pcbi-1000910-g001: Correlation of expression variability between CEU and YRI populations.Each data point represents one transcript.

Mentions: We first sought to examine whether these genes have similar level of within-population variability in different populations. For each gene, we quantified the within-population expression variability by calculating its coefficient of variation η, which is the ratio of the standard deviation of its expression (across 30 individuals within a population) to the mean value [21]–[23]. Although other metrics can be used to quantify the expression variability, η is known to be one of the most robust and unbiased metrics [21]. Greater η implies higher expression variability for a particular gene across individuals within a population, while a significant reduction in η suggests that the gene might be dosage sensitive and thus under severe selection to minimize expression variability. The η values were calculated for each of the 18,081 mRNAs across individuals within the CEU and YRI populations separately (see Table S1 for genes with their calculated expression variability in each population). Between the CEU and YRI populations, most of the human genes exhibit a similar level of within-population variability, as η in CEU is well correlated with that in YRI (r = 0.88, P≈0; Figure 1). Pair-wise comparison of expression variability between all HapMap populations further confirmed this trend (r>0.85, P≈0). The same trend was recapitulated on another independent dataset of smaller sample size based on Affymetrix Human Focus Arrays [16], suggesting this observation was not resultant from a technical artifact. Therefore such a strong correlation of within-population expression variability between the two populations suggests either expression variability of most genes is subject to similar levels of constraints in both populations, or the cis- or trans- regulatory mechanisms of these genes have not diverged significantly.

Li J, Liu Y, Kim T, Min R, Zhang Z - PLoS Comput. Biol. (2010)

Bottom Line: Although ∼20% of human genes show differentiated mRNA levels between populations, our results show that expression variability of most human genes in one population is not significantly deviant from another population, except for a small fraction that do show substantially higher expression variability in a particular population.By associating expression variability with sequence polymorphism, intriguingly, we found SNPs in the untranslated regions (5' and 3'UTRs) of these variable genes show consistently elevated population heterozygosity.We performed differential expression analysis on a genome-wide scale, and found substantially reduced expression variability for a large number of genes, prohibiting them from being differentially expressed between populations.

Affiliation: Department of Molecular Genetics, University of Toronto, Toronto, Canada.

Abstract: Variations in gene expression level might lead to phenotypic diversity across individuals or populations. Although many human genes are found to have differential mRNA levels between populations, the extent of gene expression that could vary within and between populations largely remains elusive. To investigate the dynamic range of gene expression, we analyzed the expression variability of ∼18, 000 human genes across individuals within HapMap populations. Although ∼20% of human genes show differentiated mRNA levels between populations, our results show that expression variability of most human genes in one population is not significantly deviant from another population, except for a small fraction that do show substantially higher expression variability in a particular population. By associating expression variability with sequence polymorphism, intriguingly, we found SNPs in the untranslated regions (5' and 3'UTRs) of these variable genes show consistently elevated population heterozygosity. We performed differential expression analysis on a genome-wide scale, and found substantially reduced expression variability for a large number of genes, prohibiting them from being differentially expressed between populations. Functional analysis revealed that genes with the greatest within-population expression variability are significantly enriched for chemokine signaling in HIV-1 infection, and for HIV-interacting proteins that control viral entry, replication, and propagation. This observation combined with the finding that known human HIV host factors show substantially elevated expression variability, collectively suggest that gene expression variability might explain differential HIV susceptibility across individuals.