Leveraging supervised learning for functionally-informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs

Wang, Qingbo; Kelley, David R.; Ulirsch, Jacob C.; Kanai, Masahiro; Sadhuka, Shuvom; Cui, Ran; Albors, Carlos; Cheng, Nathan; Okada, Yukinori; Aguet, François; Ardlie, Kristin; MacArthur, Daniel G.; Finucane, Hilary

doi:10.1101/2020.10.20.347294

Cited by 7 publications

(15 citation statements)

References 64 publications

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“…Figures 4A and 6C-D). Finally, we have released credible sets from statistical fine mapping analysis, which can help to further characterise loci with multiple independent signals and paves the way for fine-mapping-based colocalisation approaches (25). We will progressively expand the resource to all accessible human datasets.…”

Section: Discussionmentioning

confidence: 99%

“…Recent methodological advances have made it feasible to fine map genetic associations to small credible sets of putative causal variants and distinguish between multiple independent genetic signals in the region (23,24). These fine mapping results can be directly used in colocalisation analysis (25). They can also help avoid the many false negative colocalisations missed by approaches that assume a single causal variant in the region of interest (18).…”

Section: Introductionmentioning

confidence: 99%

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eQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLs

Kerimov

Hayhurst

Manning

et al. 2020

Preprint

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An increasing number of gene expression quantitative trait locus (QTL) studies have made summary statistics publicly available, which can be used to gain insight into human complex traits by downstream analyses such as fine-mapping and colocalisation. However, differences between these datasets in their variants tested, allele codings, and in the transcriptional features quantified are a barrier to their widespread use. Here, we present the eQTL Catalogue, a resource which contains quality controlled, uniformly re-computed QTLs from 19 eQTL publications. In addition to gene expression QTLs, we have also identified QTLs at the level of exon expression, transcript usage, and promoter, splice junction and 3ʹ end usage. Our summary statistics can be downloaded by FTP or accessed via a REST API and are also accessible via the Open Targets Genetics Portal. We demonstrate how the eQTL Catalogue and GWAS Catalog APIs can be used to perform colocalisation analysis between GWAS and QTL results without downloading and reformatting summary statistics. New datasets will continuously be added to the eQTL Catalogue, enabling systematic interpretation of human GWAS associations across a large number of cell types and tissues. The eQTL Catalogue is available at https

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

eQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLs

Kerimov

Hayhurst

Manning

et al. 2020

Preprint

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“…Our final set of local/cis-based predictors resulted in 1,031 models of protein with significant cis-SNP heritability (p-value < 0.05). We fit penalized linear models using SuSiE ( 32 ) and performed downstream PWAS using the tool FUSION ( 33 ) with the same quality controlled genome-wide association statistics as above.…”

Section: Methodsmentioning

confidence: 99%

Integrative analyses identify susceptibility genes underlying COVID-19 hospitalization

Pathak

Singh

Wendt

et al. 2020

Preprint

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Despite rapid progress in characterizing the role of host genetics in SARS-Cov-2 infection, there is limited understanding of genes and pathways that contribute to COVID-19. Here, we integrated a genome-wide association study of COVID-19 hospitalization (7,885 cases and 961,804 controls from COVID-19 Host Genetics Initiative) with mRNA expression, splicing, and protein levels (n=18,502). We identified 27 genes related to inflammation and coagulation pathways whose genetically predicted expression was associated with COVID-19 hospitalization. We functionally characterized the 27 genes using phenome- and laboratory-wide association scans in Vanderbilt Biobank (BioVU; n=85,460) and identified coagulation-related clinical symptoms, immunologic, and blood-cell-related biomarkers. We replicated these findings across trans-ethnic studies and observed consistent effects in individuals of diverse ancestral backgrounds in BioVU, pan-UK Biobank, and Biobank Japan. Our study highlights putative causal genes impacting COVID-19 severity and symptomology through the host inflammatory response.

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“…Although linkage disequilibrium generally results in GTEx eQTL associations that can only be attributed to a set of frequently co-occurring variants, the latest GTEx release includes many thousands of associations in loci with simple linkage patterns, which have been fine-mapped to a single high probability causal variant 23 . To assess the utility of Enformer predictions for identifying of causal variants, we defined a classification task for each tissue to discriminate likely causal variants (causal probability>0.9, as determined by the population-based finemapping model SuSiE 24 ) from likely spurious eQTLs (causal probability<0.01), which were matched for the eGene when possible (Methods).…”

Section: Fig 3: Enformer Improves Variant Effect Prediction On Eqtl Data As Measured By Sldp Regression and Fine-mapped Variant Classificmentioning

confidence: 99%

Effective gene expression prediction from sequence by integrating long-range interactions

Avsec

Agarwal

Visentin

et al. 2021

Preprint

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The next phase of genome biology research requires understanding how DNA sequence encodes phenotypes, from the molecular to organismal levels. How noncoding DNA determines gene expression in different cell types is a major unsolved problem, and critical downstream applications in human genetics depend on improved solutions. Here, we report substantially improved gene expression prediction accuracy from DNA sequence through the use of a new deep learning architecture called Enformer that is able to integrate long-range interactions (up to 100 kb away) in the genome. This improvement yielded more accurate variant effect predictions on gene expression for both natural genetic variants and saturation mutagenesis measured by massively parallel reporter assays. Notably, Enformer outperformed the best team on the critical assessment of genome interpretation (CAGI5) challenge for noncoding variant interpretation with no additional training. Furthermore, Enformer learned to predict promoter-enhancer interactions directly from DNA sequence competitively with methods that take direct experimental data as input. We expect that these advances will enable more effective fine-mapping of growing human disease associations to cell-type-specific gene regulatory mechanisms and provide a framework to interpret cis-regulatory evolution. To foster these downstream applications, we have made the pre-trained Enformer model openly available, and provide pre-computed effect predictions for all common variants in the 1000 Genomes dataset.

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Leveraging supervised learning for functionally-informed fine-mapping of cis-eQTLs identifies an additional 20,913 putative causal eQTLs

Cited by 7 publications

References 64 publications

eQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLs

eQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLs

Integrative analyses identify susceptibility genes underlying COVID-19 hospitalization

Effective gene expression prediction from sequence by integrating long-range interactions

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