BACKGROUND: Congenital malformations of the female genital tract (CM-FGT) are characterized by abnormal development of the fallopian tubes, uterus, and vagina, often accompanied by malformations in the urinary system, bones and hearing. However, no definitive pathogenic genes and molecular genetic causes have been identified.
METHODS: We present the largest whole-genome sequencing study of CM-FGT to date, analyzing 590 individuals in China: 95 patients, 442 case-controls, and 53 familial controls.
RESULTS: Among the patients, 5.3% carried known CM-FGT-related variants. Pedigree and case-control analyses in two dimensions of coding and non-coding regulatory regions revealed seven novel de novo copy number variations, 12 rare single-nucleotide variations, and 10 rare 3\' untranslated region (UTR) mutations in genes related to CM-FGT, particularly highlighting ASH1L as a pathogenic gene. Single-cell sequencing data showed that the majority of CM-FGT-related risk genes are spatiotemporally specifically expressed early in uterus development.
CONCLUSIONS: In conclusion, this study identified novel variants related to CM-FGT, particularly highlighting ASH1L as a pathogenic gene. The findings provide insights into the genetic variants underlying CM-FGT, with single-cell sequencing data revealing spatiotemporal specific expression patterns of key risk genes early in uterine development. This study significantly advances the understanding of CM-FGT etiology and genetic landscape, offering new opportunities for prenatal screening.

This study offers insights into the genetic and biological connections between nine common metabolic diseases using data from genome-wide association studies. Our goal is to unravel the genetic interactions and biological pathways of these complex diseases, enhancing our understanding of their genetic architecture. We employed a range of advanced analytical techniques to explore the genetic correlations and shared genetic variants of these diseases. These methods include Linked Disequilibrium Score Regression, High-Definition Likelihood (HDL), genetic analysis combining multiplicity and annotation (GPA), two-sample Mendelian randomization analyses, analysis under the multiplicity-complex null hypothesis (PLACO), and Functional mapping and annotation of genetic associations (FUMA). Additionally, Bayesian co-localization analyses were used to examine associations of specific loci across traits. Our study discovered significant genomic correlations and shared loci, indicating complex genetic interactions among these metabolic diseases. We found several shared single nucleotide variants and risk loci, notably highlighting the role of the immune system and endocrine pathways in these diseases. Particularly, rs2476601 and its associated gene PTPN22 appear to play a crucial role in the connection between type 2 diabetes mellitus, hypothyroidism/mucous oedema and hypoglycaemia. These findings enhance our understanding of the genetic underpinnings of these diseases and open new potential avenues for targeted therapeutic and preventive strategies. The results underscore the importance of considering pleiotropic effects in deciphering the genetic architecture of complex diseases, especially metabolic ones.

Germline genetic variants, including single-nucleotide variants (SNVs) and copy number variants (CNVs), account for interpatient heterogeneity. In the past several decades, genome-wide association studies (GWAS) have identified multiple lung cancer-associated SNVs in Caucasian and Chinese populations. These variants either reside within coding regions and change the structure and function of cancer-related proteins or reside within non-coding regions and alter the expression level of cancer-related proteins. The variants can be used not only for cancer risk assessment and prevention but also for the development of new therapies. In this review, we discuss the lung cancer-associated SNVs identified to date, their contributions to lung tumorigenesis and prognosis, and their potential use in predicting prognosis and implementing therapeutic strategies.

BACKGROUND: Genome-wide association studies (GWAS) have revealed a multitude of candidate genetic variants affecting the risk of developing complex traits and diseases. However, the highlighted regions are typically in the non-coding genome, and uncovering the functional causative single nucleotide variants (SNVs) is challenging. Prioritization of variants is commonly based on genomic annotation with markers of active regulatory elements, but current approaches still poorly predict functional variants. To address this, we systematically analyze six markers of active regulatory elements for their ability to identify functional variants.
RESULTS: We benchmark against molecular quantitative trait loci (molQTL) from assays of regulatory element activity that identify allelic effects on DNA-binding factor occupancy, reporter assay expression, and chromatin accessibility. We identify the combination of DNase footprints and divergent enhancer RNA (eRNA) as markers for functional variants. This signature provides high precision, but with a trade-off of low recall, thus substantially reducing candidate variant sets to prioritize variants for functional validation. We present this as a framework called FINDER-Functional SNV IdeNtification using DNase footprints and eRNA.
CONCLUSIONS: We demonstrate the utility to prioritize variants using leukocyte count trait and analyze variants in linkage disequilibrium with a lead variant to predict a functional variant in asthma. Our findings have implications for prioritizing variants from GWAS, in development of predictive scoring algorithms, and for functionally informed fine mapping approaches.

An effective strategy for accurately detecting single nucleotide variants (SNVs) is of great significance for genetic research and diagnostics. However, strict amplification conditions, complex experimental instruments, and specialized personnel are required to obtain a satisfactory tradeoff between sensitivity and selectivity for SNV discrimination. In this study, we present a CRISPR-based transistor biosensor for the rapid and highly selective detection of SNVs in viral RNA. By introducing a synthetic mismatch in the crRNA, the CRISPR-Cas13a protein can be engineered to capture the target SNV RNA directly on the surface of the graphene channel. This process induces a fast electrical signal response in the transistor, obviating the need for amplification or reporter molecules. The biosensor exhibits a detection limit for target RNA as low as 5 copies in 100 μL, which is comparable to that of real-time quantitative polymerase chain reaction (PCR). Its operational range spans from 10 to 5 × 105 copy mL-1 in artificial saliva solution. This capability enables the biosensor to discriminate between wild-type and SNV RNA within 15 min. By introducing 10 μL of swab samples during clinical testing, the biosensor provides specific detection of respiratory viruses in 19 oropharyngeal specimens, including influenza A, influenza B, and variants of SARS-CoV-2. This study emphasizes the CRISPR-transistor technique as a highly accurate and sensitive approach for field-deployable nucleic acid screening or diagnostics.

UNASSIGNED: Type 2 diabetes mellitus (T2DM) is a complex heterogenic metabolic with a wide range of etiology. Purinergic receptors have pivotal roles in different processes and are hypothesized to have roles in the pathogenesis of T2DM.
UNASSIGNED: Three hundred subjects affected by T2DM and 300 healthy subjects were genotyped by amplification refractory mutation system polymerase chain reaction (ARMS-PCR). SPSS V16.0 was recruited for statistical analysis.
UNASSIGNED: The findings showed that the G allele of rs25644A > G increases the risk of T2DM in our population statistically (OR = 1.51, 95% CI = 1.14-1.99, p = 0.003). This allele in some genotype models, including the dominant model, caused an increase in the risk of T2DM. The interaction of genotypes between studied variants in the P2XR4 gene increased the risk of T2DM. Haplotype analysis showed that Ars1169727/Grs25644 haplotype caused an increase in the risk of T2DM.
UNASSIGNED: The findings suggest that rs25644A > G plays a role in our population\'s increased risk of T2DM.

Keratoconus is a bilateral ocular condition characterized by irregularities and the thinning of the cornea. Decreased central corneal thickness is a hallmark of the condition, and numerous genes have played a role in altering corneal thickness and the subsequent development of keratoconus. Variants in the structural and regulatory genes of the extracellular matrix have been highly associated with keratoconus, as well as with pectus excavatum, a chest wall deformity commonly seen in connective tissue disorders. This report describes a patient with a c.1720-11T>A intronic variant in the collagen-encoding gene, COL5A1, who was diagnosed with early-onset keratoconus and demonstrated a significant pectus excavatum. This report associates a COL5A1 variant with these seemingly unrelated phenotypic associations, further advancing the literature on the topic.

Discoveries in the field of genomics have revealed that non-coding genomic regions are not merely \"junk DNA\", but rather comprise critical elements involved in gene expression. These gene regulatory elements (GREs) include enhancers, insulators, silencers, and gene promoters. Notably, new evidence shows how mutations within these regions substantially influence gene expression programs, especially in the context of cancer. Advances in high-throughput sequencing technologies have accelerated the identification of somatic and germline single nucleotide mutations in non-coding genomic regions. This review provides an overview of somatic and germline non-coding single nucleotide alterations affecting transcription factor binding sites in GREs, specifically involved in cancer biology. It also summarizes the technologies available for exploring GREs and the challenges associated with studying and characterizing non-coding single nucleotide mutations. Understanding the role of GRE alterations in cancer is essential for improving diagnostic and prognostic capabilities in the precision medicine era, leading to enhanced patient-centered clinical outcomes.

Spontaneous tumour formation in higher plants can occur in the absence of pathogen invasion, depending on the plant genotype. Spontaneous tumour formation on the taproots is consistently observed in certain inbred lines of radish (Raphanus sativus var. radicula Pers.). In this paper, using Oxford Nanopore and Illumina technologies, we have sequenced the genomes of two closely related radish inbred lines that differ in their ability to spontaneously form tumours. We identified a large number of single nucleotide variants (amino acid substitutions, insertions or deletions, SNVs) that are likely to be associated with the spontaneous tumour formation. Among the genes involved in the trait, we have identified those that regulate the cell cycle, meristem activity, gene expression, and metabolism and signalling of phytohormones. After identifying the SNVs, we performed Sanger sequencing of amplicons corresponding to SNV-containing regions to validate our results. We then checked for the presence of SNVs in other tumour lines of the radish genetic collection and found the ERF118 gene, which had the SNVs in the majority of tumour lines. Furthermore, we performed the identification of the CLAVATA3/ESR (CLE) and WUSCHEL (WOX) genes and, as a result, identified two unique radish CLE genes which probably encode proteins with multiple CLE domains. The results obtained provide a basis for investigating the mechanisms of plant tumour formation and also for future genetic and genomic studies of radish.

The intestinal microbiota is a crucial environmental factor in the development of inflammatory bowel disease (IBD). The abundance of Faecalibacterium prausnitzii is significantly decreased in IBD patients, which is used as a biomarker for IBD diagnosis. However, this can be observed in both IBD and colorectal cancer, which would confound the diagnostic results. Thus, we first established a new model for predicting Crohn\'s disease (CD) with high precision according to gene characteristics based on single nucleotide variants (SNVs). Next, five gene markers belonging to two species, F. prausnitzii and Eubacterium rectale, that were enriched in the CD group were obtained to build a CD prediction model, and high accuracy in distinguishing the CD and control groups was observed in the discovery (area under curve [AUC] = 91.13%) and validation cohorts (AUC = 79.55%). The model still maintained high accuracy after expanding the healthy cohort (AUC = 89.75%). High disease specificity in distinguishing CD and CRC groups (AUC = 95.74%) was also proven. This study establishes a novel diagnostic method for predicting IBD that also provides unprecedented insight for the early, painless diagnosis of other non-communicable diseases.