Large-Scale trans-eQTLs Affect Hundreds of Transcripts and Mediate Patterns of Transcriptional Co-regulation

Abstract

Efforts to decipher the causal relationships between differences in gene regulation and corresponding differences in phenotype have been stymied by several basic technical challenges. Although detecting local, cis-eQTLs is now routine, trans-eQTLs, which are distant from the genes of origin, are far more difficult to find because millions of SNPs must currently be compared to thousands of transcripts. Here, we demonstrate an alternative approach: we looked for SNPs associated with the expression of many genes simultaneously and found that hundreds of trans-eQTLs each affect hundreds of transcripts in lymphoblastoid cell lines across three African populations. These trans-eQTLs target the same genes across the three populations and show the same direction of effect. We discovered that target transcripts of a high-confidence set of trans-eQTLs encode proteins that interact more frequently than expected by chance, are bound by the same transcription factors, and are enriched for pathway annotations indicative of roles in basic cell homeostasis. We thus demonstrate that our approach can uncover trans-acting transcriptional control circuits that affect co-regulated groups of genes: a key to understanding how cellular pathways and processes are orchestrated.

Keywords: cross phenotype meta analysis; master regulator; regulatory network; trans-eQTL; transcription.

Figure 1

Hundreds of Putative trans-eQTLs across the Genome Affect the Same Genes in the Same Direction across Three African HapMap Populations We considered all autosomal variants with nominal evidence of association to multiple transcript levels (p_cpma< 0.05). We find that the targets of these trans-eQTLs overlap significantly in the three populations (empirical assessment of trans-eQTL target overlaps between YRI and LWK [A], YRI and MKK [B], and LWK and MKK [C]). We also find that trans-eQTL allelic effects are consistently in the same direction across the three populations (empirical assessment of trans-eQTL sign tests between YRI and LWK [D], YRI and MKK [E], and LWK and MKK [F]). These bulk results indicate that trans-eQTLs affect the expression of the same target genes, in the same direction, across populations.

Figure 2

A trans-eQTL at rs6899963 on Chromosome 6 Affects the Expression Levels of Many Genes across Three African HapMap Populations (A) Meta-analysis p values for 9,085 transcript eQTLs at rs6899963. (B) Effect directions are consistent across the three populations. In each population (x axis), we select SNPs where the minor allele increases (left) and decreases (right) expression and show the direction of effect in the other two populations as violin plots (trans-eQTL effect size, beta, on the y axis). The overwhelming majority of effects are consistent across all three populations. (C) The target genes of the rs6899963 trans-eQTL form a large subnetwork, which is enriched for multiple Gene Ontology biological processes. Here, we show the interplay between the top two enriched terms: GO:0007088 (regulation of mitotic nuclear division) and GO:1901564 (organonitrogen compound metabolic process).

Figure 3

A trans-eQTL at rs10520643 on Chromosome 15 Affects the Expression Levels of Many Genes across Three African HapMap Populations (A) Meta-analysis p values for 9,085 transcript eQTLs at rs10520643. (B) Effect directions are consistent across the three populations. In each population (x axis), we select SNPs where the minor allele increases (left) and decreases (right) expression and show the direction of effect in the other two populations as violin plots (trans-eQTL effect size, beta, on the y axis). The overwhelming majority of effects are consistent across all three populations. (C) The target genes of the rs10520643 trans-eQTL form a large subnetwork, which is enriched for multiple Gene Ontology biological processes. Here, we show the term GO:0046483 (heterocycle metabolic process).