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.

The science of wellbeing has come a long way from the early days of measuring wellbeing via a nation's GDP, and wellbeing measures and concepts continue to proliferate to capture its various elements. Yet, much of this activity has reflected concepts from Western cultures, despite the emphasis placed on wellbeing in all corners of the globe. To meet the challenges and opportunities arising from cross-disciplinary research worldwide, the Well-Being for Planet Earth Foundation and the Gallup World Poll have joined forces to add more culturally relevant constructs and questions to existing Gallup modules. In this white paper, we review the discussion from the international well-being summit in Kyoto, Japan (August 2019), where nine such additions were proposed and highlight why a more global view of wellbeing is needed. Overall, the new items reflect a richer view of wellbeing than life satisfaction alone and include hedonic and eudaimonic facets of wellbeing, social wellbeing, the role of culture, community, nature, and governance. These additions allow for the measurement of a broader conceptualization of wellbeing, more refined and nuanced cross-cultural comparisons, and facilitate a better examination of the causes of variation in global wellbeing. The new Gallup World Poll additions will be trialled in 2020, with additional inclusions from this summit to be made in 2021.

The interrelations among well-being, neuroticism, and depression can be captured in a so-called well-being spectrum (3-phenotype well-being spectrum, 3-WBS). Several other human traits are likely linked to the 3-WBS. In the present study, we investigate how the 3-WBS can be expanded. First, we constructed polygenic risk scores for the 3-WBS and used this score to predict a series of traits that have been associated with well-being in the literature. We included information on loneliness, big five personality traits, self-rated health, and flourishing. The 3-WBS polygenic score predicted all the original 3-WBS traits and additionally loneliness, self-rated health, and extraversion (R 2 between 0.62% and 1.58%). Next, using LD score regression, we calculated genetic correlations between the 3-WBS and the traits of interest. From all candidate traits, loneliness and self-rated health were found to have the strongest genetic correlations ( r g = − 0.79, and r g = 0.64, respectively) with the 3-WBS. Lastly, we use Genomic SEM to investigate the factor structure of the proposed spectrum. The best model fit was obtained for a two-factor model including the 5-WBS traits, with two highly correlated factors representing the negative- and positive end of the spectrum. Based on these analyses we propose to include loneliness and self-rated health in the WBS and use a 5-phenotype well-being spectrum in future studies to gain more insight into the determinants of human well-being. Electronic supplementary material The online version of this article (10.1007/s10519-019-09951-0) contains supplementary material, which is available to authorized users.

The corona virus disease 2019 (COVID-19) pandemic and the restrictions to reduce the spread of the virus has had a large impact on daily life. We investigated the individual differences in the effect of the COVID-19 pandemic and first lockdown on optimism and meaning in life in a sample from the Netherlands Twin Register. Participants completed surveys before (N = 9964, Mean age: 48.2, SD = 14.4) and during the first months of the pandemic (i.e. April–May 2020, N = 17,464, Mean age: 44.6 SD = 14.8), with a subsample completing both surveys (N = 6461, Mean age T1: 48.8, SD = 14.5). We applied genetic covariance structure models to twin data to investigate changes in the genetic architecture of the outcome traits due to the pandemic and the interaction of genes with the environmental exposure. Although 56% and 35% of the sample was negatively affected by the pandemic in their optimism and meaning in life, many participants were stable (32% and 43%) or even showed increased optimism and meaning in life (11% and 22%). Subgroups, specifically women, higher educated people, and people with poorer health, experienced larger negative effects. During the first months of the pandemic, slightly lower heritability estimates for optimism and meaning in life (respectively 20% and 25%) were obtained compared to pre-pandemic (respectively 26% and 32%), although confidence intervals overlap. The lower than unity genetic correlations across time (.75 and .63) suggest gene-environment interactions, where the expression of genes that influence optimism and meaning in life differs before and during the pandemic. The COVID-19 pandemic is a strong exposure that leads to imbalanced effects on the well-being of individuals. Some people decrease in well-being, while others get more optimistic and consider their lives as more meaningful during the pandemic. These differences are partly explained by individual differences in genetic sensitivity to extreme environmental change. More knowledge on the person-specific response to specific environmental variables underlying these individual differences is urgently needed to prevent further inequality.