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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

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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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Updated: Dec 25, 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 Mendelian Randomization.

Frank Dudbridge¹

¹Department of Health Sciences, University of Leicester, Leicester LE1 7RH, United Kingdom.

Cold Spring Harbor Perspectives in Medicine

|April 2, 2020

Summary

This summary is machine-generated.

Polygenic Mendelian randomization (MR) uses many genetic variants to improve causal inference. Advanced methods and sensitivity analyses help address pleiotropy and strengthen evidence for genetic effects on health outcomes.

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Area of Science:

Genetics
Epidemiology
Statistical Genetics

Background:

Many exposures in Mendelian randomization (MR) studies are polygenic, influenced by numerous genetic variants.
Utilizing multiple single-nucleotide polymorphisms (SNPs) as instrumental variables explains more exposure variation, enhancing MR precision.

Purpose of the Study:

To review concepts and assumptions of commonly used polygenic MR methods.
To discuss methods for relaxing MR assumptions, particularly regarding pleiotropic effects.

Main Methods:

Polygenic scores as instruments, equivalent to a weighted mean of individual SNP results.
Median and mode-based MR methods for estimating causal effects.
Outlier detection for identifying and excluding pleiotropic SNPs.
Bayesian approaches incorporating prior beliefs about pleiotropy.

Main Results:

Polygenic instruments increase precision and allow for relaxed MR assumptions.
Different averaging methods (mean, median, mode) offer alternative causal effect estimates.
Sensitivity analyses using outlier detection and Bayesian methods help assess the robustness of findings.

Conclusions:

Polygenic MR methods provide a robust framework for causal inference in the presence of complex genetic architectures.
A suite of sensitivity analyses is crucial for triangulating evidence and strengthening causal claims in MR studies.