Andrew David Grotzinger

The paper introduces an ultra-fast analytic solution for multivariate GWAS in Genomic SEM, reducing computation time by over 800 times and enabling efficient analysis on standard hardware.

This study uses genomic structural equation modeling to identify genetic overlaps between psychiatric and physical illnesses, finding shared risk factors across multiple domains.

This study analyzes GWAS data from nearly 1 million cases to show widespread genetic overlap between neurological and psychiatric disorders, with shared biological processes and divergent effect sizes.

This study models the genomic architecture of adiposity and anthropometrics across the lifespan, identifying shared and distinct genetic factors and their associations with health outcomes.

This study identifies genetic links between clusters of immune-mediated diseases and psychiatric disorders using genomic structural equation modeling and a new heterogeneity metric.

This paper evaluates methods to estimate lifetime brain atrophy from a single MRI scan and identifies genetic correlates, finding that the residual method best captures brain shrinkage and has strong heritability.

This study evaluates transdiagnostic polygenic risk scores for psychiatric disorders using the All of Us Research Program data, finding that a common psychiatric genetic factor PRS better predicts comorbidity burden than single-disorder scores.

The paper compares the multivariate genetic architecture of eight psychiatric disorders between males and females using Genomic Structural Invariance, finding similarities and sex-specific differences in genetic factor loadings.

This study identifies two distinct genetic factors influencing executive function and attentional control, showing unique links to psychiatric disorders and cognitive phenotypes.

The paper identifies five genomic factors explaining most genetic variance across 14 psychiatric disorders and finds shared and disorder-specific genetic signals with distinct biological functions.

This paper introduces GNA, a framework that uses network modeling on GWAS data to identify genetic architecture and trait-specific biology.

This study identifies genetic pathways unique to autism spectrum disorder (ASD) using Genomic SEM and related methods, finding unique gene expression patterns and genetic correlations with cognitive and psychiatric traits.

The paper uses genomic structural equation modeling to partition genetic variation between behavioral disinhibition, substance use, and substance use disorder traits.

This study uses Genomic SEM to analyze genetic differences between bipolar I and II subtypes, finding distinct genetic overlaps with internalizing traits and major depression.

This study identifies genetic subtypes of major depressive symptoms by analyzing data from clinical and community cohorts, finding distinct factors for appetite/weight symptoms and missing data patterns.

This study shows noncognitive skills become more predictive of academic achievement over time and their genetic contribution increases with age.

The paper models genetic relationships between cortical thickness and surface area using Genomic SEM, identifies gene categories associated with pleiotropy, and links these traits to psychiatric and cognitive outcomes.

The paper shows that standard methods for liability-scale heritability estimation in GWAS meta-analysis can be downwardly biased and proposes a simple correction based on effective sample size summation.

This study uses T-SEM to identify gene expression patterns linked to genetic overlap and disorder-specific risk in psychiatric disorders, finding potential drug repurposing targets.

GWAS of 2,347 neuroimaging phenotypes identifies genetic loci and structures related to cortical development, cellular processes, and neurodevelopmental disorders.

This study introduces a method to analyze gene-gene interactions across multiple traits and tissues, identifying novel associations and pathways through transcriptome-wide interaction studies.

This study performs a multivariate GWAS on psychiatric disorders and symptoms to identify two genetic liabilities distinguishing common mood disturbances from serious mental illnesses.

This study identifies four genetic factors underlying 11 psychiatric disorders and finds limited evidence for a single dimension of genetic risk across them.

This paper conducts a multivariate GWAS of AUDIT items to correct biases and identify novel genetic associations between alcohol consumption, misuse, and health outcomes.

The paper combines GWAS and GWAX data using a multivariate method to estimate SNP heritability of Alzheimer's Disease, finding higher heritability than previous SNP-based estimates.

The paper introduces T-SEM to analyze gene expression's role in cognitive traits, identifying 184 genes linked to general cognitive function and enrichments in specific genomic categories.

The paper identifies novel genetic risk loci for depression and anxiety using factor analysis and genomic SEM, revealing shared and trait-specific genetic influences.

This study identifies 157 genome-wide significant loci associated with non-cognitive contributions to educational attainment, explaining 57% of genetic variance.

The paper uses multivariate genomic analysis of 1.5 million people to identify genetic loci related to addiction, antisocial behavior, and health, finding over 500 loci enriched for brain-expressed genes.

This paper introduces within-family Mendelian randomization methods to reduce biases from family structure and shows they yield different results compared to population-based analyses.