Behcet\'s disease (BD) is one of the most vision-threatening clinical entities of uveitis. Although the etiopathogenesis of BD remains obscure, accumulating evidence has demonstrated that both genetic and environmental factors may contribute to the development of BD. Genome-wide association studies (GWAS) and candidate association studies have identified several genetic variants strongly associated with BD, including variants in human leukocyte antigen (HLA) -A02, -A03, -A24, -A26, -A31, -B15, -B27, -B35, -B49, -B51, -B57, -B58, -C0704, CIITA, ERAP1, MICA, IL1A-IL1B, IL10, IL12, IL23R, IL-23R/IL-12RB2, IL1RL1-IL18R1, STAT4, TFCP2L1, TRAF5, TNFAIP3, CCR1/CCR3, RIPK2, ADO-ZNF365-EGR2, KLRC4, LACC1, MEFV, IRF8, FUT2, CEBPB-PTPN1, ZMIZ1, RPS6KA4, IL10RA, SIPA1-FIBP-FOSL1, VAMP1, JRKL/CTCN5, IFNGR1 and miRNA-146a. Epigenetic modifications are also reported to play essential roles in the development of BD, including DNA methylation and histone modification. We review here the recent advances in the genetic and epigenetic factors associated with the BD pathogenesis.

BACKGROUND: A large number of challenging medically relevant genes (CMRGs) are situated in complex or highly repetitive regions of the human genome, hindering comprehensive characterization of genetic variants using next-generation sequencing technologies. In this study, we employed long-read sequencing technology, extensively utilized in studying complex genomic regions, to characterize genetic alterations, including short variants (single nucleotide variants and short insertions and deletions) and copy number variations, in 370 CMRGs across 41 individuals from 19 global populations.
RESULTS: Our analysis revealed high levels of genetic variants in CMRGs, with 68.73% exhibiting copy number variations and 65.20% containing short variants that may disrupt protein function across individuals. Such variants can influence pharmacogenomics, genetic disease susceptibility, and other clinical outcomes. We observed significant differences in CMRG variation across populations, with individuals of African ancestry harboring the highest number of copy number variants and short variants compared to samples from other continents. Notably, 15.79% to 33.96% of short variants were exclusively detectable through long-read sequencing. While the T2T-CHM13 reference genome significantly improved the assembly of CMRG regions, thereby facilitating variant detection in these regions, some regions still lacked resolution.
CONCLUSIONS: Our results provide an important reference for future clinical and pharmacogenetic studies, highlighting the need for a comprehensive representation of global genetic diversity in the reference genome and improved variant calling techniques to fully resolve medically relevant genes.

The massive structural variations and frequent introgression highly contribute to the genetic diversity of wheat, while the huge and complex genome of polyploid wheat hinders efficient genotyping of abundant varieties towards accurate identification, management, and exploitation of germplasm resources.
We develop a novel workflow that identifies 1240 high-quality large copy number variation blocks (CNVb) in wheat at the pan-genome level, demonstrating that CNVb can serve as an ideal DNA fingerprinting marker for discriminating massive varieties, with the accuracy validated by PCR assay. We then construct a digitalized genotyping CNVb map across 1599 global wheat accessions. Key CNVb markers are linked with trait-associated introgressions, such as the 1RS·1BL translocation and 2NvS translocation, and the beneficial alleles, such as the end-use quality allele Glu-D1d (Dx5 + Dy10) and the semi-dwarf r-e-z allele. Furthermore, we demonstrate that these tagged CNVb markers promote a stable and cost-effective strategy for evaluating wheat germplasm resources with ultra-low-coverage sequencing data, competing with SNP array for applications such as evaluating new varieties, efficient management of collections in gene banks, and describing wheat germplasm resources in a digitalized manner. We also develop a user-friendly interactive platform, WheatCNVb ( http://wheat.cau.edu.cn/WheatCNVb/ ), for exploring the CNVb profiles over ever-increasing wheat accessions, and also propose a QR-code-like representation of individual digital CNVb fingerprint. This platform also allows uploading new CNVb profiles for comparison with stored varieties.
The CNVb-based approach provides a low-cost and high-throughput genotyping strategy for enabling digitalized wheat germplasm management and modern breeding with precise and practical decision-making.

Copy number variation (CNV) is a key genetic characteristic for cancer diagnostics and can be used as a biomarker for the selection of therapeutic treatments. Using data sets established in our previous study, we benchmark the performance of cancer CNV calling by six most recent and commonly used software tools on their detection accuracy, sensitivity, and reproducibility. In comparison to other orthogonal methods, such as microarray and Bionano, we also explore the consistency of CNV calling across different technologies on a challenging genome.
While consistent results are observed for copy gain, loss, and loss of heterozygosity (LOH) calls across sequencing centers, CNV callers, and different technologies, variation of CNV calls are mostly affected by the determination of genome ploidy. Using consensus results from six CNV callers and confirmation from three orthogonal methods, we establish a high confident CNV call set for the reference cancer cell line (HCC1395).
NGS technologies and current bioinformatics tools can offer reliable results for detection of copy gain, loss, and LOH. However, when working with a hyper-diploid genome, some software tools can call excessive copy gain or loss due to inaccurate assessment of genome ploidy. With performance matrices on various experimental conditions, this study raises awareness within the cancer research community for the selection of sequencing platforms, sample preparation, sequencing coverage, and the choice of CNV detection tools.

Copy number variation (CNV) serves as a significant source of genetic diversity in mammals and exerts substantial effects on various complex traits. Pingliang red cattle, an outstanding indigenous resource in China, possess remarkable breeding value attributed to their tender meat and superior marbling quality. However, the genetic mechanisms influencing carcass and meat quality traits in Pingliang red cattle are not well understood. We generated a comprehensive genome-wide CNV map for Pingliang red cattle using the GGP Bovine 100K SNP chip. A total of 755 copy number variable regions (CNVRs) spanning 81.03 Mb were identified, accounting for approximately 3.24% of the bovine autosomal genome. Among these, we discovered 270 potentially breed-specific CNVRs in Pingliang red cattle, including 143 gains, 73 losses, and 54 mixed events. Functional annotation analysis revealed significant associations between these specific CNVRs and important traits such as carcass and meat quality, reproduction, exterior traits, growth traits, and health traits. Additionally, our network and transcriptome analysis highlighted CACNA2D1, CYLD, UBXN2B, TG, NADK, and ITGA9 as promising candidate genes associated with carcass weight and intramuscular fat deposition. The current study presents a genome-wide CNV map in Pingliang red cattle, highlighting breed-specific CNVRs, and transcriptome findings provide valuable insights into the underlying genetic characteristics of Pingliang red cattle. These results offer potential avenues for enhancing meat quality through a targeted breeding program.

Copy number variation (CNV) is an important structural variation used to elucidate complex economic traits. In this study, we sequenced 25 Wannan spotted pigs (WSPs) to detect their CNVs and identify their selection signatures compared with those of 10 Asian wild boars. A total of 14,161 CNVs were detected in the WSPs, accounting for 0.72% of the porcine genome. The fixation index (Fst) was used to identify the selection signatures, and 195 CNVs with the top 1% of the Fst value were selected. Eighty genes were identified in the selected CNV regions. Functional GO and KEGG analyses revealed that the genes within these selected CNVs are associated with key traits such as reproduction (GAL3ST1 and SETD2), fatty acid composition (PRKG1, ACACA, ACSL3, UGT8), immune system (LYZ), ear size (WIF1), and feed efficiency (VIPR2). The findings of this study contribute novel insights into the genetic CNVs underlying WSP characteristics and provide essential information for the protection and utilization of WSP populations.

Buffaloes are vital contributors to the global dairy industry. Understanding the genetic basis of milk production traits in buffalo populations is essential for breeding programs and improving productivity. In this study, we conducted whole-genome resequencing on 387 buffalo genomes from 29 diverse Asian breeds, including 132 river buffaloes, 129 swamp buffaloes, and 126 crossbred buffaloes. We identified 36,548 copy number variant (CNVs) spanning 133.29 Mb of the buffalo genome, resulting in 2,100 copy number variant regions (CNVRs), with 1,993 shared CNVRs being found within the studied buffalo types. Analyzing CNVRs highlighted distinct genetic differentiation between river and swamp buffalo subspecies, verified by evolutionary tree and principal component analyses. Admixture analysis grouped buffaloes into river and swamp categories, with crossbred buffaloes displaying mixed ancestry. To identify candidate genes associated with milk production traits, we employed 3 approaches. First, we used Vst-based population differentiation, revealing 11 genes within CNVRs that exhibited significant divergence between different buffalo breeds, including genes linked to milk production traits. Second, expression quantitative loci (eQTL) analysis revealed differential expression of CNVR-driven genes (DECGs) associated with milk production traits. Notably, known milk production-related genes were among these DECGs, validating their relevance. Last, a genome-wide association study (GWAS) identified 3 CNVRs significantly linked to peak milk yield. Our study provides comprehensive genomic insights into buffalo populations and identifies candidate genes associated with milk production traits. These findings facilitate genetic breeding programs aimed at increasing milk yield and improving quality in this economically important livestock species.

Copy number variation (CNV) is an essential genetic driving factor of cancer formation and progression, making intelligent classification based on CNV feasible. However, there are a few challenges in the current machine learning and deep learning methods, such as the design of base classifier combination schemes in ensemble methods and the selection of layers of neural networks, which often result in low accuracy. Therefore, an adaptive bilinear dynamic cascade model (Adap-BDCM) is developed to further enhance the accuracy and applicability of these methods for intelligent classification on CNV datasets. In this model, a feature selection module is introduced to mitigate the interference of redundant information, and a bilinear model based on the gated attention mechanism is proposed to extract more beneficial deep fusion features. Furthermore, an adaptive base classifier selection scheme is designed to overcome the difficulty of manually designing base classifier combinations and enhance the applicability of the model. Lastly, a novel feature fusion scheme with an attribute recall submodule is constructed, effectively avoiding getting stuck in local solutions and missing some valuable information. Numerous experiments have demonstrated that our Adap-BDCM model exhibits optimal performance in cancer classification, stage prediction, and recurrence on CNV datasets. This study can assist physicians in making diagnoses faster and better.

Wolfberry (Lycium, of the family Solanaceae) has special nutritional benefits due to its valuable metabolites. Here, 16 wolfberry-specific metabolites were identified by comparing the metabolome of wolfberry with those of six species, including maize, rice, wheat, soybean, tomato and grape. The copy numbers of the riboflavin and phenyllactate degradation genes riboflavin kinase (RFK) and phenyllactate UDP-glycosyltransferase (UGT1) were lower in wolfberry than in other species, while the copy number of the phenyllactate synthesis gene hydroxyphenyl-pyruvate reductase (HPPR) was higher in wolfberry, suggesting that the copy number variation of these genes among species may be the main reason for the specific accumulation of riboflavin and phenyllactate in wolfberry. Moreover, the metabolome-based neighbor-joining tree revealed distinct clustering of monocots and dicots, suggesting that metabolites could reflect the evolutionary relationship among those species. Taken together, we identified 16 specific metabolites in wolfberry and provided new insight into the accumulation mechanism of species-specific metabolites at the genomic level.

Noninvasive prenatal diagnosis (NIPD) for autosomal recessive nonsyndromic hearing loss (ARNSHL) has been rarely reported until recent years. Additionally, the existing method can not be used for challenging genome loci (eg, copy number variations, deletions, inversions, or gene recombinants) or on families without proband genotype. This study assessed the performance of relative haplotype dosage analysis (RHDO)-based NIPD for identifying fetal genotyping in pregnancies at risk of ARNSHL. Fifty couples carrying pathogenic variants associated with ARNSHL in either GJB2 or SLC26A4 were recruited. The RHDO-based targeted linked-read sequencing combined with whole gene coverage probes was used to genotype the fetal cell-free DNA of 49 families who met the quality control standard. Fetal amniocyte samples were genotyped using invasive prenatal diagnosis (IPD) to assess the performance of NIPD. The NIPD results showed 100% (49/49) concordance with those obtained through IPD. Two families with copy number variation and recombination were also successfully identified. Sufficient specific informative single-nucleotide polymorphisms for haplotyping, as well as the fetal cell-free DNA concentration and sequencing depth, are prerequisites for RHDO-based NIPD. This method has the merits of covering the entire genes of GJB2 and SLC26A4, qualifying for copy number variation and recombination analysis with remarkable sensitivity and specificity. Therefore, it has clinical potential as an alternative to traditional IPD for ARNSHL.