Genomic Markers Research Articles

Korea-Registries to Overcome Dementia and Accelerate Dementia Research (K-ROAD): A Cohort for Dementia Research and Ethnic-Specific Insights.

Dementia, particularly Alzheimer's disease, is a significant global health concern, with early diagnosis and treatment development being critical goals. While numerous cohorts have advanced dementia research, there is a lack of comprehensive data on ethnic differences, particularly for the Korean population. The Korea-Registries to Overcome Dementia and Accelerate Dementia Research (K-ROAD) aims to establish a large-scale, hospital-based dementia cohort to address this gap, with a focus on understanding disease progression, developing early diagnostics, and supporting treatment advancements specific to the Korean population. K-ROAD comprises multiple prospective cohorts. Participants underwent clinical evaluations, neuroimaging, and biomarker analysis, with data collected on a range of clinical and genomic markers. As of December 2023, K-ROAD has recruited over 5,800 participants, including individuals across the Alzheimer's clinical syndrome, subcortical vascular cognitive impairment, and frontotemporal dementia spectra. Preliminary findings highlight significant ethnic differences in amyloid positivity, cognitive decline, and biomarker profiles, compared to Western cohorts. The K-ROAD cohort offers a unique and critical resource for dementia research, providing insights into ethnic-specific disease characteristics and biomarker profiles. These findings will contribute to the development of personalized diagnostic and therapeutic approaches to dementia, enhancing global understanding of the disease.

Advances in Physalis molecular research: applications in authentication, genetic diversity, phylogenetics, functional genes, and omics.

The plants of the genus Physalis L. have been extensively utilized in traditional and indigenous Chinese medicinal practices for treating a variety of ailments, including dermatitis, malaria, asthma, hepatitis, and liver disorders. The present review aims to achieve a comprehensive and up-to-date investigation of the genus Physalis, a new model crop, to understand plant diversity and fruit development. Several chloroplast DNA-, nuclear ribosomal DNA-, and genomic DNA-based markers, such as psbA-trnH, internal-transcribed spacer (ITS), simple sequence repeat (SSR), random amplified microsatellites (RAMS), sequence-characterized amplified region (SCAR), and single nucleotide polymorphism (SNP), were developed for molecular identification, genetic diversity, and phylogenetic studies of Physalis species. A large number of functional genes involved in inflated calyx syndrome development (AP2-L, MPF2, MPF3, and MAGO), organ growth (AG1, AG2, POS1, and CNR1), and active ingredient metabolism (24ISO, DHCRT, P450-CPL, SR, DUF538, TAS14, and 3β-HSB) were identified contributing to the breeding of novel Physalis varieties. Various omic studies revealed and functionally identified a series of reproductive organ development-related factors, environmental stress-responsive genes, and active component biosynthesis-related enzymes. The chromosome-level genomes of Physalis floridana Rydb., Physalis grisea (Waterf.) M. Martínez, and Physalis pruinosa L. have been recently published providing a valuable resource for genome editing in Physalis crops. Our review summarizes the recent progress in genetic diversity, molecular identification, phylogenetics, functional genes, and the application of omics in the genus Physalis and accelerates efficient utilization of this traditional herb.

Prediction of Treatment Recommendations Via Ensemble Machine Learning Algorithms for Non-Small Cell Lung Cancer Patients in Personalized Medicine.

The primary goal of this research is to develop treatment-related genomic predictive markers for non-small cell lung cancer by integrating various machine learning algorithms that recommends near-optimal individualized patient treatment for chemotherapy in an effort to maximize efficacy or minimize treatment-related toxicity. This research can contribute toward developing a more refined, accurate and effective therapy accounting for specific patient needs. To accomplish our research goal, we implement ensemble learning algorithms, bagging with regularized Cox regression models and nonparametric tree-based models via Random Survival Forests. A comprehensive meta-database was compiled from the NCBI Gene Expression Omnibus data repository for lung cancer patients to capture and utilize complex genomic patterns that can predict treatment outcomes more accurately. The developed novel prediction algorithm demonstrates the ability to support complex clinical decision-making processes in the treatment of NSCLC. It effectively addresses patient heterogeneity, offering predictions that are both refined and personalized in improving the precision of chemotherapy regimens prescribed to the eligible patients. This research should contribute substantial advancement of cancer treatments by improving the accuracy and efficacy of chemotherapy treatments for a targeted group of patients who need the right treatment. The integration of complex machine learning techniques with genomic data holds substantial potential to transform current cancer treatment paradigms by providing robust support in clinical decision-making.

In vitro co-culture models for studying organoids-macrophages interaction: the golden technology of cancer immunotherapy.

Macrophages, as the largest immune cell group in tumour tissues, play a crucial role in influencing various malignant behaviours of tumour cells and tumour immune evasion. As the research on macrophages and cancer immunotherapy develops, the importance of appropriate research models becomes increasingly evident. The development of organoids has bridged the gap between traditional two-dimensional (2D) cultures and animal experiments. Recent studies have demonstrated that organoids exhibit similar physiological characteristics to the source tissue and closely resemble the in vivo genome and molecular markers of the source tissue or organ. However, organoids still lack an immune component. Developing a co-culture model of organoids and macrophages is crucial for studying the interaction and mechanisms between tumour cells and macrophages. This paper presents an overview of the establishment of co-culture models, the current research status of organoid macrophage interactions, and the current status of immunotherapy. In addition, the application prospects and shortcomings of the model are explained. Ultimately, it is hoped that the co-culture model will offer a preclinical testing platform for maximising a precise cancer immunotherapy strategy.

Optical detection of infectious SARS-CoV-2 virions by counting spikes.

Existing methods for the mass detection of viruses are limited to the registration of small amounts of a viral genome or specific protein markers. In spite of high sensitivity, the applied methods cannot distinguish between virulent viral particles and non-infectious viral particle debris. We report an approach to solve this long-standing challenge using the SARS-CoV-2 virus as an example. We show that wide-field optical microscopy with the state-of-the-art mesoscopic fluorescent labels, formed by a core-shell plasmonic nanoparticle with fluorescent dye molecules in the core-shell that are strongly coupled to the plasmonic nanoparticle, not only rapidly, i.e. in less than 20 minutes after sampling, detects SARS-CoV-2 virions directly in a patient sample without a pre-concentration step, but can also distinguish between infectious and non-infectious virus strains by counting the spikes on the lipid envelope of individual viral particles.

Computational Approaches for Clinical, Genomic and Proteomic Markers of Response to GLP-1 Therapy in Type-2 Diabetes Mellitus: An Exploratory Analysis With Machine Learning Algorithms.

Computational Approaches for Clinical, Genomic and Proteomic Markers of Response to GLP-1 Therapy in Type-2 Diabetes Mellitus: An Exploratory Analysis With Machine Learning Algorithms.

Multi-modal deep learning from imaging genomic data for schizophrenia classification.

Schizophrenia (SZ) is a psychiatric condition that adversely affects an individual's cognitive, emotional, and behavioral aspects. The etiology of SZ, although extensively studied, remains unclear, as multiple factors come together to contribute toward its development. There is a consistent body of evidence documenting the presence of structural and functional deviations in the brains of individuals with SZ. Moreover, the hereditary aspect of SZ is supported by the significant involvement of genomics markers. Therefore, the need to investigate SZ from a multi-modal perspective and develop approaches for improved detection arises. Our proposed method employed a deep learning framework combining features from structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and genetic markers such as single nucleotide polymorphism (SNP). For sMRI, we used a pre-trained DenseNet to extract the morphological features. To identify the most relevant functional connections in fMRI and SNPs linked to SZ, we applied a 1-dimensional convolutional neural network (CNN) followed by layerwise relevance propagation (LRP). Finally, we concatenated these obtained features across modalities and fed them to the extreme gradient boosting (XGBoost) tree-based classifier to classify SZ from healthy control (HC). Experimental evaluation on clinical dataset demonstrated that, compared to the outcomes obtained from each modality individually, our proposed multi-modal approach performed classification of SZ individuals from HC with an improved accuracy of 79.01%. We proposed a deep learning based framework that selects multi-modal (sMRI, fMRI and genetic) features efficiently and fuse them to obtain improved classification scores. Additionally, by using Explainable AI (XAI), we were able to pinpoint and validate significant functional network connections and SNPs that contributed the most toward SZ classification, providing necessary interpretation behind our findings.

Phylogeography of the Colombian water snake Helicopsdanieli Amaral, 1938 (Reptilia, Squamata, Dipsadidae) with comments on the systematics and evolution of the genus Helicops Wagler, 1828.

The genus Helicops Wagler, 1828 comprises 20 species of semiaquatic snakes. It is mostly distributed in the cis-Andean region of South America, with only two trans-Andean species (H.danieli, H.scalaris). Helicopsdanieli is endemic to Colombia and occurs through most of the trans-Andean region. Herein two mitochondrial and two nuclear genomic markers were sequenced for 16 samples of H.danieli across most of its distribution range to understand its phylogeography. A dated tree was also generated with additional sequences from previous studies to infer the divergence times between H.danieli and its cis-Andean congeners and of lineages within H.danieli. Using previously published data, ancestral states were estimated for putative phenotypic synapomorphies for the major clades of Helicops. For H.danieli, four clades corresponding to the main river basins within its distribution were recovered. Our dated tree suggests that the ancestor of H.danieli diverged from its closest congeners in the late Miocene (8.7 Mya), which can be associated with the closure of the Andalucia Pass, south of the Eastern Cordillera. Divergence within H.danieli commenced 1.1 Mya. Within the genus Helicops, two distinct hemipenial morphologies were observed, which are suggested as putative synapomorphies for the two most basal clades. Recognition of these two clades as distinct subgenera, Helicops sensu stricto and Tachynectes Fitzinger, 1843 is proposed. For the junior homonym Tachynectes von der Mark, 1863, rarely applied to fossil fishes, the replacement name Ichthyotachynectes nom. nov. is introduced. Furthermore, the evolution of another four phenotypic traits in Helicops and their phylogenetic utility are discussed.

Genome-wide association studies for diarrhoea outcomes identified genomic regions affecting resistance to a severe enteropathy in suckling rabbits.

Selection and breeding strategies to improve resistance to enteropathies are essential to reaching the sustainability of the rabbit production systems. However, disease heterogeneity (having only as major visible symptom diarrhoea) and low disease heritability are two barriers for the implementation of these strategies. Diarrhoea condition can affect rabbits at different life stages, starting from the suckling period, with large negative economic impacts. In this study, from a commercial population of suckling rabbits (derived from 133 litters) that experienced an outbreak of enteropathy, we first selected a few animals that died with severe symptoms of diarrhoea and characterized their microbiota, using 16S rRNA gene sequencing data. Clostridium genus was consistently present in all affected specimens. In addition, with the aim to identify genetic markers in the rabbit genome that could be used as selection tools, we performed genome-wide association studies for symptoms of diarrhoea in the same commercial rabbit population. These studies were also complemented with FST analyses between the same groups of rabbits. A total of 332 suckling rabbits (151 with severe symptoms of diarrhoea, 42 with mild symptoms and 129 without any symptoms till the weaning period), derived from 45 different litters (a subset of the 133 litters) were genotyped with the Affymetrix Axiom OrcunSNP Array. In both genomic approaches, rabbits within litters were paired to constitute two groups (susceptible and resistant, including the mildly affected in one or the other group) and run case and control genome-wide association analyses. Genomic heritability estimated in the designed experimental structure integrated in a commercial breeding scheme was 0.19-0.21 (s.e. 0.09-0.10). A total of eight genomic regions on rabbit chromosome 2 (OCU2), OCU3, OCU7, OCU12, OCU13, OCU16 and in an unassembled scaffold had significant single nucleotide polymorphisms (SNPs) and/or markers that trespassed the FST percentile distribution. Among these regions, three main peaks of SNPs were identified on OCU12, OCU13 and OCU16. The QTL region on OCU13 encompasses several genes that encode members of a family of immunoglobulin Fc receptors (FCER1G, FCRLA, FCRLB and FCGR2A) involved in the immune innate system, which might be important candidate genes for this pathogenic condition. The results obtained in this study demonstrated that resistance to an enteropathy occurring in suckling rabbits is in part genetically determined and can be dissected at the genomic level, providing DNA markers that could be used in breeding programmes to increase resistance to enteropathies in meat rabbits.

Label-Free Citrate-Stabilized Silver Nanoparticles-Based, Highly Sensitive, Cost-Effective, and Rapid Visual Method for the Differential Detection of Mycobacterium tuberculosis and Mycobacterium bovis.

Tuberculosis poses a global health challenge, and it demands improved diagnostics and therapies. Distinguishing between Mycobacterium tuberculosis (M. tb) and Mycobacterium bovis (M. bovis) infections holds critical "One Health" significance due to the zoonotic nature of these infections and inherent resistance of M. bovis to pyrazinamide, a key part of the directly observed treatment, short-course (DOTS) regimen. Furthermore, most of the currently used molecular detection methods fail to distinguish between the two species. To address this, our study presents an innovative molecular-biosensing strategy. We developed a label-free citrate-stabilized silver nanoparticle aggregation assay that offers sensitive, cost-effective, and swift detection. For molecular detection, genomic markers unique to M. tb and M. bovis were targeted using species-specific primers. In addition to amplifying species-specific regions, these primers also aid the detection of characteristic deletions in each of the mycobacterial species. Post polymerase chain reaction (PCR), we compared two highly sensitive visual detection methods with respect to the traditional agarose gel electrophoresis. The paramagnetic bead-based bridging flocculation assay successfully discriminates M. tb from M. bovis with a sensitivity of ∼40 bacilli. The second strategy exploits citrate-stabilized silver nanoparticles, which aggregate in the absence of amplified dsDNA on the addition of sodium chloride (NaCl). This technique enables the precise, sensitive, and differential detection of as few as ∼4 bacilli. Our study hence advances tuberculosis detection, overcoming the challenges of M. tb and M. bovis differentiation and offering a quicker alternative to time-consuming methods.

MegaSSR: a web server for large scale microsatellite identification, classification, and marker development.

Next-generation sequencing technologies have opened new avenues for using genomic data to study and develop molecular markers and improve genetic resources. Simple Sequence Repeats (SSRs) as genetic markers are increasingly used in molecular diversity and molecular breeding programs that require bioinformatics pipelines to analyze the large amounts of data. Therefore, there is an ongoing need for online tools that provide computational resources with minimal effort and maximum efficiency, including automated development of SSR markers. These tools should be flexible, customizable, and able to handle the ever-increasing amount of genomic data. Here we introduce MegaSSR (https://bioinformatics.um6p.ma/MegaSSR), a web server and a standalone pipeline that enables the design of SSR markers in any target genome. MegaSSR allows users to design targeted PCR-based primers for their selected SSR repeats and includes multiple tools that initiate computational pipelines for SSR mining, classification, comparisons, PCR primer design, in silico PCR validation, and statistical visualization. MegaSSR results can be accessed, searched, downloaded, and visualized with user-friendly web-based tools. These tools provide graphs and tables showing various aspects of SSR markers and corresponding PCR primers. MegaSSR will accelerate ongoing research in plant species and assist breeding programs in their efforts to improve current genomic resources.

Development of genomic SSR markers and genetic diversity of Sphaerulina musiva in China

AbstractSphaerulina musiva is an important poplar pathogenic fungus that causes leaf spot and stem canker. SSR markers serve as excellent molecular markers for assessing genetic diversity in fungi. To date, few SSR markers have been developed and applied in S. musiva. In this study, genomic‐SSR markers were developed from the S. musiva genome, and applied to analyse the genetic diversity of S. musiva isolates from Henan province in China. A total of 9923 SSR loci were identified with a relative abundance of 343.15 SSR loci per Mb. Tri‐nucleotide repeats were the most abundant in five types of SSRs, and accounted for 56.82% of the total SSR loci. Twelve genomic‐SSR markers were successfully developed from tri‐nucleotide SSR loci. The number of alleles per locus ranged from 3 to 8 with an average of 4.08 alleles. The polymorphism information content of these loci ranged from 0.428 to 0.770, with an average of 0.553. Based on the SSR markers, a total of 48 haplotypes were identified from 48 S. musiva isolates. Forty‐eight isolates were divided into four main genetic groups at a genetic distance of 0.21. Genetic relationships of the isolates had no obvious correlation with geographic origin. It is the first time to develop genomic SSR markers in S. musiva. The developed SSR markers will be a useful tool to evaluate genetic diversity and population structure of S. musiva.

A community‐driven captive‐breeding and reintroduction program maintains genetic diversity in a threatened freshwater fish

AbstractCollaborative approaches to conservation management are critical to respond to the ongoing biodiversity crisis. However, local community involvement in many conservation actions is lacking, especially within translocation and reintroduction programs. Similarly, rapid integration of genetic information into collaborative programs with local communities is rarely conducted. Here, we describe a community‐based and collaborative reintroduction program for a threatened Australian freshwater fish, the southern pygmy perch (Nannoperca australis). We integrate on‐the‐ground translocation efforts by volunteers from local communities, captive breeding by a private aquarium business, and genetic analyses done by a research institution to provide a holistic framework for the reintroduction of southern pygmy perch. We evaluated genetic diversity, population structure, relatedness, and inbreeding across the duration of the reintroduction program using data from neutral and adaptive genomic markers. This allowed us to assess the ability of such a program to minimize inbreeding and retain genomic variation, and to promote adaptive potential of the reintroduced population. While genetic variation for the source populations was very low, we found no decrease in genetic diversity or increase in inbreeding across the program. These genetic findings support the efforts made by local communities and will further inform future reintroductions as part of a collaborative conservation framework. We expand on our empirical case study by describing a theoretical framework for integrating conservation genomics research with community‐led conservation management programs and identifying the benefits of such a collaboration. Our study highlights the importance of multifaceted and integrated conservation management approaches to effectively protect and manage threatened species.

Delimiting phylogeographic diversity in the genomic era: application to an Iberian endemic frog

Abstract The rich genetic and phenotypic diversity of species complexes is best recognized through formal taxonomic naming, but one must first assess the evolutionary history of phylogeographic lineages to identify and delimit candidate taxa. Using genomic markers, mitochondrial DNA barcoding, and morphometric analyses, we examined lineage diversity and distribution in the Iberian endemic frog Rana parvipalmata. We confirmed two deep phylogeographic lineages, one relatively homogenous genetically, found in Asturias and adjacent areas (T2), and one more fragmented and locally genetically impoverished, restricted to Galicia (T1). Analyses of their hybrid zone suggested a shallow transition characterized by far-ranging admixture, which was modelled by a wide geographic cline (~60 km for the genome average) and no obvious barrier loci (i.e. loci showing disproportionally restricted introgression). The relatively young T1 and T2 have thus remained reproductively compatible, which argues against their distinction as biological species, and we accordingly describe T2 as a new subspecies, Rana parvipalmata asturiensis ssp. nov. Remarkably, we highlight striking discordances between mitochondrial and nuclear distributions across their hybrid zones, as well as between their genetic and morphological differentiation. Our study illustrates how genomic-based phylogeographic frameworks can help decipher the high genetic and phenotypic variation of species complexes and substantiate the taxonomic assessment of candidate lineages.

Tumor radiogenomics in gliomas with Bayesian layered variable selection.

We propose a statistical framework to analyze radiological magnetic resonance imaging (MRI) and genomic data to identify the underlying radiogenomic associations in lower grade gliomas (LGG). We devise a novel imaging phenotype by dividing the tumor region into concentric spherical layers that mimics the tumor evolution process. MRI data within each layer is represented by voxel-intensity-based probability density functions which capture the complete information about tumor heterogeneity. Under a Riemannian-geometric framework these densities are mapped to a vector of principal component scores which act as imaging phenotypes. Subsequently, we build Bayesian variable selection models for each layer with the imaging phenotypes as the response and the genomic markers as predictors. Our novel hierarchical prior formulation incorporates the interior-to-exterior structure of the layers, and the correlation between the genomic markers. We employ a computationally-efficient Expectation-Maximization-based strategy for estimation. Simulation studies demonstrate the superior performance of our approach compared to other approaches. With a focus on the cancer driver genes in LGG, we discuss some biologically relevant findings. Genes implicated with survival and oncogenesis are identified as being associated with the spherical layers, which could potentially serve as early-stage diagnostic markers for disease monitoring, prior to routine invasive approaches. We provide a R package that can be used to deploy our framework to identify radiogenomic associations.

Clinical forecasting of acute myeloid leukemia using ex vivo drug-sensitivity profiling

Current treatment selection for acute myeloid leukemia (AML) patients depends on risk stratification based on cytogenetic and genomic markers. However, the forecasting accuracy of treatment response remains modest, with most patients receiving intensive chemotherapy. Recently, exvivo drug screening has gained traction in personalized treatment selection and as a tool for mapping patient groups based on relevant cancer dependencies. Here, we systematically evaluated the use of drug sensitivity profiling for predicting patient survival and clinical response to chemotherapy in a cohort of AML patients. We compared computational methodologies for scoring drug efficacy and characterized tools to counter noise and batch-related confounders pervasive in high-throughput drug testing. We show that exvivo drug sensitivity profiling is a robust and versatile approach to patient prognostics that comprehensively maps functional signatures of treatment response and disease progression. In conclusion, exvivo drug profiling can assess risk for individual AML patients and may guide clinical decision-making.

Genome Survey Sequencing of the Medical Plant Dioscorea futschauensis: Genomic Characteristics Estimation and Genomic Resources Development

Dioscorea futschauensis Uline ex R. Knuth, a perennial climbing herb belonging to the Stenophora section of the genus Dioscorea L. (Dioscoreaceae), originates from Southeast China and is highly regarded for its medicinal properties. Despite its medicinal significance, the absence of genomic information has impeded the effective utilization and conservation of this species. Here, we conducted genome survey sequencing of D. futschauensis to unveil its genomic characteristics and identify species-specific genomic markers, including whole plastome sequence, plastome-divergent hotspots, single-copy nuclear genes and polymorphic nuclear SSRs. The genome of D. futschauensis was approximated to be 1392.76 Mb in length, accompanied by a substantial heterozygosity rate of 1.75% and a repeat content of 67.43%. Comparative plastome analysis illuminated the conserved genome structure and gene content within D. futschauensis, and revealed four highly divergent hotspots (atpF intron, psbA-matK, matK -rps16, and rrn16-trnI) that could serve as molecular markers for D. futschauensis and its closely related species. Furthermore, more than 200 single-copy nuclear genes and 78 polymorphic nuclear SSRs were identified within D. futschauensis. Overall, the comprehensive genomic information and abundant genetic resources provided herein will not only facilitate extended research in the realms of population genetics, phylogenetics and conservation biology of D. futschauensis, but also hold considerable value for its cultivation and molecular breeding applications.

Human Impacts on the Vegetation of the Juan Fernández (Robinson Crusoe) Archipelago

The human footprint on marine and terrestrial ecosystems of the planet has been substantial, largely due to the increase in the human population with associated activities and resource utilization. Oceanic islands have been particularly susceptible to such pressures, resulting in high levels of loss of biodiversity and reductions in the numbers and sizes of wild populations. One archipelago that has suffered from human impact has been the Juan Fernández (Robinson Crusoe) Archipelago, a Chilean national park located 667 km west of Valparaíso at 33° S. latitude. The park consists of three principal islands: Robinson Crusoe Island (48 km2); Santa Clara Island (2.2 km2); and Alejandro Selkirk Island (50 km2). The latter island lies 181 kms further west into the Pacific Ocean. No indigenous peoples ever visited or lived on any of these islands; they were first discovered by the Spanish navigator, Juan Fernández, in 1574. From that point onward, a series of European visitors arrived, especially to Robinson Crusoe Island. They began to cut the forests, and such activity increased with the establishment of a permanent colony in 1750 that has persisted to the present day. Pressures on the native and endemic flora increased due to the introduction of animals, such as goats, rats, dogs, cats, pigs, and rabbits. Numerous invasive plants also arrived, some deliberately introduced and others arriving inadvertently. At present, more than three-quarters of the endemic and native vascular species of the flora are either threatened or endangered. The loss of vegetation has also resulted in a loss of genetic variability in some species as populations are reduced in size or go extinct. It is critical that the remaining genetic diversity be conserved, and genomic markers would provide guidelines for the conservation of the diversity of the endemic flora. To preserve the unique flora of these islands, further conservation measures are needed, especially in education and phytosanitary monitoring.

Development of a 39 MM-InDel multiplex assay for the forensic application.

Insertion/deletion polymorphisms (InDels) are a category of highly prevalent markers in the human genome, characterized by their distinctive attributes, including short amplicon sizes and low mutation rates, which have shown great potential in forensic applications. Multi-allelic InDel and multi-InDel markers, collectively abbreviated as MM-InDels, were developed to enhance polymorphism by the introduction of novel alleles. Nevertheless, the relatively low mutation rates of InDels, coupled with the founder effect, result in distinct allele frequency distributions among populations. The divergent characteristics of InDels in different populations also pose challenges to the establishment of universally efficient InDel multiplex assays. To enhance the system efficiency of the InDel assay and its applicability across diverse populations, 39 MM-InDels with high polymorphism in five different ancestry superpopulations were selected from the 1000 Genomes Project dataset and combined with an amelogenin gender marker to construct a multiplex assay (named MMIDplex). The combined power of discrimination and the cumulative probability of exclusion of 39 MM-InDels were 1-1.3×10-23 and 1-9.83×10-6 in the Chinese Han population, and larger than 1-10-19 and 1-10-4 in the reference populations, relatively. These results demonstrate that the MMIDplex assay has the potential to obtain sufficient power for individual identification and paternity test in global populations.

Optical Genome Mapping Reveals Novel Structural Variants in Lymphoblastic Lymphoma.

Accurate histologic and molecular genetic diagnosis is critical for the pathogenesis study of pediatric patients with lymphoblastic lymphoma (LBL). Optical genome mapping (OGM) as all-in-one process allows the detection of most major genomic risk markers, which addresses some of the limitations associated with conventional cytogenomic testing, such as low resolution and throughput, difficulty in ascertaining genomic localization, and orientation of segments in duplication, inversions, and insertions. Here, for the first time, we examined the cytogenetics of 5 children with LBL using OGM. OGM was used to analyze 5 samples of pediatric LBL patients treated according to the modified NHL-BFM95 backbone regimen. Whole-exon Sequencing (WES) was used to confirm the existence of structural variants (SVs) identified by OGM with potentially clinical significance on MGI Tech (DNBSEQ-T7) platform. According to the fusion exon sequences revealed by WES, the HBS1L :: AHI1 fusion mRNA in case 4 was amplified by cDNA-based PCR. In total, OGM identified 251 rare variants (67 insertions, 129 deletions, 3 inversion, 25 duplications, 15 intrachromosomal translocations, and 12 interchromosomal translocations) and 229 copy number variants calls (203 gains and 26 losses). Besides all of the reproducible and pathologically significant genomic SVs detected by conventional cytogenetic techniques, OGM identified more SVs with definite or potential pathologic significance that were not detected by traditional methods, including 2 new fusion genes, HBS1L :: AHI1 and GRIK1::NSDHL , which were confirmed by WES and/or Reverse Transcription-Polymerase Chain Reaction. Our results demonstrate the feasibility of OGM to detect genomic aberrations, which may play an important role in the occurrence and development of lymphomagenesis as an important driving factor.