We conducted genome-wide association studies of three phenotypes: subjective well-being (N = 298,420), depressive symptoms (N = 161,460), and neuroticism (N = 170,910). We identified three variants associated with subjective well-being, two with depressive symptoms, and eleven with neuroticism, including two inversion polymorphisms. The two depressive symptoms loci replicate in an independent depression sample. Joint analyses that exploit the high genetic correlations between the phenotypes (|ρ̂| ≈ 0.8) strengthen the overall credibility of the findings, and allow us to identify additional variants. Across our phenotypes, loci regulating expression in central nervous system and adrenal/pancreas tissues are strongly enriched for association.

Cannabis use is a heritable trait that has been associated with adverse mental health outcomes. In the largest genome-wide association study (GWAS) for lifetime cannabis use to date (N = 184,765), we identified eight genome-wide significant independent single nucleotide polymorphisms in six regions. All measured genetic variants combined explained 11% of the variance. Gene-based tests revealed 35 significant genes in 16 regions, and S-PrediXcan analyses showed that 21 genes had different expression levels for cannabis users versus nonusers. The strongest finding across the different analyses was CADM2, which has been associated with substance use and risk-taking. Significant genetic correlations were found with 14 of 25 tested substance use and mental health-related traits, including smoking, alcohol use, schizophrenia and risk-taking. Mendelian randomization analysis showed evidence for a causal positive influence of schizophrenia risk on cannabis use. Overall, our study provides new insights into the etiology of cannabis use and its relation with mental health.

We introduce two novel methods for multivariate genome-wide association meta-analysis (GWAMA) of related traits that correct for sample overlap. A broad range of simulation scenarios supports the added value of our multivariate methods relative to univariate GWAMA. We applied the novel methods to life satisfaction, positive affect, neuroticism, and depressive symptoms, collectively referred to as the well-being spectrum (N obs = 2,370,390), and found 304 significant independent signals. Our multivariate approaches resulted in a 26% increase in the number of independent signals relative to the four univariate GWAMA, and in a ~ 57% increase in the predictive power of polygenic risk scores. Supporting transcriptome -and methylome-wide analyses (TWAS/MWAS) uncovered an additional 17 and 75 independent loci, respectively. Bioinformatic analyses, based on gene expression in brain tissues and cells, showed that genes differentially expressed in the subiculum and GABAergic interneurons are enriched in their effect on the wellbeing spectrum.In the past decade, genome-wide association studies (GWAS) have provided insights into the genetic basis of quantitative variation in complex traits 1 . With summary statistics of these GWASs becoming public and the development of linkage disequilibrium score regression (LDSC) 2,3 , genetic correlations between traits can be systematically estimated (e.g. Brainstorm consortium 4 ). Levering this widely observed genetic overlap between traits, we introduce two novel methods for multivariate genome-wide association metaanalysis, where we define a multivariate model as a model where the effect of a single SNP is considered for multiple traits: 1) N-weighted multivariate GWAMA (N-GWAMA), with a unitary effect of the SNP on all traits, and 2) model averaging GWAMA (MA-GWAMA), where we relaxed the assumption of a unitary effect of the SNP on all traits. Both methods are well equipped to deal with (unknown) sample overlap. The dependence between effect sizes (error correlation) induced by possible sample overlap is estimated from the univariate GWAMA using LDSC 2,3 . Furthermore, the univariate LDSC intercept is used to correct for population stratification and cryptic relatedness. Both methods have advantages over existing methods. In contrast to MultiPhen 5 , CCA (mv-PLINK) 6 , Combined-PC 7 , and mv-BIMBAM 8 , both our methods can be applied without the need of individual-level genotypic data as only GWAS/GWAMA summary-statistics are required. Additionally, in contrast to S Hom 9 , N -and MA-GWAMA take a more precise estimate of the error correlation into account. In contrast to MTAG 10 , MA-GWAMA , similar to S Het 9 , generates trait specific estimates for each SNP allowing for a certain degree of heterogeneity (see online methods). Finally, in contrast to TATES 11 , both N-GWAMA and MA-GWAMA generate effect sizes for the multivariate effect where TATES only generates a P-value. The absence of a signed statistic in TATES complicates or even prohibits polygenic prediction.

Feelings of well-being and happiness fluctuate over time and contexts. Ecological Momentary Assessment (EMA) studies can capture fluctuations in momentary behavior, and experiences by assessing these multiple times per day. Traditionally, EMA was performed using pen and paper. Recently, due to technological advances EMA studies can be conducted more easily with smartphones, a device ubiquitous in our society. The goal of this review was to evaluate the literature on smartphone-based EMA in well-being research in healthy subjects. The systematic review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Searching PubMed and Web of Science, we identified 53 studies using smartphone-based EMA of well-being. Studies were heterogeneous in designs, context, and measures. The average study duration was 12.8 days, with well-being assessed 2–12 times per day. Half of the studies included objective data (e.g. location). Only 47.2% reported compliance, indicating a mean of 71.6%. Well-being fluctuated daily and weekly, with higher well-being in evenings and weekends. These fluctuations disappeared when location and activity were accounted for. On average, being in nature and physical activity relates to higher well-being. Working relates to lower well-being, but workplace and company do influence well-being. The important advantages of using smartphones instead of other devices to collect EMAs are the easier data collection and flexible designs. Smartphone-based EMA reach far larger maximum sample sizes and more easily add objective data to their designs than palm-top/PDA studies. Smartphone-based EMA research is feasible to gain insight in well-being fluctuations and its determinants and offers the opportunity for parallel objective data collection. Most studies currently focus on group comparisons, while studies on individual differences in well-being patterns and fluctuations are lacking. We provide recommendations for future smartphone-based EMA research regarding measures, objective data and analyses.