Jurgens, S. J. | Search bioRxiv

bioRxiv2025

The Brugada syndrome associated gene WT1 impacts on SCN5A expression and cardiac conduction

Brugada syndrome (BrS) is an inherited cardiac arrhythmic disorder caused by conduction slowing primarily affecting the right ventricular (RV) outflow tract (RVOT). A recent genome-wide association study (GWAS) implicated a genomic region in chromosome 11, overlapping the transcription factor WT1, in BrS susceptibility. Here, we investigated the role of WT1 on cardiac conduction using a heterozygous knockout mouse model (Wt1+/-). Transcriptomic analysis revealed increased Scn5a predominantly in Wt1+/- cardiomyocytes located subepicardially in the RV and RVOT without any changes in electrical properties. To unmask an effect on cardiac conduction, we performed optical mapping in a severely challenged setting offered by Scn5a haploinsufficiency, ageing, and exposure to the sodium channel blocker ajmaline and found that diminished Wt1 improved the observed slowed conduction. Examination of human single-nuclei cardiac datasets indicated a strong negative correlation between WT1 and SCN5A expression. In line with this observation, cardiac samples from patients carrying mutations in SCN5A showed increased WT1 protein abundance in histological sections, suggesting that increased WT1, and not loss, is associated with BrS pathophysiology. By deleting the mouse orthologue of a BrS-associated noncoding region (RE) harboring a candidate regulatory element, we established that this RE controls expression of Wt1 specifically in the (sub)epicardium of the RV. Lastly, transient overexpression of WT1 in hiPSC-derived cardiomyocytes resulted in notably reduced sodium current density. Our study thereby identifies the transcription factor WT1 as a novel contributor to the pathophysiology of BrS, at least in part, through SCN5A.

genetics↗

bioRxiv2021

Adjusting for Common Variant Polygenic Scores Improves Yield in Rare Variant Association Analyses

With the emergence of large-scale sequencing data, methods for improving power in rare variant analyses (RVAT) are needed. Here, we show that adjusting for common variant polygenic scores improves the yield in gene-based RVAT across 65 quantitative traits in the UK Biobank (up to 20% increase at =2.6x10-6), without a marked increase in false-positive rates or genomic inflation. Our results illustrate how adjusting for common variant effects can aid in rare variant association discovery.

genomics↗

bioRxiv2020

Rare Genetic Variation Underlying Human Diseases and Traits: Results from 200,000 Individuals in the UK Biobank

BackgroundMany human diseases are known to have a genetic contribution. While genome-wide studies have identified many disease-associated loci, it remains challenging to elucidate causal genes. In contrast, exome sequencing provides an opportunity to identify new disease genes and large-effect variants of clinical relevance. We therefore sought to determine the contribution of rare genetic variation in a curated set of human diseases and traits using a unique resource of 200,000 individuals with exome sequencing data from the UK Biobank. Methods and ResultsWe included 199,832 participants with a mean age of 68 at follow-up. Exome-wide gene-based tests were performed for 64 diseases and 23 quantitative traits using a mixed-effects model, testing rare loss-of-function and damaging missense variants. We identified 51 known and 23 novel associations with 26 diseases and traits at a false-discovery-rate of 1%. There was a striking risk associated with many Mendelian disease genes including: MYPBC3 with over a 100-fold increased odds of hypertrophic cardiomyopathy, PKD1 with a greater than 25-fold increased odds of chronic kidney disease, and BRCA2, BRCA1, ATM and PALB2 with 3 to 10-fold increased odds of breast cancer. Notable novel findings included an association between GIGYF1 and type 2 diabetes (OR 5.6, P=5.35x10-8), elevated blood glucose, and lower insulin-like-growth-factor-1 levels. Rare variants in CCAR2 were also associated with diabetes risk (OR 13, P=8.5x10-8), while COL9A3 was associated with cataract (OR 3.4, P=6.7x10-8). Notable associations for blood lipids and hypercholesterolemia included NR1H3, RRBP1, GIGYF1, SCGN, APH1A, PDE3B and ANGPTL8. A number of novel genes were associated with height, including DTL, PIEZO1, SCUBE3, PAPPA and ADAMTS6, while BSN was associated with body-mass-index. We further assessed putatively pathogenic variants in known Mendelian cardiovascular disease genes and found that between 1.3 and 2.3% of the population carried likely pathogenic variants in known cardiomyopathy, arrhythmia or hypercholesterolemia genes. ConclusionsLarge-scale population sequencing identifies known and novel genes harboring high-impact variation for human traits and diseases. A number of novel findings, including GIGYF1,represent interesting potential therapeutic targets. Exome sequencing at scale can identify a meaningful proportion of the population that carries a pathogenic variant underlying cardiovascular disease.

genetics↗