Genetic correlations estimated from GWAS reveal pervasive pleiotropy across a wide variety of phenotypes. We introduce genomic structural equation modeling (Genomic SEM), a multivariate method for analyzing the joint genetic architecture of complex traits. Genomic SEM synthesizes genetic correlations and SNP-heritabilities inferred from GWAS summary statistics of individual traits from samples with varying and unknown degrees of overlap. Genomic SEM can be used to model multivariate genetic associations among phenotypes, identify variants with effects on general dimensions of cross-trait liability, calculate more predictive polygenic scores, and identify loci that cause divergence between traits. We demonstrate several applications of Genomic SEM, including a joint analysis of summary statistics from five psychiatric traits. We identify 27 independent SNPs not previously identified in the contributing univariate GWASs. Polygenic scores from Genomic SEM consistently outperform those from univariate GWAS. Genomic SEM is flexible, open ended, and allows for continuous innovation in multivariate genetic analysis.

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data (http://www.brainchart.io/). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.

Little is known about the genetic architecture of traits affecting educational attainment other than cognitive ability. We used Genomic Structural Equation Modeling and prior genome-wide association studies (GWAS) of educational attainment ( n = 1,131,881) and cognitive test performance ( n = 257,841) to estimate SNP associations with educational attainment variation that is independent of cognitive ability.We identified 157 genome-wide significant loci and a polygenic architecture accounting for 57% of genetic variance in educational attainment. Non-cognitive genetics were enriched in the same brain tissues and cell types as cognitive performance but showed different associations with gray-matter brain volumes. Non-cognitive genetics were further distinguished by associations with personality traits, less risky behavior,and increased risk for certain psychiatric disorders.For socioeconomic success and longevity, non-cognitive and cognitive-performance genetics demonstrated similar-magnitude associations. By conducting a GWAS of a phenotype that was not directly measured, we offer a first view of genetic architecture of non-cognitive skills influencing educational success.

Estimates from genome-wide association studies (GWAS) represent a combination of the effect of inherited genetic variation (direct effects), demography (population stratification, assortative mating) and genetic nurture from relatives (indirect genetic effects). GWAS using family-based designs can control for demography and indirect genetic effects, but large-scale family datasets have been lacking. We combined data on 159,701 siblings from 17 cohorts to generate population (between-family) and within-sibship (within-family) estimates of genome-wide genetic associations for 25 phenotypes. We demonstrate that existing GWAS associations for height, educational attainment, smoking, depressive symptoms, age at first birth and cognitive ability overestimate direct effects. We show that estimates of SNP-heritability, genetic correlations and Mendelian randomization involving these phenotypes substantially differ when calculated using within-sibship estimates. For example, genetic correlations between educational attainment and height largely disappear. In contrast, analyses of most clinical phenotypes (e.g. LDL-cholesterol) were generally consistent between population and within-sibship models. We also report compelling evidence of polygenic adaptation on taller human height using within-sibship data. Large-scale family datasets provide new opportunities to quantify direct effects of genetic variation on human traits and diseases.

Whereas traditional sports psychology interventions emphasize controlling or reducing distress, mindfulness-based interventions teach tolerance and acceptance of negative thoughts, feelings, and emotions. In the present pilot study, an entire men's Division I athletic team (n ϭ 13) provided voluntary consent and participated in a brief mindfulness-based intervention. Over 5 weeks, the team attended eight 90-min group intervention sessions immediately followed by 1-hr Hatha yoga sessions. Completer analyses showed that following the intervention, participants reported greater mindfulness, greater goal-directed energy, and less perceived stress than before the intervention. Compared with a nonrandomized control group (student athletes from various club sports; n ϭ 13), intervention participants reported greater goal-directed energy and mindfulness. We also explored written feedback from players to identify ways to improve the intervention. Implications for practitioners for improving mindfulnessbased interventions are discussed.