PascalX: a Python library for GWAS gene and pathway enrichment tests

Krefl, Daniel; Cammarata, Alessandro B; Bergmann, Sven

doi:10.1093/bioinformatics/btad296

Cited by 11 publications

(5 citation statements)

References 18 publications

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“…Because we are studying very recent GWAS that could have identified additional loci since the previous report, we first re-evaluated this overlap. To do so, we used PascalX [ 21 ] to estimate gene-level p -values from the GWAS summary statistics of the prostate, breast, skin and colon cancer studies using 19,922 protein-coding genes (Ensembl, release 94) and a window size of 25 kb around each gene (Table S3 ). The resulting gene-level p -values denote to what extent variants within and near these genes show an association signal in the GWAS.…”

Section: Resultsmentioning

confidence: 99%

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes

Urzúa-Traslaviña,

van Lieshout,

Boulogne

et al. 2024

BMC Med Genomics

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Background The genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes. Results We studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin. Conclusion We show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.

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Section: Resultsmentioning

confidence: 99%

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes

Urzúa-Traslaviña,

van Lieshout,

Boulogne

et al. 2024

BMC Med Genomics

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“…We used PascalX [9, 51] to compute gene scores based on GWAS summary statistics. The software takes as input GWAS p-values, gene annotations and LD structure.…”

Section: Methodsmentioning

confidence: 99%

Multi-layered genetic approaches to identify approved drug targets

Sadler

Auwerx

Deelen

et al. 2023

Preprint

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Drugs targeting genes that harbor natural variations associated with the disease the drug is indicated for have increased odds to be approved. Various approaches have been proposed to identify likely causal genes for complex diseases, including gene-based genome-wide association studies (GWAS), rare variant burden tests in whole exome sequencing studies (Exome) or integration of GWAS with expression/protein quantitative trait loci (eQTL-GWAS/pQTL-GWAS). Here, we compare gene-prioritization approaches on 30 common clinical traits and benchmarked their ability to recover drug target genes defined using a combination of five drug databases. Across all traits, the top prioritized genes were enriched for drug targets with odds ratios (ORs) of 2.17, 2.04, 1.81 and 1.31 for the GWAS, eQTL-GWAS, Exome and pQTL-GWAS methods, respectively. We quantified the performance of these methods using the area under the receiver operating characteristic curve as metric, and adjusted for differences in testable genes and data origins. GWAS performed significantly better (54.3%) than eQTL (52.8%) and pQTL-GWAS (51.3%), but not significantly so against the Exome approach (51.7% vs 52.8% for GWAS restricted to UK Biobank data). Furthermore, our analysis showed increased performance when diffusing gene scores on gene networks. However, substantial improvements in the protein-protein interaction network may be due to circularity in the data generation process, leading to the node (gene) degree being the best predictor for drug target genes (OR = 8.7, 95% CI = 7.3-10.4) and warranting caution when applying this strategy. In conclusion, we systematically assessed strategies to prioritize drug target genes highlighting promises and potential pitfalls of current approaches.

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“…SNP-wise heritabilities and genetic correlations between IDPs were derived using LDSR [52]. Gene and pathway scores were computed using PascalX [54, 74]. Both protein-coding genes and lincRNAs were scored using the novel, approximate “saddle” method, taking into account all SNPs within a 50kb window around each gene.…”

Section: Methodsmentioning

confidence: 99%

“…SNP-wise heritabilities and genetic correlations between IDPs were derived using LDSR [52]. Gene and pathway scores were computed using PascalX [54,74].…”

Section: Genome-wide Analysesmentioning

confidence: 99%

Phenotypic and Genetic Characteristics of Retinal Vascular Parameters and their Association with Diseases

Vela

Beyeler

Trofimova

et al. 2023

Preprint

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Fundus images allow for non-invasive assessment of the retinal vasculature whose features provide important information on health. Using a fully automated image processing pipeline, we extracted 17 different morphological vascular phenotypes, including median vessels diameter, diameter variability, main temporal angles, vascular density, central retinal equivalents, the number of bifurcations, and tortuosity, from over 130k fundus images of close to 72k UK BioBank subjects. We performed genome-wide association studies (GWAS) of these parameters. From this, we estimated their individual heritabilities, ranging between 5 and 25%, and genetic cross-trait correlations, which mostly mirrored the corresponding phenotypic correlations, but tended to be slightly larger. Projecting our genetic association signals onto genes and pathways revealed remarkably low overlap suggesting largely decoupled mechanisms modulating the different traits. Our disease trait associations confirmed some previously known findings and revealed many novel connections. Notably, diameter variability, especially for the veins, seems to have many new and interesting associations with multiple disease traits, including age of death, pulmonary embolism, and heart attack. Our analyses provide evidence that large-scale analysis of image-derived vascular traits has sufficient power for obtaining functional, as well as some initial causal insights into the processes modulating the retinal vasculature.

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PascalX: a Python library for GWAS gene and pathway enrichment tests

Cited by 11 publications

References 18 publications

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes

Multi-layered genetic approaches to identify approved drug targets

Phenotypic and Genetic Characteristics of Retinal Vascular Parameters and their Association with Diseases

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