Brendan K Bulik-Sullivan

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

This study identifies somatic copy number variants (sCNVs) in schizophrenia cases, revealing their contribution to risk and specific NRXN1 disruptions.

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.

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

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

Genetic correlations across 25 brain disorders (215,683 cases) reveal broad psychiatric common-variant sharing, largely distinct neurological disorders, and migraine as the only significant cross-category link.

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

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 quantifies linkage disequilibrium (LD) dependent architecture of complex traits, finding that low LD SNPs have higher heritability, likely due to negative selection.

The paper analyzes the genetic architecture of schizophrenia and nine complex diseases using a fast variance components analysis, finding polygenic patterns and significant genetic correlations.

BOLT-LMM improves genetic association power in large cohorts by using a Bayesian mixture prior to model non-infinitesimal genetic architectures more realistically.

The paper introduces LD Score regression to differentiate between confounding and polygenicity in genome-wide association studies.