Niamh Mullins

This study identifies shared and distinct genetic factors underlying suicidality phenotypes using GWAS and structural equation modelling.

This study investigates genetic relationships between OCD, BD, SCZ, and MDD using GWAS data, finding causal effects of OCD on BD, SCZ, and MDD.

This study uses Mendelian Randomization to identify causal relationships between neuroimaging phenotypes and bipolar disorder, finding that white matter tract variations have larger causal effects on BD.

The paper introduces Clorinn, a convex optimization method to decompose genetic variant associations into shared and distinct components across complex traits, showing improved performance in simulations and application to GWAS data.

The paper uses polygenic risk scores from GWAS to assess their association with suicidal thoughts and behaviors in a sample with alcohol use disorders, finding significant contributions from multiple PRS.

This paper conducts a multi-ancestry GWAS meta-analysis of suicide attempt, identifying 12 genome-wide significant loci and finding shared genetic risks with ADHD, smoking, and risk tolerance.

This study relaxes GWAS significance thresholds and uses GRIN to identify additional genes linked to suicidal behavior, which replicate across cohorts and show stronger biological connections.

This study identifies genetic loci associated with suicide attempts and shows shared genetic architecture with psychiatric disorders and non-psychiatric risk factors.

Meta-analysis of 41,917 European-ancestry bipolar disorder cases versus 371,549 controls yields 64 risk loci (33 novel), neuronal and synaptic gene enrichment, and 15 eQTL-prioritized genes.

This paper conducts a genome-wide association study of suicide death and shows polygenic scores predict suicide case-control status and related psychiatric traits.

This study identifies traits associated with polygenic risk for depression using UK Biobank data and finds causal links between depression risk and brain structure, as well as environmental interactions.

This study examines genetic risk for bipolar disorder in extended families, finding that both common and rare variants contribute to susceptibility, with no significant segregation of large-effect variants.

RICOPILI is a Perl-based pipeline for efficient GWAS analysis, including quality control, imputation, and meta-analysis, with automated parallelization for rapid data processing.

The paper applies polygenic risk scoring to a large family with bipolar and depressive disorders to study genetic risk patterns and anticipation.

This paper conducts a GWAS on suicide attempt across psychiatric disorders, identifying genetic loci and showing polygenic risk for major depression predicts suicide attempt in multiple disorders.

This study identifies 114 genome-wide significant loci for schizophrenia and bipolar disorder, with specific subphenotype associations and four genes distinguishing the disorders.

Genome-wide association meta-analysis of 130,664 major depression cases and 330,470 controls identifies 44 independent risk loci enriched in cortical neurons, conserved mammalian regions, splicing networks, and antidepressant targets.