Menachem Fromer

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.

A genome-wide association study of 69,369 schizophrenia cases and 236,642 controls identifies 270 risk loci, prioritizes 130 genes, and links common and rare risk variants to neuronal synaptic biology.

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 investigates the role of single-gene copy number variants in schizophrenia risk using exome-sequencing data from a Swedish cohort, identifying significant deletions in voltage-gated calcium channels.

This study identifies 99 genes associated with autism spectrum disorder through large-scale exome sequencing, showing differences in mutation frequencies between ASD and severe neurodevelopmental delay groups.

This study examines the contribution of protein-coding de novo mutations to schizophrenia risk using whole-exome sequencing data from parent-offspring trios, finding a modest but polygenic effect.

The paper introduces a Bayesian framework to prioritize risk genes for complex diseases by integrating gene-set information with variant counts, identifying novel epilepsy risk genes and linking them to immune pathways and drug targets.

The paper analyzes allelic expression bias in schizophrenia using RNA-seq data to assess imprinted gene expression and its relationship with age, ancestry, and disease.

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 compares LDSC and GREML methods for estimating genetic correlation, showing GREML provides higher accuracy, especially in the presence of genetic heterogeneity.

The paper uses Bayesian modeling to analyze rare exonic variants in schizophrenia and neurodevelopmental disorders, identifying 1,551 risk genes for schizophrenia and 98 new risk genes for developmental disorders.

The paper uses transcriptomic imputation to identify schizophrenia risk genes across multiple brain regions, finding 71 associated genes and 36 enriched pathways.

This study uses transcriptomic imputation to identify 29 novel genes associated with bipolar disorder and its subtypes, revealing biological pathways related to oxidative stress and circadian rhythms.

The paper uses gene expression data from schizophrenia cases and controls to show that common variants at 20% of schizophrenia risk loci regulate brain gene expression, with functional impacts on neural progenitor cells and zebrafish neuroanatomy.