Genome-wide association analysis and replication in 810,625 individuals identifies novel therapeutic targets for varicose veins

Ahmed, W.-U.-R.; Wiberg, A.; Ng, M.; Wang, W.; Auton, A.; 23andMe Research Team,; Lee, R.; Handa, A.; Zondervan, K. T.; Furniss, D.

doi:10.1101/2020.05.14.095653

bioRxiv · 10.1101/2020.05.14.095653

Genome-wide association analysis and replication in 810,625 individuals identifies novel therapeutic targets for varicose veins

Ahmed, W.-U.-R.·Wiberg, A.·Ng, M.·Wang, W.·Auton, A.·23andMe Research Team,·Lee, R.·Handa, A.·Zondervan, K. T.·Furniss, D.

View original source ↗Export metadata ↓

Abstract

BackgroundVaricose veins (VVs) affect one-third of Western society, with a significant subset of patients developing venous ulceration, and ongoing management of venous leg ulcers costing around $14.9 billion annually in the USA. There is no current medical management for VVs, with approaches limited to compression stockings, ablation techniques, or open surgery for more advanced disease. A significant proportion of patients report a positive family history, and heritability is ~17%, suggesting a strong genetic component. We aimed to identify novel therapeutic targets by improving our understanding of the aetiopathology and genetic architecture of VVs. MethodsWe performed the largest two-stage genome-wide association study of VVs in 401,656 subjects from UK Biobank, and replication in 408,969 subjects from 23andMe (total 135,514 varicose veins cases and 675,111 controls). We constructed a genetic risk score for VVs to investigate its use as a prognostic tool. Genes and pathways were prioritised using a suite of bioinformatic tools, and therapeutic targets identified using the Open Targets Platform. ResultsWe discovered 49 signals at 46 susceptibility loci associated with VVs, including 29 previously unreported genetic associations (28 susceptibility loci). We demonstrated that patients with VVs requiring surgery have a higher genetic risk score than those managed non-surgically. We map 237 genes to these loci, many of which are biologically relevant and tractable to therapeutic targeting or repurposing (notably VEGFA, COL27A1, EFEMP1, PPP3R1 and NFATC2). Tissue enrichment analyses implicated vascular tissue, and several genes were enriched in biological pathways relating to extracellular matrix biology, inflammation, angiogenesis, lymphangiogenesis, vascular smooth muscle cell migration, and apoptosis. ConclusionsGenes and pathways identified represent biologically plausible contributors to the pathobiology of VVs, identifying promising candidates for further investigation of venous biology and potential therapeutic targets. We have provided the proof-of-principle that genetic risk score correlates with disease severity, which represents a first step in personalised medicine approaches to varicose veins.

Explore related subjects

genetics Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Ahmed, W.-U.-R., Wiberg, A., Ng, M., Wang, W., Auton, A., 23andMe Research Team,, Lee, R., Handa, A., Zondervan, K. T., Furniss, D.. 2020-05-16. Genome-wide association analysis and replication in 810,625 individuals identifies novel therapeutic targets for varicose veins. https://doi.org/10.1101/2020.05.14.095653

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

SARDINE unlocks EWAS summary statistics for large-scale epigenetic biomarker analysis

Epigenetic clocks are increasingly used to study biological aging, yet their interpretation and utility remain uncertain. Meanwhile, the growing wealth of epigenome-wide association study (EWAS) summary statistics remains largely untapped for evaluating these biomarkers. Here, we introduce SARDINE, a statistical framework that estimates epigenetic clock-phenotype associations using EWAS summary statistics, clock weights, and reference methylation covariance, without individual-level data. SARDINE accurately reproduced individual-level association estimates across 38 epigenetic biomarkers and four phenotypes in 15,677 individuals. Applying SARDINE to diverse EWAS datasets revealed substantial heterogeneity in biomarker associations with aging, diseases, and lifestyle factors. By transforming CpG-level EWAS results into interpretable biomarker-level associations, SARDINE unlocks a largely underutilized data resource and establishes a scalable paradigm for systematic biomarker evaluation and discovery in aging research.

genetics↗

bioRxiv2026

Deep transcriptomic analysis of molecular mediators of complex traits in heterogeneous stock (HS) rats

Rats are a powerful model organism for genome-wide association studies (GWAS) of physiological and behavioral traits. Heterogeneous Stock (HS) rats, which were created by intercrossing 8 inbred rat strains, are heavily used for this purpose because of their genetic diversity and accumulated recombinations due to over 100 generations of outbreeding, which improves mapping precision. However, functional genomics resources for post-GWAS interpretation of genomic findings have been limited. We processed 3,160 RNA-seq samples, 1,932 of which were previously unpublished, from 13 tissues as part of RatGTEx. We used Pantry to quantify six modalities of transcriptome regulation (expression, isoform ratio, intron excision ratio, alternative transcription start site (TSS), alternative poly(A), and RNA stability) in each sample, which allowed us to identify 144,876 independent quantitative trait loci (QTL) for 22,599 genes. We performed a transcriptome-wide association study (TWAS) with 19,796 unique genes across tissues and transcriptional modalities and 308 traits collected from 14 studies using FUSION, which identified 7,473 significant gene-trait associations. This work presents a significant expansion of the RatGTEx resource in terms of samples, modes of transcriptome regulation, and genetic analyses. Our findings provide molecular evidence for the involvement of specific genes in mediating GWAS signals through a range of transcriptome regulatory mechanisms. These gene-to-phenotype relationships can be easily applied to genetic analyses in humans and other species. Further experiments can use this evidence to confirm these molecular mediators and target them for the treatment of behavioral and physiological traits and disorders. To facilitate use of these results, we have updated results on RatGTEx.org and provided a new interactive TWAS web portal (twas.ratgtex.org).

genetics↗

bioRxiv2026

Genomic and Epigenomic Relationships Capture Different Patterns of Genotype-by-Environment Interaction in Grapevine Clones

Genotype-by-environment interaction (GEI) challenges selection in clonally propagated perennial crops by reducing the consistency of clone performance across years. In perennial crops, both genetic and epigenetic variation are likely to contribute to complex trait variation among clones. However, the extent to which each molecular source contribute to GEI remains poorly understood. Here, we used UMR-seq, an assay that recovers SNPs and unmethylated-region (UMR; DNA methylation) profiles from a single library, to generate paired genomic and epigenomic profiles for 206 Riesling (Vitis vinifera L.) clones phenotyped for yield and juice quality traits across nine growing seasons. We then partitioned the contribution of each omic layer to clonal variation and GEI, and examined the genomic distribution of their effects. In models without explicit GEI, environmental and residual effects were the largest source of phenotypic variance and the clonal main effect was modest. Genomic relationships accounted for most of the clonal variance in four of five traits, and adding an epigenomic relationship matrix (ERM) to the genomic relationship matrix (GRM) had little effect. In contrast, when GEI effects were modelled using factor-analytic covariance structures, the epigenomic layer captured a substantial proportion of GEI variance and exceeded the genomic contribution in four of five traits. The two omic layers differed not only in the amount of GEI captured, but also in the pattern of clone re-ranking across years. When back-solved effects were aggregated into genomic windows, the largest genomic and epigenomic effects tended to fall in different regions, with overlap no greater than expected by chance. Together, these results show that genomic and epigenomic relationships capture complementary components of year-to-year variation in clone performance, with the epigenomic contribution expressed primarily through GEI. These findings provide a basis for investigating the molecular drivers of GEI and for testing whether genomic and epigenomic information can inform selection for stable clonal performance across variable environments.

genetics↗