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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...

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Comparing Within- and Between-Family Polygenic Score Prediction.

Saskia Selzam¹, Stuart J Ritchie¹, Jean-Baptiste Pingault²

¹Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK.

American Journal of Human Genetics

|July 16, 2019

Summary

Genome-wide polygenic scores (GPS) predict traits within families, but prediction is stronger between families due to socio-economic status influencing genotype-environment correlation (rGE). This study clarifies rGE

Keywords:

complex trait prediction gene-environment correlation gene-environment interplay genetic nurture polygenic score prediction socio-economic status within-family analysis

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Area of Science:

Behavioral Genetics
Quantitative Genetics
Human Genetics

Background:

Polygenic scores (GPS) predict complex traits but can be confounded by population stratification, assortative mating, and genotype-environment correlation (rGE).
Distinguishing these effects requires comparing prediction within and between families.

Purpose of the Study:

To compare genome-wide polygenic score (GPS) prediction within and between families for multiple complex traits.
To identify sources of prediction bias in GPS estimates, particularly genotype-environment correlation (rGE).

Main Methods:

Utilized dizygotic (DZ) twin pairs (up to 2,366) from the Twins Early Development Study (TEDS).
Employed mixed-effects modeling to estimate within- and between-family GPS prediction for eight outcomes (anthropometric, cognitive, personality, health).
Assessed GPS prediction for target and cross-trait predictions, controlling for family socio-economic status (SES).

Main Results:

DZ twin GPS differences predicted differences in height, BMI, intelligence, educational achievement, and ADHD symptoms.
GPS prediction for cognitive traits (intelligence, educational achievement) was ~60% greater between families than within families.
The between-family advantage for cognitive traits diminished after controlling for family SES, implicating rGE.

Conclusions:

Socio-economic status (SES) is a significant driver of between-family GPS prediction for cognitive traits via rGE.
This study disentangles prediction sources, highlighting rGE's role and ruling out population stratification and assortative mating as primary confounders for cognitive traits.