Assessment and ascertainment in psychiatric molecular genetics: challenges and opportunities for cross-disorder research.

Psychiatric disorders are highly comorbid, heritable, and genetically correlated [1-4]. The primary objective of cross-disorder psychiatric genetics research is to identify and characterize both the shared genetic factors that contribute to convergent disease etiologies and the unique genetic factors that distinguish between disorders [4, 5]. This information can illuminate the biological mechanisms underlying comorbid presentations of psychopathology, improve nosology and prediction of illness risk and trajectories, and aid the development of more effective and targeted interventions.

Genomic structural equation modeling of reward-related traits: exploring the genetic factor structure and its relationship with psychopathology.

Reward sensitivity has a partial genetic background, and extreme levels may increase vulnerability to psychopathology. This study explores the genetic factor structure underlying reward-related traits and examines how genetic variance links to psychopathology. We modeled GWAS data from ten reward-related traits: risk tolerance (N = 975,353), extraversion (N = 122,886), sensation seeking (N = 132,395), (lack of) premeditation (N = 132,667), (lack of) perseverance (N = 133,517), positive urgency (N = 132,132), negative urgency (N = 132,559), attentional impulsivity (N = 124,739), motor impulsivity (N = 124,104), and nonplanning impulsivity (N = 123,509) to derive their genetic factor structure.

Advancing Gene Discovery for Substance Use Disorders Using Additional Traits Related to Behavioral Disinhibition.

Importance: Substance use disorders (SUDs) frequently co-occur with each other and with other traits related to behavioral disinhibition, a spectrum of outcomes referred to as externalizing. Nevertheless, genome-wide association studies (GWAS) typically study individual SUDs separately. This single-disorder approach ignores genetic covariance between SUDs and other traits and may contribute to the relatively limited genetic discoveries to date.

Psychiatric neuroimaging at a crossroads: Insights from psychiatric genetics.

Thanks to methodological advances, large-scale data collections, and longitudinal designs, psychiatric neuroimaging is better equipped than ever to identify the neurobiological underpinnings of youth mental health problems. However, the complexity of such endeavors has become increasingly evident, as the field has been confronted by limited clinical relevance, inconsistent results, and small effect sizes. Some of these challenges parallel those historically encountered by psychiatric genetics.

Brain structures with stronger genetic associations are not less associated with family- and state-level economic contexts.

We investigate whether neural, cognitive, and psychopathology phenotypes that are more strongly related to genetic differences are less strongly associated with family- and state-level economic contexts (N = 5374 individuals with 1KG-EUR-like genotypes with 870 twins, from the Adolescent Behavior and Cognitive Development study). We estimated the twin- and SNP-based heritability of each phenotype, as well as its association with an educational attainment polygenic index (EA PGI). We further examined associations with family socioeconomic status (SES) and tested whether SES-related differences were moderated by state cost of living and social safety net programs (Medicaid expansion and cash assistance).

Executive Function and Impulsivity Predict Distinct Genetic Variance in Internalizing Problems, Externalizing Problems, Thought Disorders, and Compulsive Disorders: A Genomic Structural Equation Modeling Study.

Individual differences in self-control predict many health and life outcomes. Building on twin literature, we used genomic structural equation modeling to test the hypothesis that genetic influences on executive function and impulsivity predict independent variance in mental health and other outcomes. The impulsivity factor (comprising urgency, lack of premeditation, and other facets) was only modestly genetically correlated with low executive function ( =.

A sex-stratified analysis of the genetic architecture of human brain anatomy.

Large biobanks have dramatically advanced our understanding of genetic influences on human brain anatomy. However, most studies have combined rather than compared male and female participants. Here we screen for sex differences in the common genetic architecture of over 1000 neuroanatomical phenotypes in the UK Biobank and establish a general concordance between male and female participants in heritability estimates, genetic correlations, and variant-level effects.

Shared Genetic Liability across Systems of Psychiatric and Physical Illness.

Epidemiological literature has shown that there are extensive comorbidity patterns between psychiatric and physical illness. However, our understanding of the multivariate systems of relationships underlying these patterns is poorly understood. Using Genomic SEM and Genomic E-SEM, an extension for genomic exploratory factor analysis that we introduce and validate, we evaluate the extent to which latent genomic factors from eight domains, encompassing 76 physical outcomes across 1.

Multi-ancestry meta-analysis of tobacco use disorder identifies 461 potential risk genes and reveals associations with multiple health outcomes.

Tobacco use disorder (TUD) is the most prevalent substance use disorder in the world. Genetic factors influence smoking behaviours and although strides have been made using genome-wide association studies to identify risk variants, most variants identified have been for nicotine consumption, rather than TUD. Here we leveraged four US biobanks to perform a multi-ancestral meta-analysis of TUD (derived via electronic health records) in 653,790 individuals (495,005 European, 114,420 African American and 44,365 Latin American) and data from UK Biobank (n = 898,680).

Transcriptional cartography integrates multiscale biology of the human cortex.

The cerebral cortex underlies many of our unique strengths and vulnerabilities, but efforts to understand human cortical organization are challenged by reliance on incompatible measurement methods at different spatial scales. Macroscale features such as cortical folding and functional activation are accessed through spatially dense neuroimaging maps, whereas microscale cellular and molecular features are typically measured with sparse postmortem sampling. Here, we integrate these distinct windows on brain organization by building upon existing postmortem data to impute, validate, and analyze a library of spatially dense neuroimaging-like maps of human cortical gene expression.

Multi-ancestry study of the genetics of problematic alcohol use in over 1 million individuals.

Problematic alcohol use (PAU), a trait that combines alcohol use disorder and alcohol-related problems assessed with a questionnaire, is a leading cause of death and morbidity worldwide. Here we conducted a large cross-ancestry meta-analysis of PAU in 1,079,947 individuals (European, N = 903,147; African, N = 122,571; Latin American, N = 38,962; East Asian, N = 13,551; and South Asian, N = 1,716 ancestries). We observed a high degree of cross-ancestral similarity in the genetic architecture of PAU and identified 110 independent risk variants in within- and cross-ancestry analyses.

The molecular genetic landscape of human brain size variation.

Human brain size changes dynamically through early development, peaks in adolescence, and varies up to 2-fold among adults. However, the molecular genetic underpinnings of interindividual variation in brain size remain unknown. Here, we leveraged postmortem brain RNA sequencing and measurements of brain weight (BW) in 2,531 individuals across three independent datasets to identify 928 genome-wide significant associations with BW.

Characterizing the phenotypic and genetic structure of psychopathology in UK Biobank.

Mental conditions exhibit a higher-order transdiagnostic factor structure which helps to explain the widespread comorbidity observed in psychopathology. However, the phenotypic and genetic structures of psychopathology may differ, raising questions about the validity and utility of these factors. Here, we study the phenotypic and genetic factor structures of ten psychiatric conditions using UK Biobank and public genomic data.

Guidelines for Evaluating the Comparability of Down-Sampled GWAS Summary Statistics.

Proprietary genetic datasets are valuable for boosting the statistical power of genome-wide association studies (GWASs), but their use can restrict investigators from publicly sharing the resulting summary statistics. Although researchers can resort to sharing down-sampled versions that exclude restricted data, down-sampling reduces power and might change the genetic etiology of the phenotype being studied. These problems are further complicated when using multivariate GWAS methods, such as genomic structural equation modeling (Genomic SEM), that model genetic correlations across multiple traits.

Salivary Epigenetic Measures of Body Mass Index and Social Determinants of Health Across Childhood and Adolescence.

Importance: Children who are socioeconomically disadvantaged are at increased risk for high body mass index (BMI) and multiple diseases in adulthood. The developmental origins of health and disease hypothesis proposes that early life conditions affect later-life health in a manner that is only partially modifiable by later-life experiences.

Objective: To examine whether epigenetic measures of BMI developed in adults are valid biomarkers of childhood BMI and if they are sensitive to early life social determinants of health.

A multivariate approach to understanding the genetic overlap between externalizing phenotypes and substance use disorders.

Substance use disorders (SUDs) are phenotypically and genetically correlated with each other and with other psychological traits characterized by behavioural under-control, termed externalizing phenotypes. In this study, we used genomic structural equation modelling to explore the shared genetic architecture among six externalizing phenotypes and four SUDs used in two previous multivariate genome-wide association studies of an externalizing and an addiction risk factor, respectively. We first evaluated five confirmatory factor analytic models, including a common factor model, alternative parameterizations of two-factor structures and a bifactor model.

A sex-stratified analysis of the genetic architecture of human brain anatomy.

Large biobanks have dramatically advanced our understanding of genetic influences on human brain anatomy. However, most studies have combined rather than compared males and females - despite theoretical grounds for potential sex differences. By systematically screening for sex differences in the common genetic architecture of > 1000 neuroanatomical phenotypes in the UK Biobank, we establish a general concordance between males and females in heritability estimates, genetic correlations and variant-level effects.

Robust estimation of cortical similarity networks from brain MRI.

Structural similarity is a growing focus for magnetic resonance imaging (MRI) of connectomes. Here we propose Morphometric INverse Divergence (MIND), a new method to estimate within-subject similarity between cortical areas based on the divergence between their multivariate distributions of multiple MRI features. Compared to the prior approach of morphometric similarity networks (MSNs) on n > 11,000 scans spanning three human datasets and one macaque dataset, MIND networks were more reliable, more consistent with cortical cytoarchitectonics and symmetry and more correlated with tract-tracing measures of axonal connectivity.