Spencer Grant

The genetics of Parkinson’s disease (PD) in underrepresented populations remain poorly characterized, potentially overlooking population-specific contributions. We analyzed 461 Chilean PD cases from a movement disorders center. Pathogenic, likely pathogenic, or GBA1 risk variants were identified in 58 individuals (12.6%), mainly in LRRK2 (50%) and GBA1 (44.8%), while PRKN, SNCA , and SQSTM1 collectively represented 5.2%. All LRRK2 variants were p.G2019S, with an overall frequency of 6.3%, the highest reported in South America, and enrichment of Ashkenazi Jewish ancestry at this locus. These findings characterize the genetic landscape of PD in Chile and support its relevance for LRRK2 -targeted studies.

Background: LRRK2 variants are major contributors to Parkinsons disease (PD). Many pathogenic variants increase kinase activity, underscoring the value of functional assays in nominating therapeutic targets and kinase inhibitors as potential disease-modifying therapies. Objective: To develop an interactive resource that provides functional context and ancestry-specific variant frequencies. Methods: Genotyping and short-read sequencing data were analyzed for 101,678 individuals (61,709 PD, 39,969 controls) from the Global Parkinsons Genetics Program (GP2) and integrated with clinical and in-vitro biochemical kinase activity information. Results: The LRRK2 Browser (http://gp2.org/lrrk2browser) displays ancestry-specific genetic data for 19,596 LRRK2 variants (968 exonic, 14 disease-associated) across 11 populations, and functional data for 171 variants. Clinical annotations include age, age at onset, and family history of PD. Discussion: The publicly available LRRK2 Browser represents an open-access, multi-ancestry resource to support LRRK2 variant interpretation. It aims to enhance the translational potential of genetic and functional data for precision medicine and the implementation of gene-targeted therapies in diverse populations.

Parkinson’s disease (PD) is a complex neurodegenerative disorder with environmental and genetic influences. Using genotyping array data of 661 South African PD cases and 737 controls, we conduct a polygenic risk score (PRS) analysis using PRSice-2. Summary statistics from two PD association studies have been used as base datasets. We split the target dataset into training (70%; n = 979) and validation (30%; n = 419) cohorts. We test various clumping window sizes, linkage disequilibrium thresholds, and p -value thresholds to determine the optimal combination for risk prediction. Additionally, we investigate the variance explained by different combinations of covariates. Overall, we observe modest predictive performance (AUC: 0.5847-0.6183). Age at recruitment emerges as the strongest individual predictor, while sex contributes the least. These findings provide the first evaluation of PRS performance for PD in a highly admixed South African cohort, underscoring the importance of including underrepresented populations in genetic risk prediction. By systematically assessing predictive performance across two base datasets, we highlight how ancestry composition and study design affect risk estimation in diverse populations. This work lays a foundation for refining genomic prediction in admixed populations and contributes to ongoing efforts to ensure that advances in precision medicine are globally relevant.

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 multiomics 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.

Parkinson’s disease (PD) is a complex neurodegenerative disorder with environmental and genetic influences. Using genotyping array data of 661 South African PD cases and 737 controls, a polygenic risk score (PRS) analysis was conducted using PRSice-2. Summary statistics were used from two PD association studies as base datasets. The target dataset was split into training (70%; n=979) and validation (30%; n=419) cohorts. Various clumping window sizes, linkage disequilibrium thresholds, and p -value thresholds were tested to determine the optimal combination for risk prediction. Additionally, we investigated the variance explained by different combinations of covariates. Finally, top contributing variants were identified and cross-referenced with inferred local ancestry to assess ancestry-specific effects. Overall, modest predictive performance was observed (AUC: 0.6307-0.6311). Age at recruitment was the strongest individual predictor, while sex contributed the least. Local ancestry analysis indicated that the top contributing variants were not ancestry-specific risk variants. These findings provide the first evaluation of PRS performance for PD in a highly admixed South African cohort, underscoring the importance of including underrepresented populations in genetic risk prediction.

Spatial patterns of cells and other biological elements drive both physiologic and pathologic processes within tissues. While many imaging and transcriptomic methods document tissue organization, discerning these patterns is challenging, especially when they involve multiple elements in complex arrangements. To address this challenge, we present Spatial Patterning Analysis of Cellular Ensembles (SPACE), an R package for analysis of high-plex spatial data. SPACE is compatible with any data collection modality that records values (i.e., categorical cell/structure types or quantitative expression levels) at fixed spatial coordinates (i.e., 2d pixels or 3d voxels). SPACE detects not only broad patterns of co-occurrence but also context-dependent associations, quantitative gradients and orientations, and other organizational complexities. Via a robust information theoretic framework, SPACE explores all possible ensembles of tissue elements – single elements, pairs, triplets, and so on – and ranks the most strongly patterned ensembles. For single images, rankings reflect patterns that differ from random assortment. For sets of images, rankings reflect patterns that differ across sample groups (e.g., genotypes, treatments, timepoints, etc.). Further tools then thoroughly characterize the nature of each pattern for intuitive interpretation. We validate SPACE and demonstrate its advantages using murine lymph node images for which ground truth has been defined. We then use SPACE to detect new patterns across varied datasets, including tumors and tuberculosis granulomas.

A central question in immunology is what features allow the immune system to respond in a timely manner to a variety of pathogens encountered at unanticipated times and diverse body sites. Two decades of advanced and static dynamic imaging methods have now revealed several major principles facilitating host defense. Suborgan spatial prepositioning of distinct cells promotes time‐efficient interactions upon pathogen sensing. Such pre‐organization also provides an effective barrier to movement of pathogens from parenchymal tissues into the blood circulation. Various molecular mechanisms maintain effective intercellular communication among otherwise rapidly moving cells. These and related discoveries have benefited from recent increases in the number of parameters that can be measured simultaneously in a single tissue section and the extension of such multiplex analyses to 3D tissue volumes. The application of new computational methods to such imaging data has provided a quantitative, in vivo context for cell trafficking and signaling pathways traditionally explored in vitro or with dissociated cell preparations. Here, we summarize our efforts to devise and employ diverse imaging tools to probe immune system organization and function, concluding with a commentary on future developments, which we believe will reveal even more about how the immune system operates in health and disease.