Detection of gene-environment interaction in pedigree data using genome-wide genotypes

Area of Science:

• Quantitative Genetics
• Statistical Genetics
• Genomics

Background:

• Heritability estimation traditionally uses family data or genome-wide single nucleotide polymorphism (SNP) data separately.
• Existing models may not fully capture the dynamic interplay of genetic and environmental factors across different life stages or cohorts.

Purpose of the Study:

• To develop and validate a novel statistical model combining family and SNP data for heritability estimation.
• To investigate the moderation effects of measured variables (e.g., age, birth year) on genetic and environmental variance components.
• To apply the model to understand the genetic architecture of complex traits like BMI, height, attention problems, and anxiety/depression symptoms.

Main Methods:

• Re-parameterization of the Zaitlen et al. model to integrate family and SNP-based heritability estimation.
• Extension of the model to incorporate moderation by measured variables.
• Data simulation to verify Type 1 error rates and parameter accuracy.
• Application to real-world data for traits including BMI, height, attention problems, and anxiety/depression symptoms, considering age and birth year as moderators.

Main Results:

• The proposed method demonstrated accurate parameter estimation and correct Type 1 error rates through simulations.
• Genetic variance for BMI increased with age, while environmental variance increased more substantially, decreasing BMI heritability.
• Environmental variance for height increased with birth year, and for attention problems, it increased with age.
• The study successfully illustrated the assessment of moderation on genetic and environmental variance components.

Conclusions:

• The integrated approach provides a robust framework for estimating heritability by combining family and SNP data.
• Moderation analysis reveals that genetic and environmental influences on complex traits are not static and can vary with factors like age and birth year.
• Understanding these dynamic effects enhances the comprehension of the genetic architecture underlying complex human traits.