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Multi-Trait Genomic Risk Stratification for Type 2 Diabetes.

Palle Duun Rohde1,2, Mette Nyegaard2,3, Mads Kjolby3,4,5,6

  • 1Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark.

Frontiers in Medicine
|September 27, 2021
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Summary

Developing multi-trait genetic risk scores (MT-GRS) improves type 2 diabetes mellitus (T2DM) prediction accuracy by incorporating correlated traits. This enhanced approach offers better risk stratification than single-trait GRS alone.

Keywords:
GRSUK Biobankgenetic risk scoresmulti-trait analysisprecision medicine

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Area of Science:

  • Genetics and Genomics
  • Metabolic Diseases
  • Biostatistics

Background:

  • Type 2 diabetes mellitus (T2DM) prevalence is increasing globally, posing a significant health burden.
  • T2DM has a strong genetic component, with heritability estimated between 40-70%.
  • Current genome-wide genetic risk scores (GRS) explain only a small fraction of T2DM risk.

Purpose of the Study:

  • To develop and evaluate multi-trait genetic risk scores (MT-GRS) for improved T2DM risk prediction.
  • To leverage correlated traits to enhance the accuracy of genetic risk assessment for T2DM.
  • To investigate the impact of incorporating information from correlated traits on GRS performance.

Main Methods:

  • Utilized UK Biobank data for phenotype and genotype information.
  • Estimated marker effects for T2DM and seven correlated traits (height, BMI, pulse, blood pressure, smoking, medication use).
  • Developed MT-GRS by combining summary statistics from UK Biobank and two independent T2DM studies.

Main Results:

  • MT-GRS improved prediction accuracy by 12.5% compared to single-trait GRS.
  • MT-GRS strategy elevated accuracy by 50-94% in two independent T2DM studies.
  • Incorporating seven correlated traits further increased prediction accuracy by 34%; BMI and medication use had the largest weights.

Conclusions:

  • Leveraging correlated trait information significantly enhances the predictive power of genetic risk scores.
  • MT-GRS offers a more accurate method for individual risk stratification of T2DM.
  • Constructing GRS using both disease-specific and correlated trait genomic data is advisable for improved risk assessment.