Personalized Polygenic Profiling for Lipid Metabolism in a Russian Population

A genetic dataset from a genome-wide association study of 8,732 individuals from the Russian population, focusing on total cholesterol, LDL-C, and HDL-C levels. The study, authored by Aleksandra Mamchur and last updated in 2026, identified associations with genes including HMGCR, APOE, and CETP. It also includes polygenic score models tested on an additional sample of 3,954 patients with cardiovascular diseases.

Use Cases

• Training polygenic risk score models based on identified genetic variants for lipid traits.
• Investigating sex-specific genetic predictors of cholesterol levels, such as variants in the SMARCA4 and LDLR genes.
• Assessing the link between constructed polygenic scores and diseases like atherosclerosis and myocardial infarction.
• Developing personalized cardiovascular disease risk assessment tools that consider age and BMI as modifiable factors.

Strengths

• Includes genetic data from a substantial cohort of 8,732 individuals for discovery.
• Validates polygenic score models on an independent clinical sample of 3,954 patients.
• Considers modifiable factors like age and BMI in its profiling approach.
• Identifies specific gene associations (e.g., HMGCR, APOE, CETP) for different lipid types.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count for the primary genetic data is unknown, which may limit suitability assessment.
• Data is specific to the Russian population, which may limit generalizability to other genetic ancestries.

Provenance

Source

figshare, author Aleksandra Mamchur.

Collection Method

Genome-wide association studies conducted on a population sample, with machine learning methods used to construct polygenic scores.

Freshness

Last updated 2026-06-02 05:40:24; freshness should be verified.

Geography

Russian population.