Genetic diagnostics is driven by technological advances, forming a tight interface between research, clinic and industry, which enables rapid implementation of new technologies. Short-read genome and exome sequencing, the current state of the art in clinical genetics, can detect a broad spectrum of genetic variants across the genome. However, despite these advancements, more than half of individuals with rare diseases remain undiagnosed after genomic investigations. Long-read whole-genome sequencing (LR-WGS) is a promising technology that identifies previously difficult-to-detect variants while also enabling phasing and methylation analysis and has the potential of generating complete personal assemblies. To pave the way for clinical use of LR-WGS, the clinical genomic community must establish standardized protocols and quality parameters while also developing innovative tools for data analysis and interpretation. In this Perspective, we explore the key challenges and benefits in integrating LR-WGS into routine clinical diagnostics.

De novo variants adjacent to the canonical splicing sites or in the well-defined splicing-related regions are more likely to impair splicing but remain under-investigated in autism spectrum disorder (ASD). By analyzing large, recent ASD genome sequencing cohorts, we find a significant burden of de novo potential splicing-disrupting variants (PSDVs) in 5048 probands compared to 4090 unaffected siblings. We identified 55 genes with recurrent de novo PSDVs that were highly intolerant to variation. Forty-six of these genes have not been strongly implicated in ASD or other neurodevelopmental disorders previously, including GSK3B. Through international, multicenter collaborations, we assembled genotype and phenotype data for 15 individuals with GSK3B variants and identified common phenotypes including developmental delay, ASD, sleeping disturbance, and aggressive behavior. Using available single-cell transcriptomic data, we show that GSK3B is enriched in dorsal progenitors and intermediate forms of excitatory neurons in the developing brain. We showed that Gsk3b knockdown in mouse excitatory neurons interferes with dendrite arborization and spine maturation which could not be rescued by de novo missense variants identified from affected individuals. In summary, our findings suggest that PSDVs may play an important role in the genetic etiology of ASD and allow for the prioritization of new ASD candidate genes. Importantly, we show that genetic variation resulting in GSK3B loss-of-function can lead to a neurodevelopmental disorder with core features of ASD and developmental delay.

Accurate variant calling from whole-exome sequencing (WES) data is vital for understanding genetic diseases. Recently, commercial variant calling software have emerged that do not require bioinformatics or programming expertise, hence enabling independent analysis of WES data by smaller laboratories and clinics and circumventing the need for dedicated and expensive computers and bioinformatics staff. This study benchmarks four non-programming variant calling software namely, Illumina BaseSpace Sequence Hub (Illumina), CLC Genomics Workbench (CLC), Partek Flow, and Varsome Clinical, for the variant calling of three Genome in a Bottle (GIAB) whole-exome sequencing datasets (HG001, HG002 and HG003). Following alignment of sequence reads to the human reference genome GRCh38, variants were compared against high-confidence regions from GIAB datasets and assessed using the Variant Calling Assessment Tool (VCAT). Illumina's DRAGEN Enrichment achieved the highest precision and recall scores for single nucleotide variant (SNV) and insertions/deletion (indel) calling at over 99% for SNVs and 96% for indels while Partek Flow using unionised variant calls from Freebayes and Samtools had the lowest indel calling performance. Illumina had the highest true positives (TP) variant counts for all samples and all four software shared 98-99% similarity of TP variants. Run times were shortest for CLC and Illumina ranging from 6 to 25 min and 29 to 36 min respectively, while Partek Flow took the longest (3.6 to 29.7 h). This study provides information for clinicians and biologists without programming expertise in their selection of software for variant analysis that balance accuracy, sensitivity, and runtime.

Next-generation sequencing (NGS) has emerged as a pivotal technology in the field of oncology, transforming the approach to cancer diagnosis and treatment. This paper provides a comprehensive overview of the integration of NGS into clinical settings, emphasizing its significant contributions to precision medicine. NGS enables detailed genomic profiling of tumors, identifying genetic alterations that drive cancer progression and facilitating personalized treatment plans targeting specific mutations, thereby improving patient outcomes. This capability facilitates the development of personalized treatment plans targeting specific mutations, leading to improved patient outcomes and the potential for better prognosis. The application of NGS extends beyond identifying actionable mutations; it is instrumental in detecting hereditary cancer syndromes, thus aiding in early diagnosis and preventive strategies. Furthermore, NGS plays a crucial role in monitoring minimal residual disease, offering a sensitive method to detect cancer recurrence at an early stage. Its use in guiding immunotherapy by identifying biomarkers that predict response to treatment is also highlighted. Ethical issues related to genetic testing, such as concerns around patient consent and data privacy, are also important considerations that need to be addressed for the broader implementation of NGS. These include the complexities of data interpretation, the need for robust bioinformatics support, cost considerations, and ethical issues related to genetic testing. Addressing these challenges is essential for the widespread adoption of NGS. Looking forward, advancements such as single-cell sequencing and liquid biopsies promise to further enhance the precision of cancer diagnostics and treatment. This review emphasizes the transformative impact of NGS in oncology and advocates for its incorporation into routine clinical practice to promote molecularly driven cancer care.

The continued evolution of genomic technologies over the past few decades has revolutionized the field of neurogenetics, offering profound insights into the genetic underpinnings of neurological disorders. Identification of causal genes for numerous monogenic neurological conditions has informed key aspects of disease mechanisms and facilitated research into critical proteins and molecular pathways, laying the groundwork for therapeutic interventions. However, the question remains: has this transformative trend reached its zenith? In this review, we suggest that despite significant strides in genome sequencing and advanced computational analyses, there is still ample room for methodological refinement. We anticipate further major genetic breakthroughs corresponding with the increased use of long-read genomes, variant calling software, AI tools, and data aggregation databases. Genetic progress has historically been driven by technological advancements from the commercial sector, which are developed in response to academic research needs, creating a continuous cycle of innovation and discovery. This review explores the potential of genomic technologies to address the challenges of neurogenetic disorders. By outlining both established and modern resources, we aim to emphasize the importance of genetic technologies as we enter an era poised for discoveries.

This is a protocol for a Cochrane Review (intervention). The objectives are as follows: To evaluate the benefits and harms of using NGS techniques compared to conventional newborn screening alone for pre-symptomatic identification of genetic diseases in newborns.

to explore equity and ethical issues in the application of the new techniques, to inform healthcare decisions by families, carers, and policymakers.

Large and highly repetitive genomes are common. However, research interests usually lie within the non-repetitive parts of the genome, as they are more likely functional, and can be used to answer questions related to adaptation, selection and evolutionary history. Exome capture is a cost-effective method for providing sequencing data from protein-coding parts of the genes. C0t-1 DNA blockers consist of repetitive DNA and are used in exome captures to prevent the hybridisation of repetitive DNA sequences to capture baits or bait-bound genomic DNA. Universal blockers target repetitive regions shared by many species, while species-specific c0t-1 DNA is prepared from the DNA of the studied species, thus perfectly matching the repetitive DNA contents of the species. So far, the use of species-specific c0t-1 DNA has been limited to a few model species. Here, we evaluated the performance of blocker treatments in exome captures of Pinus sylvestris, a widely distributed conifer species with a large (> 20 Gbp) and highly repetitive genome. We compared treatment with a commercial universal blocker to treatments with species-specific c0t-1 (30,000 and 60,000 ng). Species-specific c0t-1 captured more unique exons than the initial set of targets leading to increased SNP discovery and reduced sequencing of tandem repeats compared to the universal blocker. Based on our results, we recommend optimising exome captures using at least 60,000 ng of species-specific c0t-1 DNA. It is relatively easy and fast to prepare and can also be used with existing bait set designs.

Genetic sequencing technologies are powerful tools for identifying rare variants and genes associated with Mendelian and complex traits; indeed, whole-exome and whole-genome sequencing are increasingly popular methods for population-scale genetic studies. However, careful quality control steps should be taken to ensure study accuracy and reproducibility, and sequencing data require extensive quality filtering to delineate true variants from technical artifacts. Although processing standards are harmonized across pipelines to call variants from sequencing reads, there currently exists no standardized pipeline for conducting quality filtering on variant-level datasets for the purpose of population-scale association analysis. In this Tutorial, we discuss key quality control parameters, provide guidelines for conducting quality filtering of samples and variants, and compare commonly used software programs for quality control of samples, variants and genotypes from sequencing data. As sequencing data continue to gain popularity in genetic research, establishing standardized quality control practices is crucial to ensure consistent, reliable and reproducible results across studies.

The completion of the Human Genome Project in 2003 has led to significant advances in patient care in medicine, particularly in diagnosing and managing genetic diseases and cancer. In the realm of genetic diseases, approximately 15% of critically ill infants born in the U.S.A. are diagnosed with genetic disorders, which comprise a significant cause of mortality in neonatal and pediatric intensive care units. The introduction of rapid whole-genome sequencing (rWGS) as a first-tier test in critically ill children with suspected, undiagnosed genetic diseases is a breakthrough in the diagnosis and subsequent clinical management of such infants and older children in intensive care units. Rapid genome sequencing is currently being used clinically in the USA, the UK, the Netherlands, Sweden, and Australia, among other countries. This review is intended for students and clinical practitioners, including non-experts in genetics, for whom it provides a historical background and a chronological review of the relevant published literature for the progression of pediatric diagnostic genomic sequencing leading to the development of pediatric rWGS in critically ill infants and older children with suspected but undiagnosed genetic diseases. Factors that will help to develop rWGS as a clinical test in critically ill infants and the limitations are briefly discussed, including an evaluation of the clinical utility and accessibility of genetic testing, education for parents and providers, cost-effectiveness, ethical challenges, consent issues, secondary findings, data privacy concerns, false-positive and false-negative results, challenges in variant interpretation, costs and reimbursement, the limited availability of genetic counselors, and the development of evidence-based guidelines, which would all need to be addressed to facilitate the implementation of pediatric genomic sequencing in an effective widespread manner in the era of precision medicine.

The integration of genomics into personalized medicine has the potential to transform healthcare by customizing treatments according to individual genetic profiles. This paper examines the diverse applications of genomics, including the identification of disease susceptibility, improvement of diagnostic methods, optimization of drug therapies, and monitoring of treatment responses. It also explores the expanding global market for genetic testing and the increasing implementation of whole-genome sequencing in clinical practice, with a focus on pilot programs that are advancing comprehensive genomic analysis. Despite challenges such as high costs, data interpretation complexities, and ethical concerns, significant efforts are being made to address these issues. Additionally, the creation of biobanks as vital resources for preserving high-quality biosamples and supporting research highlights the critical need for infrastructure development in genomics. By fostering interdisciplinary collaboration and establishing robust ethical and regulatory frameworks, personalized medicine can ensure equitable access to tailored therapies and enhance health outcomes for everyone. This abstract provides an overview of the transformative potential of genomics and personalized medicine in ushering in a new era of precision healthcare.

Immune checkpoint inhibitors (ICIs) have demonstrated significantly improved clinical efficacy in a minority of patients with advanced melanoma, whereas non-responders potentially suffer from severe side effects and delays in other treatment options. Predicting the response to anti-PD1 treatment in melanoma remains a challenge because the current FDA-approved gold standard, the nonsynonymous tumor mutation burden (nsTMB), offers limited accuracy.

In this study, we developed a multi-omics-based machine learning model that integrates genomic and transcriptomic biomarkers to predict the response to anti-PD1 treatment in patients with advanced melanoma. We employed least absolute shrinkage and selection operator (LASSO) regression with 49 biomarkers extracted from tumor-normal whole-exome and RNA sequencing as input features. The performance of the multi-omics AI model was thoroughly compared to that of nsTMB alone and to models that use only genomic or transcriptomic biomarkers.

We used publicly available DNA and RNA-seq datasets of melanoma patients for the training and validation of our model, forming a meta-cohort of 449 patients for which the outcome was recorded as a RECIST score. The model substantially improved the prediction of anti-PD1 outcomes compared to nsTMB alone, with an ROC AUC of 0.7 in the training set and an ROC AUC of 0.64 in the test set. Using SHAP values, we demonstrated the explainability of the model's predictions on a per-sample basis.

We demonstrated that models using only RNA-seq or multi-omics biomarkers outperformed nsTMB in predicting the response of melanoma patients to ICI. Furthermore, our machine learning approach improves clinical usability by providing explanations of its predictions on a per-patient basis. Our findings underscore the utility of multi-omics data for selecting patients for treatment with anti-PD1 drugs. However, to train clinical-grade AI models for routine applications, prospective studies collecting larger melanoma cohorts with consistent application of exome and RNA sequencing are required.

Pain is a common and complex non-motor symptom in people with Parkinson's disease (PWP). Little is known about the genetic drivers of pain in PWP, and progress in its study has been challenging. Here, we conducted two genome-wide association studies (GWAS) to identify genetic variants associated with pain experienced during the earliest stages of Parkinson's disease. The study population consisted of 4,159 PWP of European ancestry who were mapped to five previously-described, longitudinal pain trajectories. In the first GWAS, the extreme pain trajectories (highest burden versus no significant pain over time) were compared, and in the second GWAS, a multinomial approach was undertaken. While no variant reached genome-wide significance, we identified promising associations, such as rs117108018 (ORGWAS-Extreme=8.96, pGWAS-Extreme=2.5 × 10- 7), a brain/nerve eQTL for L3MBTL3 and EPB41L2, and rs61881484 (pGWAS-Multinomial=2 × 10- 7), which intersects a transcription factor peak targeting CREB1, critical in sensory neuron synaptic plasticity and neuropathic pain regulation. Gene-based tests implicated CTNNB1 (pGWAS-Extreme=3.2 × 10- 5), KLK7 (pGWAS-Extreme=7 × 10- 5), and SLITRK3 (pGWAS-Multinomial=3.2 × 10- 5), which have been associated with neurodevelopment. At the pathway-level, there was an enrichment for genes involved in neurotransmitter regulation and opioid dependence. This study implicates neuropathic pain mechanisms as prominent drivers of elevated pain in PWP, suggests potential therapeutic genetic targets for further research.

Effective subset selection from complex oligonucleotide libraries is crucial for genomics, synthetic biology, and DNA data storage. The polymerase chain reaction, foundational for amplifying target subsets is limited by primer design and length for specificity, which constrains the scalability of oligo libraries and increases the synthesis burden for primers. We introduce an oligo subset selection methodology that utilizes sequence-specific cyclic nucleotide synthesis and blocking of the template oligos. This approach eliminates the need for primers for selective hybridization and enables the encoding and selection of hundreds of subsets with barcode lengths of fewer than five nucleotides. Moreover, cyclic selection enables a hierarchical data structure in the oligo library, enhancing the programmability. This advancement offers a scalable and cost-effective solution for handling complex oligo libraries.

Structural variation (SV) typically refers to alterations in DNA fragments at least 50 base pairs long in the human genome. It can alter thousands of DNA nucleotides and thus significantly influence human health, disease, and clinical phenotypes. There is a shared and growing recognition that the emergence of effective computational tools and high-throughput technologies such as short-read sequencing and long-read sequencing offers novel insight into SV and, by extension, diseases affecting planetary health. However, numerous available SV tools exist with varying strengths and weaknesses. This is currently hampering the abilities of scholars to select the optimal tools to study SVs. Here, we reviewed 175 tools developed in the past two decades for SV detection, annotation, visualization, and downstream analysis of human genomics. In this expert review, we provide a comprehensive catalog of SV-related tools across different technology platforms and summarize their features, strengths, and limitations with an eye to accelerate systems science and planetary health innovations.

Common clinical findings in patients with 19p13.3 duplication include intrauterine growth restriction, intellectual disability, developmental delay, microcephaly, and distinctive facial features. In this study, we report the case of a patient with 19p13.3 microduplication and novel clinical findings, specifically nephrotic syndrome.

A 4-year-old girl was admitted to our hospital in December 2020 with a fever and cough that had persisted for 3 days. A series of treatments, chromosomal microarray analysis (CMA) and whole exome sequencing (WES) were performed. Relevant literature was reviewed using the search terms "19p13.3" and "19p13.3 microduplication syndrome" in the China Knowledge Network, Wanfang Database, Weipu Journal Service Platform, and PubMed (date range: database establishment to September 2023). In addition to common symptoms, such as developmental delay, microcephaly, distinctive facial features, and congenital heart defects, the patient also had nephrotic syndrome, a previously unreported phenomenon. CMA results showed a 3.6 Mb fragment duplication (copy number: 3) in the chr19p13.3 region, containing 127 protein-coding genes (including CELF5, NFIC, SMIM24, PIAS4, ATCAY, MAP2K2, and ZBTB7A). WES revealed a filamin C mutation (p.Glu309Valfs × 11). The mutation status of the patient and her father was heterozygous, whereas the mutation was not detected in the mother.

Microduplication in the 19p13.3 region could be one of the genetic factors contributing to the observed clinical phenotypes. However, patients with developmental delay, microcephaly, distinctive facial features, congenital heart defects, and urogenital system disorders may exhibit these manifestations due to various genetic syndromes; therefore, simply considering the possibility of 19p13.3 microduplication syndrome based on these non-specific features is not sufficient. Further comprehensive evaluations, including CMA, should be conducted in conjunction with other genetic tests and detailed clinical examinations to accurately determine the underlying genetic causes.

Familial neurohypophyseal diabetes insipidus is a rare genetic disease caused by AVP gene variants and is characterized by progressive polyuria and polydipsia in early childhood. Herein, we have reported the clinical symptoms and genetic test results of a Japanese patient with a family history of polyuria and polydipsia for over five generations. The proband was a 6-yr-old boy who was referred for the evaluation of polyuria and polydipsia. A hypertonic saline infusion test showed no increase in AVP levels and a water deprivation test followed by vasopressin administration confirmed the diagnosis of central diabetes insipidus. Genetic analyses of the patient and his affected mother revealed a novel heterozygous missense variant (c.308T>A, p.V103D). This variant was located in the region encoding the neurophysin II moiety. Computational analysis predicted that p.V103D is pathogenic, and a structural change was detected by viewing the three-dimensional structure of the protein model. To our knowledge, this is the first study to identify a novel missense variant, p.V103D, in a Japanese family with central diabetes insipidus. These findings expand the panel of AVP variants and facilitate the genetic diagnosis of familial neurohypophyseal diabetes insipidus.

The high incidence and mortality rates of colorectal cancer (CRC) in Alabama African Americans (AAs) and Oklahoma American Indians (AIs) are recognized as cancer disparities, yet the underlying causes have been poorly demonstrated. By evaluating CRC whole-exome sequencing and mutational profiles, here we report sets of mutated genes whose frequencies differed significantly (p < 0.05) in a race-specific manner. Secondary screening with cancer database identified "survival-critical genes (SCGs)" (i.e., genes whose mutations/alterations are associated with significant differences in the patients' survival rates) among the differentially mutated genes. Notable SCGs with race-pronounced variants were different from DEGs and their involved pathways included nucleotide catabolism and cell cycle checkpoints for AAs, and extracellular matrix organization for AIs. The inclusion of these SCGs with race-pronounced variants in the clinical CRC next-generation sequencing panels and the development of targeting drugs will serve as refinements for precision medicine to overcome racial disparities in health outcomes of CRC.

The discovery and development of immune checkpoint inhibitors (ICIs) has revolutionised the management of human cancers. However, only a subset of patients responds to ICI therapy, even though immune evasion is a hallmark of cancer. Initially, treatment was administered to patients on the basis of expression levels of one of the targets of ICI therapy, programmed cell death ligand 1. In clinical trials, the high response rate of melanoma and non-small cell lung cancer patients to ICI therapy supported the basic premise of cancer immunotherapy, that tumour-specific mutated proteins trigger an immune response. Tumour mutational burden subsequently emerged as a potential biomarker for response to ICI therapy. This review summarises the evidence supporting the scientific rationale for TMB as a biomarker for ICI therapy and focuses on some of the major challenges associated with incorporation of TMB into routine clinical practice.

Biologists currently have an assortment of high-throughput sequencing techniques allowing the study of population dynamics in increasing detail. The utility of genetic estimates depends on their ability to recover meaningful approximations while filtering out noise produced by artifacts. In this study, we empirically compared the congruence of two reduced representation approaches (genotyping-by-sequencing, GBS, and whole-exome sequencing, WES) in estimating genetic diversity and population structure using SNP markers typed in a small number of wild jaguar (Panthera onca) samples from South America. Due to its targeted nature, WES allowed for a more straightforward reconstruction of loci compared to GBS, facilitating the identification of true polymorphisms across individuals. We therefore used WES-derived metrics as a benchmark against which GBS-derived indicators were compared, adjusting parameters for locus assembly and SNP filtering in the latter. We observed significant variation in SNP call rates across samples in GBS datasets, leading to a recurrent miscalling of heterozygous sites. This issue was further amplified by small sample sizes, ultimately impacting the consistency of summary statistics between genotyping methods. Recognizing that the genetic markers obtained from GBS and WES are intrinsically different due to varying evolutionary pressures, particularly selection, we consider that our empirical comparison offers valuable insights and highlights critical considerations for estimating population genetic attributes using reduced representation datasets. Our results emphasize the critical need for careful evaluation of missing data and stringent filtering to achieve reliable estimates of genetic diversity and differentiation in elusive wildlife species.

Cancer nanomedicine has emerged as a promising approach to overcome the limitations of conventional cancer therapies, offering enhanced efficacy and safety in cancer management. However, the inherent heterogeneity of tumors presents increasing challenges for the application of cancer nanomedicine in both diagnosis and treatment. This heterogeneity necessitates the integration of advanced and high-throughput analytical techniques to tailor nanomedicine strategies to individual tumor profiles. Omics technologies, encompassing genomics, epigenomics, transcriptomics, proteomics, metabolomics, and more, provide unparalleled insights into the molecular and cellular mechanisms underlying cancer. By dissecting tumor heterogeneity across multiple levels, these technologies offer robust support for the development of personalized and precise cancer nanomedicine strategies. In this review, the principles, techniques, and applications of key omics technologies are summarized. Especially, the synergistic integration of omics and nanomedicine in cancer therapy is explored, focusing on enhanced diagnostic accuracy, optimized therapeutic strategies and the assessment of nanomedicine-mediated biological responses. Moreover, this review addresses current challenges and outlines future directions in the field of omics-enhanced nanomedicine. By offering valuable insights and guidance, this review aims to advance the integration of omics with nanomedicine, ultimately driving improved diagnostic and therapeutic strategies for cancer.

Variant detection from long-read genome sequencing (lrGS) has proven to be more accurate and comprehensive than variant detection from short-read genome sequencing (srGS). However, the rate at which lrGS can increase molecular diagnostic yield for rare disease is not yet precisely characterized. We performed lrGS using Pacific Biosciences "HiFi" technology on 96 short-read-negative probands with rare diseases that were suspected to be genetic. We generated hg38-aligned variants and de novo phased genome assemblies, and subsequently annotated, filtered, and curated variants using clinical standards. New disease-relevant or potentially relevant genetic findings were identified in 16/96 (16.7%) probands, nine of which (8/96, ∼9.4%) harbored pathogenic or likely pathogenic variants. Nine probands (∼9.4%) had variants that were accurately called in both srGS and lrGS and represent changes to clinical interpretation, mostly from recently published gene-disease associations. Seven cases included variants that were only correctly interpreted in lrGS, including copy-number variants (CNVs), an inversion, a mobile element insertion, two low-complexity repeat expansions, and a 1 bp deletion. While evidence for each of these variants is, in retrospect, visible in srGS, they were either not called within srGS data, were represented by calls with incorrect sizes or structures, or failed quality control and filtration. Thus, while reanalysis of older srGS data clearly increases diagnostic yield, we find that lrGS allows for substantial additional yield (7/96, 7.3%) beyond srGS. We anticipate that as lrGS analysis improves, and as lrGS data sets grow allowing for better variant-frequency annotation, the additional lrGS-only rare disease yield will grow over time.

Coffin-Siris syndrome is a clinically elusive and rare genetic disease characterized by a wide range of clinical manifestations. This study deeply analyzed and identified the clinical phenotype and genetic variant location in a pediatric patient with Coffin-Siris syndrome, aiming to enhance the understanding of this syndrome and assist in its screening and diagnosis.

A combination of advanced diagnostic tools, including high-throughput whole-exome sequencing (WES) and first-generation sequencing technologies, was employed to ascertain the etiology of the disease in the child.

The clinical phenotype was characterized by stunted growth, reduced stature, spina bifida, enuresis, and a ventricular septal defect. WES revealed a de novo variant in the SOX11 gene locus (c.700G > T), identified as pathogenic. It is noteworthy that this variant has not been previously reported.

The combination of clinical presentation and genetic testing results supports that the patient suffers from Coffin-Siris syndrome due to a genetic variant in the SOX11 gene. This de novo variant expands our understanding of human gene variation, which is conducive to genetic counseling and screening for early diagnosis of Coffin-Siris syndrome.

Hearing loss is a common disease. More than 100 genes have been reported to be associated with hereditary hearing loss. However, the distribution of these genes and their variants across diverse populations remains unclear. In this study, we gathered 347 hearing-impaired patients from four language families (Sinitic, Tibeto-Burman, Kra-Dai, and Hmong-Mien) in Southwestern China, excluding cases caused by common mutations in the GJB2 gene. By using next generation sequencing, 122 genes associated with hereditary hearing loss were analyzed on these patients. Rare candidate variants were identified in 71.93 % (264/347) of patients with hearing loss. The diagnostic rate varied around 10 % across different language families. The most frequently identified causative genes in successfully diagnosed cases were SLC26A4, MYO7A and TMPRSS3. Moreover, a substantial number of variants of unknown significance (VUS) were identified in our patient cohort. This underscores the critical need for establishing ethnicity-specific genomic databases for hearing loss. It will significantly improve the clinical diagnostic rate for hearing loss in this region.

Variant annotations are crucial for efficient identification of pathogenic variants. In this study, we retrospectively analyzed the utility of four annotation tools (allele frequency, ClinVar, SpliceAI, and Phenomatcher) in identifying 271 pathogenic single nucleotide and small insertion/deletion variants (SNVs/small indels). Although variant filtering based on allele frequency is essential for narrowing down on candidate variants, we found that 13 de novo pathogenic variants in autosomal dominant or X-linked dominant genes are registered in gnomADv4.0 or 54KJPN, with an allele frequency of less than 0.001%, suggesting that very rare variants in large cohort data can be pathogenic de novo variants. Notably, 38.4% candidate SNVs/small indels are registered in the ClinVar database as pathogenic or likely pathogenic, which highlights the significance of this database. SpliceAI can detect candidate variants affecting RNA splicing, leading to the identification of four variants located 11 to 50 bp away from the exon-intron boundary. Prioritization of candidate genes by proband phenotype using the PhenoMatcher module revealed that approximately 95% of the candidate genes had a maximum PhenoMatch score ≥ 0.6, suggesting the utility of phenotype-based variant prioritization. Our results suggest that a combination of multiple annotation tools and appropriate evaluation can improve the diagnosis of rare diseases.

X-linked chronic granulomatous disease (X-CGD) is an inborn error of immunity (IEI) resulting from genetic mutations in the cytochrome b-245 beta chain (CYBB) gene. The applicability of base editors (BEs) to correct mutations that cause X-CGD is constrained by the requirement of Cas enzymes to recognize specific protospacer adjacent motifs (PAMs). Our recently engineered PAMless Cas enzyme, SpRY, can overcome the PAM limitation. However, the efficiency, specificity, and applicability of SpRY-based BEs to correct mutations in human hematopoietic stem and progenitor cells (HSPCs) have not been thoroughly examined. Here, we demonstrated that the adenine BE ABE8e-SpRY can access a range of target sites in HSPCs to correct mutations causative of X-CGD. For the prototypical X-CGD mutation CYBB c.676C>T, ABE8e-SpRY achieved up to 70% correction, reaching efficiencies greater than three-and-one-half times higher than previous CRISPR nuclease and donor template approaches. We profiled potential off-target DNA edits, transcriptome-wide RNA edits, and chromosomal perturbations in base-edited HSPCs, which together revealed minimal off-target or bystander edits. Edited alleles persisted after transplantation of the base-edited HSPCs into immunodeficient mice. Together, these investigational new drug-enabling studies demonstrated efficient and precise correction of an X-CGD mutation with PAMless BEs, supporting a first-in-human clinical trial (NCT06325709) and providing a potential blueprint for treatment of other IEI mutations.

Oral mucosal melanomas (OMMs) are aggressive neoplasms commonly found in dogs but rare in humans. Utilizing whole exome sequencing (WES), which focuses on protein-coding regions to reveal mutation profiles, we conducted a comparative analysis of canine OMM and human melanomas. This study involved DNA extraction, exome enrichment, and sequencing from three canine OMM cell lines (CMGD-2, CMGD-5, TLM-1), five canine OMM frozen samples, a human OMM cell line (MEMO), and a human commercial skin melanoma cell line (SK-MEL-28). The sequencing and subsequent analysis of FASTQ files yielded final variant files, leading to the identification of mutations. Our findings revealed a total of 500 mutated genes in canine OMM, including significant ones such as EP300, FAT4, JAK3, LRP1B, NCOR1, and NOTCH1. Notably, 82 shared mutations were identified between human melanomas and canine OMM genomes. These mutations were categorized based on the gene functions. The identification of these mutations provides critical insights that can pave the way for the development of novel therapeutic strategies for both canine and human OMM, offering hope for more effective treatments in the future.

Congenital hypothyroidism (CH), characterized by insufficient thyroid hormone production due to abnormalities in the hypothalamic-pituitary-thyroid axis, is the most common congenital endocrine disorder. We previously conducted comprehensive genetic screening of 102 patients with permanent CH born in Kanagawa Prefecture, Japan and identified mutations in several genes in 19 CH patients, including defects in genes encoding dual oxidase 2, thyroglobulin, thyrotropin receptor, thyroid peroxidase, and paired-box 8. Despite these findings, approximately 80% of cases remain unexplained. CH pedigrees unexplained by known genetic forms of CH have been reported in the literature and registered as congenital hypothyroidism, nongoitrous, 3 (CHNG3; %609893) in Online Mendelian Inheritance in Man. We also identified a Japanese pedigree of CH that was compatible with CHNG3. However, the exact genetic cause of CHNG3 was not revealed by standard analysis methods such as exome sequencing and array comparative genomic hybridization. We therefore took a combined approach and analyzed a total of 11 undiagnosed CH pedigrees by whole genome sequencing to analyze a 3-Mb linkage region, and found a disease-causing variant affecting a TTTG microsatellite in a noncoding region on chromosome 15. Further analysis revealed that 13.9% of 989 Japanese CH patients had abnormalities involving the TTTG microsatellite, with a substantial proportion (41.5%) of familial CH cases carrying these mutations. Identification of the genetic cause of CHNG3 provides new insights into the pathogenesis of CH, and highlights the need for continued exploration of noncoding genomic regions in Mendelian disorders of unknown etiology.

To summarize the clinical manifestations of craniofacial fibrous dysplasia (CFD) patients with ocular complications, and find effective methods to diagnose early.

Nine CFD patients with ocular complications, and their parents were recruited in this study. All patients underwent ocular and systemic examinations. Bone lesions from all patients and peripheral blood from patients and their parents were collected for whole exome sequencing (WES). According to the screening for low-frequency deleterious variants, and bioinformatics variants prediction software, possible disease-causing variants were found in multiple CFD patients. The variants were validated by Sanger sequencing. Trio analysis was performed to verify the genetic patterns of CFD.

All patients were diagnosed with CFD, according to the clinical manifestations, classic radiographic appearance, and pathological biopsy. The main symptoms of the 9 CFD patients, included visual decline (9/9), craniofacial deformity (3/9) and strabismus (2/9), with few extraocular manifestations. The family backgrounds of all the CFD patients indicated that only the patient was affected, and their immediate family members were normal. GNAS variants were identified in all bone lesions from CFD patients, including two variant types: c.601C > T:p.R201C(6/9) and c.602G > A:p.R201H (3/9) in exon 8. The detection rate reached 100% by WES, but only 77.8% by Sanger sequencing. Interestingly, we found GNAS variants could not be detected in peripheral blood samples from CFD patients or their parents, and other potentially disease-causing gene variants related to CFD were not found.

For CFD patients with bone lesions involving the optic canal or sphenoid sinus regions, ocular symptoms should also be considered. Furthermore, we confirmed that CFD is not inherited, somatic variants in the GNAS gene are the main pathogenic gene causing CFD. Compared to the traditional methods in molecular genetic diagnosis of CFD, WES is more feasible and effective but limited in the type of samples.

In the last two decades, the development of nanotechnology has resulted in inorganic nanoparticles playing crucial roles in key industries, ranging from healthcare to energy technologies. For instance, gold and silver nanoparticles are widely used in rapid COVID-19 and flu tests, titania and zinc oxide nanoparticles are commonly found in cosmetic products, and superparamagnetic iron oxide nanoparticles have been clinically exploited as contrast agents and anti-anemia medicines. As a result, human exposure to nanomaterials is continuously increasing, raising concerns about their potential adverse health effects. Historically, the study of nanoparticle toxicity has largely relied on macroscopic observations obtained in different in vitro and in vivo models, resulting in readouts such as median lethal dose, biodistribution profile, and/or histopathological assessment. In recent years, omics methodologies, including transcriptomics, epigenomics, proteomics, metabolomics, and lipidomics, are increasingly used to characterize the biological interactions of nanomaterials, providing a better and broader understanding of their impact and mechanisms of toxicity. These approaches have been able to identify important genes and gene products that mediate toxicological effects, as well as endogenous functions and pathways dysregulated by nanoparticles. Omics methods improve our understanding of nanoparticle biology, and unravel mechanistic insights into nanomedicine-based therapies. This review aims to provide a deeper understanding and new perspectives of omics approaches to characterize the toxicity and biological interactions of inorganic nanoparticles, and improve the safety of nanoparticle applications.

In recent years, an increasing number of genes associated with male and female infertility have been identified. The genetics of infertility is no longer limited to the analysis of karyotypes or specific genes, and it is now possible to analyse several dozen infertility genes simultaneously. Here, we present the diagnostic activity over the past two years including 140 patients (63 women and 77 men). Targeted sequencing revealed causative variants in 17 patients, representing an overall diagnostic rate of 12.1%, with prevalence rates in females and males of 11% and 13%, respectively. The gene-disease relationship (GDR) was re-evaluated for genes due to the addition of new patients and/or variants in the actual study. Five genes changed categories: two female genes (MEIOB and TBPL2) moved from limited to moderate; two male genes (SOHLH1 and GALNTL5) moved from no evidence to strong and from limited to moderate; and SEPTIN12, which was unable to classify male infertility, was reclassified as limited. Many infertility genes have yet to be identified. With the increasing integration of genetics in reproductive medicine, the scope of intervention extends to include other family members, in addition to individual patients or couples. Genetic counselling consultations and appropriate staffing will need to be established in fertility centres. Trial registration number: Not applicable.

Kinship and pedigree, used for estimating inbreeding, heritability, selection, and gene flow, are useful for breeding and animal conservation. However, as the size of crossbred populations increases, inaccurate generation and parentage assignment in livestock farms increase. Restriction-site-associated DNA sequencing is a cost-effective platform for single nucleotide polymorphism (SNP) discovery and genotyping. Here, we performed a kinship analysis and pedigree reconstruction for Angus and Xiangxi yellow cattle. A total of 975 cattle, including 923 offspring with 24 known sires and 28 known dams, were sampled and subjected to SNP discovery and genotyping. The identified SNP panel included 7,305 SNPs capturing the maximum difference between paternal and maternal genome information, allowing us to distinguish F1 from F2 generations with 90% accuracy. In conclusion, we provided a low-cost and efficient SNP panel for kinship analyses and the improvement of local genetic resources, which are valuable for breed improvement, local resource utilization, and conservation.

Immune checkpoint inhibitors (ICIs) activate anti-cancer immunity by blocking T cell checkpoint molecules such as programmed death 1 (PD-1) and cytotoxic T lymphocyte-associated protein 4 (CTLA-4). Although ICIs induce some durable responses in various cancer patients, they also have disadvantages, including low response rates, the potential for severe side effects, and high treatment costs. Therefore, selection of patients who can benefit from ICI treatment is critical, and identification of biomarkers is essential to improve the efficiency of ICIs. In this review, we provide updated information on established predictive biomarkers (tumor programmed death-ligand 1 [PD-L1] expression, DNA mismatch repair deficiency, microsatellite instability high, and tumor mutational burden) and potential biomarkers currently under investigation such as tumor-infiltrated and peripheral lymphocytes, gut microbiome, and signaling pathways related to DNA damage and antigen presentation. In particular, this review aims to summarize the current knowledge of biomarkers, discuss issues, and further explore future biomarkers.

Dyslexia is a learning disability that negatively affects reading, writing, and spelling development at the word level in 5%-9% of children. The phenotype is variable and complex, involving several potential cognitive and physical concomitants such as sensory dysregulation and immunodeficiencies. The biological pathogenesis is not well-understood. Toward a better understanding of the biological drivers of dyslexia, we conducted the first joint exome and metabolome investigation in a pilot sample of 30 participants with dyslexia and 13 controls. In this analysis, eight metabolites of interest emerged (pyridoxine, kynurenic acid, citraconic acid, phosphocreatine, hippuric acid, xylitol, 2-deoxyuridine, and acetylcysteine). A metabolite-metabolite interaction analysis identified Krebs cycle intermediates that may be implicated in the development of dyslexia. Gene ontology analysis based on exome variants resulted in several pathways of interest, including the sensory perception of smell (olfactory) and immune system-related responses. In the joint exome and metabolite analysis, the olfactory transduction pathway emerged as the primary pathway of interest. Although the olfactory transduction and Krebs cycle pathways have not previously been described in dyslexia literature, these pathways have been implicated in other neurodevelopmental disorders including autism spectrum disorder and obsessive-compulsive disorder, suggesting the possibility of these pathways playing a role in dyslexia as well. Immune system response pathways, on the other hand, have been implicated in both dyslexia and other neurodevelopmental disorders.

The skin is a complex and dynamic organ where homeostasis is maintained through the intricate interplay between the immune system and metabolism, particularly cholesterol metabolism. Various factors such as cytokines, inflammatory mediators, cholesterol metabolites, and metabolic enzymes play crucial roles in facilitating these interactions. Dysregulation of this delicate balance contributes to the pathogenic pathways of inflammatory skin conditions, notably psoriasis. In this article, we provide an overview of omics biomarkers associated with psoriasis in relation to cholesterol metabolism. We explore multi-omics approaches that reveal the communication between immunometabolism and psoriatic inflammation. Additionally, we summarize the use of multi-omics strategies to uncover the complexities of multifactorial and heterogeneous inflammatory diseases. Finally, we highlight potential future perspectives related to targeted drug therapies and research areas that can advance precise medicine. This review aims to serve as a valuable resource for those investigating the role of cholesterol metabolism in psoriasis.

Multiple sclerosis (MS) age at onset (AAO) is a clinical predictor of long-term disease outcomes, independent of disease duration. Little is known about the genetic and biological mechanisms underlying age of first symptoms. We conducted a genome-wide association study (GWAS) to investigate associations between individual genetic variation and the MS AAO phenotype.

The study population was comprised participants with MS in 6 clinical trials: ADVANCE (N = 655; relapsing-remitting [RR] MS), ASCEND (N = 555; secondary-progressive [SP] MS), DECIDE (N = 1,017; RRMS), OPERA1 (N = 581; RRMS), OPERA2 (N = 577; RRMS), and ORATORIO (N = 529; primary-progressive [PP] MS). Altogether, 3,905 persons with MS of European ancestry were analyzed. GWAS were conducted for MS AAO in each trial using linear additive models controlling for sex and 10 principal components. Resultant summary statistics across the 6 trials were then meta-analyzed, for a total of 8.3 × 10-6 single nucleotide polymorphisms (SNPs) across all trials after quality control and filtering for heterogeneity. Gene-based tests of associations, pathway enrichment analyses, and Mendelian randomization analyses for select exposures were also performed.

Four lead SNPs within 2 loci were identified (p < 5 × 10-8), including a) 3 SNPs in the major histocompatibility complex and their effects were independent of HLA-DRB1*15:01 and b) a LOC105375167 variant on chromosome 7. At the gene level, the top association was HLA-C (p = 1.2 × 10-7), which plays an important role in antiviral immunity. Functional annotation revealed the enrichment of pathways related to T-cell receptor signaling, autoimmunity, and the complement cascade. Mendelian randomization analyses suggested a link between both earlier age at puberty and shorter telomere length and earlier AAO, while there was no evidence for a role for either body mass index or vitamin D levels.

Two genetic loci associated with MS AAO were identified, and functional annotation demonstrated an enrichment of genes involved in adaptive and complement immunity. There was also evidence supporting a link with age at puberty and telomere length. The findings suggest that AAO in MS is multifactorial, and the factors driving onset of symptoms overlap with those influencing MS risk.

Since US Food and Drug Administration approval of programmed death ligand 1 (PD-L1) as the first companion diagnostic for immune checkpoint inhibitors (ICIs) in non-small cell lung cancer, many patients have experienced increased overall survival. To improve selection of ICI responders versus nonresponders, microsatellite instability/mismatch repair deficiency (MSI/MMR) and tumor mutation burden (TMB) came into play. Clinical data show PD-L1, MSI/MMR, and TMB are independent predictive immunotherapy biomarkers. Harmonization of testing methodologies, optimization of assay design, and results analysis are ongoing. Future algorithms to determine immunotherapy eligibility might involve complementary use of current and novel biomarkers. Artificial intelligence could facilitate algorithm implementation to convert complex genetic data into recommendations for specific ICIs.

Variant detection from long-read genome sequencing (lrGS) has proven to be considerably more accurate and comprehensive than variant detection from short-read genome sequencing (srGS). However, the rate at which lrGS can increase molecular diagnostic yield for rare disease is not yet precisely characterized. We performed lrGS using Pacific Biosciences "HiFi" technology on 96 short-read-negative probands with rare disease that were suspected to be genetic. We generated hg38-aligned variants and de novo phased genome assemblies, and subsequently annotated, filtered, and curated variants using clinical standards. New disease-relevant or potentially relevant genetic findings were identified in 16/96 (16.7%) probands, eight of which (8/96, 8.33%) harbored pathogenic or likely pathogenic variants. Newly identified variants were visible in both srGS and lrGS in nine probands (~9.4%) and resulted from changes to interpretation mostly from recent gene-disease association discoveries. Seven cases included variants that were only interpretable in lrGS, including copy-number variants, an inversion, a mobile element insertion, two low-complexity repeat expansions, and a 1 bp deletion. While evidence for each of these variants is, in retrospect, visible in srGS, they were either: not called within srGS data, were represented by calls with incorrect sizes or structures, or failed quality-control and filtration. Thus, while reanalysis of older data clearly increases diagnostic yield, we find that lrGS allows for substantial additional yield (7/96, 7.3%) beyond srGS. We anticipate that as lrGS analysis improves, and as lrGS datasets grow allowing for better variant frequency annotation, the additional lrGS-only rare disease yield will grow over time.

Comprehending the mechanism behind human diseases with an established heritable component represents the forefront of personalized medicine. Nevertheless, numerous medically important genes are inaccurately represented in short-read sequencing data analysis due to their complexity and repetitiveness or the so-called 'dark regions' of the human genome. The advent of PacBio as a long-read platform has provided new insights, yet HiFi whole-genome sequencing (WGS) cost remains frequently prohibitive. We introduce a targeted sequencing and analysis framework, Twist Alliance Dark Genes Panel (TADGP), designed to offer phased variants across 389 medically important yet complex autosomal genes. We highlight TADGP accuracy across eleven control samples and compare it to WGS. This demonstrates that TADGP achieves variant calling accuracy comparable to HiFi-WGS data, but at a fraction of the cost. Thus, enabling scalability and broad applicability for studying rare diseases or complementing previously sequenced samples to gain insights into these complex genes. TADGP revealed several candidate variants across all cases and provided insight into LPA diversity when tested on samples from rare disease and cardiovascular disease cohorts. In both cohorts, we identified novel variants affecting individual disease-associated genes (e.g., IKZF1, KCNE1). Nevertheless, the annotation of the variants across these 389 medically important genes remains challenging due to their underrepresentation in ClinVar and gnomAD. Consequently, we also offer an annotation resource to enhance the evaluation and prioritization of these variants. Overall, we can demonstrate that TADGP offers a cost-efficient and scalable approach to routinely assess the dark regions of the human genome with clinical relevance.

Early-onset systemic lupus erythematosus presents with a more severe disease and is associated with a greater genetic burden, especially in patients from Black, Asian or Hispanic ancestries. Next-generation sequencing techniques, notably whole exome sequencing, have been extensively used in genomic interrogation studies to identify causal disease variants that are increasingly implicated in the development of autoimmunity. This Review discusses the known casual variants of polygenic and monogenic systemic lupus erythematosus and its implications under certain genetic disparities while suggesting an age-based sequencing strategy to aid in clinical diagnostics and patient management for improved patient care.