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Polygenic Traits01:18

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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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Heritability is a statistical concept that measures the degree to which genetic differences among individuals contribute to trait variations within a population. It is a fundamental idea in genetics, often prone to misinterpretation. Heritability is expressed as a percentage, reflecting the proportion of variation in a specific trait across a population that can be linked to genetic differences. However, it's important to understand that heritability does not determine how "genetic"...
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Genetic variation is the diversity in DNA sequences found among individuals of the same species. This diversity is crucial for a species' survival because it helps organisms adapt to environmental changes. Genetic variation begins with fertilization, where an egg and sperm cell merge. Each of these cells carries 23 chromosomes, up to 46 in the fertilized egg. Chromosomes are long DNA strands that contain genes, the basic units of heredity.
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MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups.

Jin Jin1, Jianan Zhan2, Jingning Zhang3

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19103, USA.

Cell Genomics
|April 11, 2024
PubMed
Summary

MUSSEL improves polygenic risk score (PRS) prediction across diverse ancestries by leveraging genome-wide association study (GWAS) data. This ancestry-specific method shows significant gains, especially in underrepresented populations.

Keywords:
Bayesian hierarchical modelingeffect-size distributionensemble learninggenome-wide association studiesmulti-ancestry polygenic predictionpolygenic architecture

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Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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Area of Science:

  • Genetics
  • Statistical genetics
  • Population genetics

Background:

  • Polygenic risk scores (PRSs) predict complex traits but show performance disparities across ancestral populations.
  • Existing methods struggle to bridge the prediction gap in diverse ancestries.

Purpose of the Study:

  • To develop and evaluate MUSSEL, a novel method for ancestry-specific polygenic prediction.
  • To improve PRS accuracy in underrepresented populations by integrating multi-ancestry GWAS summary statistics.

Main Methods:

  • MUSSEL employs Bayesian hierarchical modeling and ensemble learning to borrow information across ancestry groups.
  • The method utilizes summary statistics from genome-wide association studies (GWASs).
  • Evaluated through simulations and analyses of four large studies with diverse participants (5.7 million total).

Main Results:

  • MUSSEL demonstrated substantial prediction R-squared gains compared to existing methods (e.g., PRS-CSx, CT-SLEB) in African ancestry populations.
  • Average gains of 40.2% and 49.3% were observed for 11 continuous traits.
  • Optimal method performance varied based on GWAS sample size, target ancestry, trait architecture, and LD reference panels.

Conclusions:

  • MUSSEL offers a promising approach for enhancing PRS accuracy in diverse populations.
  • A combination of methods may be necessary for generating the most robust PRSs across all ancestries.
  • Further development is needed to optimize PRS for global applicability.