Abstract
Population structure in genotype data has been extensively studied, and is revealed by looking at the principal components of the genotype matrix. However, no similar analysis of population structure in gene expression data has been conducted, in part because a naïve principal components analysis of the gene expression matrix does not cluster by population. We identify a linear projection that reveals population structure in gene expression data. Our approach relies on the coupling of the principal components of genotype to the principal components of gene expression via canonical correlation analysis. Our method is able to determine the significance of the variance in the canonical correlation projection explained by each gene. We identify 3,571 significant genes, only 837 of which had been previously reported to have an associated eQTL in the GEUVADIS results. We show that our projections are not primarily driven by differences in allele frequency at known cis-eQTLs and that similar projections can be recovered using only several hundred randomly selected genes and SNPs. Finally, we present preliminary work on the consequences for eQTL analysis. We observe that using our projection co-ordinates as covariates results in the discovery of slightly fewer genes with eQTLs, but that these genes replicate in GTEx matched tissue at a slightly higher rate.
Highlights
Genes mirror geography to the extent that in global populations without admixture, individuals can be localized to within hundreds of kilometers purely on the basis of their genotype [1,2,3]
We show that the coupling of principal component analysis to canonical correlation analysis offers an efficient approach to exploratory analysis of this kind of data
We apply this method to the GEUVADIS dataset of genotype and gene expression values of European and Yoruba individuals, finding as-of-yet unstudied population structure in gene expression abundances
Summary
Genes mirror geography to the extent that in global populations without admixture, individuals can be localized to within hundreds of kilometers purely on the basis of their genotype [1,2,3]. Population structure in genotypes is revealed via projection of single nucleotide polymorphism (SNP) data onto the first few principal components of the population-genotype matrix. While PCA has been successful in revealing population structure from SNP data, it does not identify such structure in some other genomic data types. In the case of gene expression data, PCA has not revealed obvious population signatures (Supporting Information Fig 1A, [4]). We show that the first two principal components of expression data do not capture population structure, there are other projections that do. One approach to finding such a projection is the coupling of dimension reduction to correlation maximization.
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