Overlap in the Genetic Architecture of Stroke Risk, Early Neurological Changes, and Cardiovascular Risk Factors

[Background and Purpose] The genetic relationships between stroke risk, stroke severity, and early neurological changes are complex and not completely understood. Genetic studies have identified 32 all stroke risk loci. Polygenic risk scores can be used to compare the genetic architecture of related traits. In this study, we compare the genetic architecture of stroke risk, stroke severity, and early neurological changes with that of 2 stroke risk factors: type 2 diabetes mellitus (T2DM) and hypertension.[Methods] We assessed the degree of overlap in the genetic architecture of stroke risk, T2DM, hypertension, and 2 acute stroke phenotypes based on the National Institutes of Health Stroke Scale (NIHSS), which ranges from 0 for no stroke symptoms to 21 to 42 for a severe stroke: baseline (within 6 hours after onset) and change in NIHSS (ΔNIHSS=NIHSS at baseline−NIHSS at 24 hours). This was done by (1) single-nucleotide polymorphism by single-nucleotide polymorphism comparison, (2) weighted polygenic risk scores with sentinel variants, and (3) whole-genome polygenic risk scores using multiple P thresholds.[Results] We found evidence of genetic architecture overlap between stroke risk and T2DM (P=2.53×10−169), hypertension (P=3.93×10−04), and baseline NIHSS (P=0.03). However, there was no evidence of overlap between ΔNIHSS and stroke risk, T2DM, or hypertension.[Conclusions] The genetic architecture of stroke risk is correlated with that of T2DM, hypertension, and initial stroke severity (NIHSS within 6 hours of stroke onset). However, the genetic architecture of early neurological change after stroke (ΔNIHSS) is not correlated with that of ischemic stroke risk, T2DM, or hypertension. Thus, stroke risk and early neurological change after stroke have distinct genetic architectures.This study was supported by Emergency Medicine Foundation Career Development Grant; American Heart Association (AHA) Mentored Clinical and Population Research Award (14CRP18860027); NIH/NINDS-R01-NS085419; NIH/NINDS-K23-NS099487-01; Barnes-Jewish Hospital Foundation; Helsinki University Central Hospital; Finnish Medical Foundation; Finland government subsidiary funds; Spanish Ministry of Science and Innovation; Instituto de Salud Carlos III (grants Registro Base de Datos de Ictus del Hospital del Mar (BASICMAR) Funding for Research in Health [PI051737], Genome Wide Association Study in Spanish Population (GWALA Project) from Fondos de Investigación Sanitaria Instituto de Salud Carlos III [PI10/02064, PI12/01238, and PI15/00451]); Fondo Europeo de Desarrollo Regional (FEDER/EDRF) Red de Investigación Cardiovascular (RD12/0042/0020); Fundació la Marató TV3; Genestroke Consortium (76/C/2011); and Recercaixa’13 (JJ086116). Dr Fernandez-Cadenas is supported by Miguel Servet II Program, Generacion project (PI15/01978), Pretest project (PMP15/00022), Invictus plus Network (RD16/0019), from Instituto de Salud Carlos III and Fondos Feder; Agaur; and Epigenesis project from Marató TV3 Foundation. The MEGASTROKE project received funding from sources specified at http://www.megastroke.org/acknowledgments.html.Peer reviewe

Molecular pathways in patients with systemic lupus erythematosus revealed by gene-centred DNA sequencing

Objectives Systemic lupus erythematosus (SLE) is an autoimmune disease with extensive heterogeneity in disease presentation between patients, which is likely due to an underlying molecular diversity. Here, we aimed at elucidating the genetic aetiology of SLE from the immunity pathway level to the single variant level, and stratify patients with SLE into distinguishable molecular subgroups, which could inform treatment choices in SLE. Methods We undertook a pathway-centred approach, using sequencing of immunological pathway genes. Altogether 1832 candidate genes were analysed in 958 Swedish patients with SLE and 1026 healthy individuals. Aggregate and single variant association testing was performed, and we generated pathway polygenic risk scores (PRS). Results We identified two main independent pathways involved in SLE susceptibility: T lymphocyte differentiation and innate immunity, characterised by HLA and interferon, respectively. Pathway PRS defined pathways in individual patients, who on average were positive for seven pathways. We found that SLE organ damage was more pronounced in patients positive for the T or B cell receptor signalling pathways. Further, pathway PRS-based clustering allowed stratification of patients into four groups with different risk score profiles. Studying sets of genes with priors for involvement in SLE, we observed an aggregate common variant contribution to SLE at genes previously reported for monogenic SLE as well as at interferonopathy genes. Conclusions Our results show that pathway risk scores have the potential to stratify patients with SLE beyond clinical manifestations into molecular subsets, which may have implications for clinical follow-up and therapy selection

Multi-Ancestry GWAS reveals excitotoxicity associated with outcome after ischaemic stroke

Funding: This work was supported by grants from the Emergency Medicine Foundation Career Development Grant; AHA Mentored Clinical & Population Research Award (14CRP18860027); NIH/NINDS-R01-NS085419 (C.C., J.M.L.); NIH/NINDS-R37-NS107230, NIH/NINDS U24-NS107230 (J.M.L.); NIH/NINDS-K23-NS099487 (L.H.); NIH/NIA-K99-AG062723 (L.I.); Barnes-Jewish Hospital Foundation (J.M.L.); Biogen (C.C., J.M.L.); Bright Focus Foundation, US Department of Defense, Helsinki University Central Hospital; Finnish Medical Foundation; Finland government subsidiary funds; Spanish Ministry of Science and Innovation; Instituto de Salud Carlos III (grants ‘Registro BASICMAR’ Funding for Research in Health (PI051737), ‘GWALA project’ from Fondos de Investigación Sanitaria ISC III (PI10/02064, PI12/01238 and PI15/00451), JR18/00004); Fondos FEDER/EDRF Red de Investigación Cardiovascular (RD12/0042/0020); Fundació la Marató TV3; Genestroke Consortium (76/C/2011); Recercaixa’13 (JJ086116). Tomás Sobrino (CPII17/00027), Francisco Campos (CPII19/00020) and Israel Fernandez are supported by Miguel Servet II Program from Instituto de Salud Carlos III and Fondos FEDER. I.F. is also supported by Maestro project (PI18/01338) and Pre-test project (PMP15/00022) from Instituto de Salud Carlos III and Fondos Feder, Agaur; and Epigenesis project from Marató TV3 Foundation. J.C., J.M., A.D., J.M.-F., J.A. and I.F. are supported by Invictus plus Network (RD16/0019) from Instituto de Salud Carlos III and Fondos Feder. Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP-2013/07559-3) (I.L.-C.), Sigrid Juselius Foundation. The MEGASTROKE project received funding from sources specified at http://www.megastroke.org/acknowledgments.html. B.S., B.A. and F.S. are supported by NIH awards NS097000, NS101718, NS075035, NS079153 and NS106950.During the first hours after stroke onset, neurological deficits can be highly unstable: some patients rapidly improve, while others deteriorate. This early neurological instability has a major impact on long-term outcome. Here, we aimed to determine the genetic architecture of early neurological instability measured by the difference between the National Institutes of Health Stroke Scale (NIHSS) within 6h of stroke onset and NIHSS at 24h.  A total of 5876 individuals from seven countries (Spain, Finland, Poland, USA, Costa Rica, Mexico and Korea) were studied using a multi-ancestry meta-analyses. We found that 8.7% of NIHSS at 24h of variance was explained by common genetic variations, and also that early neurological instability has a different genetic architecture from that of stroke risk. Eight loci (1p21.1, 1q42.2, 2p25.1, 2q31.2, 2q33.3, 5q33.2, 7p21.2 and 13q31.1) were genome-wide significant and explained 1.8% of the variability suggesting that additional variants influence early change in neurological deficits. We used functional genomics and bioinformatic annotation to identify the genes driving the association from each locus. Expression quantitative trait loci mapping and summary data-based Mendelian randomization indicate that ADAM23 (log Bayes factor = 5.41) was driving the association for 2q33.3. Gene-based analyses suggested that GRIA1 (log Bayes factor = 5.19), which is predominantly expressed in the brain, is the gene driving the association for the 5q33.2 locus. These analyses also nominated GNPAT (log Bayes factor = 7.64) ABCB5 (log Bayes factor = 5.97) for the 1p21.1 and 7p21.1 loci. Human brain single-nuclei RNA-sequencing indicates that the gene expression of ADAM23 and GRIA1 is enriched in neurons. ADAM23, a presynaptic protein and GRIA1, a protein subunit of the AMPA receptor, are part of a synaptic protein complex that modulates neuronal excitability.  These data provide the first genetic evidence in humans that excitotoxicity may contribute to early neurological instability after acute ischaemic stroke.PostprintPeer reviewe