CONQUER: an interactive toolbox to understand functional consequences of GWAS hits

Bouland, Gerard A; Beulens, Joline W.J.; Nap, Joey; Slik, Arno R. van der; Zaldumbide, Arnaud; Hart, Leen M. ‘t; Slieker, Roderick C.

doi:10.1093/nargab/lqaa085

Cited by 3 publications

(2 citation statements)

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“…We focused on 79 AD risk variants identified by Wightman et al 4 and Bellenguez et al 5 . Using CONQUER 31 , we identified 2,528 genes near these 79 variants (see methods). Of these genes, 975 were present in our collection of single cell datasets (SFig.…”

Section: Differential Correlation-based Gene Prioritization For Alzhe...mentioning

confidence: 99%

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Single-cell RNA sequencing data reveals rewiring of transcriptional relationships in Alzheimer’s Disease associated with risk variants

Bouland,

Marinus,

van Kesteren

et al. 2023

Preprint

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Understanding how genetic risk variants contribute to Alzheimer's Disease etiology remains a challenge. Single-cell RNA sequencing (scRNAseq) allows for the investigation of cell type specific effects of genomic risk loci on gene expression. Using seven scRNAseq datasets totalling >1.3 million cells, we investigated differential correlation of genes between healthy individuals and individuals diagnosed with Alzheimer's Disease. Using the number of differential correlations of a gene to estimate its involvement and potential impact, we present a prioritization scheme for identifying probable causal genes near genomic risk loci. Besides prioritizing genes, our approach pin-points specific cell types and provides insight into the rewiring of gene-gene relationships associated with Alzheimer's.

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Section: Differential Correlation-based Gene Prioritization For Alzhe...mentioning

confidence: 99%

“…In total 79 AD risk variants were extracted. For each variant, CONQUER 31 was used to identify genes genomically located near the respective risk variants. Besides defining a fixed window of 1 Mb around the respective variant, CONQUER uses chromatin interaction to dynamically expand the search space.…”

Section: Gene Prioritizationmentioning

confidence: 99%

Single-cell RNA sequencing data reveals rewiring of transcriptional relationships in Alzheimer’s Disease associated with risk variants

Bouland,

Marinus,

van Kesteren

et al. 2023

Preprint

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Diabetes risk loci-associated pathways are shared across metabolic tissues

et al. 2022

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Aims/hypothesis Numerous genome-wide association studies have been performed to understand the influence of genetic variation on type 2 diabetes etiology. Many identified risk variants are located in non-coding and intergenic regions, which complicates understanding of how genes and their downstream pathways are influenced. An integrative data approach will help to understand the mechanism and consequences of identified risk variants. Methods In the current study we use our previously developed method CONQUER to overlap 403 type 2 diabetes risk variants with regulatory, expression and protein data to identify tissue-shared disease-relevant mechanisms. Results One SNP rs474513 was found to be an expression-, protein- and metabolite QTL. Rs474513 influenced LPA mRNA and protein levels in the pancreas and plasma, respectively. On the pathway level, in investigated tissues most SNPs linked to metabolism. However, in eleven of the twelve tissues investigated nine SNPs were linked to differential expression of the ribosome pathway. Furthermore, seven SNPs were linked to altered expression of genes linked to the immune system. Among them, rs601945 was found to influence multiple HLA genes, including HLA-DQA2, in all twelve tissues investigated. Conclusion Our results show that in addition to the classical metabolism pathways, other pathways may be important to type 2 diabetes that show a potential overlap with type 1 diabetes.

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