB and substance use phenotypes were in the same direction across multiple p-value thresholds and examined enrichment in overlap of the strongest associated SNPs. RESULTS: We found nominally significant genetic correlations of ASB with lifetime cannabis use (rg = 0.69, p=.016) and cigarettes per day (rg = 0.59, p = 0.036) but not with weekly alcohol consumption or ever smoking. Sign-tests revealed consistent directions of effect of SNPs for ASB and cannabis use for all p-value thresholds except the most stringent one, whereas for ASB with cigarettes per day no consistent evidence was found. We found no evidence of enrichment in overlap of the most associated SNPs across these traits. CONCLUSION: Using measured genetic variants, we found preliminary support for a genetic correlation of ASB with lifetime cannabis use and cigarettes per day.