Olav Bjerkehagen Smeland

The paper quantifies how heritability is distributed across genomic regions for different polygenicity levels in complex traits, showing exons contribute less and intergenic regions more in highly polygenic traits.

The paper identifies shared genetic variants between major psychiatric disorders and retinal traits, revealing molecular mechanisms linking retinal morphology to neurodevelopmental and neurotransmission processes.

This study investigates genetic overlaps between psychiatric disorders, cardiometabolic diseases, and brain structure to understand shared and distinct genetic pathways contributing to comorbidity.

This paper uses genetic data from psychiatric disorders to prioritize drug targets and compounds, identifying genetic support for existing medications and opportunities for drug repurposing.

Bivariate GSA-MiXeR is a tool that models genetic correlations between two traits to identify genomic regions associated with psychiatric and neurodegenerative disorders using single-cell RNA sequencing data.

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 identifies distinct genetic overlaps between Alzheimer's and Parkinson's disease and metabolic markers, revealing disease-specific pathways involving lipid metabolism and synaptic processes.

The paper uses normative modeling to assess cerebellar morphology differences in 15q11.2 CNV carriers compared to non-carriers, finding modest and individualized effects.

This study maps the genetic continuum of autoimmune and autoinflammatory disorders using GWAS and statistical methods, identifying four genetic clusters with potential implications for treatment.

The paper identifies extensive phenotypic and genetic links between systemic metabolism and brain morphology using UK Biobank data.

This study identifies shared genetic loci between common epilepsies and general cognitive ability using LDSC, MiXeR, and conjFDR analyses.

The paper maps cerebellar anatomical variations across the lifespan and clinical illnesses, revealing heterogeneity in conditions like autism, schizophrenia, and Alzheimer's.

This study identifies shared and distinct genetic factors between cognitive traits and schizophrenia using genomic structural equation modelling and GWAS data.

The paper introduces trivariate MiXeR to quantify genetic overlap between three complex phenotypes using GWAS summary statistics, demonstrating its ability to detect complex patterns of genetic overlap.

MiXeR-Pred improves polygenic prediction by leveraging genetic overlap between complex phenotypes, outperforming primary GWAS and MTAG approaches.

The paper investigates the genetic architecture of the corpus callosum and its overlap with psychiatric and substance use phenotypes using GWAS summary statistics and causal mixture models.

This study identifies 114 novel genomic loci for anxiety and finds extensive genetic overlap with schizophrenia, bipolar disorder, major depression, and ADHD.

This study identifies 72 genetic loci associated with basal ganglia volumes and finds significant genetic overlaps with Parkinson's disease, schizophrenia, and migraine.

This study identifies 14 novel opioid use disorder loci and 7 shared loci with schizophrenia, bipolar disorder, and major depression using genome-wide association summary statistics.

The paper introduces Meta-MOSTest, an extension of MOSTest for meta-analysis of multivariate GWAS, showing it maintains statistical power and type I error control across multiple cohorts.

This study uses a multivariate genetic approach to analyze personality and cognitive traits, identifying pleiotropy and improving polygenic prediction.

This study uses a human iPSC-derived astrocyte model to identify SCZ-specific transcriptomic and functional changes in astrocytes, revealing altered gene expression and biological processes.

The paper uses a multivariate GWAS approach to identify 780 genetic loci associated with cortical morphology, showing a 10-fold increase in loci compared to univariate methods.

This study identifies genetic overlap between Alzheimer's disease and depression using GWAS data and biostatistical tools, finding shared variants and brain morphology associations.

This study links brain morphology and body composition using MRI data from 19,330 UK Biobank participants, finding significant associations between body measures and brain structures.

This study identifies 42 genetic loci associated with thalamus and its nuclei volumes and finds genetic overlaps with cortical regions and ten brain disorders.

The paper investigates the association between predicted brain C4A expression and cognitive performance and brain imaging measures in the UK Biobank, finding significant negative associations and structural mediation effects.

The paper extends a Gaussian mixture model to account for linkage disequilibrium and heterozygosity in GWAS summary statistics, improving estimates of genetic quantities like heritability.

The paper introduces MOSTest, a multivariate statistical method for GWAS, identifying 347 genetic loci associated with brain morphology in UK Biobank data.

The paper introduces AI-MiXeR, a method to estimate variant counts and effect sizes across functional annotations using GWAS summary statistics, revealing phenotype-specific polygenicity and effect size differences.