Jordan W. Smoller

The paper models the genetic architecture of cortical expansion using GWAS data and links it to neuropsychiatric disorders.

This paper conducts a multi-ancestry GWAS of stimulant use disorder, identifies 19 genome-wide significant loci, and finds genetic correlations with other psychiatric and substance use disorders.

The paper performs large-scale GWAS and RVAS on 392,030 whole genomes from All of Us, identifying 49,863 associations and 193 novel pLoF gene-phenotype links through meta-analysis with UK Biobank.

The paper examines how intersectional sociodemographic factors affect genetic risk estimates from polygenic scores across multiple diseases.

This study identifies 1,294 genomic regions and 961 effector genes associated with externalizing disorders, revealing neural mechanisms and polygenic predictors of variance in these conditions.

This study conducts the first comprehensive genetic analysis of alcohol use disorder in a Nepalese population, identifying a novel genome-wide significant locus and highlighting the need for replication and improved polygenic prediction.

This paper introduces mixWAS, a one-shot, lossless algorithm for integrating distributed EHR datasets to identify cross-cohort genetic associations across multiple outcomes.

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 evaluates the instability of polygenic risk scores (PRS) across different publications and introduces an integrative scoring framework to improve classification consistency.

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 UNSemblePRS, an unsupervised ensemble method for integrating pre-trained polygenic risk scores without phenotype data, showing robust performance across diverse populations.

This study evaluates how different modeling choices affect the performance of clinical risk prediction models integrating genetic and clinical data, finding that PRS inclusion benefits vary by integration method and data type.

The paper investigates the N-glycome changes during neuronal differentiation and finds that brain-specific N-glycans like Man-5 are programmed independently of the transcriptome.

ALADYNOULLI integrates longitudinal health records and genetic data to model disease progression, identifying interpretable disease signatures with cross-population consistency and improving clinical prediction.

The paper evaluates factors affecting brain-age prediction models using structural neuroimaging data from 35,683 individuals, finding optimal model performance with no site harmonization, age-binning, and a sample size of over 1,600.

The paper analyzes pathogenic variant frequencies in the All of Us cohort by ancestry, revealing disparities and potential biases in genomic data.

This study identifies 928 genes associated with brain weight variation across three datasets and links them to neurodevelopmental processes and brain function.

The paper analyzes genetic, ancestral, and geospatial data from a biobank to explore their relationships with health outcomes in a historically diverse population.

The paper examines the association between psychiatric polygenic scores and healthcare utilization and comorbidity burden in individuals of European ancestry, finding significant links for major depressive disorder.

This study identifies somatic copy number variants (sCNVs) in schizophrenia cases, revealing their contribution to risk and specific NRXN1 disruptions.

This study identifies modifiable comorbidities of schizophrenia by analyzing EHR data and polygenic risk scores, finding conditions with no shared genetic risk that may be influenced by treatment or environment.

The paper selects and validates ten polygenic risk scores for clinical use in diverse populations, focusing on chronic diseases and addressing disparities in predictive performance.

This study performs a multi-ancestry meta-analysis of tobacco use disorder using electronic health records, identifying 88 risk loci and 461 potential risk genes, and linking TUD to various health outcomes.

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 benchmarks and optimizes a normative modeling framework for brain morphometry across the lifespan using MFPR, showing excellent accuracy and stability.

This study evaluates polygenic risk score methods across diverse biobanks and ancestries, finding that European reference panels often yield better performance, while PRS-CS outperforms P+T for high-heritability traits.

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.