Brielin C. Brown

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 maps interaction QTLs (iQTLs) for cell type, age, and other variables in blood multi-omic data to identify context-specific genetic regulatory effects.

The paper uses interventional data to infer causal networks for blood traits, revealing regulatory relationships among 788 genes.

This paper introduces MCFA, an unsupervised integration method for multi-omic data, and applies it to five omics types across 614 samples to infer shared and private factors.

The paper introduces Welch-weighted Egger regression to reduce false positives in Mendelian randomization due to correlated pleiotropy, showing improved performance on real and simulated data.

This paper introduces bimmer, a method to infer causal networks from observational genomics data by estimating direct and mediated effects using bidirectional mediated Mendelian randomization.

This study identifies schizophrenia genetic associations in East Asian and European populations, finding similar genetic effects across ancestries but highlighting challenges in polygenic risk prediction across groups.

The paper identifies population structure in gene expression data using canonical correlation analysis between genotype and expression principal components, finding genes associated with diseases affecting African Americans.