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Blood Lipid Polygenic Risk Score Development and Application for Atherosclerosis Ultrasound Parameters.

Marija Zaicenoka1,2, Alexandra I Ershova1, Anna V Kiseleva1

  • 1National Medical Research Center for Therapy and Preventive Medicine, Ministry of Healthcare of the Russian Federation, 10-3, Petroverigsky per., 101000 Moscow, Russia.

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Summary

Developing polygenic risk scores (PRS) for blood lipids in Russian populations improves prediction of atherosclerosis risk. Fine-tuning PRS with advanced methods enhances their performance for clinical utility.

Keywords:
atherosclerosiscardiovascular diseasehigh-density lipoprotein cholesterollow-density lipoprotein cholesterolpolygenic risk scorestotal cholesteroltriglycerides

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Area of Science:

  • Genetics
  • Cardiovascular Disease Epidemiology
  • Biostatistics

Background:

  • Investigates the application of existing genome-wide association studies (GWAS) data on blood lipids.
  • Focuses on developing polygenic risk scores (PRS) for European Russian populations.
  • Utilizes two distinct population samples from the Ivanovo and Vologda regions.

Purpose of the Study:

  • To assess the feasibility of creating and refining PRS for lipid levels in a Russian cohort.
  • To evaluate the performance of different PRS development methods, including PRSice-2 and LDpred2.
  • To determine the clinical utility of PRS in predicting atherosclerosis.

Main Methods:

  • Employed three PRS development strategies: standard PRS, PRSice-2 fine-tuning, and LDpred2 fine-tuning.
  • Constructed 56 PRS scales for four lipid phenotypes: LDL cholesterol, HDL cholesterol, total cholesterol, and triglycerides.
  • Analyzed PRS associations with carotid and femoral artery atherosclerosis using ultrasound data.

Main Results:

  • Achieved a 2-4% increase in R-squared for PRS prediction of lipid phenotypes compared to previous Russian population studies.
  • PRS estimates reached performance levels comparable to those reported in other global populations.
  • Found significant correlations between PRS for total cholesterol, LDL cholesterol, and triglycerides with atherosclerosis markers (ρ = 0.09-0.13, p < 0.001).
  • Observed an inverse correlation between HDL cholesterol PRS and femoral artery plaque count (ρ = -0.08, p = 8.71 × 10-3).

Conclusions:

  • Fine-tuning PRS using PRSice-2 and LDpred2 significantly enhances the predictive performance of blood lipid PRS.
  • Blood lipid PRS demonstrate potential for predicting atherosclerosis risk in the studied population.
  • The findings support the broader application of PRS in cardiovascular risk assessment.