Judit García-González

BACKGROUND: Genetic differences may contribute to interindividual variability in LDL-C (low-density lipoprotein cholesterol) lowering with statin therapy. Polygenic risk scores may help identify individuals unlikely to achieve guideline-concordant LDL-C targets on statins, enabling earlier therapy intensification. METHODS: We developed a multiancestry polygenic risk score for statin non-goal response (PRS-NGR) and evaluated its association with failure to achieve an on-statin LDL-C level of ≤70 mg/dL and with percent LDL-C reduction. This longitudinal cohort study used genotyping and electronic health record-linked data from the All of Us Research Program (2018–2025), the UK Biobank (2014–2023), and the Biobank Japan (2003–2008). Participants were statin users with at least 1 prestatin and 1 on-statin LDL-C measurement. Associations were assessed overall and by genetic ancestry, with replication in the UK Biobank and Biobank Japan. RESULTS: The study included 46 564 participants from All of Us, 37 009 from the UK Biobank, and 3613 from Biobank Japan. In All of Us, higher PRS-NGR was associated with increased odds of non-goal response (odds ratio per SD, 1.43 [95% CI, 1.37–1.49]). Compared with the middle quintile, individuals in the top 1% had a higher risk, whereas those in the bottom 1% had a lower risk of non-goal response. Associations of PRS-NGR were consistent across African, European, and Latin American ancestry groups. Each SD increase in PRS-NGR corresponded to a 1.2-percentage-point smaller LDL-C reduction. Findings were replicated in the UK Biobank (odds ratio per SD, 2.39 [95% CI, 2.23–2.57]) and in Biobank Japan (odds ratio per SD, 1.31 [95% CI, 1.17–1.46]). Integration of PRS-NGR with guideline-based criteria identified individuals who derived a higher LDL-C% change and increased identification of statin-eligible individuals. CONCLUSIONS: We developed and validated a multiancestry polygenic risk score that estimates the risk of non-goal LDL-C response to statin therapy. Incorporation of polygenic risk into lipid-lowering treatment paradigms may improve risk stratification and support more tailored therapy intensification strategies.

Background: Fine-mapping and gene-prioritisation techniques applied to the latest genome-wide association study (GWAS) results have prioritised hundreds of genes as causally associated with disease. Here we leverage these recently compiled lists of high-confidence causal genes to interrogate where in the body disease genes operate, providing a more direct approach than previous studies, which have primarily relied on the enrichment of GWAS signals among genes with cell- or tissue-specific expression. Results: By integrating GWAS summary statistics, gene prioritisation results, and RNA-seq data from 46 tissues and 204 cell types, we directly analyse the gene expression of putative disease genes across the body in relation to 11 major diseases and cancers. In tissues and cell types with established disease relevance, disease genes show higher and more specific gene expression compared to control genes. Moreover, we detect elevated expression in tissues and cell types without previous links to the corresponding disease. While some of these results may be explained by cell types that span multiple tissues, such as macrophages in brain, blood, lung and spleen in relation to Alzheimer’s disease ( P -values < 10 ^–3 ), the cause for others is unclear and warrants further investigation. To support functional follow-up studies of disease genes, we identify technical and biological factors influencing their expression. Finally, we highlight tissue-disease pairs in which significantly elevated expression is associated with increased odds of inclusion in drug development programmes. Conclusions: We provide our systematic testing framework as an open-source, publicly available tool that can be utilised to offer novel insights into the genes, tissues and cell types involved in any disease, with the potential for informing drug development and delivery strategies.

A key goal of human genetics research is to understand how the effects of genetic variants combine to produce the risk of complex disease. Here we discuss and contrast three conceptual models developed to explain how multigenic risk is generated. The polygenic model, derived from the century-old infinitesimal model, has been the dominant framework for understanding the genetic inheritance of complex traits. More recently, two mechanistic models have been proposed: the omnigenic model, which hypothesizes core genes with direct effects on disease and peripheral genes with regulatory, indirect effects, and what we call the ‘stratagenic’ model, in which the genetic risk of disease is stratified across genomic pathways of functional relevance. There are key differences in the implications of these models for research, drug development and precision medicine. Therefore, it is essential to determine which model is most accurate for each disease or whether a single model is broadly optimal across complex diseases.

Genome wide association studies of schizophrenia reveal a complex polygenic risk architecture comprised of hundreds of risk variants; most are common in the population, non-coding, and act by genetically regulating the expression of one or more gene targets (“eGenes”). It remains unclear how the myriad genetic variants that are predicted to confer individually small effects combine to yield substantial clinical impacts in aggregate. Here, we demonstrate that convergence (i.e., the shared downstream transcriptomic changes with a common direction of effect), resulting from one-at-a-time perturbation of schizophrenia eGenes, influences the outcome when eGenes are manipulated in combination. In total, we apply pooled and arrayed CRISPR approaches to target 21 schizophrenia eGenes in human induced pluripotent stem cell-derived glutamatergic neurons, finding that functionally similar eGenes yield stronger and more specific convergent effects. Points of convergence constrain additive relationships between polygenic risk loci: consistent with a liability threshold model, combinatorial perturbations of these same schizophrenia eGenes reveal that pathway-level convergence predicts when observed effects will fail to sum to levels predicted by an additive model. Targeting points of convergence as novel therapeutic targets may prove more efficacious than individually reversing the effects of multiple risk loci.

Mutations in the RBFOX1 gene are associated with psychiatric disorders but how RBFOX1 influences psychiatric disorder vulnerability remains unclear. Recent studies showed that RBFOX proteins mediate the alternative splicing of PAC1, a critical HPA axis activator. Further, RBFOX1 dysfunction is linked to dysregulation of BDNF/TRKB, a pathway promoting neuroplasticity, neuronal survival and stress resilience. Hence, RBFOX1 dysfunction may increase psychiatric disorder vulnerability via HPA axis dysregulation, leading to disrupted development and allostatic overload. To test this hypothesis, we generated a zebrafish rbfox1 loss of function (LoF) line and examined behavioural and molecular effects during development. We found that rbfox1 LoF mutants exhibited hyperactivity, impulsivity and heightened arousal, alongside alterations in proliferation – traits associated with neurodevelopmental and stress-related disorders. In adults, loss of rbfox1 function led to decreased fertility and survival, consistent with allostatic overload. At the molecular level, at larval stages rbfox1 mutants showed increased cortisol levels and disrupted expression of key stress-related genes (bdnf, trkb2, pac1a-hop, crhb, nr3c2). Pharmacological intervention targeting TRKB restored crhb and nr3c2 gene expression and hyperactive and hyperarousal behaviours. In adults, dysregulation of crhb, nr3c2 and bdnf/trkb2 genes was only seen following acute stress exposure. Our findings reveal a fundamental role for RBFOX1 in integrating stress responses through its regulation of BDNF/TRKB and neuroendocrine signalling.

Genome-wide association studies of common disorders reveal polygenic architectures. While these variants may converge into biologically relevant pathways, how polygenicity aggregates across pathways remains unknown. We studied polygenicity at the pathway level in anorexia nervosa (AN) - a psychiatric disorder with multi-systemic clinical manifestations. We constructed pathway-based polygenic risk scores (pathway PRS) to model genetic risk at the pathway level for 3,687 individuals with AN and 11,257 controls. We identified 497 AN-associated pathways after Bonferroni correction, including pathways involved in the brain, metabolism, immunity/stress, and reproduction/development. A strong positive correlation was observed between number of pathways ranking at top 10% and AN case proportion (r = 0.74, P = 7.98x10 ^-21 ). The positive correlation between pathway count and AN risk was also observed when restricted to individuals with low overall genetic risk of AN. Pathways ranking at top 10% among cases were more diverse than those in controls. Higher AN risk was observed for pathway aggregation within function (e.g., brain or metabolism) and across functions (e.g., brain + metabolism). Individuals at top 10% risk of brain-brain and brain-metabolism pathway pairs had the highest average AN risk compared to other pathway pairs (e.g., brain-immune, metabolism-metabolism). Further, pathway PRS provided higher prediction power of AN than overall genome-wide PRS and contributed to the genetic liability of AN among AN cases with low overall genetic risk. Together, our results demonstrate that polygenicity exists at the pathway level, opening new avenues for disease prediction and identification of actionable targets.

BACKGROUND: Coronary artery disease (CAD) is a complex, heterogeneous disease with distinct etiological mechanisms. These different etiologies may give rise to multiple subtypes of CAD that could benefit from alternative preventions and treatments. However, so far, there have been no systematic efforts to predict CAD subtypes using clinical and genetic factors. METHODS: Here, we trained and applied statistical models incorporating clinical and genetic factors to predict CAD subtypes in 26 036 patients with CAD in the UK Biobank. We performed external validation of the UK Biobank models in the US-based All of Us cohort (8598 patients with CAD). Subtypes were defined as high versus normal LDL (low-density lipoprotein) levels, high versus normal Lpa (lipoprotein A) levels, ST-segment–elevation myocardial infarction versus non–ST-segment–elevation myocardial infarction, occlusive versus nonocclusive CAD, and stable versus unstable CAD. Clinical predictors included levels of ApoA, ApoB, HDL (high-density lipoprotein), triglycerides, and CRP (C-reactive protein). Genetic predictors were genome-wide and pathway-based polygenic risk scores (PRSs). RESULTS: Results showed that both clinical-only and genetic-only models can predict CAD subtypes, while combining clinical and genetic factors leads to greater predictive accuracy. Pathway-based PRSs had higher discriminatory power than genome-wide PRSs for the Lpa and LDL subtypes and provided insights into their etiologies. The 10-pathway PRS most predictive of the LDL subtype involved cholesterol metabolism. Pathway PRS models had poor generalizability to the All of Us cohort. CONCLUSIONS: In summary, we present the first systematic demonstration that CAD subtypes can be distinguished by clinical and genomic risk factors, which could have important implications for stratified cardiovascular medicine.

Fine-mapping and gene-prioritisation techniques applied to the latest Genome-Wide Association Study (GWAS) results have prioritised hundreds of genes as causally associated with disease. Here we leverage these recently compiled lists of high-confidence causal genes to interrogate where in the body disease genes operate, which, in previous studies, has mostly been investigated by testing for enrichment of GWAS signal among genes with cell/tissue specific expression. By integrating GWAS summary statistics, gene prioritisation results, and RNA-seq data from 46 tissues and 204 cell types, we directly analyse the gene expression of putative disease genes across the body in relation to 11 major diseases and cancers. In tissues and cell types with established disease relevance, disease genes show higher and more specific gene expression compared to control genes. However, we also detect elevated expression in tissues and cell types without previous links to the corresponding disease. While some of these results may be explained by cell types that span multiple tissues, such as macrophages in brain, blood, lung and spleen in relation to Alzheimer’s disease ( P -values < 10 ^-3 ), the cause for others is unclear and warrants further investigation. To support functional follow-up studies of disease genes, we identify technical and biological factors influencing their expression, and highlight tissues in which higher expression is associated with increased odds of inclusion in drug development programs. We provide our systematic testing framework as an open-source, publicly available tool that can be utilised to offer novel insights into the genes, tissues and cell types involved in any disease, with the potential for informing drug development and delivery strategies.