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Expression reflects population structure.

Brielin C Brown1, Nicolas L Bray2,3, Lior Pachter4

  • 1Department of Computer Science, University of California Berkeley, Berkeley, California, United States of America.

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This study introduces a new method to reveal population structure in gene expression data using genotype information. The approach identifies significant genes and improves the replication rate of expression quantitative trait loci (eQTL) findings.

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Area of Science:

  • Genomics
  • Population Genetics
  • Bioinformatics

Background:

  • Population structure is well-characterized in genotype data using principal component analysis.
  • Similar analysis for population structure in gene expression data is lacking, as standard methods do not reveal population clustering.
  • Gene expression data presents unique challenges for population structure analysis.

Purpose of the Study:

  • To develop a method for identifying population structure within gene expression data.
  • To link genotype principal components with gene expression principal components.
  • To identify genes significantly contributing to population structure in expression data and assess their impact on expression quantitative trait loci (eQTL) analysis.

Main Methods:

  • Canonical Correlation Analysis (CCA) was employed to couple principal components of genotype data with those of gene expression data.
  • A linear projection was derived to visualize population structure in gene expression.
  • Statistical significance of gene contributions to the projection was assessed.

Main Results:

  • A novel linear projection successfully revealed population structure in gene expression data.
  • 3,571 significant genes were identified, with 837 previously unreported in GEUVADIS eQTL results.
  • The method is robust, not solely driven by cis-eQTL allele frequencies, and can utilize a reduced set of genes and SNPs.

Conclusions:

  • The developed method effectively uncovers population structure in gene expression data.
  • Incorporating these projections as covariates in eQTL analysis slightly reduces gene discovery but enhances replication rates in matched tissues.
  • This approach offers a valuable tool for population genomics and genetic association studies using gene expression data.