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Detecting Genotype-Population Interaction Effects by Ancestry Principal Components.

Chenglong Yu1,2,3, Guiyan Ni1,4,5, Julius van der Werf5

  • 1Australian Centre for Precision Health, University of South Australia Cancer Research Institute, University of South Australia, Adelaide, SA, Australia.

Frontiers in Genetics
|May 7, 2020
PubMed
Summary
This summary is machine-generated.

Genetic effects on complex traits vary across populations. This study introduces genotype-by-population (G × P) interactions using ancestry principal components (PCs) to reveal significant genetic heterogeneity in behavioral traits.

Keywords:
SNP-based heritabilityUK Biobankcomplex traitsgenetic heterogeneitygenotype-phenotype relationshipselection bias

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Area of Science:

  • Genetics
  • Population Genetics
  • Quantitative Genetics

Background:

  • Complex traits exhibit phenotypic heterogeneity across populations.
  • Understanding genetic effects heterogeneity is crucial for complex trait research.
  • Previous studies on genetic heterogeneity often used discrete population groups, losing individual variation information.

Purpose of the Study:

  • To introduce and apply a novel concept of genotype-by-population (G × P) interaction.
  • To examine G × P interactions for complex traits using ancestry principal components (PCs).
  • To identify population-specific genetic effects on complex traits.

Main Methods:

  • Applied a reaction norm model to 70 complex traits with significant SNP-heritability.
  • Utilized the first and second ancestry principal components (PCs) to define populations.
  • Analyzed data from UK Biobank, including white British and other white Europeans (N = 22,229).

Main Results:

  • Demonstrated significant population genetic heterogeneity for behavioral traits.
  • Identified significant G × P interactions for traits like age at first sexual intercourse and academic qualification.
  • Showcased the utility of PCs in defining population structure for genetic analysis.

Conclusions:

  • The G × P interaction model effectively captures genetic heterogeneity across populations.
  • Ancestry PCs provide a robust way to define population structure, minimizing confounding factors.
  • This approach enhances our understanding of the genetic architecture of complex traits and their modulation across diverse populations.