White matter hyperintensities (WMH) are prevalent Alzheimer’s disease (AD). However, the role of WMH in the etiology of AD is debated. Specifically, a key question is whether higher WMH is predictive of higher beta-amyloid (Aβ) and fibrillar tau accumulation. Here, we assessed whether a genetic predisposition to higher WMH (assessed via polygenic score [PGS] of WMH based on a large-scale population-based GWAS) is associated with higher rates of WMH volume and amyloid- and tau-PET accumulation in elderly individuals. We included 338 participants within the AD spectrum defined by amyloid-PET positivity, encompassing 135 cognitively normal (CN Aβ+), 122 mild cognitive impairment (MCI Aβ+), 81 AD dementia, and in addition 287 amyloid-negative controls (CN Aβ-) from from ADNI. Flortaucipir-PET was available in a subset of 92 Aβ+ & 85 CN Aβ- participants. WMH were segmented via a deep learning-based algorithm on FLAIR images and partitioned into 5 lobar volumes. Based on GWAS in 35,000 individuals from the UKBiobank with global WMH volume as the dependent variable, we generated a PGS including 20 SNPs (p<5×10 −8 ). Centiloids of global cortical amyloid-PET and temporal-meta ROI values of tau-PET SUVR were computed. All linear regression analyses were stratified by group (CN Aβ- vs pooled Aβ+) and controlled for age, sex, education, systolic blood pressure, BMI, APOE e4 genotype, and population genetic components. A higher PGS was associated with higher cross-sectional WMH volume in the frontal, parietal, and temporal lobes in both the CN Aβ- and the whole Aβ+ group (except for temporal WMH; Figure 1). Longitudinally, higher PGS was associated with faster increases in frontal WMH volume in CN Aβ- (β=0.18, p=.03) and in the temporal lobe in the Aβ+ group (β=0.18, p=.01, Figure 2) over 2.8 years on average. Conversely, neither PGS nor baseline WMH volume (or modifiable risk factors) predicted increase in amyloid- or tau-PET (mean follow-ups: 3.9 & 2.8 years). Genetic predisposition to WMH was associated with higher WMH volume in a region-dependent manner, but the increases in WMH were associated neither with Aβ- nor tau-PET accumulation, suggesting that white matter lesions develop independently from AD pathologies.

Traumatic brain injury leads to a highly orchestrated immune- and glial cell response partially responsible for long-lasting disability and the development of secondary neurodegenerative diseases. A holistic understanding of the mechanisms controlling the responses of specific cell types and their crosstalk is required to develop an efficient strategy for better regeneration. Here, we combine spatial and single-cell transcriptomics to chart the transcriptomic signature of the injured male murine cerebral cortex, and identify specific states of different glial cells contributing to this signature. Interestingly, distinct glial cells share a large fraction of injury-regulated genes, including inflammatory programs downstream of the innate immune-associated pathways Cxcr3 and Tlr1/2. Systemic manipulation of these pathways decreases the reactivity state of glial cells associated with poor regeneration. The functional relevance of the discovered shared signature of glial cells highlights the importance of our resource enabling comprehensive analysis of early events after brain injury.

Spatially resolved transcriptomics has revolutionized RNA studies by aligning RNA abundance with tissue structure, enabling direct comparisons between histology and gene expression. Traditional approaches to identifying signature genes often involve preliminary data grouping, which can overlook subtle expression patterns in complex tissues. We present Spatial Gradient Screening, an algorithm which facilitates the supervised detection of histology-associated gene expression patterns without prior data grouping. Utilizing spatial transcriptomic data along with single-cell deconvolution from injured mouse cortex, and TCR-seq data from brain tumors, we compare our methodology to standard differential gene expression analysis. Our findings illustrate both the advantages and limitations of cluster-free detection of gene expression, offering more profound insights into the spatial architecture of transcriptomes. The algorithm is embedded in SPATA2, an open-source framework written in R, which provides a comprehensive set of tools for investigating gene expression within tissue.

HomeStrokeVol. 54, No. 3Novel Polygenic Risk Score for Intracranial Aneurysms Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessEditorialPDF/EPUBNovel Polygenic Risk Score for Intracranial Aneurysms Simon Frerich and John W. Cole Simon FrerichSimon Frerich Correspondence to: Simon Frerich, MSc, Institute for Stroke and Dementia Research, University Hospital, Ludwig Maximilian University (LMU) of Munich, Feodor-Lynen-Str. 17, 81377 Munich, Germany. Email E-mail Address: [email protected] https://orcid.org/0000-0002-8275-6113 Institute for Stroke and Dementia Research, University Hospital (S.F.), LMU Munich, Germany. Graduate School of Systemic Neurosciences (GSN) (S.F.), LMU Munich, Germany. and John W. ColeJohn W. Cole https://orcid.org/0000-0001-9263-8930 Department of Neurology, Maryland Stroke Center, Baltimore (J.W.C.). Baltimore VA Medical Center and University of Maryland School of Medicine (J.W.C.). Originally published19 Jan 2023https://doi.org/10.1161/STROKEAHA.122.041807Stroke. 2023;54:819–820This article is a commentary on the followingGenetic Risk Score for Intracranial Aneurysms: Prediction of Subarachnoid Hemorrhage and Role in Clinical HeterogeneityOther version(s) of this articleYou are viewing the most recent version of this article. Previous versions: January 19, 2023: Ahead of Print Since the completion of the first genome-wide association studies in the early 2000s, polygenic risk prediction has raised considerable expectations for research and clinical use.1 Despite methodological concerns about construction and validity of polygenic risk scores (PRS),2 multiple studies have shown that PRS strongly associate with disease status,3 and the number of studies related to PRS is increasing exponentially (based on a PubMed search for the terms: "polygenic risk score*" OR "genetic risk score*" OR "genetic score*" OR "polygenic score*," as of 23/11/2022). Most commonly, polygenic risk scores, also known as genetic risk scores (GRS), represent a defined set of risk variants based on findings from genome-wide association studies. For each individual, the number of risk alleles is summed and weighted by its effect size.3 Therefore, PRS can discriminate individuals based on their genetic susceptibility to a particular trait or disease.See related article, p 810Much of the evidence supporting the clinical utility of PRS comes from well-powered studies of cardiovascular diseases, type 2 diabetes, cancer, and Alzheimer disease.3 Conversely, previous PRS studies on intracranial aneurysms (IAs) are small and scarce.4 Whereas the first PRS study on IA could not find an association between a PRS and aneurysm size,5 follow-up work showed that the PRS is higher in individuals with IAs located at the middle cerebral artery as compared with all other locations.6 Another study found that an improved PRS associated with aneurysm diameter and volume but not with aneurysm presence.7 Notably, the cited studies were based on no more than 10 genetic variants, which amounted to a SNP-based heritability of approximately 4%.5,6 In addition, associations with IA outcomes were evaluated in small cohorts of fewer than 2000 cases.5–7 The statistical power of these studies to detect meaningful associations was therefore limited.In this issue of Stroke, Bakker et al8 address this gap by creating a novel genetic risk score of IA. The authors leverage a recent genome-wide association study of IA and aneurysmal subarachnoid hemorrhage (ASAH)9 along with association data of 17 IA-related traits, to create a "meta Genetic Risk Score" (metaGRS). Among the 17 selected traits are established IA risk factors, such as blood pressure and smoking, and diseases genetically correlated with IA, including ischemic stroke.8 To construct the score, the authors utilized an "elastic net regression" methodology in a training sample from the UK Biobank (1161 IA cases, 407 392 controls). In this way, a total of 7 078 955 SNPs were included in the score. First, the authors evaluated associations of the metaGRS with ASAH incidence and IA presence in the HUNT study (828 IA cases, 68 568 controls). While the score improved ASAH incidence prediction above a model including clinical risk factors sex, blood pressure, and smoking (C-index 0.63 to 0.65), the prediction of IA presence did not increase after including the score. The authors also showed that the prediction of ASAH incidence was stronger in women than in men by using scores trained and validated in women and men separately. Next, a higher score independently associated with 3 out of 9 tested IA patient characteristics in the ISGC-IA cohort (5560 IA cases), namely hypertension, smoking status, and lower age at ASAH. In addition, a lower metaGRS was observed in patients with a single IA compared to multiple IAs, and in patients with an IA at the internal carotid artery. The latter 2 associations, however, were not independent of smoking and hypertension in a multivariate model.The predictive value of the metaGRS remains limited, given that it did not improve prediction of IA presence above a model including clinical risk factors. One reason for this could be the presence of undetected and unruptured IAs in the control group, as suggested by the authors,8 which would attenuate the statistical power to predict IAs. Only an improved characterization of the controls via brain vessel imaging (eg, MRA, CTA) can resolve this issue. Furthermore, a previously shown association with IAs located at the middle cerebral artery6 could not be replicated. Conversely, a decreased genetic load in patients with an IA at the internal carotid artery was identified, a location that was not included in the previous study. Last, the PRS is based on individuals of European ancestry, resulting in reduced predictive accuracy in other populations.3Despite these limitations, the novel PRS by Bakker et al8 is a major improvement over previous scores. The statistical power is considerably larger, as the utilized genome-wide association study more than doubled the number of cases compared to previous studies (7495 IA/ASAH cases, 71 934 controls).9 SNP-based heritability increased to 21.6%, thereby explaining more than half of the twin-based heritability of IA (h2=41%).9 More than 7000 IA cases were used for creating and evaluating the metaGRS.9 Further, the authors used 3 different cohorts to train and validate their score (UK Biobank, HUNT, ISGC-IA).In closing, the investigators constructed a metaGRS with predictive ability for ASAH, although with limited added value over standard clinical risk factors. This finding emphasizes the need for patient-specific vascular risk factor control in the setting of IA, specifically optimizing blood pressure and smoking cessation. However, their findings that prediction by the metaGRS for ASAH performed better in women than in men, and in subjects at a younger age, independent of both hypertension and smoking, further highlights that genetic drivers also play a key role. While these results do not indicate that regular use of the metaGRS is clinically warranted, they certainly highlight that both genetic and environmental factors jointly contribute to disease risk and that additional research in needed to further clarify these relationships.Article InformationAcknowledgmentsS. Frerich and Dr Cole drafted and revised the article, and analyzed and interpreted the data.Disclosures Dr Cole receives royalty payments from Springer; and is partially supported by an American Heart Association (AHA)-Bayer Discovery Grant (Grant 17IBDG33700328), the AHA Cardiovascular Genome-Phenome Study (Grant-15GPSPG23770000), NIH (Grants: R01-NS114045; R01-NS100178; R01-NS105150), and the US Department of Veterans Affairs.FootnotesThe opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.For Disclosures, see page 820.Correspondence to: Simon Frerich, MSc, Institute for Stroke and Dementia Research, University Hospital, Ludwig Maximilian University (LMU) of Munich, Feodor-Lynen-Str. 17, 81377 Munich, Germany. Email simon.frerich@med.uni-muenchen.deReferences1. Bell J. Predicting disease using genomics.Nature. 2004; 429:453–456. doi: 10.1038/nature02624CrossrefGoogle Scholar2. Janssens ACJ. Validity of polygenic risk scores: are we measuring what we think we are?Hum Mol Genet. 2019; 28:R143–R150. doi: 10.1093/hmg/ddz205CrossrefMedlineGoogle Scholar3. Lewis CM, Vassos E. Polygenic risk scores: from research tools to clinical instruments.Genome Med. 2020; 12:44. doi: 10.1186/s13073-020-00742-5CrossrefMedlineGoogle Scholar4. Bakker MK, Ruigrok YM. Genetics of intracranial aneurysms.Stroke. 2021; 52:3004–3012. doi: 10.1161/strokeaha.120.032621LinkGoogle Scholar5. Kleinloog R, van 't Hof FNG, Wolters FJ, Rasing I, van der Schaaf IC, Rinkel GJE, Ruigrok YM. The association between genetic risk factors and the size of intracranial aneurysms at time of rupture.Neurosurgery. 2013; 73:705–708. doi: 10.1227/NEU.0000000000000078CrossrefMedlineGoogle Scholar6. van 't Hof FNG, Kurki MI, Kleinloog R, de Bakker PIW, von und zu Fraunberg M, Jaaskelainen JE, Gaal EI, Lehto H, Kivisaari R, Laakso A, et al. Genetic risk load according to the site of intracranial aneurysms.Neurology. 2014; 83:34–39. doi: 10.1212/wnl.0000000000000547CrossrefMedlineGoogle Scholar7. Peymani A, Adams HHH, Cremers LGM, Krestin G, Hofman A, van Duijn CM, Uitterlinden AG, van der Lugt A, Vernooij MW, Ikram MA. Genetic determinants of unruptured intracranial aneurysms in the general population.Stroke. 2015; 46:2961–2964. doi: 10.1161/STROKEAHA.115.010414LinkGoogle Scholar8. Bakker MK, Kanning JP, Abraham G, Martinsen AE, Winsvold BS, Zwart J-A, Bourcier R, Sawada T, Koido M, Kamatani Y, et al. Genetic risk score for intracranial aneurysms: prediction of subarachnoid hemorrhage and role in clinical heterogeneity.Stroke. 2023; 54:810–818. doi: 10.1161/STROKEAHA.122.040715LinkGoogle Scholar9. Bakker MK, van der Spek RAA, van Rheenen W, Morel S, Bourcier R, Hostettler IC, Alg VS, van Eijk KR, Koido M, Akiyama M, et al. Genome-wide association study of intracranial aneurysms identifies 17 risk loci and genetic overlap with clinical risk factors.Nat Genet. 2020; 52:1303–1313. doi: 10.1038/s41588-020-00725-7CrossrefMedlineGoogle Scholar eLetters(0)eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.Sign In to Submit a Response to This Article Previous Back to top Next FiguresReferencesRelatedDetailsRelated articlesGenetic Risk Score for Intracranial Aneurysms: Prediction of Subarachnoid Hemorrhage and Role in Clinical HeterogeneityMark K. Bakker, et al. Stroke. 2023;54:810-818 March 2023Vol 54, Issue 3 Advertisement Article InformationMetrics © 2023 American Heart Association, Inc.https://doi.org/10.1161/STROKEAHA.122.041807PMID: 36655556 Originally publishedJanuary 19, 2023 Keywordsintracranial aneurysmsEditorialsPDF download Advertisement SubjectsGenetic, Association StudiesGeneticsIntracranial HemorrhagePrecision Medicine

ObjectivePolygenic variation accounts for a substantial portion of the risk of Alzheimer's disease (AD), but its effect on the rate of fibrillar‐tau accumulation as a key driver of dementia symptoms is unclear.MethodsWe combined the to‐date largest number of genetic risk variants of AD (n = 85 lead single‐nucleotide polymorphisms [SNPs]) from recent genome‐wide association studies (GWAS) to generate a polygenic score (PGS). We assessed longitudinal tau‐positron emission tomography (PET), amyloid‐PET, and cognition in 231 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Using the PGS, together with global amyloid‐PET, we predicted the rate of tau‐PET increases in Braak‐stage regions‐of‐interest and cognitive decline. We also assessed PGS‐risk enrichment effects on the required sample size in clinical trials targeting tau pathology.ResultsWe found that a higher PGS was associated with higher rates of tau‐PET accumulation, in particular at elevated amyloid‐PET levels. The tau‐PET increases mediated the association between PGS and faster cognitive decline. Risk enrichment through high PGS afforded sample size savings by 34%.InterpretationOur results demonstrate that the PGS predicts faster tau progression and thus cognitive decline, showing utility to enhance statistical power in clinical trials. ANN NEUROL 2023;93:819–829

Introduction: Stroke etiology remains unknown in 30% of cases, hindering secondary prevention efforts. We leveraged human genetic data in order to identify evidence of overlap between stroke of undetermined source and defined stroke etiologies as well as causal relationships with modifiable risk factors. Methods: We analyzed genome-wide data from 16,851 ischemic stroke cases and 32,473 controls from the NINDS Stroke Genetics Network with TOAST- and CCS-defined subtypes. Using genetic risk scores for large artery, cardioembolic, and small vessel stroke (LAS, CES, SVS) we assessed the degree of overlap with stroke of undetermined source and used pairwise GWAS to search for shared loci. We then applied Mendelian randomization (MR) to identify causal risk factors for stroke of undetermined source. Results: There was significant overlap between stroke of undetermined source and all defined stroke subtypes at a genome-wide and locus level (19 shared loci with LAS, 2 with CES, 5 with SVS). Shared loci pointed to altered gene expression in arterial tissue and blood and atherosclerosis-related mechanisms. Further, genetically predicted carotid intima media thickness was associated with stroke of undetermined source. While genetic liability to atrial fibrillation also showed a significant association, this was attenuated in analyses excluding cases with incomplete diagnostic workup. MR analyses showed significant associations in blood pressure, diabetes, waist-to-hip ratio, inflammatory pathways (IL-6 signaling, MCP-1 levels), and elevated factor XI levels with stroke of undetermined source (Figure). Conclusion: These analyses suggest that stroke of undetermined source shares genetic and modifiable risk factors with defined stroke subtypes. Together, they raise the hypothesis that refinement of current subtyping approaches can reduce the proportion of cases classified as undetermined and optimize secondary prevention strategies.

Progression of fibrillar tau is a key driver of neurodegeneration and cognitive decline in Alzheimer’s disease (AD), however which factors predict the rate of tau accumulation at the patient-level is unclear. Here we propose to employ a polygenic score (PGS) for the prediction of tau accumulation and cognitive changes in AD, which could be useful for selection of individuals with faster tau progression in clinical trials. We included 231 ADNI participants with longitudinal measurements of tau-PET, amyloid-PET, cognitive data and genotype data. We computed a PGS based on 85 independent lead SNPs from two recent large GWAS (DOI:10.1101/2020.10.01.20200659; DOI:10.1038/s41588-021-00921-z), excluding all APOE variants. Using linear mixed effect models, we computed the individual rates of change in each of the biomarkers. In linear regression models we tested whether PGS can predict the rate of tau-PET changes in Braak-stage ROIs ( Figure 1A ) and cognitive changes. In order to assess whether amyloid mediates the effect of PGS on tau accumulation we performed sensitivity analyses in subgroups categorized by amyloid status as well as tested the interaction between the PGS and global amyloid-PET. We further estimated sample size required for detection of hypothetical treatment effect on the rate tau accumulation using power analysis. Higher PGS was associated with higher accumulation rates of tau-PET in cortical regions (Braak 3+4:β=0.306, p FDR <0.001; Braak 5+6:β=0.262, p FDR <0.001; Figure 1B ). A higher PGS was further associated with faster decline in episodic memory (β=-0.223, p FDR <0.001; Figure 1C ) and global cognition (β=0.280, p FDR <0.001; Figure 1C ). The observed effects of PGS on cognitive changes were mediated by higher tau-PET accumulation ( Figure 2 ). We found a synergistic effect between PGS and elevated amyloid-PET levels on the rate of tau-PET accumulation such that the PGS effects on tau-PET accumulation were stronger particularly at elevated amyloid levels ( Figure 3 ). Selection of individuals with highest PGS yielded a 32-33% saving in required sample size to detect a treatment effect on tau accumulation. Higher polygenic score is associated with faster tau accumulation and may be a useful tool for risk stratification in disease-modifying treatments on tau.

BACKGROUND AND OBJECTIVES:To investigate whether antemortem MRI-based atrophy subtypes of Alzheimer disease (AD) differ in neuropathologic features and comorbid non-AD pathologies at postmortem. METHODS:From the Alzheimer's Disease Neuroimaging Initiative cohort, we included individuals with antemortem MRI evaluating brain atrophy within 2 years before death, antemortem diagnosis of AD dementia/mild cognitive impairment, and postmortem-confirmed AD neuropathologic change. Antemortem atrophy subtypes were modeled as continuous phenomena based on a recent conceptual framework: typicality (spanning limbic-predominant AD to hippocampal-sparing AD) and severity (spanning typical AD to minimal atrophy AD). Postmortem neuropathologic evaluation included AD hallmarks, β-amyloid, and tau as well as non-AD pathologies, alpha-synuclein and TAR DNA-binding protein 43 (TDP-43). We also investigated the overall concomitance across these pathologies. Partial correlations assessed the associations between antemortem atrophy subtypes and postmortem neuropathologic outcomes. RESULTS:In 31 individuals (26 AD dementia/5 mild cognitive impairment, mean age = 80 years, 26% females), antemortem typicality was significantly negatively associated with neuropathologic features, including β-amyloid (rho = -0.39 overall), tau (rho = -0.38 regionally), alpha-synuclein (rho = -0.39 regionally), TDP-43 (rho = -0.49 overall), and concomitance of pathologies (rho = -0.59 regionally). Limbic-predominant AD was associated with higher Thal phase, neuritic plaque density, and presence of TDP-43 compared with hippocampal-sparing AD. Regionally, limbic-predominant AD showed a higher presence of tau and alpha-synuclein pathologies in medial temporal structures, a higher presence of TDP-43, and concomitance of pathologies subcortically/cortically compared with hippocampal-sparing AD. Antemortem severity was significantly negatively associated with concomitance of pathologies (rho = -0.43 regionally), such that typical AD showed higher concomitance of pathologies than minimal atrophy AD. DISCUSSION:We provide a direct antemortem-to-postmortem validation, highlighting the importance of understanding atrophy-based heterogeneity in AD relative to AD and non-AD pathologies. We suggest that (1) typicality and severity in atrophy reflect differential aspects of susceptibility of the brain to AD and non-AD pathologies; and (2) limbic-predominant AD and typical AD subtypes share similar biological pathways, making them more vulnerable to AD and non-AD pathologies compared with hippocampal-sparing AD, which may follow a different biological pathway. Our findings provide a deeper understanding of associations of atrophy subtypes in AD with different pathologies, enhancing the prevailing knowledge of biological heterogeneity in AD and could contribute toward tracking disease progression and designing clinical trials in the future.

White matter hyperintensities (WMH) are among the most common radiological abnormalities in the ageing population and an established risk factor for stroke and dementia. While common variant association studies have revealed multiple genetic loci with an influence on their volume, the contribution of rare variants to the WMH burden in the general population remains largely unexplored. We conducted a comprehensive analysis of this burden in the UK Biobank using publicly available whole-exome sequencing data (n up to 17 830) and found a splice-site variant in GBE1, encoding 1,4-alpha-glucan branching enzyme 1, to be associated with lower white matter burden on an exome-wide level [c.691+2T>C, β = -0.74, standard error (SE) = 0.13, P = 9.7 × 10-9]. Applying whole-exome gene-based burden tests, we found damaging missense and loss-of-function variants in HTRA1 (frequency of 1 in 275 in the UK Biobank population) to associate with an increased WMH volume (P = 5.5 × 10-6, false discovery rate = 0.04). HTRA1 encodes a secreted serine protease implicated in familial forms of small vessel disease. Domain-specific burden tests revealed that the association with WMH volume was restricted to rare variants in the protease domain (amino acids 204-364; β = 0.79, SE = 0.14, P = 9.4 × 10-8). The frequency of such variants in the UK Biobank population was 1 in 450. The WMH volume was brought forward by ∼11 years in carriers of a rare protease domain variant. A comparison with the effect size of established risk factors for WMH burden revealed that the presence of a rare variant in the HTRA1 protease domain corresponded to a larger effect than meeting the criteria for hypertension (β = 0.26, SE = 0.02, P = 2.9 × 10-59) or being in the upper 99.8% percentile of the distribution of a polygenic risk score based on common genetic variants (β = 0.44, SE = 0.14, P = 0.002). In biochemical experiments, most (6/9) of the identified protease domain variants resulted in markedly reduced protease activity. We further found EGFL8, which showed suggestive evidence for association with WMH volume (P = 1.5 × 10-4, false discovery rate = 0.22) in gene burden tests, to be a direct substrate of HTRA1 and to be preferentially expressed in cerebral arterioles and arteries. In a phenome-wide association study mapping ICD-10 diagnoses to 741 standardized Phecodes, rare variants in the HTRA1 protease domain were associated with multiple neurological and non-neurological conditions including migraine with aura (odds ratio = 12.24, 95%CI: 2.54-35.25; P = 8.3 × 10-5]. Collectively, these findings highlight an important role of rare genetic variation and the HTRA1 protease in determining WMH burden in the general population.

ABSTRACTWhite matter hyperintensities (WMH) are among the most common radiological abnormalities in the ageing population and an established risk factor for stroke and dementia. While common variant association studies have revealed multiple genetic loci with an influence on WMH volume, the contribution of rare variants to WMH burden in the general population remains largely unexplored. We conducted a comprehensive analysis of WMH burden in the UK Biobank using publicly available whole-exome sequencing data (N=16,511) and found a splice-site variant in GBE1, encoding 1,4-alpha-glucan branching enzyme 1, to be associated with lower white matter burden on an exome-wide level (c.691+2T>C, beta=-0.74, se=0.13, p=9.7E-9). Applying whole-exome gene-based burden tests, we found damaging missense and loss-of-function variants in HTRA1 to associate with increased WMH volume (p=5.5E-6, FDR=0.04). HTRA1 encodes a secreted serine protease implicated in familial forms of small vessel disease. Domain-specific burden tests revealed that the association with WMH volume was restricted to rare variants in the protease domain (amino acids 204-364; beta=0.79, se=0.14, p=9.4E-8). The frequency of such variants in the UK Biobank population was 1 in 450. WMH volume was brought forward by approximately 11 years in carriers of a rare protease domain variant. A comparison with the effect size of established risk factors for WMH burden revealed that the presence of a rare variant in the HTRA1 protease domain corresponded to a larger effect than meeting the criteria for hypertension (beta=0.26, se=0.02, p=2.9E-59) or being in the upper 99.8% percentile of the distribution of a polygenic risk score based on common genetic variants (beta=0.44, se=0.14, p=0.002). In biochemical experiments, most (6/9) of the identified protease domain variants resulted in a markedly reduced protease activity. We further found EGFL8, which showed suggestive evidence for association with WMH volume (p=1.5E-4, FDR=0.22) in gene burden tests, to be a direct substrate of HTRA1 and to be preferentially expressed in cerebral arterioles and arteries. In a phenome-wide association study (PheWAS) mapping ICD-10 diagnoses to 741 standardized Phecodes, rare variants in the HTRA1 protease domain were associated with multiple neurological and non-neurological conditions including migraine with aura (OR=12.24, 95%CI [2.54-35.25], p=8.3E-5). Collectively, these findings highlight an important role of rare genetic variation and of the HTRA1 protease in determining WMH burden in the general population.

Objectives: To investigate whether antemortem atrophy-based subtypes of Alzheimer's disease (AD) may be differentially susceptible to individual or concomitance of AD and non-AD (co)-pathologies, assessed neuropathologically at postmortem. Methods: We selected 31 individuals from the AD neuroimaging initiative with: an antemortem magnetic resonance imaging scan evaluating brain atrophy available within two years before death; an antemortem diagnosis of AD dementia or prodromal AD; and postmortem neuropathological confirmation of AD. Antemortem atrophy-based subtypes was modeled as a continuous phenomenon in terms of two recently proposed dimensions: typicality (ranging from limbic-predominant AD to hippocampal-sparing AD subtypes) and severity (ranging from typical AD to minimal atrophy AD subtypes). Postmortem neuropathological evaluation included global and regional outcomes: AD hallmark pathologies of amyloid-beta and tau; non-AD co-pathologies of alpha-synuclein Lewy body and TDP-43; and the overall concomitance across these four (co)-pathologies. Partial correlation and linear regression models were used to assess the association between antemortem atrophy-based subtypes and postmortem neuropathological outcomes. Results: We observed significant global and regional associations between antemortem typicality and postmortem (co)-pathologies including tau, alpha-synuclein Lewy bodies and TDP-43. Antemortem typicality demonstrated stronger regional associations with concomitance of multiple postmortem (co)-pathologies in comparison to antemortem severity. Our findings suggest the following susceptibilities of atrophy-based subtypes: limbic-predominant AD towards higher burden of tau and TDP-43 pathologies while hippocampal-sparing AD towards lower burdens; limbic-predominant AD and typical AD towards higher burden of alpha-synuclein Lewy body pathology while hippocampal-sparing AD and minimal-atrophy AD towards lower burdens. Discussion: Through a direct antemortem-to-postmortem validation, our study highlights the importance of understanding heterogeneity in AD in relation to concomitance of AD and non-AD pathologies. Our findings provide a deeper understanding of both global and regional vulnerabilities of the biological subtypes of AD brain towards (co)-pathologies. Relative involvement of both AD hallmark and non-AD (co)-pathologies will enhance prevailing knowledge of biological heterogeneity in AD and could thus, contribute towards tracking disease progression and designing clinical trials in the future.

Background: Observational studies suggest an association of stroke with cardiac traits beyond atrial fibrillation, the leading source of cardioembolism. However, controversy remains regarding a causal role of these traits in stroke pathogenesis. Here, we leveraged genetic data to systematically assess associations between cardiac traits and stroke risk using a Mendelian Randomization framework. Methods: We studied 66 cardiac traits including cardiovascular diseases, magnetic resonance imaging–derived cardiac imaging, echocardiographic imaging, and electrocardiographic measures, as well as blood biomarkers in a 2-sample Mendelian Randomization approach. Genetic predisposition to each trait was explored for associations with risk of stroke and stroke subtypes in data from the MEGASTROKE consortium (40 585 cases/406 111 controls). Using multivariable Mendelian Randomization, we adjusted for potential pleiotropic or mediating effects relating to atrial fibrillation, coronary artery disease, and systolic blood pressure. Results: As expected, we observed strong independent associations between genetic predisposition to atrial fibrillation and cardioembolic stroke and between genetic predisposition to coronary artery disease as a proxy for atherosclerosis and large-artery stroke. Our data-driven analyses further indicated associations of genetic predisposition to both heart failure and lower resting heart rate with stroke. However, these associations were explained by atrial fibrillation, coronary artery disease, and systolic blood pressure in multivariable analyses. Genetically predicted P-wave terminal force in V1, an electrocardiographic marker for atrial cardiopathy, was inversely associated with large-artery stroke. Conclusions: Available genetic data do not support substantial effects of cardiac traits on the risk of stroke beyond known clinical risk factors. Our findings highlight the need to carefully control for confounding and other potential biases in studies examining candidate cardiac risk factors for stroke.