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Polygenic Traits

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...

Genome-wide Association Studies-GWAS

Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...

Heritability

Heritability

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"...

Pleiotropy

Pleiotropy

Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...

Multiple Allele Traits

Multiple Allele Traits

The Concept of Multiple Allelism

Punnett Squares

Punnett Squares

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Related Experiment Video

Updated: Nov 5, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Polygenic Risk Scores.

Michael D Osterman¹, Tyler G Kinzy¹, Jessica N Cooke Bailey¹

¹Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, Ohio.

Current Protocols

|May 14, 2021

Summary

This summary is machine-generated.

Polygenic risk scores aggregate genetic variants to estimate disease risk. This guide details methods, software, and interpretation for calculating these scores for complex traits.

Keywords:

area under the curve complex traits and diseases disease prediction genetic risk score polygenic risk score

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An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations

Published on: November 3, 2010

Area of Science:

Genetics
Bioinformatics
Epidemiology

Background:

Genome-wide association studies (GWAS) identify genetic loci linked to complex traits and diseases.
Genetic risk scores (GRS) aggregate effects of associated loci for disease risk prediction.
Polygenic risk scores (PRS) extend GRS by incorporating more variants across the genome.

Purpose of the Study:

To describe common methods and software packages for calculating and interpreting polygenic risk scores.
To provide a revised overview of PRS analysis, including necessary information, planning, interpretation, and limitations.
To offer practical guidance with simulated data and software walkthroughs.

Main Methods:

Review of existing methodologies for polygenic risk score calculation.
Description of software packages designed for PRS analysis.
Inclusion of simulated data for practical application and demonstration.

Main Results:

Detailed information required for PRS analysis.
Considerations for planning and interpreting PRS results.
Discussion of limitations inherent in PRS methodologies and choices.

Conclusions:

Polygenic risk scores are a valuable tool for disease risk prediction by aggregating numerous genetic variants.
Understanding the methods, software, and interpretation is crucial for effective application of PRS.
This work provides a comprehensive resource for researchers utilizing polygenic risk scores.