Mohammad Mehdi Ahangari

The paper assesses the association between rare variant genetic loads and cognitive traits, intellectual disability, and psychiatric disorders in population-based cohorts.

This paper proposes using embryo-level autozygosity as a biomarker to prioritize embryos with lower disease risk in preimplantation genetic testing for couples with consanguinity.

This study develops and evaluates HLA-ARC, a polygenic risk score for type 1 diabetes, showing improved performance over existing methods in diverse ancestry groups.

This paper develops and validates polygenic scores for within-family disease risk prediction, showing strong performance across ancestries and demonstrating their clinical utility in embryo screening.

The paper identifies novel CNV loci and dose-dependent gene effects across six psychiatric disorders using genome-wide analysis.

The paper identifies shared and divergent genetic mechanisms across psychiatric disorders using gene-set burden analysis of copy number variants.

This study uses machine learning and functional weights to identify rare variants associated with AUDIT-P, finding significant associations in ADH1C and THRA genes.

This study evaluates how perturbations in haplotype reference panels affect genotype imputation accuracy, finding that phase and genotype errors have a greater impact than variant density.

The paper analyzes rare functional variation in genes related to ethanol metabolism in severe alcohol dependence cases using a case-only design and WES data from the island of Ireland.