Germline genetic contribution to the immune landscape of cancer.

Understanding the contribution of the host's genetic background to cancer immunity may lead to improved stratification for immunotherapy and to the identification of novel therapeutic targets. We investigated the effect of common and rare germline variants on 139 well-defined immune traits in ∼9000 cancer patients enrolled in TCGA. High heritability was observed for estimates of NK cell and T cell subset infiltration and for interferon signaling. Common variants of IFIH1, TMEM173 (STING1), and TMEM108 were associated with differential interferon signaling and variants mapping to RBL1 correlated with T cell subset abundance. Pathogenic or likely pathogenic variants in BRCA1 and in genes involved in telomere stabilization and Wnt-ß-catenin also acted as immune modulators. Our findings provide evidence for the impact of germline genetics on the composition and functional orientation of the tumor immune microenvironment. The curated datasets, variants, and genes identified provide a resource toward further understanding of tumor-immune interactions.

Discovering predisposing genes for hereditary breast cancer using deep learning.

Breast cancer (BC) is the most common malignancy affecting Western women today. It is estimated that as many as 10% of BC cases can be attributed to germline variants. However, the genetic basis of the majority of familial BC cases has yet to be identified. Discovering predisposing genes contributing to familial BC is challenging due to their presumed rarity, low penetrance, and complex biological mechanisms. Here, we focused on an analysis of rare missense variants in a cohort of 12 families of Middle Eastern origins characterized by a high incidence of BC cases. We devised a novel, high-throughput, variant analysis pipeline adapted for family studies, which aims to analyze variants at the protein level by employing state-of-the-art machine learning models and three-dimensional protein structural analysis. Using our pipeline, we analyzed 1218 rare missense variants that are shared between affected family members and classified 80 genes as candidate pathogenic. Among these genes, we found significant functional enrichment in peroxisomal and mitochondrial biological pathways which segregated across seven families in the study and covered diverse ethnic groups. We present multiple evidence that peroxisomal and mitochondrial pathways play an important, yet underappreciated, role in both germline BC predisposition and BC survival.

123VCF: an intuitive and efficient tool for filtering VCF files.

BACKGROUND: The advent of Next-Generation Sequencing (NGS) has catalyzed a paradigm shift in medical genetics, enabling the identification of disease-associated variants. However, the vast quantum of data produced by NGS necessitates a robust and dependable mechanism for filtering irrelevant variants. Annotation-based variant filtering, a pivotal step in this process, demands a profound understanding of the case-specific conditions and the relevant annotation instruments. To tackle this complex task, we sought to design an accessible, efficient and more importantly easy to understand variant filtering tool. RESULTS: Our efforts culminated in the creation of 123VCF, a tool capable of processing both compressed and uncompressed Variant Calling Format (VCF) files. Built on a Java framework, the tool employs a disk-streaming real-time filtering algorithm, allowing it to manage sizable variant files on conventional desktop computers. 123VCF filters input variants in accordance with a predefined filter sequence applied to the input variants. Users are provided the flexibility to define various filtering parameters, such as quality, coverage depth, and variant frequency within the populations. Additionally, 123VCF accommodates user-defined filters tailored to specific case requirements, affording users enhanced control over the filtering process. We evaluated the performance of 123VCF by analyzing different types of variant files and comparing its runtimes to the most similar algorithms like BCFtools filter and GATK VariantFiltration. The results indicated that 123VCF performs relatively well. The tool's intuitive interface and potential for reproducibility make it a valuable asset for both researchers and clinicians. CONCLUSION: The 123VCF filtering tool provides an effective, dependable approach for filtering variants in both research and clinical settings. As an open-source tool available at https://project123vcf.sourceforge.io , it is accessible to the global scientific and clinical community, paving the way for the discovery of disease-causing variants and facilitating the advancement of personalized medicine.

Integrated approach to generate artificial samples with low tumor fraction for somatic variant calling benchmarking.

BACKGROUND: High-throughput sequencing (HTS) has become the gold standard approach for variant analysis in cancer research. However, somatic variants may occur at low fractions due to contamination from normal cells or tumor heterogeneity; this poses a significant challenge for standard HTS analysis pipelines. The problem is exacerbated in scenarios with minimal tumor DNA, such as circulating tumor DNA in plasma. Assessing sensitivity and detection of HTS approaches in such cases is paramount, but time-consuming and expensive: specialized experimental protocols and a sufficient quantity of samples are required for processing and analysis. To overcome these limitations, we propose a new computational approach specifically designed for the generation of artificial datasets suitable for this task, simulating ultra-deep targeted sequencing data with low-fraction variants and demonstrating their effectiveness in benchmarking low-fraction variant calling. RESULTS: Our approach enables the generation of artificial raw reads that mimic real data without relying on pre-existing data by using NEAT, a fine-grained read simulator that generates artificial datasets using models learned from multiple different datasets. Then, it incorporates low-fraction variants to simulate somatic mutations in samples with minimal tumor DNA content. To prove the suitability of the created artificial datasets for low-fraction variant calling benchmarking, we used them as ground truth to evaluate the performance of widely-used variant calling algorithms: they allowed us to define tuned parameter values of major variant callers, considerably improving their detection of very low-fraction variants. CONCLUSIONS: Our findings highlight both the pivotal role of our approach in creating adequate artificial datasets with low tumor fraction, facilitating rapid prototyping and benchmarking of algorithms for such dataset type, as well as the important need of advancing low-fraction variant calling techniques.

Validated WGS and WES protocols proved saliva-derived gDNA as an equivalent to blood-derived gDNA for clinical and population genomic analyses.

BACKGROUND: Whole exome sequencing (WES) and whole genome sequencing (WGS) have become standard methods in human clinical diagnostics as well as in population genomics (POPGEN). Blood-derived genomic DNA (gDNA) is routinely used in the clinical environment. Conversely, many POPGEN studies and commercial tests benefit from easy saliva sampling. Here, we evaluated the quality of variant call sets and the level of genotype concordance of single nucleotide variants (SNVs) and small insertions and deletions (indels) for WES and WGS using paired blood- and saliva-derived gDNA isolates employing genomic reference-based validated protocols. METHODS: The genomic reference standard Coriell NA12878 was repeatedly analyzed using optimized WES and WGS protocols, and data calls were compared with the truth dataset published by the Genome in a Bottle Consortium. gDNA was extracted from the paired blood and saliva samples of 10 participants and processed using the same protocols. A comparison of paired blood-saliva call sets was performed in the context of WGS and WES genomic reference-based technical validation results. RESULTS: The quality pattern of called variants obtained from genomic-reference-based technical replicates correlates with data calls of paired blood-saliva-derived samples in all levels of tested examinations despite a higher rate of non-human contamination found in the saliva samples. The F1 score of 10 blood-to-saliva-derived comparisons ranged between 0.8030-0.9998 for SNVs and between 0.8883-0.9991 for small-indels in the case of the WGS protocol, and between 0.8643-0.999 for SNVs and between 0.7781-1.000 for small-indels in the case of the WES protocol. CONCLUSION: Saliva may be considered an equivalent material to blood for genetic analysis for both WGS and WES under strict protocol conditions. The accuracy of sequencing metrics and variant-detection accuracy is not affected by choosing saliva as the gDNA source instead of blood but much more significantly by the genomic context, variant types, and the sequencing technology used.

Genome sequencing data analysis for rare disease gene discovery.

Rare diseases occur in a smaller proportion of the general population, which is variedly defined as less than 200 000 individuals (US) or in less than 1 in 2000 individuals (Europe). Although rare, they collectively make up to approximately 7000 different disorders, with majority having a genetic origin, and affect roughly 300 million people globally. Most of the patients and their families undergo a long and frustrating diagnostic odyssey. However, advances in the field of genomics have started to facilitate the process of diagnosis, though it is hindered by the difficulty in genome data analysis and interpretation. A major impediment in diagnosis is in the understanding of the diverse approaches, tools and datasets available for variant prioritization, the most important step in the analysis of millions of variants to select a few potential variants. Here we present a review of the latest methodological developments and spectrum of tools available for rare disease genetic variant discovery and recommend appropriate data interpretation methods for variant prioritization. We have categorized the resources based on various steps of the variant interpretation workflow, starting from data processing, variant calling, annotation, filtration and finally prioritization, with a special emphasis on the last two steps. The methods discussed here pertain to elucidating the genetic basis of disease in individual patient cases via trio- or family-based analysis of the genome data. We advocate the use of a combination of tools and datasets and to follow multiple iterative approaches to elucidate the potential causative variant.

Genetic variant detection in a South African haemophilia B population.

BACKGROUND: Haemophilia B is characterised by a deficiency of factor IX (FIX) protein due to genetic variants in the FIX gene (F9). Genetic testing may have a vital role in effectively managing haemophilia B. However, in many developing countries, comprehensive genetic variant detection is unavailable. This study aimed to address the lack of genetic data in our country by conducting genetic variant detection on people affected by haemophilia B in our region. METHODS: Twenty-one participants were screened with a direct Sanger sequencing method to identify variants in the F9 gene. The identified variants were then compared to previously published variants and/or to a reference database. RESULTS AND DISCUSSION: A total of ten F9 genetic changes were detected, with five of them being novel. These identified variants were distributed across different domains of the FIX protein. Only one participant had a history of inhibitor formation against FIX replacement therapy. Notably, this participant had two distinct genetic changes present adjacent to each other. Thus, we hypothesise that the presence of multiple variants within the same functional region of the gene may increase the risk for inhibitor development. CONCLUSION: The discovery of novel pathogenic variations in the F9 gene highlights the importance of genetic analysis in specific geographical regions. The possible link between a complex variant and inhibitor formation illustrates the potential role that genetic screening has as a pre-treatment tool in predicting treatment reactions and outcomes.

Comprehensive prognostic assessment in kidney cancer: A multidimensional approach analyzing Fufang Sanling granules, genetic variants, and immune infiltration.

This study delves into the potential therapeutic benefits of Fufang Sanling Granules for kidney cancer, focusing on their active components and the underlying mechanisms of their interaction with cancer-related targets. By constructing a drug-active component-target network based on eight herbs, key active compounds such as kaempferol, quercetin, and linolenic acid were identified, suggesting their pivotal roles in modulating immune responses and cellular signaling pathways relevant to cancer progression. The research further identified 51 central drug-disease genes through comprehensive bioinformatics analyses, implicating their involvement in crucial biological processes and pathways. A novel risk score model, encompassing six genes with significant prognostic value for renal cancer, was established and validated, showcasing its effectiveness in predicting patient outcomes through mutation analysis and survival studies. The model's predictive power was further confirmed by its ability to stratify patients into distinct risk groups with significant survival differences, highlighting its potential as a prognostic tool. Additionally, the study explored the relationship between gene expression within the identified black module and the risk score, uncovering significant associations with the extracellular matrix and immune infiltration patterns. This reveals the complex interplay between the tumor microenvironment and cancer progression. The integration of the risk score with clinical parameters through a nomogram significantly improved the model's predictive accuracy, offering a more comprehensive tool for predicting kidney cancer prognosis. In summary, by combining detailed molecular analyses with clinical insights, this study presents a robust framework for understanding the therapeutic potential of Fufang Sanling Granules in kidney cancer. It not only sheds light on the active components and their interactions with cancer-related genes but also introduces a reliable risk score model, paving the way for personalized treatment strategies and improved patient management in the future.

Whole-genome sequencing and comparative genomic analysis of potential biotechnological strains of Trichoderma harzianum, Trichoderma atroviride, and Trichoderma reesei.

Trichoderma atroviride and Trichoderma harzianum are widely used as commercial biocontrol agents against plant diseases. Recently, T. harzianum IOC-3844 (Th3844) and T. harzianum CBMAI-0179 (Th0179) demonstrated great potential in the enzymatic conversion of lignocellulose into fermentable sugars. Herein, we performed whole-genome sequencing and assembly of the Th3844 and Th0179 strains. To assess the genetic diversity within the genus Trichoderma, the results of both strains were compared with strains of T. atroviride CBMAI-00020 (Ta0020) and T. reesei CBMAI-0711 (Tr0711). The sequencing coverage value of all genomes evaluated in this study was higher than that of previously reported genomes for the same species of Trichoderma. The resulting assembly revealed total lengths of 40 Mb (Th3844), 39 Mb (Th0179), 36 Mb (Ta0020), and 32 Mb (Tr0711). A genome-wide phylogenetic analysis provided details on the relationships of the newly sequenced species with other Trichoderma species. Structural variants revealed genomic rearrangements among Th3844, Th0179, Ta0020, and Tr0711 relative to the T. reesei QM6a reference genome and showed the functional effects of such variants. In conclusion, the findings presented herein allow the visualization of genetic diversity in the evaluated strains and offer opportunities to explore such fungal genomes in future biotechnological and industrial applications.

Prenatal exome and genome sequencing for fetal structural abnormalities.

As prenatal exome sequencing becomes integrated into clinical care, it is critical that providers caring for women with fetal anomalies recognize not only the benefits, but also the challenges and considerations related to this technology. This overview of prenatal sequencing includes information about indications for sequencing, methods, diagnostic yield, clinical utility, variant interpretation, ethical considerations and dilemmas, practical considerations (ie, turnaround time and cost), pre- and posttest counseling points, and psychological impact of testing on families.

A Highly Efficient Workflow for Detecting and Identifying Sequence Variants in Therapeutic Proteins with a High Resolution LC-MS/MS Method.

Large molecule protein therapeutics have steadily grown and now represent a significant portion of the overall pharmaceutical market. These complex therapies are commonly manufactured using cell culture technology. Sequence variants (SVs) are undesired minor variants that may arise from the cell culture biomanufacturing process that can potentially affect the safety and efficacy of a protein therapeutic. SVs have unintended amino acid substitutions and can come from genetic mutations or translation errors. These SVs can either be detected using genetic screening methods or by mass spectrometry (MS). Recent advances in Next-generation Sequencing (NGS) technology have made genetic testing cheaper, faster, and more convenient compared to time-consuming low-resolution tandem MS and Mascot Error Tolerant Search (ETS)-based workflows which often require ~6 to 8 weeks data turnaround time. However, NGS still cannot detect non-genetic derived SVs while MS analysis can do both. Here, we report a highly efficient Sequence Variant Analysis (SVA) workflow using high-resolution MS and tandem mass spectrometry combined with improved software to greatly reduce the time and resource cost associated with MS SVA workflows. Method development was performed to optimize the high-resolution tandem MS and software score cutoff for both SV identification and quantitation. We discovered that a feature of the Fusion Lumos caused significant relative under-quantitation of low-level peptides and turned it off. A comparison of common Orbitrap platforms showed that similar quantitation values were obtained on a spiked-in sample. With this new workflow, the amount of false positive SVs was decreased by up to 93%, and SVA turnaround time by LC-MS/MS was shortened to 2 weeks, comparable to NGS analysis speed and making LC-MS/MS the top choice for SVA workflow.

Wholistic approach: Transcriptomic analysis and beyond using archival material for molecular diagnosis.

Many neoplasms remain unclassified after histopathological examination, which requires further molecular analysis. To this regard, mesenchymal neoplasms are particularly challenging due to the combination of their rarity and the large number of subtypes, and many entities still lack robust diagnostic hallmarks. RNA transcriptomic profiles have proven to be a reliable basis for the classification of previously unclassified tumors and notably for mesenchymal neoplasms. Using exome-based RNA capture sequencing on more than 5000 samples of archival material (formalin-fixed, paraffin-embedded), the combination of expression profiles analyzes (including several clustering methods), fusion genes, and small nucleotide variations has been developed at the Centre Léon Bérard (CLB) in Lyon for the molecular diagnosis of challenging neoplasms and the discovery of new entities. The molecular basis of the technique, the protocol, and the bioinformatics algorithms used are described herein, as well as its advantages and limitations.

Genomic data integration and user-defined sample-set extraction for population variant analysis.

BACKGROUND: Population variant analysis is of great importance for gathering insights into the links between human genotype and phenotype. The 1000 Genomes Project established a valuable reference for human genetic variation; however, the integrative use of the corresponding data with other datasets within existing repositories and pipelines is not fully supported. Particularly, there is a pressing need for flexible and fast selection of population partitions based on their variant and metadata-related characteristics. RESULTS: Here, we target general germline or somatic mutation data sources for their seamless inclusion within an interoperable-format repository, supporting integration among them and with other genomic data, as well as their integrated use within bioinformatic workflows. In addition, we provide VarSum, a data summarization service working on sub-populations of interest selected using filters on population metadata and/or variant characteristics. The service is developed as an optimized computational framework with an Application Programming Interface (API) that can be called from within any existing computing pipeline or programming script. Provided example use cases of biological interest show the relevance, power and ease of use of the API functionalities. CONCLUSIONS: The proposed data integration pipeline and data set extraction and summarization API pave the way for solid computational infrastructures that quickly process cumbersome variation data, and allow biologists and bioinformaticians to easily perform scalable analysis on user-defined partitions of large cohorts from increasingly available genetic variation studies. With the current tendency to large (cross)nation-wide sequencing and variation initiatives, we expect an ever growing need for the kind of computational support hereby proposed.

Rare coding variants in RCN3 are associated with blood pressure.

BACKGROUND: While large genome-wide association studies have identified nearly one thousand loci associated with variation in blood pressure, rare variant identification is still a challenge. In family-based cohorts, genome-wide linkage scans have been successful in identifying rare genetic variants for blood pressure. This study aims to identify low frequency and rare genetic variants within previously reported linkage regions on chromosomes 1 and 19 in African American families from the Trans-Omics for Precision Medicine (TOPMed) program. Genetic association analyses weighted by linkage evidence were completed with whole genome sequencing data within and across TOPMed ancestral groups consisting of 60,388 individuals of European, African, East Asian, Hispanic, and Samoan ancestries. RESULTS: Associations of low frequency and rare variants in RCN3 and multiple other genes were observed for blood pressure traits in TOPMed samples. The association of low frequency and rare coding variants in RCN3 was further replicated in UK Biobank samples (N = 403,522), and reached genome-wide significance for diastolic blood pressure (p = 2.01 × 10- 7). CONCLUSIONS: Low frequency and rare variants in RCN3 contributes blood pressure variation. This study demonstrates that focusing association analyses in linkage regions greatly reduces multiple-testing burden and improves power to identify novel rare variants associated with blood pressure traits.

ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies.

Set-based analysis that jointly tests the association of variants in a group has emerged as a popular tool for analyzing rare and low-frequency variants in sequencing studies. The existing set-based tests can suffer significant power loss when only a small proportion of variants are causal, and their powers can be sensitive to the number, effect sizes, and effect directions of the causal variants and the choices of weights. Here we propose an aggregated Cauchy association test (ACAT), a general, powerful, and computationally efficient p value combination method for boosting power in sequencing studies. First, by combining variant-level p values, we use ACAT to construct a set-based test (ACAT-V) that is particularly powerful in the presence of only a small number of causal variants in a variant set. Second, by combining different variant-set-level p values, we use ACAT to construct an omnibus test (ACAT-O) that combines the strength of multiple complimentary set-based tests, including the burden test, sequence kernel association test (SKAT), and ACAT-V. Through analysis of extensively simulated data and the whole-genome sequencing data from the Atherosclerosis Risk in Communities (ARIC) study, we demonstrate that ACAT-V complements the SKAT and the burden test, and that ACAT-O has a substantially more robust and higher power than those of the alternative tests.

Dynamic Scan Procedure for Detecting Rare-Variant Association Regions in Whole-Genome Sequencing Studies.

Whole-genome sequencing (WGS) studies are being widely conducted in order to identify rare variants associated with human diseases and disease-related traits. Classical single-marker association analyses for rare variants have limited power, and variant-set-based analyses are commonly used by researchers for analyzing rare variants. However, existing variant-set-based approaches need to pre-specify genetic regions for analysis; hence, they are not directly applicable to WGS data because of the large number of intergenic and intron regions that consist of a massive number of non-coding variants. The commonly used sliding-window method requires the pre-specification of fixed window sizes, which are often unknown as a priori, are difficult to specify in practice, and are subject to limitations given that the sizes of genetic-association regions are likely to vary across the genome and phenotypes. We propose a computationally efficient and dynamic scan-statistic method (Scan the Genome [SCANG]) for analyzing WGS data; this method flexibly detects the sizes and the locations of rare-variant association regions without the need to specify a prior, fixed window size. The proposed method controls for the genome-wise type I error rate and accounts for the linkage disequilibrium among genetic variants. It allows the detected sizes of rare-variant association regions to vary across the genome. Through extensive simulated studies that consider a wide variety of scenarios, we show that SCANG substantially outperforms several alternative methods for detecting rare-variant-associations while controlling for the genome-wise type I error rates. We illustrate SCANG by analyzing the WGS lipids data from the Atherosclerosis Risk in Communities (ARIC) study.

Clinical impact of whole-genome sequencing in patients with early-onset dementia.

BACKGROUND: In the clinical setting, identification of the genetic cause in patients with early-onset dementia (EOD) is challenging due to multiple types of genetic tests required to arrive at a diagnosis. Whole-genome sequencing (WGS) has the potential to serve as a single diagnostic platform, due to its superior ability to detect common, rare and structural genetic variation. METHODS: WGS analysis was performed in 50 patients with EOD. Point mutations, small insertions/deletions, as well as structural variants (SVs) and short tandem repeats (STRs), were analysed. An Alzheimer's disease (AD)-related polygenic risk score (PRS) was calculated in patients with AD. RESULTS: Clinical genetic diagnosis was achieved in 7 of 50 (14%) of the patients, with a further 8 patients (16%) found to have established risk factors which may have contributed to their EOD. Two pathogenic variants were identified through SV analysis. No expanded STRs were found in this study cohort, but a blinded analysis with a positive control identified a C9orf72 expansion accurately. Approximately 37% (7 of 19) of patients with AD had a PRS equivalent to >90th percentile risk. DISCUSSION: WGS acts as a single genetic test to identify different types of clinically relevant genetic variations in patients with EOD. WGS, if used as a first-line clinical diagnostic test, has the potential to increase the diagnostic yield and reduce time to diagnosis for EOD.

Identification of an at-risk subpopulation with high immune infiltration based on the peroxisome pathway and TIM3 in colorectal cancer.

BACKGROUND: Peroxisomes are pivotal metabolic organelles that exist in almost all eukaryote cells. A reduction in numbers and enzymatic activities of peroxisomes was found in colon adenocarcinomas. However, the role of peroxisomes or the peroxisome pathway in colorectal cancer (CRC) is not defined. METHODS: In the current study, a peroxisome score was calculated to indicate the activity of the peroxisome pathway using gene set variant analysis based on transcriptomic datasets. CIBERSORTx was chosen to infer enriched immune cells for tumors among subgroups. The SubMap algorithm was applied to predict its sensitivity to immunotherapy. RESULTS: The patients with a relatively low peroxisome score and high level of T-cell immunoglobulin and mucin domain 3 (TIM-3) presented the worse overall survival than others. Moreover, low peroxisome scores were associated with high infiltration of lymphocytes and poor prognosis in those CRC patients. Thus, a PERLowTIM3High CRC risk subpopulation was identified and characterized by high immune infiltration. The results also showed that CD8 T cells and macrophages highly infiltrated tumors of the PERLowTIM3High group, regardless of consortium molecular subtype and microsatellite instability status. This subgroup had the highest tumor mutational burden and overexpression of immune checkpoint genes. Further, the PERLowTIM3High group showed a higher probability of responding to programmed cell death protein-1-based immunotherapy. In addition, genes involved in peroxisomal metabolic processes in CRC were also investigated since peroxisome is a rather pleiotropic and highly metabolic organelle in cell. The results indicated that only those genes involved in fatty acid alpha oxidation could be used to stratify CRC patients as similar as peroxisome pathway genes. CONCLUSIONS: We revealed the favorable prognostic value of the peroxisome pathway in CRC and provided a new CRC stratification based on peroxisomes and TIM3, which might be helpful for CRC diagnostics and personalized treatment.

Analysis of high-risk pedigrees identifies 11 candidate variants for Alzheimer's disease.

INTRODUCTION: Analysis of sequence data in high-risk pedigrees is a powerful approach to detect rare predisposition variants. METHODS: Rare, shared candidate predisposition variants were identified from exome sequencing 19 Alzheimer's disease (AD)-affected cousin pairs selected from high-risk pedigrees. Variants were further prioritized by risk association in various external datasets. Candidate variants emerging from these analyses were tested for co-segregation to additional affected relatives of the original sequenced pedigree members. RESULTS: AD-affected high-risk cousin pairs contained 564 shared rare variants. Eleven variants spanning 10 genes were prioritized in external datasets: rs201665195 (ABCA7), and rs28933981 (TTR) were previously implicated in AD pathology; rs141402160 (NOTCH3) and rs140914494 (NOTCH3) were previously reported; rs200290640 (PIDD1) and rs199752248 (PIDD1) were present in more than one cousin pair; rs61729902 (SNAP91), rs140129800 (COX6A2, AC026471), and rs191804178 (MUC16) were not present in a longevity cohort; and rs148294193 (PELI3) and rs147599881 (FCHO1) approached significance from analysis of AD-related phenotypes. Three variants were validated via evidence of co-segregation to additional relatives (PELI3, ABCA7, and SNAP91). DISCUSSION: These analyses support ABCA7 and TTR as AD risk genes, expand on previously reported NOTCH3 variant identification, and prioritize seven additional candidate variants.

Functional characterization of novel variants in SMPD1 in Indian patients with acid sphingomyelinase deficiency.

Pathogenic variations in SMPD1 lead to acid sphingomyelinase deficiency (ASMD), that is, Niemann-Pick disease (NPD) type A and B (NPA, NPB), which is a recessive lysosomal storage disease. The knowledge of variant spectrum in Indian patients is crucial for early and accurate NPD diagnosis and genetic counseling of families. In this study, we recruited 40 unrelated pediatric patients manifesting symptoms of ASMD and subnormal ASM enzyme activity. Variations in SMPD1 were studied using Sanger sequencing for all exons, followed by interpretation of variants based on American College of Medical Genetics and Genomics & Association for Molecular Pathology (ACMG/AMP) criteria. We identified 18 previously unreported variants and 21 known variants, including missense, nonsense, deletions, duplications, and splice site variations with disease-causing potential. Eight missense variants were functionally characterized using in silico molecular dynamic simulation and in vitro transient transfection in HEK293T cells, followed by ASM enzyme assay, immunoblot, and immunofluorescence studies. All the variants showed reduced ASM activity in transfected cells confirming their disease-causing potential. The study provides data for efficient prenatal diagnosis and genetic counseling of families with NPD type A and B.