30-Second Takeaway
- Large multi-ancestry and protein-based datasets are sharpening genetic risk prediction for aortic stenosis and common diseases.
- Joint, oligogenic, and cell-type-specific maps refine gene discovery and mechanistic interpretation in developmental and cancer genetics.
- Read-aware imputation extends accurate genotyping to low-coverage, long-read, and prenatal settings using biobank-scale panels.
Week ending December 20, 2025
Genomic tools refine disease mechanisms, risk prediction, and variant interpretation across cardiovascular, developmental, oncologic, and repeat-expansion disorders
Multi-ancestry GWAS of aortic stenosis maps 244 loci and nominates druggable targets and a new PRS
A multi-ancestry GWAS meta-analysis of 86,864 aortic stenosis (AS) cases among 2.85 million individuals identified 241 autosomal and 3 X-chromosome loci. Sex- and ancestry-stratified analyses yielded additional sex-specific and ancestry-specific loci, refining AS genetic architecture across populations. A valve-based transcriptome-wide association study highlighted 54 genes whose genetically predicted expression influenced AS risk, prioritizing valve-relevant pathways. Silencing CMKLR1 and LTBP4 in human valvular interstitial cells reduced mineralization, implicating chemerin and TGF-β signaling as therapeutic candidates.
Disease-context pQTL mapping identifies 110 likely causal proteins and improves cross-disease risk prediction
Investigators profiled 2,901 plasma proteins in 7,626 healthy individuals and 28,064 patients across 42 disease states to map pQTLs. They identified 25,987 independent pQTL associations across 2,224 regions, with many showing disease-specific regulatory effects on protein levels. pQTLs discovered in specific diseases were more likely to correspond to disease risk variants, connecting protein regulation to pathology. Integrated Mendelian randomisation supported 110 high-confidence causal proteins for 21 diseases, including Apolipoprotein(a) and ACE.
Joint de novo variant analysis across neurodevelopmental and cardiac disorders expands risk genes and shared pathways
A two-trait Bayesian analysis of 43,287 trios with ASD, UDD/ID, CHD, or schizophrenia jointly evaluated de novo coding variants. At posterior probability >0.80, the study implicated 180 ASD, 315 UDD/ID, 49 CHD, and 47 schizophrenia candidate risk genes, including novel genes. Pairwise cross-disorder analyses identified shared risk genes, enriched for synaptic and epigenetic pathways in ASD–UDD/ID, ASD–schizophrenia, and UDD/ID–schizophrenia. CHD–ASD shared genes were enriched for cell-cycle phase transition, while CHD–UDD/ID overlaps highlighted cardiac-development pathways.
References
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Additional Reads
Optional additional studies from this edition.