Profiling RNA-Seq at multiple resolutions markedly increases the number of causal eQTLs

Genome-wide association studies have identified hundreds of risk loci for autoimmune disease, yet only a minority (~25%) share genetic effects with changes to gene expression (eQTLs) in immune cells. RNA-Seq based quantification at whole-gene resolution, where abundance is estimated by culminating expression of all transcripts or exons of the same gene, is likely to account for this observed lack of colocalisation as subtle isoform switches and expression variation in independent exons can be concealed.

Researchers at King’s College London performed integrative cis-eQTL analysis using association statistics from twenty autoimmune diseases (560 independent loci) and RNA-Seq data from 373 individuals of the Geuvadis cohort profiled at gene-, isoform-, exon-, junction-, and intron-level resolution in lymphoblastoid cell lines. After stringently testing for a shared causal variant using both the Joint Likelihood Mapping and Regulatory Trait Concordance frameworks, they found that gene-level quantification significantly underestimated the number of causal cis-eQTLs. Only 5.0-5.3% of loci were found to share a causal cis-eQTL at gene-level compared to 12.9-18.4% at exon-level and 9.6-10.5% at junction-level. More than a fifth of autoimmune loci shared an underlying causal variant in a single cell type by combining all five quantification types; a marked increase over current estimates of steady-state causal cis-eQTLs. Causal cis-eQTLs detected at different quantification types localised to discrete epigenetic annotations. The researchers applied a linear mixed-effects model to distinguish cis-eQTLs modulating all expression elements of a gene from those where the signal is only evident in a subset of elements. Exon-level analysis detected disease-associated cis-eQTLs that subtly altered transcription globally across the target gene. They dissected in detail the genetic associations of systemic lupus erythematosus and functionally annotated the candidate genes. Many of the known and novel genes were concealed at gene-level (e.g. IKZF2, TYK2, LYST). Their findings are provided as a web resource http://insidegen.com/.

Breakdown of autoimmune associated causal cis-eQTLs using RNA-Seq

rna-seq

(A) Percentage and number of causal cis-eQTL associations detected per RNA-Seq quantification type, following LD pruning of associated SNPs from twenty autoimmune diseases to 560 independent susceptibly loci. The top chart shows the number of causal cis-eQTLs when combining all RNA-Seq profiling types together (20%). (B) Sharing of causal cis-eQTL associations per quantification type (110 detected in total). Percentage of causal cis-eQTLs captured are shown as a percentage of the 110 total. (C) Total causal cis-eQTLs per disease across all five levels of RNA-Seq quantification, using the 20 diseases of the ImmunoBase resource. In orange are disease-associated SNPs that show no shared association with expression across any quantification type. In blue are the disease-associated SNPs that are also causal cis-eQTLs. (D) Causal cis-eQTLs and candidate genes per disease broken down by quantification type

Odhams CA, Cunninghame Graham DS, Vyse TJ (2017) Profiling RNA-Seq at multiple resolutions markedly increases the number of causal eQTLs in autoimmune disease. PLoS Genet 13(10): e1007071. [article]

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