Gary J Donohoe

The paper introduces dGAMLSS, a distributed algorithm for fitting GAMLSS models to construct population reference charts without sharing patient data.

This study identifies FOXP1-regulated genes across developmental stages and shows their enrichment for schizophrenia heritability, particularly in early childhood and adolescence.

This study identifies two ASD subgroups with distinct brain morphology using cross-cultural datasets, highlighting key brain regions associated with subgroup characteristics and autistic traits.

This study identifies genetic loci associated with social participation and occupational engagement using UK Biobank data, finding genetic correlations with neuropsychiatric disorders and socioeconomic factors.

The paper identifies genetically distinct patient subgroups across diseases by analyzing polygenic risk and pathway convergence.

This study identifies transcriptomic changes in mouse hippocampus due to social isolation and links them to human genetic risk factors for neurodevelopmental disorders.

The paper identifies MEF2C direct target genes enriched for schizophrenia and cognitive function genes, showing their involvement in neurogenesis and mitochondrial function.

The paper uses brain cell-type-specific protein interactomes to interpret genetic data in schizophrenia and identifies enriched networks and genes associated with the disease.

The paper examines the relationship between methylation age and brain age in adolescents, finding they are largely independent and influenced by different factors like smoking and BMI.

The paper introduces brain charts as a standardized resource for benchmarking brain morphology across the human lifespan using MRI data from a large and diverse cohort.

The paper identifies 13 genetic loci associated with cognitive resilience using GWAS-by-subtraction in UK Biobank data, showing enrichment in brain regions and cell types.

This study identifies sex-dependent genetic risk loci shared and specific to mood and psychotic disorders using genome-wide interaction analysis.

This paper identifies potential nootropic drug targets by integrating large-scale cognitive GWAS and transcriptomic data to find credible druggable genes.

The paper compares ten polygenic score methods for psychiatric disorders across multiple cohorts, finding that some methods significantly improve prediction statistics over the baseline PC+T method.

The paper identifies genes regulated by MEF2C during neurodevelopment that are associated with schizophrenia, intelligence, and neurodevelopmental disorders, with different genetic variants affecting distinct cell types and phenotypes.

This study identifies genetic variants linked to longitudinal brain structure changes across the lifespan using MRI data from 15,640 individuals.

This study identifies DNA methylation changes in blood samples from psychosis patients, including treatment-resistant schizophrenia, and links them to biological age, smoking, and cellular composition.

The paper uses pleiotropic meta-analysis to identify genetic loci associated with cognition, education, and schizophrenia, revealing distinct biological mechanisms for concordant and discordant genetic correlations.

The paper links reduced cortical morphometric similarity in psychosis to the expression of schizophrenia-related genes in specific brain regions.

The paper develops deep learning models to assess the quality of subcortical brain shape models from MRI, reducing human rater time by 46-70%.

The paper finds a genetic association between schizophrenia risk and age at first birth in women, with different genetic correlations in younger and older age groups.

This study identifies 206 new genomic loci and 1,041 genes associated with intelligence using a large GWAS meta-analysis of 279,930 individuals.

This study compares LDSC and GREML methods for estimating genetic correlation, showing GREML provides higher accuracy, especially in the presence of genetic heterogeneity.

The paper identifies an excess of rare loss-of-function and missense mutations in voltage-gated sodium channels and synaptic genes associated with schizophrenia using large-scale sequencing and meta-analysis.

This study performs a large-scale GWAS of cognitive ability, identifies 70 genomic loci, and finds tissue-specific gene expression and potential drug targets.

This work uses machine learning to automate quality control of 3D brain shape models, reducing human workload by 30-70% across multiple datasets and diseases.