Thread by @kousikbioinfo

Thrilled to share our work on fine-mapping of immune-mediated diseases (IMD) using regulatory quantitative trait loci (QTLs) finally out in @NatureGenet
nature.com/articles/s4158…

@SoranzoTeam @sangerinstitute @Cambridge_Uni
#immunediseases #autoimmune #GWAS #QTL
🧵 1/14

Here we extended the evaluation of regulatory QTLs generated as part of the @blueprint_eu project to systematically map molecular mechanisms and causal variants at 12 immune-mediated diseases with publicly-available summary statistics. 2/14

📢 The BLUEPRINT "phase 2" genotype data and regulatory QTL summary statistics are available in the @EGAarchive (ega-archive.org/datasets/) under accession codes EGAD00001005192, EGAD00001005199 and EGAD00001005200.
3/14

In our study, we identified 340 unique immune-mediated disease (IMD) loci that colocalised with high posterior probability (≥98%) with regulatory QTLs. #colocalisation 4/14

We showed that fine-mapping using regulatory QTLs produced smaller credible sets compared to disease summary statistics. More importantly, they were also enriched for more functionally interpretable candidate causal variants. #finemapping 5/14

We showed that using multiple layers of regulatory QTLs could refine 3.5 times more IMD loci than using eQTLs only, indicating the value of regulatory QTLs to complement and enhance the biological and contextual interpretation of causal effects. 6/14

We used massively parallel reporter assays (MPRAs) to evaluate candidate causal variants at the ITGA4 locus associated with inflammatory bowel disease (IBD). #IBD 7/14

We reported many other high confidence fine-mapped IMD loci, e.g., BACH2, ANKRD55, UBASH3A. Our fine-mapped variant: rs72928038 for BACH2 gene in T-cells recently shown to be a candidate causal Type 1 diabetes (T1D) variant. #T1D @ccrobertson01

nature.com/articles/s4158…
8/14

We used low read-depth WGS data followed by a genotype imputation strategy that produced an almost complete catalogue of common variants. This revealed that specific classes of causal variants (particularly INDELs) are systematically under-represented in disease GWAS. 9/14

The main advantage of regulatory QTLs is that, owing to their average large effect sizes, they require order-of-magnitude fewer study samples to detect associations compared to disease endpoints, making it cost-effective to use WGS data. 10/14

QTL-based fine-mapping provides direct insight into the underlying molecular mechanisms of disease associations, pointing to putative effector genes, cell types and information on the direction of effect for causal variants, which is not achievable just by using GWAS data. 11/14

Overall, our study demonstrates for the first time the utility of regulatory QTLs as an alternative approach to complement information coming from disease fine-mapping studies. 12/14

Many thanks to all the people who contributed to this work.
@manueltard @alicel_mann @VasquezLouella @dvonsfran @KlaudiaMWalter @Morphogen_IX @anderson_carl
13/14

Last by not least, thank you @nicolesoranzo for your continuous guidance and supervision. Thank you very much for being a great mentor.
#grateful 14/14