Kensuke Daida

While induced pluripotent stem cells (iPSCs) have gained popularity in studying neurodegenerative diseases, the heterogeneity of stem cells used across studies impacts cross-study comparison. The iPSC Neurodegenerative Disease Initiative (iNDI) selected the KOLF2.1J cell line and prioritized its use as a reference standard for studying the effects of pathogenic variants on cell biology due to its stability and neutral neurodegenerative disease genetic risk. This cell line, and its derivatives expressing over 100 variants related to Alzheimer’s disease, Parkinson’s disease, and other neurological diseases, are available for academic and industry access. Current genomic data analyses are limited by the use of a human reference genome that does not capture the complete genetic background of a given iPSC line. While in the future this issue may be partially mitigated by the creation of a comprehensive human pangenome, previous work has shown that generating custom genomes is of value both to characterize the variation present and to serve as a more appropriate genomic reference. Here, we generated and characterized a custom complete genome assembly from KOLF2.1J. Mapping of sequencing reads to a personalized diploid assembly results in more comprehensive mapping compared to traditional linear references (i.e GRCh38). In addition, we provide a comprehensive custom gene annotation along with isoform expression and differential methylation analyses across multiple cell types. The assembly and all additional data is browsable and publicly available. This resource will enable more accurate investigation of the KOLF2.1J cell line and any genomics data generated compared to using traditional generalized references, while also serving as a foundational approach for establishing custom reference assemblies for other high-value iPSC lines.

Objective: Long‐read sequencing and optical genome mapping technologies have the ability to detect large and complex structural variants. This has led to the discovery of novel pathogenic variants in neurodegenerative movement disorders. Thus, we aimed to systematically compare the SV detection capabilities of OGM and ONT in Parkinson's disease. Methods: Ultra‐high molecular weight DNA was derived from blood and fibroblast cultures of 19 patients with mostly early‐onset Parkinson's disease, and used for Nanopore sequencing and optical genome mapping. The size distributions of deletions and insertions were compared, and variants were filtered for rare or potentially pathogenic variants in 134 known movement disorder genes. Results: Both methods identified SVs > 50 kb; however, optical mapping identified fewer structural variants (49,677) compared to Nanopore sequencing (94,400), but detected six times more in the range 50–80 kb. In general, it detected significantly larger deletions and insertions ( p < 2.2 × 10 ⁻¹⁶ ). Both methods detected a benign intergenic deletion (195 kb) near ITPR1 , and optical mapping validated a previously published 7‐Mb PRKN inversion. Small heterozygous deletions in ATXN2 , SUCLA2 , and PNKD detected by optical mapping were identified to be intronic by Nanopore sequencing. No causal variants were identified in movement disorder genes. Interpretation: Optical mapping can be a powerful first‐line method for detecting large structural variants, but it requires a high‐resolution method to refine breakpoint positions. Despite certain limitations, Nanopore sequencing was highly capable of detecting large variants independently and allows for a highly complementary assessment and validation of structural variation in combination with optical mapping.

Many non-coding variants influence complex traits and diseases through gene regulation, yet the mechanisms linking these variants to downstream biology remain poorly understood. Here, we present eQTLGen Phase 2 , a comprehensive genome-wide analysis of gene expression quantitative trait loci (eQTLs) in 43,301 blood samples from 52 datasets. Beyond local cis -effects, this sample size enabled the first systematic mapping of trans -eQTLs at scale. We identify cis -eQTLs for nearly all expressed genes (94.7%) and trans -eQTLs for over half (56.2%). Second, by colocalizing cis -eQTLs with trans -eQTLs, we infer a directed gene regulatory network comprising 47,554 directed gene regulatory relationships. These networks reveal how genetic perturbations in upstream regulators produce dose-dependent downstream effects, supported by Perturb-seq and ChIP-seq data. Third, integrating this network with 87 genome-wide association studies allows us to systematically prioritize trait-relevant pathways and candidate genes. Variants exerting both cis – and trans -effects are markedly more likely to colocalize with trait associations than cis -only variants, delineating a subset of functionally active cis -eQTLs from a large group with limited downstream impact. This distinction provides a conceptual framework for identifying regulatory variants that truly mediate complex trait biology. Together, these results provide a publicly available resource of cis – and trans -eQTLs and an in vivo scaffold for human gene-regulatory networks, elucidating how propagation of cis -effects modulates complex disease.

TheAPOEgene encodes a key lipid transport protein and plays a central role in Alzheimer’s disease (AD) pathogenesis. Three commonAPOEalleles, ε2 (rs7412(C>T), ε3 (reference), and ε4 (rs429358(T>C)), arise from two coding variants in exon 4 and confer distinct AD risk profiles, with ε4 increasing risk and ε2 providing protection. The ε3-linkedAPOEvariant rs769455[T] has also been associated with elevated AD risk in individuals of African ancestry carrying both rs769455[T] and ε4 alleles. These single nucleotide variants (SNVs) reside in a cytosine-phosphate-guanine (CpG) island, which is a region with a higher frequency of CpG sites compared to the rest of the genome. CpG sites are subject to 5-methylcytosine (5mC) methylation by DNA methyltransferases which add a methyl group to the fifth carbon on the cytosine residue of a CpG site. The presence of SNVs can disrupt this process, making these regions prime targets for differential methylation; however, allele-specific methylation patterns inAPOEremain poorly resolved due to technical limitations of conventional bisulfite and methylation array based methods, including degraded DNA quality, sparse CpG coverage, and lack of haplotype phasing. Here, we leverage high-accuracy long-read sequencing data to generate haplotype-resolved methylation profiles of theAPOElocus in 332 postmortem brain samples from two ancestrally different cohorts. This includes 201 individuals of European ancestry from the North American Brain Expression Consortium (NABEC), comprising 402 haplotypes (48 ε2 and 58 ε4 alleles), and 131 individuals of African and African admixed ancestry from the Human Brain Core Collection (HBCC), comprising 262 haplotypes (25 ε2, 64 ε4, and 7 rs769455 alleles). A linear regression analysis identified 18 novel differentially methylated CpG sites (DMCs) associated withAPOEε2, ε4, and rs769455 within a gene cluster spanningTOMM40, APOE, APOC1,andAPOC4-APOC2. This represents the most comprehensive haplotype-resolved methylation study ofAPOEin human brain tissue to date. Our results uncover distinct allele-specific methylation signatures and demonstrate the power of long-read sequencing for resolving epigenetic variation relevant to AD risk.

Background: Hereditary ataxias are genetically diverse, yet up to 75% remain undiagnosed due to technological and financial barriers. A pathogenic ZFHX3 GGC repeat expansion was recently linked to spinocerebellar ataxia type 4 (SCA4), characterized by progressive ataxia and sensory neuropathy, with all reported cases in individuals of Northern European ancestry. Methods: We performed Oxford Nanopore Technologies (ONT) genome long-read sequencing (>115 GB per sample) on a total of 15 individuals from Chile; 14 patients with suspected hereditary movement disorders and one unrelated family member. Variants were identified using PEPPER-Margin-DeepVariant 0.8 (SNVs), Sniffles 2.4 (SVs), and Vamos 2.1.3 (STRs). Ancestry was inferred using GenoTools with reference data from the 1000 Genomes Project, Human Genome Diversity Project, and an Ashkenazi Jewish panel. Haplotype analysis was conducted by phasing SNVs within ZFHX3 , and methylation profiling was performed with modbamtools. Results: We identified ZFHX3 GGC repeat expansions (47–55 repeats) in four individuals with progressive ataxia, polyneuropathy, and vermis atrophy. One case presented parkinsonism–ataxia, expanding the phenotype. Longer expansions correlated with earlier onset and greater severity. Hypermethylation was detected on the expanded allele, and haplotype analysis linked ultra-rare ZFHX3 variants to distant Swedish ancestry. Conclusion: This is the first report of SCA4 outside Northern Europe, confirming a shared founder haplotype and expansion instability. The presence of parkinsonism broadens the clinical spectrum. Comprehensive genetic testing across diverse populations is crucial, and long-read sequencing enhances diagnostic yield by detecting repeat expansions and SNVs in a single assay.

Elucidating the genetic contributions to Parkinson’s disease (PD) etiology across diverse ancestries is a critical priority for the development of targeted therapies in a global context. We conducted the largest sequencing characterization of potentially disease-causing, protein-altering and splicing mutations in 710 cases and 11,827 controls from genetically predicted African or African admixed ancestries. We explored copy number variants (CNVs) and runs of homozygosity (ROHs) in prioritized early onset and familial cases. Our study identified rare GBA1 coding variants to be the most frequent mutations among PD patients, with a frequency of 4% in our case cohort. Out of the 18 GBA1 variants identified, ten were previously classified as pathogenic or likely pathogenic, four were novel, and four were reported as of uncertain clinical significance. The most common known disease-associated GBA1 variants in the Ashkenazi Jewish and European populations, p.Asn409Ser, p.Leu483Pro, p.Thr408Met, and p.Glu365Lys, were not identified among the screened PD cases of African and African admixed ancestry. Similarly, the European and Asian LRRK2 disease-causing mutational spectrum, including LRRK2 p.Gly2019Ser and p.Gly2385Arg genetic risk factors, did not appear to play a major role in PD etiology among West African-ancestry populations. However, we found three heterozygous novel missense LRRK2 variants of uncertain significance overrepresented in cases, two of which — p.Glu268Ala and p.Arg1538Cys — had a higher prevalence in the African ancestry population reference datasets. Structural variant analyses revealed the presence of PRKN CNVs with a frequency of 0.7% in African and African admixed cases, with 66% of CNVs detected being compound heterozygous or homozygous in early-onset cases, providing further insights into the genetic underpinnings in early-onset juvenile PD in these populations. Novel genetic variation overrepresented in cases versus controls among screened genes warrants further replication and functional prioritization to unravel their pathogenic potential. Here, we created the most comprehensive genetic catalog of both known and novel coding and splicing variants potentially linked to PD etiology in an underserved population. Our study has the potential to guide the development of targeted therapies in the emerging era of precision medicine. By expanding genetics research to involve underrepresented populations, we hope that future PD treatments are not only effective but also inclusive, addressing the needs of diverse ancestral groups.

Objective: Variants in PRKN and PINK1 are the leading cause of early‐onset autosomal recessive Parkinson's disease, yet many cases remain genetically unresolved. We previously identified a 7 megabases complex structural variant in a pair of monozygotic twins using Oxford Nanopore Technologies (ONT) long‐read sequencing. This study aims to determine if ONT long‐read sequencing can detect a second variant in other unresolved early‐onset Parkinson's disease (EOPD) cases with 1 heterozygous PRKN or PINK1 variant.Methods: ONT long‐read sequencing was performed on EOPD patients with 1 reported PRKN/PINK1 pathogenic variant, with onset age under 50. Positive controls included EOPD patients with 2 known PRKN pathogenic variants. Initial testing involved short‐read targeted panel sequencing for single nucleotide variants and multiplex ligation‐dependent probe amplification for copy number variants.Results: A total of 47 patients were studied (PRKN “one‐variant,” n = 23; PINK1 “one‐variant,” n = 12; PRKN “two‐variants,” n = 12). ONT long‐read sequencing identified a second pathogenic variant in 26% of PRKN “one‐variant” patients (6/23), but none in PINK1 “one‐variant” patients (0/12). Detected variants included 1 complex inversion, 2 structural variant overlaps, and 3 duplications. In the PRKN “two‐variants” group, both variants were identified in all patients (100%, 12/12).Interpretation: ONT long‐read sequencing effectively identifies pathogenic structural variants in the PRKN locus missed by conventional methods. It should be considered for unresolved EOPD cases when a second variant is not detected through conventional approaches. ANN NEUROL 2025;97:753–765

Recently, an African ancestry-specific Parkinson disease (PD) risk signal was identified at the gene encoding glucocerebrosidase (GBA1). This variant (rs3115534-G) is carried by ~50% of West African PD cases and imparts a dose-dependent increase in risk for disease. The risk variant has varied frequencies across African ancestry groups but is almost absent in European and Asian ancestry populations. GBA1 is a gene of high clinical and therapeutic interest. Damaging biallelic protein-coding variants cause Gaucher disease and monoallelic variants confer risk for PD and dementia with Lewy bodies, likely by reducing the function of glucocerebrosidase. Interestingly, the African ancestry-specific GBA1 risk variant is a noncoding variant, suggesting a different mechanism of action. Using full-length RNA transcript sequencing, we identified partial intron 8 expression in risk variant carriers (G) but not in nonvariant carriers (T). Antibodies targeting the N terminus of glucocerebrosidase showed that this intron-retained isoform is likely not protein coding and subsequent proteomics did not identify a shorter protein isoform, suggesting that the disease mechanism is RNA based. Clustered regularly interspaced short palindromic repeats editing of the reported index variant (rs3115534) revealed that this is the sequence alteration responsible for driving the production of these transcripts containing intron 8. Follow-up analysis of this variant showed that it is in a key intronic branchpoint sequence and, therefore, has important implications in splicing and disease. In addition, when measuring glucocerebrosidase activity, we identified a dose-dependent reduction in risk variant carriers. Overall, we report the functional effect of a GBA1 noncoding risk variant, which acts by interfering with the splicing of functional GBA1 transcripts, resulting in reduced protein levels and reduced glucocerebrosidase activity. This understanding reveals a potential therapeutic target in an underserved and underrepresented population.

Copy Number Variations (CNVs) play pivotal roles in the etiology of complex diseases and are variable across diverse populations. Understanding the association between CNVs and disease susceptibility is significant in disease genetics research and often requires analysis of large sample sizes. One of the most cost-effective and scalable methods for detecting CNVs is based on normalized signal intensity values, such as Log R Ratio (LRR) and B Allele Frequency (BAF), from Illumina genotyping arrays. In this study, we present CNV-Finder, a novel pipeline integrating deep learning techniques on array data, specifically a Long Short-Term Memory (LSTM) network, to expedite the large-scale identification of CNVs within predefined genomic regions. This facilitates efficient prioritization of samples for time-consuming or costly subsequent analyses such as Multiplex Ligation-dependent Probe Amplification (MLPA), short-read, and long-read whole genome sequencing. We incorporate four genes to establish our methods—Parkin ( PRKN ), Leucine Rich Repeat And Ig Domain Containing 2 ( LINGO2 ), Microtubule Associated Protein Tau ( MAPT ), and alpha-Synuclein ( SNCA )—which may be relevant to neurological diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD), Progressive Supranuclear Palsy (PSP), or related disorders such as essential tremor (ET). By training our models on expert-annotated samples and validating them across diverse cohorts, including those from the Global Parkinson’s Genetics Program (GP2) and additional dementia-specific databases, we demonstrate the efficacy of CNV-Finder in accurately detecting deletions and duplications. Our pipeline outputs app-compatible files for visualization within CNV-Finder’s interactive web application. This interface enables researchers to review predictions and filter displayed samples by model prediction values, LRR range, and variant count in order to explore or confirm results. Our pipeline integrates this human feedback to enhance model performance and reduce false positive rates. Through a series of comprehensive analyses and validations using visual inspection, MLPA, short-read, and long-read sequencing data, we demonstrate the robustness and adaptability of CNV-Finder in identifying CNVs with regions of varied size, probe density, and noise. Our findings highlight the significance of contextual understanding and human expertise in enhancing the precision of CNV identification, particularly in complex genomic regions like 17q21.31. The CNV-Finder pipeline is a scalable, publicly available resource for the scientific community, available on GitHub ( https://github.com/GP2code/CNV-Finder ; DOI 10.5281/zenodo.14182563 ). CNV-Finder not only expedites accurate candidate identification but also significantly reduces the manual workload for researchers, enabling future targeted validation and downstream analyses in regions or phenotypes of interest.