ABCA4 is the most frequently mutated gene leading to inherited retinal disease (IRD) with over 2200 pathogenic variants reported to date. Of these, ~1% are copy number variants (CNVs) involving the deletion or duplication of genomic regions, typically >50 nucleotides in length. An in-depth assessment of the current literature based on the public database LOVD, regarding the presence of known CNVs and structural variants in ABCA4, and additional sequencing analysis of ABCA4 using single-molecule Molecular Inversion Probes (smMIPs) for 148 probands highlighted recurrent and novel CNVs associated with ABCA4-associated retinopathies. An analysis of the coverage depth in the sequencing data led to the identification of eleven deletions (six novel and five recurrent), three duplications (one novel and two recurrent) and one complex CNV. Of particular interest was the identification of a complex defect, i.e., a 15.3 kb duplicated segment encompassing exon 31 through intron 41 that was inserted at the junction of a downstream 2.7 kb deletion encompassing intron 44 through intron 47. In addition, we identified a 7.0 kb tandem duplication of intron 1 in three cases. The identification of CNVs in ABCA4 can provide patients and their families with a genetic diagnosis whilst expanding our understanding of the complexity of diseases caused by ABCA4 variants.

With the recent expansion of structural variant identification in the human genome, understanding the role of these impactful variants in disease architecture is critically important. Currently, a large proportion of genome-wide-significant genome-wide association study (GWAS) single nucleotide polymorphisms (SNPs) are functionally unresolved, raising the possibility that some of these SNPs are associated with disease through linkage disequilibrium with causal structural variants. Hence, understanding the linkage disequilibrium between newly discovered structural variants and statistically significant SNPs may provide a resource for further investigation into disease-associated regions in the genome. Here we present a resource cataloging structural variant-significant SNP pairs in high linkage disequilibrium. The database is composed of (i) SNPs that have exhibited genome-wide significant association with traits, primarily disease phenotypes, (ii) newly released structural variants (SVs), and (iii) linkage disequilibrium values calculated from unphased data. All data files including those detailing SV and GWAS SNP associations and results of GWAS-SNP-SV pairs are available at the SV-SNP LD Database and can be accessed at \'https://github.com/hliang-SchrodiLab/SV_SNPs. Our analysis results represent a useful fine mapping tool for interrogating SVs in linkage disequilibrium with disease-associated SNPs. We anticipate that this resource may play an important role in subsequent studies which investigate incorporating disease causing SVs into disease risk prediction models.

Structural variants (SVs, including large-scale insertions, deletions, inversions, and translocations) significantly impact the functions of genes in the microbial genome, and SVs in the microbiome are associated with diverse biological processes and human diseases. With the advancements in sequencing and bioinformatics technologies, increasingly, sequencing data and analysis tools are already being extensively utilized for microbiome SV analyses, leading to a higher demand for more dedicated SV analysis workflows. Moreover, due to the unique detection biases of various sequencing technologies, including short-read sequencing (such as Illumina platforms) and long-read sequencing (e.g., Oxford Nanopore and PacBio), SV discovery based on multiple platforms is necessary to comprehensively identify the wide variety of SVs. Here, we establish an integrated pipeline MetaSVs combining Nanopore long reads and Illumina short reads to analyze SVs in the microbial genomes from gut microbiome and further identify differential SVs that can be reflective of metabolic differences. Our pipeline provides researchers easy access to SVs and relevant metabolites in the microbial genomes without the requirement of specific technical expertise, which is particularly useful to researchers interested in metagenomic SVs but lacking sophisticated bioinformatic knowledge.

Copy-number variants (CNVs) are genome-wide structural variations involving the duplication or deletion of large nucleotide sequences. While these types of variations can be commonly found in humans, large and rare CNVs are known to contribute to the development of various neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD). Nevertheless, given that these NDD-risk CNVs cover broad regions of the genome, it is particularly challenging to pinpoint the critical gene(s) responsible for the manifestation of the phenotype. In this study, we performed a meta-analysis of CNV data from 11,614 affected individuals with NDDs and 4,031 control individuals from SFARI database to identify 41 NDD-risk CNV loci, including 24 novel regions. We also found evidence for dosage-sensitive genes within these regions being significantly enriched for known NDD-risk genes and pathways. In addition, a significant proportion of these genes was found to (1) converge in protein-protein interaction networks, (2) be among most expressed genes in the brain across all developmental stages, and (3) be hit by deletions that are significantly over-transmitted to individuals with ASD within multiplex ASD families from the iHART cohort. Finally, we conducted a burden analysis using 4,281 NDD cases from Decipher and iHART cohorts, and 2,504 neurotypical control individuals from 1000 Genomes and iHART, which resulted in the validation of the association of 162 dosage-sensitive genes driving risk for NDDs, including 22 novel NDD-risk genes. Importantly, most NDD-risk CNV loci entail multiple NDD-risk genes in agreement with a polygenic model associated with the majority of NDD cases.

Whole exome sequencing discovers causative mutations in less than 50 % of rare disease patients, suggesting the presence of additional mutations in the non-coding genome. So far, non-coding mutations have been identified in less than 0.2 % of individuals with genetic diseases listed in the ClinVar database and exhibit highly diverse molecular mechanisms. In contrast to our capability to sequence the whole genome, our ability to discover and functionally confirm such non-coding mutations is lagging behind severely. We discuss the problems and present examples of confirmed mutations in deep intronic sequences, non-coding triplet repeats, enhancers, and larger structural variants and highlight their proposed disease mechanisms. Finally, we discuss the type of data that would be required to establish non-coding mutation detection in routine diagnostics.

High-throughput sequencing techniques have significantly increased the molecular diagnosis rate for patients with monogenic disorders. This is primarily due to a substantially increased identification rate of disease mutations in the coding sequence, primarily SNVs and indels. Further progress is hampered by difficulties in the detection of structural variants and the interpretation of variants outside the coding sequence. In this review, we provide an overview about how novel sequencing techniques and state-of-the-art algorithms can be used to discover small and structural variants across the whole genome and introduce bioinformatic tools for the prediction of effects variants may have in the non-coding part of the genome.

Inherited retinal diseases (IRDs) are a group of rare monogenic diseases with high genetic heterogeneity (pathogenic variants identified in over 280 causative genes). The genetic diagnostic rate for IRDs is around 60%, mainly thanks to the routine application of next-generation sequencing (NGS) approaches such as extensive gene panels or whole exome analyses. Whole-genome sequencing (WGS) has been reported to improve this diagnostic rate by revealing elusive variants, such as structural variants (SVs) and deep intronic variants (DIVs). We performed WGS on 33 unsolved cases with suspected autosomal recessive IRD, aiming to identify causative genetic variants in non-coding regions or to detect SVs that were unexplored in the initial screening. Most of the selected cases (30 of 33, 90.9%) carried monoallelic pathogenic variants in genes associated with their clinical presentation, hence we first analyzed the non-coding regions of these candidate genes. Whenever additional pathogenic variants were not identified with this approach, we extended the search for SVs and DIVs to all IRD-associated genes. Overall, we identified the missing causative variants in 11 patients (11 of 33, 33.3%). These included three DIVs in ABCA4, CEP290 and RPGRIP1; one non-canonical splice site (NCSS) variant in PROM1 and three SVs (large deletions) in EYS, PCDH15 and USH2A. For the previously unreported DIV in CEP290 and for the NCCS variant in PROM1, we confirmed the effect on splicing by reverse transcription (RT)-PCR on patient-derived RNA. This study demonstrates the power and clinical utility of WGS as an all-in-one test to identify disease-causing variants missed by standard NGS diagnostic methodologies.

BACKGROUND: Anopheles funestus is a leading vector of malaria in most parts of East and Southern Africa, yet its ecology and responses to vector control remain poorly understood compared with other vectors such as Anopheles gambiae and Anopheles arabiensis. This study presents the first large-scale survey of the genetic and phenotypic expression of insecticide resistance in An. funestus populations in Tanzania.
METHODS: We performed insecticide susceptibility bioassays on An. funestus mosquitoes in nine regions with moderate-to-high malaria prevalence in Tanzania, followed by genotyping for resistance-associated mutations (CYP6P9a, CYP6P9b, L119F-GSTe2) and structural variants (SV4.3 kb, SV6.5 kb). Generalized linear models were used to assess relationships between genetic markers and phenotypic resistance. An interactive R Shiny tool was created to visualize the data and support evidence-based interventions.
RESULTS: Pyrethroid resistance was universal but reversible by piperonyl-butoxide (PBO). However, carbamate resistance was observed in only five of the nine districts, and dichloro-diphenyl-trichloroethane (DDT) resistance was found only in the Kilombero valley, south-eastern Tanzania. Conversely, there was universal susceptibility to the organophosphate pirimiphos-methyl in all sites. Genetic markers of resistance had distinct geographical patterns, with CYP6P9a-R and CYP6P9b-R alleles, and the SV6.5 kb structural variant absent or undetectable in the north-west but prevalent in all other sites, while SV4.3 kb was prevalent in the north-western and western regions but absent elsewhere. Emergent L119F-GSTe2, associated with deltamethrin resistance, was detected in heterozygous form in districts bordering Mozambique, Malawi and the Democratic Republic of Congo. The resistance landscape was most complex in western Tanzania, in Tanganyika district, where all five genetic markers were detected. There was a notable south-to-north spread of resistance genes, especially CYP6P9a-R, though this appears to be interrupted, possibly by the Rift Valley.
CONCLUSIONS: This study underscores the need to expand resistance monitoring to include An. funestus alongside other vector species, and to screen for both the genetic and phenotypic signatures of resistance. The findings can be visualized online via an interactive user interface and could inform data-driven decision-making for resistance management and vector control. Since this was the first large-scale survey of resistance in Tanzania\'s An. funestus, we recommend regular updates with greater geographical and temporal coverage.

While short-read sequencing currently dominates genetic research and diagnostics, it frequently falls short of capturing certain structural variants (SVs), which are often implicated in the etiology of neurodevelopmental disorders (NDDs). Optical genome mapping (OGM) is an innovative technique capable of capturing SVs that are undetectable or challenging-to-detect via short-read methods. This study aimed to investigate NDDs using OGM, specifically focusing on cases that remained unsolved after standard exome sequencing. OGM was performed in 47 families using ultra-high molecular weight DNA. Single-molecule maps were assembled de novo, followed by SV and copy number variant calling. We identified 7 variants of interest, of which 5 (10.6%) were classified as likely pathogenic or pathogenic, located in BCL11A, OPHN1, PHF8, SON, and NFIA. We also identified an inversion disrupting NAALADL2, a gene which previously was found to harbor complex rearrangements in two NDD cases. Variants in known NDD genes or candidate variants of interest missed by exome sequencing mainly consisted of larger insertions (> 1kbp), inversions, and deletions/duplications of a low number of exons (1-4 exons). In conclusion, in addition to improving molecular diagnosis in NDDs, this technique may also reveal novel NDD genes which may harbor complex SVs often missed by standard sequencing techniques.

Parkinson\'s disease (PD) significantly impacts millions of individuals worldwide. Although our understanding of the genetic foundations of PD has advanced, a substantial portion of the genetic variation contributing to disease risk remains unknown. Current PD genetic studies have primarily focused on one form of genetic variation, single nucleotide variants (SNVs), while other important forms of genetic variation, such as structural variants (SVs), are mostly ignored due to the complexity of detecting these variants with traditional sequencing methods. Yet, these forms of genetic variation play crucial roles in gene expression and regulation in the human brain and are causative of numerous neurological disorders, including forms of PD. This review aims to provide a comprehensive overview of our current understanding of the involvement of coding and noncoding SVs in the genetic architecture of PD.