William Byerley

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

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

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.

Exomes of 24,248 schizophrenia cases and 97,322 controls implicate ultra-rare damaging variants in 10 genes, including GRIN2A and GRIA3, with odds ratios of 3 to 50.

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

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 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 examines common genetic variants' contribution to bipolar disorder risk in multiplex families and finds higher polygenic risk scores in affected individuals compared to controls and unaffected family members.

This study identifies 73 genome-wide significant loci for mood disorders, revealing shared and distinct genetic factors between major depressive disorder and bipolar disorder.

This study identifies 30 genome-wide significant loci associated with bipolar disorder, including novel genes involved in ion channels, neurotransmitters, and synaptic components.

SummaryAUC approximates the AUC of polygenic risk scores using only summary statistics from validation datasets, enabling efficient evaluation of PRS models.

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.

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.

This study identifies eight genome-wide significant CNV loci associated with schizophrenia using a large cohort of 41,321 subjects.