Modeling the genomic architecture of adiposity and anthropometrics across the lifespan.

Obesity-related conditions are among the leading causes of preventable death and are increasing in prevalence worldwide. Body size and composition are complex traits that are challenging to characterize due to environmental and genetic influences, longitudinal variation, heterogeneity between sexes, and differing health risks based on adipose distribution. Here, we construct a 4-factor genomic structural equation model using 18 measures, unveiling shared and distinct genetic architectures underlying birth size, abdominal size, adipose distribution, and adiposity.

Uncovering the multivariate genetic architecture of frailty with genomic structural equation modeling.

Frailty is a multifaceted clinical state associated with accelerated aging and adverse health outcomes. Informed etiological models of frailty hold promise for producing widespread health improvements across the aging population. Frailty is currently measured using aggregate scores, which obscure etiological pathways that are only relevant to subcomponents of frailty.

Examining the genetic links between clusters of immune-mediated diseases and psychiatric disorders.

Extant phenotypic and genetic literature has established consistent relationships between autoimmune, autoinflammatory, and psychiatric disorders. However, a comprehensive model investigating the association between a broad range of psychiatric disorders and immune-mediated disease in a multivariate framework is lacking. We utilized Genomic Structural Equation Modeling (Genomic SEM) to establish a factor structure across 11 immune-mediated diseases.

Measurement characteristics and genome-wide correlates of lifetime brain atrophy estimated from a single MRI.

As a cardinal marker of brain ageing, lifetime brain atrophy obtained from a cross-sectional magnetic resonance image promises to boost statistical power to uncover novel genetic mechanisms of neurodegeneration. By analysing five young and old adult cohorts, we perform the most definitive study on lifetime brain atrophy's measurement and correlates. It is simply calculated from the relationship between total brain volume and intracranial volume, using the difference, ratio, or regression-residual method.

The Landscape of Shared and Divergent Genetic Influences across 14 Psychiatric Disorders.

Psychiatric disorders display high levels of comorbidity and genetic overlap. Genomic methods have shown that even for schizophrenia and bipolar disorder, two disorders long-thought to be etiologically distinct, the majority of genetic signal is shared. Furthermore, recent cross-disorder analyses have uncovered over a hundred pleiotropic loci shared across eight disorders.

Examining the genetic relationship between Alzheimer's disease, schizophrenia and their shared risk factors using genomic structural equation modelling.

Epidemiological studies have demonstrated an association between dementia and schizophrenia. There is a significant symptom overlap between the two disorders-psychosis is seen in 50% of patients with Alzheimer's disease and cognitive impairment is a key feature of schizophrenia. Whether these overlapping clinical presentations reflect shared aetiology is unclear.

Transdiagnostic Polygenic Risk Models for Psychopathology and Comorbidity: Cross-Ancestry Analysis in the Research Program.

Psychiatric disorders exhibit substantial genetic overlap, raising questions about the utility of transdiagnostic genetic risk models. Using data from the Research Program (N=102,091), we evaluated common psychiatric genetic (CPG) factor-based polygenic risk scores (PRSs) compared to standard disorder-specific PRSs. The CPG PRS consistently outperformed disorder-specific scores in predicting individual disorder risk, explaining 1.

Comparison of the multivariate genetic architecture of eight major psychiatric disorders across sex.

Differences in the patterning of genetic sharing between groups of individuals may arise from biological pathways, social mechanisms, phenotyping and ascertainment. We expand genomic structural equation modeling to allow for testing genomic structural invariance (GSI), that is, the formal comparison of multivariate genetic architecture across groups. We apply GSI to compare the autosomal multivariate genetic architecture of eight psychiatric disorders spanning three factors (psychotic, neurodevelopmental and internalizing) between cisgender males and females.

Molecular genetic influences on attentional control and other executive processes and their links with psychopathology in the AFFECT study.

Background: Attentional control is a critical component of executive functioning involved in numerous psychiatric and neurological disorders, yet its etiological relationships with many cognitive and behavioral phenotypes remain underexplored.

Methods: We conducted the first multivariate characterization of molecular genetic influences on attentional control and other executive processes in a cohort of more than 20,000 individuals enriched for mood disorders. We used Genomic Structural Equation Modeling to formally model patterns of genetic covariance among these task-based measures of cognition, as well as their relationships with other cognitive, clinical, and imaging-derived phenotypes.

Impulsivity facets and substance use involvement: insights from genomic structural equation modeling.

Background: Impulsivity is a multidimensional trait associated with substance use disorders (SUDs), but the relationship between distinct impulsivity facets and stages of substance use involvement remains unclear.

Methods: We used genomic structural equation modeling and genome-wide association studies ( = 79,729-903,147) to examine the latent genetic architecture of nine impulsivity traits and seven substance use (SU) and SUD traits.

Results: We found that the SU and SUD factors were strongly genetically inter-correlated (=0.

Genomic network analysis characterizes genetic architecture and identifies trait-specific biology.

Pervasive genetic overlap across human complex traits necessitates developing multivariate methods that can parse pleiotropic and trait-specific genetic signals. Here, we introduce Genomic Network Analysis (GNA), an analytic framework that applies the principles of network modelling to estimates of genetic overlap derived from genome-wide association study (GWAS) summary statistics. The result is a genomic network that describes the conditionally independent genetic associations between traits that remain when controlling for shared signal with the broader network of traits.

Lifetime brain atrophy estimated from a single MRI: measurement characteristics and genome-wide correlates.

A measure of lifetime brain atrophy (LBA) obtained from a single magnetic resonance imaging (MRI) scan could be an attractive candidate to boost statistical power in uncovering novel genetic signals and mechanisms of neurodegeneration. We analysed data from five young and old adult cohorts (MRi-Share, Human Connectome Project, UK Biobank, Generation Scotland Subsample, and Lothian Birth Cohort 1936 [LBC1936]) to test the validity and utility of LBA inferred from cross-sectional MRI data, i.e.

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.

Examining the Genetic Links between Clusters of Immune-mediated Diseases and Psychiatric Disorders.

Importance: Autoimmune and autoinflammatory diseases have been linked to psychiatric disorders in the phenotypic and genetic literature. However, a comprehensive model that investigates the association between a broad range of psychiatric disorders and immune-mediated disease in a multivariate framework is lacking.

Objective: This study aims to establish a factor structure based on the genetic correlations of immune-mediated diseases and investigate their genetic relationships with clusters of psychiatric disorders.

Partitioning the Genomic Components of Behavioral Disinhibition and Substance Use (Disorder) Using Genomic Structural Equation Modeling.

Externalizing behaviors encompass manifestations of risk-taking, self-regulation, aggression, sensation-/reward-seeking, and impulsivity. Externalizing research often includes substance use (SUB), substance use disorder (SUD), and other (non-SUB/SUD) "behavioral disinhibition" (BD) traits. Genome-wide and twin research have pointed to overlapping genetic architecture within and across SUB, SUD, and BD.

Evidence for strong genetic correlations among internalizing psychopathology and related self-reported measures using both genomic and twin/adoptive approaches.

The internalizing construct captures shared variance underlying risk for mood and anxiety disorders. Internalizing factors based on diagnoses (or symptoms) of major depressive disorder (MDD) and generalized anxiety disorder (GAD) are well established. Studies have also integrated self-reported measures of associated traits (e.

Examining Differences in the Genetic and Functional Architecture of Attention-Deficit/Hyperactivity Disorder Diagnosed in Childhood and Adulthood.

Background: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder with diagnostic criteria requiring symptoms to begin in childhood. We investigated whether individuals diagnosed as children differ from those diagnosed in adulthood with respect to shared and unique architecture at the genome-wide and gene expression level of analysis.

Methods: We used genomic structural equation modeling (SEM) to investigate differences in genetic correlations () of childhood-diagnosed ( = 14,878) and adulthood-diagnosed ( = 6961) ADHD with 98 behavioral, psychiatric, cognitive, and health outcomes.

Disentangling differing relationships between internalizing disorders and alcohol use.

Both internalizing disorders and alcohol use have dramatic, wide-spread implications for global health. Previous work has established common phenotypic comorbidity among these disorders, as well as shared genetic variation underlying them both. We used genomic structural equation modeling to investigate the shared genetics of internalizing, externalizing, and alcohol use traits, as well as to explore whether specific domains of internalizing symptoms mediate the contrasting relationships with problematic alcohol use compared to alcohol consumption.

Beyond the factor indeterminacy problem using genome-wide association data.

Latent factors, such as general intelligence, depression and risk tolerance, are invoked in nearly all social science research where a construct is measured via aggregation of symptoms, question responses or other measurements. Because latent factors cannot be directly observed, they are inferred by fitting a specific model to empirical patterns of correlations among measured variables. A long-standing critique of latent factor theories is that the correlations used to infer latent factors can be produced by alternative data-generating mechanisms that do not include latent factors.