Fully exploiting SNP arrays: a systematic review on the tools to extract underlying genomic structure

A review of deoxyribonucleic acid-based single-nucleotide polymorphisms in diabetic kidney disease among Asian populations: Challenges and future directions

Diabetic kidney disease (DKD) is a persistent disorder that occurs as a result of long-term diabetes mellitus with genetic and environmental risk factors. The identification of DKD associated single-nucleotide polymorphisms (SNPs) is pivotal for patient screening. This manuscript briefly outlines the pathophysiology and the role of genetic factors in DKD expansion. It further discusses the utility of bioinformatic tools and laboratory techniques requried for the identification of DKD-specific SNPs along with integrated data analysis pipelines valuable to enhance the accuracy of genetic interpretation. A comparative analysis of various SNPs has also been made across diverse Asian populations in conjunction with environmental and lifestyle factors. The clinical relevance of SNPs in predicting DKD progression and stratifying patient risk is highlighted, with focus on gene-specific pathways and associated functional outcomes. The advances in genetic screening, gene-specific therapies, and microbiome-based therapy should help expand the utility of SNPs-based identification of DKD under diverse clinical settings. A structured clinical decision-making framework is proposed to support customized treatment based on SNP profiles. However, this domain has yet to gain widespread recognition with regard to variability in the effects of SNPs across diverse demographies, challenges in clinical translation, and ethical considerations in genetic testing.

Investigating the Performance of Oxford Nanopore Long-Read Sequencing with Respect to Illumina Microarrays and Short-Read Sequencing

Oxford Nanopore Technologies (ONT) long-read sequencing (LRS) has emerged as a promising genomic analysis tool, yet comprehensive benchmarks with established platforms across diverse datasets remain limited. This study aimed to benchmark LRS performance against Illumina short-read sequencing (SRS) and microarrays for variant detection across different genomic contexts and to evaluate the impact of experimental factors. We sequenced 14 human genomes using the three platforms and evaluated single nucleotide variants (SNVs), insertions/deletions (indels), and structural variants (SVs) detection, stratifying by high-complexity, low-complexity, and dark genome regions while assessing effects of multiplexing, depth, and read length. LRS SNV accuracy was slightly lower than that of SRS in high-complexity regions (F-measure: 0.954 vs. 0.967) but showed comparable sensitivity in low-complexity regions. LRS showed robust performance for small (1-5 bp) indels in high-complexity regions (F-measure: 0.869), but SRS agreement decreased significantly in low-complexity regions and for larger indel sizes. Within dark regions, LRS identified more indels than SRS, but showed lower base-level accuracy. LRS identified 2.86 times more SVs than SRS, excelling at detecting large variants (>6 kb), with SV detection improving with sequencing depth. Sequencing depth strongly influenced variant calling performance, whereas multiplexing effects were minimal. Our findings provide valuable insights for optimising LRS applications in genomic research and diagnostics.

DPImpute: A Genotype Imputation Framework for Ultra-Low Coverage Whole-Genome Sequencing and its Application in Genomic Selection

Whole-genome sequencing is pivotal for elucidating the complex relationships between genotype and phenotype. However, its widespread application is hindered by the high sequencing depth and large sample sizes required, especially for genomic selection (GS) reliant on precise phenotype prediction from high-density genotype data. To address this, DPImpute (Dual-Phase Impute) is developed, an two-step imputation pipeline enabling accurate whole-genome SNP genotyping under ultra-low coverage whole-genome sequencing (ulcWGS) depths, small testing sample sizes, and limited reference populations. DPImpute achieved 98.06% SNP imputation accuracy with minimal testing samples (≤10), reference samples (≤100), and an ultra-low sequencing depth of 0.3X, surpassing the accuracy of existing imputation methods. Moreover, this high accuracy is maintained across multi-ancestry human populations. Remarkably, DPImpute demonstrated accurate SNP imputation from low-coverage sequencing data from single blood cells and single blastocyst cells, highlighting its potential in embryo GS. To enhance the accessibility of DPImpute, a user-friendly web server (https://agdb.ecenr.com/DPImpute/home) is developed and a Docker container for seamless implementation. In summary, DPImpute can significantly expedite breeding programs through precise and cost-effective genotyping and serve as a valuable tool for diverse population genotyping, encompassing both human and animal studies.

High performance imputation of structural and single nucleotide variants using low-coverage whole genome sequencing

BACKGROUND

Whole genome sequencing (WGS), despite its advantages, is yet to replace methods for genotyping single nucleotide variants (SNVs) such as SNP arrays and targeted genotyping assays. Structural variants (SVs) have larger effects on traits than SNVs, but are more challenging to accurately genotype. Using low-coverage WGS with genotype imputation offers a cost-effective strategy to achieve genome-wide variant coverage, but is yet to be tested for SVs.

METHODS

Here, we investigate combined SNV and SV imputation with low-coverage WGS data in Atlantic salmon (Salmo salar). As the reference panel, we used genotypes for high-confidence SVs and SNVs for n = 365 wild individuals sampled from diverse populations. We also generated 15 × WGS data (n = 20 samples) for a commercial population external to the reference panel, and called SVs and SNVs with gold-standard approaches. An imputation method selected for its established performance using low-coverage sequencing data (GLIMPSE) was tested at WGS depths of 1 × , 2 × , 3 × , and 4 × for samples within and external to the reference panel.

RESULTS

SNVs were imputed with high accuracy and recall across all WGS depths, including for samples out-with the reference panel. For SVs, we compared imputation based purely on linkage disequilibrium (LD) with SNVs, to that supplemented with SV genotype likelihoods (GLs) from low-coverage WGS. Including SV GLs increased imputation accuracy, but as a trade-off with recall, requiring 3-4 × depth for best performance. Combining strategies allowed us to capture 84% of the reference panel deletions with 87% accuracy at 1 × depth. We also show that SV length affects imputation performance, with provision of SV GLs greatly enhancing accuracy for the longest SVs in the dataset.

CONCLUSIONS

This study highlights the promise of reference panel imputation using low-coverage WGS, including novel opportunities to enhance the resolution of genome-wide association studies by capturing SVs.

Deciphering new insights into copy number variations as drivers of genomic diversity and adaptation in farm animal species

The basis of all improvement in (re)production performance of animals and plants lies in the genetic variation. The underlying genetic variation can be further explored through investigations using molecular markers including single nucleotide polymorphism (SNP) and microsatellite, and more recently structural variants like copy number variations (CNVs). Unlike SNPs, CNVs affect a larger proportion of the genome, making them more impactful vis-à-vis variation at the phenotype level. They significantly contribute to genetic variation and provide raw material for natural and artificial selection for improved performance. CNVs are characterized as unbalanced structural variations that arise from four major mechanisms viz., non-homologous end joining (NHEJ), non-allelic homologous recombination (NAHR), fork stalling and template switching (FoSTeS), and retrotransposition. Various detection methods have been developed to identify CNVs, including molecular techniques and massively parallel sequencing. Next-generation sequencing (NGS)/high-throughput sequencing offers higher resolution and sensitivity, but challenges remain in delineating CNVs in regions with repetitive sequences or high GC content. High-throughput sequencing technologies utilize different methods based on read-pair, split-read, read depth, and assembly approaches (or their combination) to detect CNVs. Read-pair based methods work by mapping discordant reads, while the read-depth approach works on detecting the correlation between read depth and copy number of genetic segments or a gene. Split-read methods involve mapping segments of reads to different locations on the genome, while assembly methods involve comparing contigs to a reference or de novo sequencing. Similar to other marker-trait association studies, CNV-association studies are not uncommon in humans and farm animals. Soon, extensive studies will be needed to deduce the unique evolutionary trajectories and underlying molecular mechanisms for targeted genetic improvements in different farm animal species. The present review delineates the importance of CNVs in genetic studies, their generation along with programs and principles to efficiently identify them, and finally throw light on the existing literature on studies in farm animal species vis-à-vis CNVs.

A High-Throughput Screening Strategy for Bacillus subtilis Producing Menaquinone-7 Based on Fluorescence-Activated Cell Sorting

Menaquinone-7 (MK-7) is recognized for its important biological activity, and Bacillus subtilis is the preferred strain for its fermentative production. However, the limited phenotypic diversity among high-yielding strains complicates the development of rapid screening methods. To address this, we utilized the effect of MK-7 on transmembrane potential to develop a high-throughput screening (HTS) strategy for efficiently identifying strains with improved MK-7 production. Among various membrane potential fluorescent dyes tested, Rhodamine 123 was selected for quantifying intracellular MK-7 levels due to its effective staining and minimal impact on cell growth. By optimizing pretreatment protocols and staining conditions, we established an HTS protocol that combines fluorescence-activated cell sorting with HPLC to identify strains with increased MK-7 production. A linear correlation was observed between mean MK-7 content and average fluorescence intensity (R2 = 0.9646). This approach was applied to mutant libraries generated through atmospheric room temperature plasma mutagenesis. After three cycles of mutagenesis and screening, the mutant AR03-27 was identified, showing an 85.65% increase in MK-7 yield compared to the original SJTU2 strain. Resequencing analysis revealed that the top three mutants contained mutations in genes related to membrane transport, suggesting their potential role in enhancing MK-7 yield.

Uniparental disomy (UPD) exclusion in embryos following Preimplantation Genetic Testing for Structural Rearrangements (PGT-SR)

PURPOSE

Uniparental disomy (UPD) is a genetic condition which both copies of a chromosome are inherited from a single parent, potentially leading to imprinting disorders. This study aimed to assess the integration of Short Tandem Repeat (STR) analysis into Preimplantation Genetic Testing for Structural Rearrangements (PGT-SR) to assess UPD risk and its impact on selecting euploid embryos for embryo transfer in couples with chromosomal translocations involving imprinted chromosomes.

METHODS

This study evaluated three couples carrying balanced chromosomal translocations: 45,XX,der(13;14)(q10;q10), 46,XX,t(10;11)(q22;q13), and 45,XY,der(14;15)(q10;q10). STR analysis was performed on trophectoderm (TE) biopsies after Whole Genome Amplification (WGA) after PGT-SR analysis using parental blood samples to assess UPD risk in euploid embryos. Haplotyping was conducted with five to six STR markers specific to each rearranged chromosome to detect UPD in euploid embryos.

RESULTS

Of the four embryos analyzed across the three families, two couples had euploid embryos that tested negative for UPD. These embryos were successfully transferred, resulting in the birth of two healthy children. In the third family, the euploid embryo also tested negative for UPD but failed to implant after transfer, resulting in no pregnancy.

DISCUSSION

Despite its rarity, UPD involving imprinted chromosomes poses significant clinical risks, as seen in disorders such as Prader-Willi syndrome and Angelman syndrome. This study highlights the importance of integrating UPD screening into PGT-SR protocols, to detect both heterodisomic and isodisomic UPD events minimizing the risk of severe genetic disorders.

CONCLUSION

Integrating STR-based UPD screening within PGT-SR workflows is a reliable and cost-effective strategy that enhances embryo selection and mitigates the risk of imprinting disorders. This approach improves reproductive outcomes for families with chromosomal rearrangements, offering a practical advancement in assisted reproduction.

CRISPR-Cas technology in forensic investigations: Principles, applications, and ethical considerations

CRISPR-Cas (Clustered Regularly Interspaced Short Palindromic Repeats and CRISPR-associated proteins) systems are adaptive immune systems originally present in bacteria, where they are essential to protect against external genetic elements, including viruses and plasmids. Taking advantage of this system, CRISPR-Cas-based technologies have emerged as incredible tools for precise genome editing, thus significantly advancing several research fields. Forensic sciences represent a multidisciplinary field that explores scientific methods to investigate and resolve legal issues, particularly criminal investigations and subject identification. Consequently, it plays a critical role in the justice system, providing scientific evidence to support judicial investigations. Although less explored, CRISPR-Cas-based methodologies demonstrate strong potential in the field of forensic sciences due to their high accuracy and sensitivity, including DNA profiling and identification, interpretation of crime scene investigations, detection of food contamination or fraud, and other aspects related to environmental forensics. However, using CRISPR-Cas-based methodologies in human samples raises several ethical issues and concerns regarding the potential misuse of individual genetic information. In this manuscript, we provide an overview of potential applications of CRISPR-Cas-based methodologies in several areas of forensic sciences and discuss the legal implications that challenge their routine implementation in this research field.

genomeprofile: Unveiling the genomic profile for livestock breeding through comprehensive SNP array-based genotyping

In livestock breeding, single nucleotide polymorphism arrays have become a cornerstone of modern livestock breeding. SNP arrays facilitate the identification of genetic markers linked to economically important traits and provide a powerful tool for predicting breeding values. However, conventional breeding programs often overlook additional genomic features contained in the SNP array data that can provide valuable insights into the genetic diversity, copy number variation, inbreeding levels and potential challenges in breeding lines. Here we present genomeprofile, a tool using SNP array-based genomic data, offering a comprehensive profile of breeding animals including the identification of copy number variants and runs of homozygosity, and screening for aneuploidy. By integrating these features into the breeding landscape, genomeprofile enables a more comprehensive picture of genomic variation, ultimately enhancing precision breeding strategies. To illustrate the practicality and efficacy of genomeprofile, we applied the tool to a dataset of four pig breeding lines. The genomeprofile tool is a user-friendly tool that processes genotype data in finalreport or plink ped format efficiently into useful output. The output contains copy number variations, runs of homozygosity, selection signatures, aneuploidy and inbreeding per individual and across populations. This allows breeding companies and researchers to identify unique individuals or regions in the genome of interest based on routinely collected data.

Asthma-Genomic Advances Toward Risk Prediction

Asthma is a common complex airway disease whose prediction of disease risk and most severe outcomes is crucial in clinical practice for adequate clinical management. This review discusses the latest findings in asthma genomics and current obstacles faced in moving forward to translational medicine. While genome-wide association studies have provided valuable insights into the genetic basis of asthma, there are challenges that must be addressed to improve disease prediction, such as the need for diverse representation, the functional characterization of genetic variants identified, variant selection for genetic testing, and refining prediction models using polygenic risk scores.

Development of a next generation SNP genotyping array for wheat

High-throughput genotyping arrays have provided a cost-effective, reliable and interoperable system for genotyping hexaploid wheat and its relatives. Existing, highly cited arrays including our 35K Wheat Breeder's array and the Illumina 90K array were designed based on a limited amount of varietal sequence diversity and with imperfect knowledge of SNP positions. Recent progress in wheat sequencing has given us access to a vast pool of SNP diversity, whilst technological improvements have allowed us to fit significantly more probes onto a 384-well format Axiom array than previously possible. Here we describe a novel Axiom genotyping array, the 'Triticum aestivum Next Generation' array (TaNG), largely derived from whole genome skim sequencing of 204 elite wheat lines and 111 wheat landraces taken from the Watkins 'Core Collection'. We used a novel haplotype optimization approach to select SNPs with the highest combined varietal discrimination and a design iteration step to test and replace SNPs which failed to convert to reliable markers. The final design with 43 372 SNPs contains a combination of haplotype-optimized novel SNPs and legacy cross-platform markers. We show that this design has an improved distribution of SNPs compared to previous arrays and can be used to generate genetic maps with a significantly higher number of distinct bins than our previous array. We also demonstrate the improved performance of TaNGv1.1 for Genome-wide association studies (GWAS) and its utility for Copy Number Variation (CNV) analysis. The array is commercially available with supporting marker annotations and initial genotyping results freely available.

Potential Association of Cytochrome P450 Copy Number Alteration in Tumour with Chemotherapy Resistance in Lung Adenocarcinoma Patients

Resistance to anticancer agents is a major obstacle to efficacious tumour therapy and responsible for high cancer-related mortality rates. Some resistance mechanisms are associated with pharmacokinetic variability in anticancer drug exposure due to genetic polymorphisms of drug-metabolizing cytochrome P450 (CYP) enzymes, whereas variations in tumoural metabolism as a consequence of CYP copy number alterations are assumed to contribute to the selection of resistant cells. A high-throughput quantitative polymerase chain reaction (qPCR)-based method was developed for detection of CYP copy number alterations in tumours, and a scoring system improved the identification of inappropriate reference genes that underwent deletion/multiplication in tumours. The copy numbers of both the target (CYP2C8, CYP3A4) and the reference genes (ALB, B2M, BCKDHA, F5, CD36, MPO, TBP, RPPH1) established in primary lung adenocarcinoma by the qPCR-based method were congruent with those determined by next-generation sequencing (for 10 genes, slope = 0.9498, r2 = 0.72). In treatment naïve adenocarcinoma samples, the copy number multiplication of paclitaxel-metabolizing CYP2C8 and/or CYP3A4 was more prevalent in non-responder patients with progressive disease/exit than in responders with complete remission. The high-throughput qPCR-based method can become an alternative approach to next-generation sequencing in routine clinical practice, and identification of altered CYP copy numbers may provide a promising biomarker for therapy-resistant tumours.

Generic Platform for the Multiplexed Targeted Electrochemical Detection of Osteoporosis-Associated Single Nucleotide Polymorphisms Using Recombinase Polymerase Solid-Phase Primer Elongation and Ferrocene-Modified Nucleoside Triphosphates

Osteoporosis is a multifactorial disease influenced by genetic and environmental factors, which contributes to an increased risk of bone fracture, but early diagnosis of this disease cannot be achieved using current techniques. We describe a generic platform for the targeted electrochemical genotyping of SNPs identified by genome-wide association studies to be associated with a genetic predisposition to osteoporosis. The platform exploits isothermal solid-phase primer elongation with ferrocene-labeled nucleoside triphosphates. Thiolated reverse primers designed for each SNP were immobilized on individual gold electrodes of an array. These primers are designed to hybridize to the SNP site at their 3'OH terminal, and primer elongation occurs only where there is 100% complementarity, facilitating the identification and heterozygosity of each SNP under interrogation. The platform was applied to real blood samples, which were thermally lysed and directly used without the need for DNA extraction or purification. The results were validated using Taqman SNP genotyping assays and Sanger sequencing. The assay is complete in just 15 min with a total cost of 0.3€ per electrode. The platform is completely generic and has immense potential for deployment at the point of need in an automated device for targeted SNP genotyping with the only required end-user intervention being sample addition.

Human Pangenomics: Promises and Challenges of a Distributed Genomic Reference

A pangenome is a collection of the common and unique genomes that are present in a given species. It combines the genetic information of all the genomes sampled, resulting in a large and diverse range of genetic material. Pangenomic analysis offers several advantages compared to traditional genomic research. For example, a pangenome is not bound by the physical constraints of a single genome, so it can capture more genetic variability. Thanks to the introduction of the concept of pangenome, it is possible to use exceedingly detailed sequence data to study the evolutionary history of two different species, or how populations within a species differ genetically. In the wake of the Human Pangenome Project, this review aims at discussing the advantages of the pangenome around human genetic variation, which are then framed around how pangenomic data can inform population genetics, phylogenetics, and public health policy by providing insights into the genetic basis of diseases or determining personalized treatments, targeting the specific genetic profile of an individual. Moreover, technical limitations, ethical concerns, and legal considerations are discussed.

Cytogenomic epileptology

Molecular cytogenetic and cytogenomic studies have made a contribution to genetics of epilepsy. However, current genomic research of this devastative condition is generally focused on the molecular genetic aspects (i.e. gene hunting, detecting mutations in known epilepsy-associated genes, searching monogenic causes of epilepsy). Nonetheless, chromosomal abnormalities and copy number variants (CNVs) represent an important part of genetic defects causing epilepsy. Moreover, somatic chromosomal mosaicism and genome/chromosome instability seem to be a possible mechanism for a wide spectrum of epileptic conditions. This idea becomes even more attracting taking into account the potential of molecular neurocytogenetic (neurocytogenomic) studies of the epileptic brain. Unfortunately, analyses of chromosome numbers and structure in the affected brain or epileptogenic brain foci are rarely performed. Therefore, one may conclude that cytogenomic area of genomic epileptology is poorly researched. Accordingly, molecular cytogenetic and cytogenomic studies of the clinical cohorts and molecular neurocytogenetic analyses of the epileptic brain appear to be required. Here, we have performed a theoretical analysis to define the targets of the aforementioned studies and to highlight future directions for molecular cytogenetic and cytogenomic research of epileptic disorders in the widest sense. To succeed, we have formed a consortium, which is planned to perform at least a part of suggested research. Taking into account the nature of the communication, "cytogenomic epileptology" has been introduced to cover the research efforts in this field of medical genomics and epileptology. Additionally, initial results of studying cytogenomic variations in the Russian neurodevelopmental cohort are reviewed with special attention to epilepsy. In total, we have concluded that (i) epilepsy-associated cytogenomic variations require more profound research; (ii) ontological analyses of epilepsy genes affected by chromosomal rearrangements and/or CNVs with unraveling pathways implicating epilepsy-associated genes are beneficial for epileptology; (iii) molecular neurocytogenetic (neurocytogenomic) analysis of postoperative samples are warranted in patients suffering from epileptic disorders.

Rare copy number variation in posttraumatic stress disorder

Posttraumatic stress disorder (PTSD) is a heritable (h2 = 24-71%) psychiatric illness. Copy number variation (CNV) is a form of rare genetic variation that has been implicated in the etiology of psychiatric disorders, but no large-scale investigation of CNV in PTSD has been performed. We present an association study of CNV burden and PTSD symptoms in a sample of 114,383 participants (13,036 cases and 101,347 controls) of European ancestry. CNVs were called using two calling algorithms and intersected to a consensus set. Quality control was performed to remove strong outlier samples. CNVs were examined for association with PTSD within each cohort using linear or logistic regression analysis adjusted for population structure and CNV quality metrics, then inverse variance weighted meta-analyzed across cohorts. We examined the genome-wide total span of CNVs, enrichment of CNVs within specified gene-sets, and CNVs overlapping individual genes and implicated neurodevelopmental regions. The total distance covered by deletions crossing over known neurodevelopmental CNV regions was significant (beta = 0.029, SE = 0.005, P = 6.3 × 10-8). The genome-wide neurodevelopmental CNV burden identified explains 0.034% of the variation in PTSD symptoms. The 15q11.2 BP1-BP2 microdeletion region was significantly associated with PTSD (beta = 0.0206, SE = 0.0056, P = 0.0002). No individual significant genes interrupted by CNV were identified. 22 gene pathways related to the function of the nervous system and brain were significant in pathway analysis (FDR q < 0.05), but these associations were not significant once NDD regions were removed. A larger sample size, better detection methods, and annotated resources of CNV are needed to explore this relationship further.

Integrated Analysis of Tumor Mutation Burden and Immune Infiltrates in Hepatocellular Carcinoma

Tumor mutation burdens (TMBs) act as an indicator of immunotherapeutic responsiveness in various tumors. However, the relationship between TMBs and immune cell infiltrates in hepatocellular carcinoma (HCC) is still obscure. The present study aimed to explore the potential diagnostic markers of TMBs for HCC and analyze the role of immune cell infiltration in this pathology. We used OA datasets from The Cancer Genome Atlas database. First, the “maftools” package was used to screen the highest mutation frequency in all samples. R software was used to identify differentially expressed genes (DEGs) according to mutation frequency and perform functional correlation analysis. Then, the gene ontology (GO) enrichment analysis was performed with “clusterProfiler”, “enrichplot”, and “ggplot2” packages. Finally, the correlations between diagnostic markers and infiltrating immune cells were analyzed, and CIBERSORT was used to evaluate the infiltration of immune cells in HCC tissues. As a result, we identified a total of 359 DEGs in this study. These DEGs may affect HCC prognosis by regulating fatty acid metabolism, hypoxia, and the P53 pathway. The top 15 genes were selected as the hub genes through PPI network analysis. SRSF1, SNRPA1, and SRSF3 showed strong similarities in biological effects, NCBP2 was demonstrated as a diagnostic marker of HCC, and high NCBP2 expression was significantly correlated with poor over survival (OS) in HCC. In addition, NCBP2 expression was correlated with the infiltration of B cells (r = 0.364, p = 3.30 × 10⁻¹²), CD8⁺ T cells (r = 0.295, p = 2.71 × 10⁻⁸), CD4⁺ T cells, (r = 0.484, p = 1.37 × 10⁻²¹), macrophages (r = 0.551, p = 1.97 × 10⁻²⁸), neutrophils (r = 0.457, p = 3.26 × 10⁻¹⁹), and dendritic cells (r = 0.453, p = 1.97 × 10⁻¹⁸). Immune cell infiltration analysis revealed that the degree of central memory T-cell (Tcm) infiltration may be correlated with the HCC process. In conclusion, NCBP2 can be used as diagnostic markers of HCC, and immune cell infiltration plays an important role in the occurrence and progression of HCC.