Э. К. Хуснутдинова

PRANA adapts polygenic risk scores to non-European populations, improving predictive accuracy by 5%-20% in South Asian, East Asian, and Ashkenazi Jewish populations.

The paper introduces co-expression-based models to improve eQTL predictions and identifies novel schizophrenia-associated genes using brain RNA-seq data.

The paper uses case-control data from over 400,000 women to classify BRCA1 and BRCA2 variants, finding high consistency with ClinVar annotations for 785 previously unclassified variants.

This study examines differences in polygenic risk score distributions across European populations and shows how country-specific calibration can improve breast cancer risk prediction.

This study examines epigenetic age acceleration and adaptation mechanisms in the Yakutian population using DNA methylation data, finding higher age acceleration and region-specific methylation patterns related to climate adaptation and aging.

This study improves transcriptome-wide association studies by integrating pleiotropy-guided methods with GWAS data to identify new cancer susceptibility genes.

The paper analyzes rare copy number variants (CNVs) in breast cancer cases and controls, identifying associations with known and novel susceptibility loci.

The paper analyzes the structural variability, expression, and pharmacogenetics of the TMPRSS2 gene, identifying it as a potential target for COVID-19 therapy due to its role in SARS-CoV-2 priming and genetic diversity.

This study combines genome-wide association data from four cancers to identify shared and novel genetic associations and pleiotropic effects.