Ayman H. Fanous

Genomic and transcriptomic insights into antipsychotic-induced changes in total cholesterol and body mass index in a multi-ancestry cohort of the US veterans

The paper conducts a GWAS on antipsychotic-induced changes in total cholesterol and BMI in a multi-ancestry cohort, identifying genetic loci and pathways associated with these metabolic effects.

Multiplex Portuguese Families as a Lens into rare mutations and the Shared Genetic Architecture of Schizophrenia, Mood Disorders, and Autism Spectrum Disorders

This study analyzes 173 multiplex Portuguese families to identify rare genetic variants associated with schizophrenia, mood disorders, and autism, finding a CHD2 mutation linked to diverse mental illness phenotypes.

Gene-Set Based Rare Variant Association Analysis of Whole Genome Sequencing Data in the Portuguese Island Collection for Schizophrenia and Bipolar Disorder

The paper performs rare variant association analysis using whole-genome sequencing data from the Portuguese Island Collection to identify shared genetic pathways in schizophrenia and bipolar disorder.

Genomic dimensions deconstruct the clinical heterogeneity of bipolar disorder

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

Correlates of Risk for Disinhibited Behaviors in the Million Veteran Program Cohort

This study identifies correlates of risk for disinhibited behaviors in veterans using polygenic scores and electronic health records, finding strong associations with substance use disorders and related health outcomes.

Using brain cell-type-specific protein interactomes to interpret genetic data in schizophrenia

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.

Detecting and Adjusting for Hidden Biases due to Phenotype Misclassification in Genome-Wide Association Studies

This paper introduces PheMED, a tool to quantify effect size dilution in GWAS due to phenotypic misclassification, and demonstrates its applications in improving meta-analysis.

Exome sequencing identifies rare coding variants in 10 genes which confer substantial risk for schizophrenia

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.

Penetrance and Pleiotropy of Polygenic Risk Scores for Schizophrenia, Bipolar Disorder, and Depression in the VA Health Care System

The paper validates polygenic risk scores for schizophrenia, bipolar disorder, and depression using VA EHR data and shows their association with disease risk and hospitalization.

Sex-Dependent Shared and Non-Shared Genetic Architecture Across Mood and Psychotic Disorders

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

Polygenic risk scores lack prognostic value for adults with severe mental illness

This study evaluates the prognostic value of polygenic risk scores for schizophrenia and finds they do not improve outcome prediction beyond clinical features.

Investigation of convergent and divergent genetic influences underlying schizophrenia and alcohol use disorder

This study identifies genetic variants shared between schizophrenia and alcohol use disorder, finding both convergent and divergent effects and evidence of brain tissue expression.

A comparison of ten polygenic score methods for psychiatric disorders applied across multiple cohorts

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.

Genome wide meta-analysis identifies genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders.

Cross-disorder meta-analysis of eight psychiatric GWAS (232,964 cases, 494,162 controls) reports 109 loci shared by two or more disorders and three groups of correlated disorders.

Contribution of rare copy number variants to bipolar disorder risk is limited to schizoaffective cases

The study finds that rare copy number variants (CNVs) contribute to bipolar disorder risk primarily in schizoaffective disorder bipolar type cases, with differences in CNV burden and schizophrenia polygenic risk scores between subtypes.

Genome-wide association study of suicide attempt in psychiatric disorders identifies association with major depression polygenic risk scores

This paper conducts a GWAS on suicide attempt across psychiatric disorders, identifying genetic loci and showing polygenic risk for major depression predicts suicide attempt in multiple disorders.

Age at first birth in women is genetically associated with increased risk of schizophrenia

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.

Estimation of genetic correlation using linkage disequilibrium score regression and genomic restricted maximum likelihood

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

Genome-wide Association Study of Clinical Features in the Schizophrenia Psychiatric Genomics Consortium: Confirmation of Polygenic Effect on Negative Symptoms

The paper confirms a polygenic effect on negative symptoms in schizophrenia using GWAS, identifies novel loci, and highlights the need for larger samples.

Proof of concept: Molecular prediction of schizophrenia risk

This paper investigates how global polygenic risk scores, sex, and their interaction predict schizophrenia risk using a classification model, finding sex and polygenic risk are the strongest predictors.

FIQT: a simple, powerful method to accurately estimate effect sizes in genome scans

FIQT estimates Z-score means from genome scans more accurately and efficiently than existing methods, improving risk prediction in schizophrenia studies.