Atrial fibrillation predicted effector genes

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• Atrial fibrillation

Method:

Potential effector genes for atrial fibrillation were prioritized at GWAS loci using six types of evidence, including eQTLs, gene set enrichment analysis, and MetaXcan analysis.

Ascending aorta function predicted effector genes

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• Ascending aorta function

Method:

Genes were prioritized at loci genetically associated with three aortic properties, based on three evidence types: results from DEPICT (Data-driven Expression Prioritized Integration for Complex Traits); presence of a coding variant in high LD with the lead SNP; and proximity of a gene to the lea

COVID-19 susceptibility and severity predicted effector genes

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• COVID-19 susceptibility and severity

Method:

This study applied the COGS (Capture Hi-C Omnibus Gene Score) pipeline to promoter capture Hi-C data (PCHi-C) data, along with genetic associations from four different analyses performed by the COVID-19 Host Genetics

Systemic lupus erythematosus (SLE) predicted effector genes

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• Systemic lupus erythematosus (SLE)

Method:

This study generated effector gene predictions for SLE using genome-wide association results and promoter-focused Capture-C analysis in follicular helper T-cells from human tonsil to link genetically associated variants to the genes they may regulate.

Estimated glomerular filtration rate (eGFRcrea) predicted effector genes

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• Estimated glomerular filtration rate (serum creatinine)

Method

This study integrated results from 8 methods to prioritize putative kidney function effector genes at loci genetically associated with eGFRcrea.

Cardiometabolic trait predicted effector genes

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• Cardiometabolic traits

Method

Candidate causal genes for cardiometabolic traits were predicted by integration of genetic association results, expression and splicing quantitative trait loci (eQTLs and sQTLs), transcriptional regulation by physiological and pharmacological cardiometabolic regulators, and protein-protein interaction

Type 1 diabetes predicted effector genes

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• Type 1 diabetes

Method

These predictions synthesize genetic, perturbational, and regulatory evidence to categorize potential T1D effector genes by the strength of their supporting evidence.

Reference:

Onengut-Gumuscu S and Rich SS. Unpublished, 2022.

Type 2 diabetes predicted effector genes

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• Type 2 diabetes

Method

Candidate effector gene predictions were generated by Bayesian colocalization of T2D GWAS signals and eQTLs in T2D-relevant tissues.

CARDIoGRAMplusC4D CAD predicted effector genes

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• Coronary artery disease

Method

These coronary artery disease (CAD) effector gene predictions from the CARDIoGRAMplusC4D consortium considered 8 locus-based or similarity-based predictors at each of 239 genome-wide significant CAD loci.

Stroke predicted effector genes

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• Stroke

Method

Potential effector genes were prioritized, using multiple evidence sources, at loci genetically associated with stroke risk.