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Related Concept Videos

Multiple Allele Traits01:49

Multiple Allele Traits

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Multiple Allele Traits01:49

Multiple Allele Traits

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Polygenic Traits01:18

Polygenic Traits

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...
Polygenic Traits01:18

Polygenic Traits

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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Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
Multi-species Conserved Sequences02:51

Multi-species Conserved Sequences

Next-generation sequencing technologies have created large genomic databases of a variety of animals and plants. Ever since the human genome project was completed, scientists studied the genome of primates, mammals, and other phylogenetically distant living beings. Such large-scale  studies have provided new insights into the evolutionary relationship between organisms.
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Protocol for constructing multi-ancestry polygenic models using S4-Multi.

Ping-Hung Lai1, Jonathan P Tyrer2, John Baierl1

  • 1Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA 90069, USA.

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PubMed
Summary
This summary is machine-generated.

This study introduces a protocol for building multi-ancestry polygenic models and generating polygenic risk scores. The workflow enhances prediction accuracy using linkage disequilibrium for both single- and multi-ancestry analyses.

Keywords:
BioinformaticsComputer sciencesGenetics

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Polygenic risk scores (PRS) are valuable tools for predicting disease risk.
  • Existing methods often struggle with multi-ancestry populations, limiting their global applicability.
  • Accurate PRS require robust methods for handling diverse genetic ancestries.

Purpose of the Study:

  • To present a comprehensive protocol for constructing multi-ancestry polygenic models.
  • To detail the generation of polygenic risk scores using the S4-Multi workflow.
  • To enhance PRS prediction accuracy by incorporating linkage disequilibrium information.

Main Methods:

  • Utilizing the S4-Multi protocol for model construction.
  • Preparing reference genotype data and formatting GWAS summary statistics.
  • Employing S4_select for SNP selection and S4_shrink for Bayesian shrinkage.
  • Generating and evaluating polygenic scores for single- and multi-ancestry analyses.

Main Results:

  • A validated protocol for creating multi-ancestry polygenic models.
  • Demonstrated improvement in PRS prediction accuracy through linkage disequilibrium.
  • Support for both single- and multi-ancestry genetic risk prediction.

Conclusions:

  • The presented protocol offers a robust framework for multi-ancestry PRS.
  • This workflow can improve the accuracy and applicability of genetic risk prediction across diverse populations.
  • The method facilitates advanced genetic research in population health.