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Related Concept Videos

Polygenic Traits01:18

Polygenic Traits

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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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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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Genome-wide Association Studies-GWAS01:11

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Heritability01:06

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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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Single Nucleotide Polymorphisms-SNPs01:05

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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,...
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Related Experiment Video

Updated: Aug 5, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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An Ensemble Penalized Regression Method for Multi-ancestry Polygenic Risk Prediction.

Jingning Zhang1, Jianan Zhan2, Jin Jin3

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

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|March 30, 2023
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Summary

PROSPER improves polygenic risk scores (PRS) for diverse populations by integrating multiple ancestry GWAS data. This novel ensemble method enhances prediction accuracy, especially for minority groups, overcoming limitations of existing European-centric models.

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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 and Bioinformatics
  • Population Genetics
  • Statistical Genomics

Background:

  • Existing polygenic risk score (PRS) models predominantly use European ancestry data, limiting their applicability and accuracy in diverse global populations.
  • Accurate prediction of complex traits and diseases across ancestries is crucial for equitable healthcare and genetic research.
  • Bridging the ancestry gap in PRS is essential for reducing health disparities and advancing precision medicine.

Approach:

  • Developed PROSPER (Polygenic Risk scOres based on enSemble of PEnalized Regression), a novel method for generating multi-ancestry PRS.
  • PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations using penalized regression (LASSO and Ridge).
  • Employs a parsimonious penalty parameter specification and an ensemble approach to combine PRS across different penalty parameters for improved performance.

Key Points:

  • PROSPER significantly enhances multi-ancestry polygenic prediction across various genetic architectures compared to existing methods.
  • Demonstrated substantial improvements in out-of-sample prediction R-squared for continuous traits in African ancestry populations (average 70% increase vs. PRS-CSx).
  • The method is computationally scalable for large SNP datasets and numerous diverse populations.

Conclusions:

  • PROSPER offers a robust solution for developing accurate and transferable PRS across diverse ancestries.
  • The approach effectively addresses the limitations of current PRS methods, promoting more equitable genetic risk prediction.
  • PROSPER represents a significant advancement in leveraging diverse genomic data for improved disease risk assessment.