Data Tecnica International (United States)

SORL1, the gene encoding the SORLA protein, has arisen as a potential therapeutic target for Alzheimer’s disease (AD). Studies suggest that restoring SORLA function or its trafficking pathways, particularly the SORLA–retromer recycling system, may offer a promising strategy to slow or halt AD progression. While both rare and common SORL1 variants have been associated with increased AD risk, recent evidence suggests a potential involvement of SORL1 in other neurodegenerative conditions. This study assessed the contribution of SORL1 genetic variation to the risk of AD, related dementias (RD), and Parkinson’s disease (PD) using data from six large-scale biobanks, comprising 15,043 AD, 9,943 RD, and 42,763 PD cases, along with 111,969 controls across 11 ancestries. We identified 53 potentially disease-related SORL1 variants (CADD score > 20, MAC ≥ 2, annotated as protein-altering or splicing, and with the mutated allele present only in cases), including 41 novel and 12 previously reported variants. Three were found across multiple ancestries. Overall, 13 variants were found in AD-related cohorts, 5 in RD cohorts, and 35 in PD cohorts. Association analysis identified 10 nominally significant variants associated with AD and 5 with PD. The replication of multiple SORL1 variants across neurodegenerative diseases and ancestrally diverse populations underscores its potential broad genetic contribution to neurodegeneration and reinforces its relevance across distinct clinical phenotypes. Gene-based burden analysis did not reveal any significant cumulative effect of SORL1 variants in the populations tested. A family-based analysis identified a rare predicted-damaging variant in two East Asian families (11:121478242:G:A, p.R176Q) and two variants in two families of European ancestry (11:121514222:A:C, p.N371T; 11:121545392:G:A, p.V672M) that show some evidence of segregation in PD families. Although these variants were slightly more frequent in unrelated PD cases vs. controls, none of them showed statistically significant enrichment in PD, likely due to their very low frequency. Overall, our results extend the understanding of SORL1 beyond AD, suggesting a broader role in neurodegeneration and emphasizing the need for diverse population studies when evaluating genetic risk.

Epigenetic clocks are widely used to estimate biological aging, yet most are built from array-based data from peripheral tissues of predominantly European-ancestry individuals, limiting generalizability. Here, we present aging clocks trained using GenoML, an automated machine learning platform for clinical and multi-omics data, on DNA methylation from Oxford Nanopore long-read sequencing. These models leverage over 28 million CpG sites across individuals of African and European ancestry. Our findings highlight the power of long-read methylation data for constructing accurate, ancestry-aware aging clocks and emphasize the importance of inclusive training datasets.

Pathogenic GAA repeat expansions in FGF14 are an established cause of late-onset cerebellar ataxia, but have not been linked to Parkinson’s disease (PD). Given emerging evidence that repeat expansions in ataxia-associated genes like RFC1, can contribute to atypical or familial forms of PD, we investigated whether FGF14 expansions might play a similar role. Using long-read whole-genome sequencing on 411 individuals with PD and 197 neurologically healthy controls from the PPMI cohort, alongside 1,429 additional controls from the NIH CARD initiative, the 1000 Genomes Project, and the All of Us program, representing globally diverse populations. We identified pathogenic FGF14 GAA repeat expansions in five individuals with PD and one control. All five individuals fit the clinical criteria of PD and showed typical patterns of neurodegeneration on DaTSCAN imaging; α-synuclein aggregation was confirmed by a positive seeding assay among four individuals with available data. These findings broaden the phenotypic spectrum of FGF14 repeat-associated disease and suggest a rare, previously unrecognized genetic contributor to PD. To our knowledge, this is the first report implicating FGF14 in PD and underscores the utility of long-read sequencing for detecting hidden forms of pathogenic variation in unresolved cases.

Abstract BackgroundMost research initiatives have emerged from high‐income countries (HIC), leaving a gap in understanding the disease’s genetic basis in diverse populations like those in Latin American countries (LAC). ReDLat tackles this gap, focusing on LAC’s unique genetics and socioeconomic factors to identify specific Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD) risk factors in Mexico, Colombia, Peru, Chile, Argentina, and Brazil.MethodWe employed a comprehensive genetic analysis approach, integrating Whole Genome Sequencing (WGS), Exome Sequencing, and SNP arrays to understand the cohort’s unique genetic architecture. We conducted ancestry analysis and searched for disease‐causing variants with mendelian inheritance, genome‐wide association studies (GWAS), rare variant enrichment, and evaluation of Polygenic Risk Scores (PRS).ResultsWe recruited and genotyped an initial cohort of 1046 participants with AD, 423 with FTD, and 855 healthy controls (HC) between 2020 and 2023. Analysis is ongoing, and we expect to sequence ∼600 additional samples in the coming months. Ancestry analysis revealed tri‐continental admixture, except for Brazil, which showed an additional Asian component (Figure 1). Top candidate gene rare variant enrichment associations (SKAT p < 0.05) were TREM2 for FTD and ABCA7 and ABCA1 for AD. GWAS identified a robust association with the APOE locus on chromosome 19 in AD vs. HC.. We tested an AD PRS developed in European populations by Bellenguez et al (2020). on our cohort using 83 single‐nucleotide polymorphisms.. The PRS modestly distinguishes between all patients and HC (p = 2.4 × 10^‐12), AD vs. HC (p = 2.2 × 10^‐12), and even FTD vs. HC (p = 4.3 × 10^‐5), albeit with modest separation between groups, as expected for its application in a genetically admixed population.ConclusionOur findings represent a pivotal step in understanding the genetic landscape of AD and FTD in admixed populations. They underscore the importance of including diverse populations in genetic research, paving the way for future studies. These findings have the potential to inform more personalized approaches to the diagnosis and treatment of neurodegenerative diseases in diverse global populations, as well as identify novel targets for therapeutic development.

Blood-based RNA transcriptomics offers a promising avenue for identifying biomarkers of Parkinson’s Disease (PD) progression and may provide mechanistic insights into the systemic biological processes underlying its pathogenesis beyond the well-defined neurodegenerative features. Previous studies have indicated an age-dependent increase in neutrophil-enriched gene expression, alongside a reduction in lymphocyte counts, in individuals with PD. These immune cell changes can obscure disease-relevant transcriptomic signals. In this study, we performed differential expression (DE) analysis of whole-blood RNA sequencing data from PD cohorts, incorporating a correction for immune cell-enriched gene expression, particularly neutrophil-related pathways, to improve the resolution of PD-associated molecular changes. Using 1,254 Parkinson’s Progression Markers Initiative (PPMI) samples with complete blood count (CBC) data, we developed a predictive model to estimate neutrophil percentages in a 6,987 PPMI and Parkinson’s Disease Biomarkers Program (PDBP) samples. We mitigated the confounding effects of immune cell-enriched gene expression by integrating predicted neutrophil percentages as a covariate in DE analysis. This approach revealed a consistent and significant downregulation of SNCA across all PD cohorts, a finding previously obscured by immune cell signatures. Lowered SNCA expression was found in individuals with known predisposition genes (e.g., SNCA, GBA, LRRK2) and in non-genetic PD cohorts lacking known pathogenic mutations, suggesting it may represent a key transcriptomic hallmark of the disease.

Commercial genome-wide genotyping arrays have historically neglected coverage of genetic variation across populations. We aimed to create a multi-ancestry genome-wide array that would include a wide range of neuro-specific genetic content to facilitate genetic research in neurological disorders across multiple ancestral groups, fostering diversity and inclusivity in research studies. We developed the Illumina NeuroBooster Array (NBA), a custom high-throughput and cost-effective platform on a backbone of 1,914,934 variants from the Infinium Global Diversity Array and added custom content comprising 95,273 variants associated with more than 70 neurological conditions or traits, and we further tested its performance on more than 2000 patient samples. This novel platform includes approximately 10,000 tagging variants to facilitate imputation and analyses of neurodegenerative disease-related genome-wide association study loci across diverse populations. In this article, we describe NBA's potential as an efficient means for researchers to assess known and novel disease genetic associations in a multi-ancestry framework. The NBA can identify rare genetic variants and accurately impute more than 15 million common variants across populations. Apart from enabling sample prioritization for further whole-genome sequencing studies, we envisage that NBA will play a pivotal role in recruitment for interventional studies in the precision medicine space. From a broader perspective, the NBA serves as a promising means to foster collaborative research endeavors in the field of neurological disorders worldwide. Ultimately, this carefully designed tool is poised to make a substantial contribution to uncovering the genetic etiology underlying these debilitating conditions. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

A Inflação pode ser analisada ou medida pelo Índice de Preços ao Consumidor Amplo, que representa o aumento de preços dos produtos num determinado país ou região, durante um período. Tais índices afetam diretamente a economia do país, trazendo riscos para empreendedores, empresários e investidores de todos os setores econômicos do Brasil. Dessa forma realizou-se uma análise mensal do índice de preços ao consumidor do Brasil, no período de 4 anos consecutivos. Partindo dessa premissa, o presente trabalho teve como objetivo, analisar o índice de Preços ao Consumidor Amplo (IPCA) pela Técnica de Wavelets. Na metodologia simulou-se as Wavelets de Morlet, verificando sua tendência e Periodicidade (Flutuações de ciclos econômicos) ao longo do período. Verificou-se que as Wavelets retratam adequadamente a tendência espacial e temporal, pelo espectro de energia, com periodicidades dominantes de flutuações de ciclos econômicos da Inflação entre 2 e 4 meses, proporcionando informações úteis e estratégicas para os vários setores da economia do País

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

Genome-wide association studies (GWASs) have identified numerous susceptibility loci for Parkinson’s disease (PD), but its genetic architecture remains underexplored in populations of non-European ancestry. To identify genetic variants associated with PD in the Chinese population, we performed a GWAS using whole-genome sequencing (WGS) in 1,972 cases and 2,478 controls, and a replication study in a total of 8209 cases and 9454 controls. We identified one new risk variant rs61204179 (Pcombined = 1.47 × 10−9) with low allele frequency, four previously reported risk variants (NUCKS1/RAB29-rs11557080, SNCA-rs356182, FYN-rs997368, and VPS13C-rs2251086), as well as three risk variants in LRRK2 coding region (A419V, R1628P, and G2385R) with genome-wide significance (P < 5 × 10−8) for PD in Chinese population. Moreover, of the reported genome-wide significant risk variants found mostly in European ancestry populations, the correlation coefficient (rb) of effect size accounting for sampling errors was 0.91 between datasets and 63.6% attained P < 0.05 in Chinese population. Accordingly, we estimated a heritability of 0.14–0.18 for PD, and a moderate genetic correlation between European ancestry and Chinese populations (rg = 0.47, se = 0.21). Polygenic risk score (PRS) analysis revealed that individuals with PRS values in the highest quartile had a 3.9-fold higher risk of developing PD than the lowest quartile. In conclusion, the present GWAS identified PD-associated variants in Chinese population, as well as genetic factors shared among distant populations. Our findings shed light on the genetic homogeneity and heterogeneity of PD in different ethnic groups and suggested WGS might continue to improve our understanding of the genetic architecture of PD.

Abstract The Monogenic Network of the Global Parkinson’s Genetics Program (GP2) aims to create an efficient infrastructure to accelerate the identification of novel genetic causes of Parkinson’s disease (PD) and to improve our understanding of already identified genetic causes, such as reduced penetrance and variable clinical expressivity of known disease-causing variants. We aim to perform short- and long-read whole-genome sequencing for up to 10,000 patients with parkinsonism.