Laksh Malik

Tumor evolution is driven by various mutational processes, ranging from single-nucleotide variants (SNVs) to large structural variants (SVs) to dynamic shifts in DNA methylation. Current short-read sequencing methods struggle to accurately capture the full spectrum of these genomic and epigenomic alterations due to inherent technical limitations. To overcome that, here we introduce an approach to identify and analyze the genomic and epigenetic events in different stages of tumoral evolution from long-read sequencing of single-cell derived sublines. We then use it to profile 23 sublines of a mouse cutaneous melanoma cell line, characterized with distinct growth phenotypes and treatment responses. We develop a computational framework for harmonization and joint analysis of different variant types in the evolutionary context. Uniquely, our framework enables detection of recurrent amplifications of putative driver genes, generated by independent SVs across different lineages, suggesting parallel evolution. In addition, our approach revealed gradual and lineage-specific methylation changes associated with aggressive clonal phenotypes. We also show our set of phylogeny-constrained variant calls along with openly released sequencing data can be a valuable resource for the development and benchmarking of computational methods.

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.

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.

DNA methylation most commonly occurs as 5-methylcytosine (5mC) in the human genome and has been associated with human diseases. Recent developments in single-molecule sequencing technologies (Oxford Nanopore Technologies [ONT] and Pacific Biosciences [PacBio]) have enabled readouts of long, native DNA molecules, including cytosine methylation. ONT recently upgraded their Nanopore sequencing chemistry and kits from the R9 to the R10 version, which yielded increased accuracy and sequencing throughput. However, the effects on methylation detection have not yet been documented. Here, we performed a series of computational analyses to characterize differences in Nanopore-based 5mC detection between the ONT R9 and R10 chemistries. We compared 5mC calls in R9 and R10 for three human genome data sets: a cell line, a frontal cortex brain sample, and a blood sample. We performed an in-depth analysis on CpG islands and homopolymer regions, and documented high concordance for methylation detection among sequencing technologies. The strongest correlation was observed between Nanopore R10 and Illumina bisulfite technologies for cell line–derived data sets. Subtle differences in methylation data sets between technologies can impact analysis tools such as differential methylation calling software. Our findings show that comparisons can be drawn between methylation data from different Nanopore chemistries using guided hypotheses. This work will facilitate comparison among Nanopore data cohorts derived using different chemistries from large-scale sequencing efforts, such as the NIH CARD Long Read Initiative.

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.

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.

Parkinson’s disease (PD) is a common neurodegenerative disorder with a significant risk proportion driven by genetics. While much progress has been made, most of the heritability remains unknown. This is in-part because previous genetic studies have focused on the contribution of single nucleotide variants. More complex forms of variation, such as structural variants and tandem repeats, are already associated with several synucleinopathies. However, because more sophisticated sequencing methods are usually required to detect these regions, little is understood regarding their contribution to PD. One example is a polymorphic CT-rich region in intron 4 of the SNCA gene. This haplotype has been suggested to be associated with risk of Lewy Body (LB) pathology in Alzheimer’s Disease and SNCA gene expression, but is yet to be investigated in PD. Here, we attempt to resolve this CT-rich haplotype and investigate its role in PD. We performed targeted PacBio HiFi sequencing of the region in 1375 PD cases and 959 controls. We replicate the previously reported associations and a novel association between two PD risk SNVs (rs356182 and rs5019538) and haplotype 4, the largest haplotype. Through quantitative trait locus analyzes we identify a significant haplotype 4 association with alternative CAGE transcriptional start site usage, not leading to significant differential SNCA gene expression in post-mortem frontal cortex brain tissue. Therefore, disease association in this locus might not be biologically driven by this CT-rich repeat region. Our data demonstrates the complexity of this SNCA region and highlights that further follow up functional studies are warranted.

DNA methylation most commonly occurs as 5-methylcytosine (5-mC) in the human genome and has been associated with human diseases. Recent developments in single-molecule sequencing technologies (Oxford Nanopore Technologies (ONT) and Pacific Biosciences) have enabled readouts of long, native DNA molecules, including cytosine methylation. ONT recently upgraded their Nanopore sequencing chemistry and kits from R9 to the R10 version, which yielded increased accuracy and sequencing throughput. However the effects on methylation detection have not yet been documented. Here we performed a series of computational analyses to characterize differences in Nanopore-based 5mC detection between the ONT R9 and R10 chemistries. We compared 5mC calls in R9 and R10 for three human genome datasets: a cell line, a frontal cortex brain sample, and a blood sample. We performed an in-depth analysis on CpG islands and homopolymer regions, and documented high concordance for methylation detection among sequencing technologies. The strongest correlation was observed between Nanopore R10 and Illumina bisulfite technologies for cell line-derived datasets. Subtle differences in methylation datasets between technologies can impact analysis tools such as differential methylation calling software. Our findings show that comparisons can be drawn between methylation data from different Nanopore chemistries using guided hypotheses. This work will facilitate comparison among Nanopore data cohorts derived using different chemistries from large scale sequencing efforts, such as the NIH CARD Long Read Initiative.