Brandon J. Coombes

This study identifies genetic risk loci and factors underlying clinical subtypes of bipolar disorder using GWAS and meta-analysis.

The paper identifies FNDC4 as a gene associated with alcohol use disorder through GWAS and shows its role in regulating neurogenesis via alternative splicing and CRISPR/Cas9 experiments.

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.

The paper examines the association between insomnia genetic liability and substance use disorders in bipolar disorder patients using polygenic risk scores and electronic health records.

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 identifies three novel immune pathway genes with deleterious variants associated with Alzheimer's disease using polygenic risk scores in whole exome sequence data.

This study evaluates the association of genetic risk scores with depression and anxiety diagnoses, finding distinct genetic contributions to their comorbidity.

This study identifies genetic variants associated with AUD treatment outcomes using pharmacogenomic analyses of naltrexone and acamprosate responses.

The paper introduces BinomiRare, a test for rare variant association with binary outcomes in mixed models, showing it controls type 1 error in simulations and applies it to study genetic variants in disease traits.

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.

The paper identifies four genes, including two novel ones, co-expressed with TREM2 in Alzheimer's disease using whole-exome data from ADSP.

This paper introduces a principal component approach to improve association testing with polygenic risk scores by reducing type I error and improving power compared to permutation tests and a priori parameter selection.