Cell-type-specific effects of autism-associated 15q duplication syndrome in the human brain.

Recurrent copy-number variation represents one of the most well-established genetic drivers in neurodevelopmental disorders, including autism spectrum disorder. Duplication of 15q11-q13 (dup15q) is a well-described neurodevelopmental syndrome that increases the risk of autism more than 40-fold. However, the effects of this duplication on gene expression and chromatin accessibility in specific cell types in the human brain remain unknown. To identify the cell-type-specific transcriptional and epigenetic effects of dup15q in the human frontal cortex, we conducted single-nucleus RNA sequencing and multi-omic sequencing on dup15q-affected individuals (n = 6) as well as individuals with non-dup15q autism (n = 7) and neurotypical control individuals (n = 7). Cell-type-specific differential expression analysis identified significantly regulated genes, critical biological pathways, and differentially accessible genomic regions. Although there was overall increased gene expression across the duplicated genomic region, cellular identity represented an important factor mediating gene-expression changes. As compared to other cell types, neuronal subtypes showed greater upregulation of gene expression across a critical region within the duplication. Genes that fell within the duplicated region and had high baseline expression in control individuals showed only modest changes in dup15q, regardless of cell type. Of note, dup15q and autism had largely distinct signatures of chromatin accessibility but shared the majority of transcriptional regulatory motifs, suggesting convergent biological pathways. However, the transcriptional binding-factor motifs implicated in each condition implicated distinct biological mechanisms: neuronal JUN and FOS networks in autism vs. an inflammatory transcriptional network in dup15q microglia. This work provides a cell-type-specific analysis of how dup15q changes gene expression and chromatin accessibility in the human brain, and it finds evidence of marked cell-type-specific effects of this genetic driver. These findings have implications for guiding therapeutic development in dup15q syndrome, as well as understanding the functional effects of copy-number variants more broadly in neurodevelopmental disorders.

Exome copy number variant detection, analysis, and classification in a large cohort of families with undiagnosed rare genetic disease.

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and, with new innovative methods, can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the Genomics Research to Elucidate the Genetics of Rare Diseases consortium and analyzed using the seqr platform. The addition of CNV detection to exome analysis identified causal CNVs for 171 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb. The causal CNVs consisted of 140 deletions, 15 duplications, 3 suspected complex structural variants (SVs), 3 insertions, and 10 complex SVs, the latter two groups being identified by orthogonal confirmation methods. To classify CNV variant pathogenicity, we used the 2020 American College of Medical Genetics and Genomics/ClinGen CNV interpretation standards and developed additional criteria to evaluate allelic and functional data as well as variants on the X chromosome to further advance the framework. We interpreted 151 CNVs as likely pathogenic/pathogenic and 20 CNVs as high-interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher-resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

Assortative mating and parental genetic relatedness contribute to the pathogenicity of variably expressive variants.

We examined more than 97,000 families from four neurodevelopmental disease cohorts and the UK Biobank to identify phenotypic and genetic patterns in parents contributing to neurodevelopmental disease risk in children. We identified within- and cross-disorder correlations between six phenotypes in parents and children, such as obsessive-compulsive disorder (R = 0.32-0.38, p < 10-126). We also found that measures of sub-clinical autism features in parents are associated with several autism severity measures in children, including biparental mean Social Responsiveness Scale scores and proband Repetitive Behaviors Scale scores (regression coefficient = 0.14, p = 3.38 × 10-4). We further describe patterns of phenotypic similarity between spouses, where spouses show correlations for six neurological and psychiatric phenotypes, including a within-disorder correlation for depression (R = 0.24-0.68, p < 0.001) and a cross-disorder correlation between anxiety and bipolar disorder (R = 0.09-0.22, p < 10-92). Using a simulated population, we also found that assortative mating can lead to increases in disease liability over generations and the appearance of "genetic anticipation" in families carrying rare variants. We identified several families in a neurodevelopmental disease cohort where the proband inherited multiple rare variants in disease-associated genes from each of their affected parents. We further identified parental relatedness as a risk factor for neurodevelopmental disorders through its inverse relationship with variant pathogenicity and propose that parental relatedness modulates disease risk by increasing genome-wide homozygosity in children (R = 0.05-0.26, p < 0.05). Our results highlight the utility of assessing parent phenotypes and genotypes toward predicting features in children who carry rare variably expressive variants and implicate assortative mating as a risk factor for increased disease severity in these families.

How does genetic variation modify ND-CNV phenotypes?

Rare copy-number variants (CNVs) associated with neurodevelopmental disorders (NDDs), i.e., ND-CNVs, provide an insight into the neurobiology of NDDs and, potentially, a link between biology and clinical outcomes. However, ND-CNVs are characterised by incomplete penetrance resulting in heterogeneous carrier phenotypes, ranging from non-affected to multimorbid psychiatric, neurological, and physical phenotypes. Recent evidence indicates that other variants in the genome, or 'other hits', may partially explain the variable expressivity of ND-CNVs. These may be other rare variants or the aggregated effects of common variants that modify NDD risk. Here we discuss the recent findings, current questions, and future challenges relating to other hits research in the context of ND-CNVs and their potential for improved clinical diagnostics and therapeutics for ND-CNV carriers.

StrVCTVRE: A supervised learning method to predict the pathogenicity of human genome structural variants.

Whole-genome sequencing resolves many clinical cases where standard diagnostic methods have failed. However, at least half of these cases remain unresolved after whole-genome sequencing. Structural variants (SVs; genomic variants larger than 50 base pairs) of uncertain significance are the genetic cause of a portion of these unresolved cases. As sequencing methods using long or linked reads become more accessible and SV detection algorithms improve, clinicians and researchers are gaining access to thousands of reliable SVs of unknown disease relevance. Methods to predict the pathogenicity of these SVs are required to realize the full diagnostic potential of long-read sequencing. To address this emerging need, we developed StrVCTVRE to distinguish pathogenic SVs from benign SVs that overlap exons. In a random forest classifier, we integrated features that capture gene importance, coding region, conservation, expression, and exon structure. We found that features such as expression and conservation are important but are absent from SV classification guidelines. We leveraged multiple resources to construct a size-matched training set of rare, putatively benign and pathogenic SVs. StrVCTVRE performs accurately across a wide SV size range on independent test sets, which will allow clinicians and researchers to eliminate about half of SVs from consideration while retaining a 90% sensitivity. We anticipate clinicians and researchers will use StrVCTVRE to prioritize SVs in probands where no SV is immediately compelling, empowering deeper investigation into novel SVs to resolve cases and understand new mechanisms of disease. StrVCTVRE runs rapidly and is publicly available.

Increased copy-number variant load of associated risk genes in sporadic cases of amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is an age-related neurodegenerative disease characterized by selective loss of motor neurons in the brainstem and spinal cord. Several genetic factors have been associated to ALS, ranging from causal genes and potential risk factors to disease modifiers. The search for pathogenic variants in these genes has mostly focused on single nucleotide variants (SNVs) while relatively understudied and not fully elucidated is the contribution of structural variants, such as copy number variations (CNVs). Here, we applied an exon-centric aCGH method to investigate, in sporadic ALS patients, the load of CNVs in 131 genes previously associated to ALS. Our approach revealed that CNV load, defined as the total number of CNVs or their size, was significantly higher in ALS cases than controls. About 87% of patients harbored multiple CNVs in ALS-related genes, and 75% structural variants compromised genes directly implicated in ALS pathogenesis (C9orf72, CHCHD10, EPHA4, FUS, HNRNPA1, KIF5A, NEK1, OPTN, PFN1, SOD1, TARDBP, TBK1, UBQLN2, UNC13A, VAPB, VCP). CNV load was also associated to higher onset age and disease progression rate. Although the contribution of individual CNVs in ALS is still unknown, their extensive load in disease-related genes may have relevant implications for the diagnostic, prognostic and therapeutical management of this devastating disorder.

CNVDeep: deep association of copy number variants with neurocognitive disorders.

BACKGROUND: Copy number variants (CNVs) have become increasingly instrumental in understanding the etiology of all diseases and phenotypes, including Neurocognitive Disorders (NDs). Among the well-established regions associated with ND are small parts of chromosome 16 deletions (16p11.2) and chromosome 15 duplications (15q3). Various methods have been developed to identify associations between CNVs and diseases of interest. The majority of methods are based on statistical inference techniques. However, due to the multi-dimensional nature of the features of the CNVs, these methods are still immature. The other aspect is that regions discovered by different methods are large, while the causative regions may be much smaller. RESULTS: In this study, we propose a regularized deep learning model to select causal regions for the target disease. With the help of the proximal [20] gradient descent algorithm, the model utilizes the group LASSO concept and embraces a deep learning model in a sparsity framework. We perform the CNV analysis for 74,811 individuals with three types of brain disorders, autism spectrum disorder (ASD), schizophrenia (SCZ), and developmental delay (DD), and also perform cumulative analysis to discover the regions that are common among the NDs. The brain expression of genes associated with diseases has increased by an average of 20 percent, and genes with homologs in mice that cause nervous system phenotypes have increased by 18 percent (on average). The DECIPHER data source also seeks other phenotypes connected to the detected regions alongside gene ontology analysis. The target diseases are correlated with some unexplored regions, such as deletions on 1q21.1 and 1q21.2 (for ASD), deletions on 20q12 (for SCZ), and duplications on 8p23.3 (for DD). Furthermore, our method is compared with other machine learning algorithms. CONCLUSIONS: Our model effectively identifies regions associated with phenotypic traits using regularized deep learning. Rather than attempting to analyze the whole genome, CNVDeep allows us to focus only on the causative regions of disease.

Using Copy Number Variation Data and Neural Networks to Predict Cancer Metastasis Origin Achieves High Area under the Curve Value with a Trade-Off in Precision.

The accurate identification of the primary tumor origin in metastatic cancer cases is crucial for guiding treatment decisions and improving patient outcomes. Copy number alterations (CNAs) and copy number variation (CNV) have emerged as valuable genomic markers for predicting the origin of metastases. However, current models that predict cancer type based on CNV or CNA suffer from low AUC values. To address this challenge, we employed a cutting-edge neural network approach utilizing a dataset comprising CNA profiles from twenty different cancer types. We developed two workflows: the first evaluated the performance of two deep neural networks-one ReLU-based and the other a 2D convolutional network. In the second workflow, we stratified cancer types based on anatomical and physiological classifications, constructing shallow neural networks to differentiate between cancer types within the same cluster. Both approaches demonstrated high AUC values, with deep neural networks achieving a precision of 60%, suggesting a mathematical relationship between CNV type, location, and cancer type. Our findings highlight the potential of using CNA/CNV to aid pathologists in accurately identifying cancer origins with accessible clinical tests.

Genome sequencing detects a wide range of clinically relevant copy-number variants and other genomic alterations.

PURPOSE: Copy-number variants (CNVs) and other non-single nucleotide variant/indel variant types contribute an important proportion of diagnoses in individuals with suspected genetic disease. This study describes the range of such variants detected by genome sequencing (GS). METHODS: For a pediatric cohort of 1032 participants undergoing clinical GS, we characterize the CNVs and other non-single nucleotide variant/indel variant types that were reported, including aneuploidies, mobile element insertions, and uniparental disomies, and we describe the bioinformatic pipeline used to detect these variants. RESULTS: Together, these genetic alterations accounted for 15.8% of reported variants. Notably, 67.9% of these were deletions, 32.9% of which overlapped a single gene, and many deletions were reported together with a second variant in the same gene in cases of recessive disease. A retrospective medical record review in a subset of this cohort revealed that up to 6 additional genetic tests were ordered in 68% (26/38) of cases, some of which failed to report the CNVs/rare variants reported on GS. CONCLUSION: GS detected a broad range of reported variant types, including CNVs ranging in size from 1 Kb to 46 Mb.

Beyond IQ: executive function deficits and their relation to functional, clinical, and neuroimaging outcomes in 3q29 deletion syndrome.

BACKGROUND: 3q29 deletion syndrome (3q29del) is a rare (~1:30 000) genomic disorder associated with a wide array of neurodevelopmental and psychiatric phenotypes. Prior work by our team identified clinically significant executive function (EF) deficits in 47% of individuals with 3q29del; however, the nuances of EF in this population have not been described. METHODS: We used the Behavior Rating Inventory of Executive Function (BRIEF) to perform the first in-depth assessment of real-world EF in a cohort of 32 individuals with 3q29del (62.5% male, mean age = 14.5 ± 8.3 years). All participants were also evaluated with gold-standard neuropsychiatric and cognitive assessments. High-resolution structural magnetic resonance imaging was performed on a subset of participants (n = 24). RESULTS: We found global deficits in EF; individuals with 3q29del scored higher than the population mean on the BRIEF global executive composite (GEC) and all subscales. In total, 81.3% of study subjects (n = 26) scored in the clinical range on at least one BRIEF subscale. BRIEF GEC T scores were higher among 3q29del participants with a diagnosis of attention deficit/hyperactivity disorder (ADHD), and BRIEF GEC T scores were associated with schizophrenia spectrum symptoms as measured by the Structured Interview for Psychosis-Risk Syndromes. BRIEF GEC T scores were not associated with cognitive ability. The BRIEF-2 ADHD form accurately (sensitivity = 86.7%) classified individuals with 3q29del based on ADHD diagnosis status. BRIEF GEC T scores were correlated with cerebellar white matter and subregional cerebellar cortex volumes. CONCLUSIONS: Together, these data expand our understanding of the phenotypic spectrum of 3q29del and identify EF as a core feature linked to both psychiatric and neuroanatomical features of the syndrome.

Diagnostic yield of chromosomal microarray in the largest Latino clinical cohort.

Copy number variants (CNVs) remain a major etiological cause of neurodevelopmental delay and congenital malformations. Chromosomal microarray analysis (CMA) represents the gold standard for CNVs molecular characterization. We applied CMA throughout the patient's clinical diagnostic workup, as the patient's medical provider requested. We collected CMA results of 3380 patients enrolled for 5 years (2016-2021). We found 830 CNVs in 719 patients with potential clinical significance, that is, (i) pathogenic, (ii) likely pathogenic, and (iii) variants of uncertain significance (VUS), from which 10.6% (predominantly involving chromosomes 15 and 22) were most likely the final cause underpinning the patients' clinical phenotype. For those associated with neurodevelopmental phenotypes, the rate of pathogenic or likely pathogenic findings among the patients with CNVs was 60.75%. When considering epileptic phenotypes, it was 59%. Interestingly, our protocol identified two gains harbored in 17q21.31 and 9q34.3, internationally classified initially as VUS. However, because of their high frequency, we propose that these two VUS be reclassified as likely benign in this widely heterogeneous phenotypic population. These results support the diagnostic yield efficiency of CMA in characterizing CNVs to define the final molecular cause of genetic diseases in this cohort of Colombian patients, the most significant sample of patients from a Latino population, and define new benign polymorphic CNVs.

First copy number variant in trans with single nucleotide variant in CCN6 causing progressive pseudorheumatoid dysplasia revealed by genome sequencing and deep phenotyping in monozygotic twins.

Biallelic pathogenic variants in CCN6 cause progressive pseudorheumatoid dysplasia (PPD), a rare skeletal dysplasia. The predominant features include noninflammatory progressive joint stiffness and enlargement, which are not unique to this condition. Nearly 100% of the reported variants are single nucleotide variants or small indels, and missing of a second variant has been reported. Genome sequencing (GS) covers various types of variants and deep phenotyping (DP) provides detailed and precise information facilitating genetic data interpretation. The combination of GS and DP improves diagnostic yield, especially in rare and undiagnosed diseases. We identified a novel compound heterozygote involving a disease-causing copy number variant (g.112057664_112064205del) in trans with a single nucleotide variant (c.624dup(p.Cys209MetfsTer21)) in CCN6 in a pair of monozygotic twins, through the methods of GS and DP. The twins had received three nondiagnostic results before. The g.112057664_112064205del variant was missed by all the tests, and the recorded phenotypes were inaccurate or even misleading. The twins were diagnosed with PPD, ending a 13-year diagnostic odyssey. There may be other patients with PPD experiencing underdiagnosis and misdiagnosis due to inadequate genetic testing or phenotyping methods. This case highlights the critical role of GS and DP in facilitating an accurate and timely diagnosis.

Brain functional connectivity mirrors genetic pleiotropy in psychiatric conditions.

Pleiotropy occurs when a genetic variant influences more than one trait. This is a key property of the genomic architecture of psychiatric disorders and has been observed for rare and common genomic variants. It is reasonable to hypothesize that the microscale genetic overlap (pleiotropy) across psychiatric conditions and cognitive traits may lead to similar overlaps at the macroscale brain level such as large-scale brain functional networks. We took advantage of brain connectivity, measured by resting-state functional MRI to measure the effects of pleiotropy on large-scale brain networks, a putative step from genes to behaviour. We processed nine resting-state functional MRI datasets including 32 726 individuals and computed connectome-wide profiles of seven neuropsychiatric copy-number-variants, five polygenic scores, neuroticism and fluid intelligence as well as four idiopathic psychiatric conditions. Nine out of 19 pairs of conditions and traits showed significant functional connectivity correlations (rFunctional connectivity), which could be explained by previously published levels of genomic (rGenetic) and transcriptomic (rTranscriptomic) correlations with moderate to high concordance: rGenetic-rFunctional connectivity = 0.71 [0.40-0.87] and rTranscriptomic-rFunctional connectivity = 0.83 [0.52; 0.94]. Extending this analysis to functional connectivity profiles associated with rare and common genetic risk showed that 30 out of 136 pairs of connectivity profiles were correlated above chance. These similarities between genetic risks and psychiatric disorders at the connectivity level were mainly driven by the overconnectivity of the thalamus and the somatomotor networks. Our findings suggest a substantial genetic component for shared connectivity profiles across conditions and traits, opening avenues to delineate general mechanisms-amenable to intervention-across psychiatric conditions and genetic risks.

Atypicality index as an add-on to combined first-trimester screening for chromosomal aberrations.

OBJECTIVES: To compute a set of atypicality indices based on combined first-trimester screening (cFTS) markers and second-trimester estimated fetal weight (EFW), and to demonstrate their potential in identifying pregnancies at reduced or increased risk of chromosomal aberrations following a low-risk cFTS result. METHODS: The atypicality index quantifies the unusualness of an individual set of measurements relative to a reference distribution and can be computed from any variables or measurements available. A score of 0% on the atypicality index represents the most typical profiles, while a score of 100% indicates the highest level of atypicality. From the Danish Fetal Medicine Database, we retrieved data on all pregnant women seen for cFTS in the Central Denmark Region between January 2008 and December 2018. All pregnancies with a cytogenetic or molecular analysis obtained prenatally, postnatally or following pregnancy loss or termination were identified. A first-trimester atypicality index (AcFTS) was computed based on nuchal translucency (NT) thickness, maternal serum free ß-human chorionic gonadotropin (ß-hCG) and pregnancy-associated plasma protein-A (PAPP-A). Furthermore, a second-trimester index (AcFTS + EFW) was computed from cFTS markers and EFW from a routine second-trimester anomaly scan. All pregnancies were stratified into subgroups based on their atypicality levels and their cFTS risk estimates. The risk of chromosomal aberrations in each subgroup was then compared with the overall prevalence, and a graphical presentation of the multivariate measurement profiles was developed. RESULTS: We retrieved data on 145 955 singleton pregnancies, of which 9824 (6.7%) were genetically examined. Overall, 1 in 122 (0.82% (95% CI, 0.77-0.87%)) of all pregnancies seen for cFTS were affected by a fetal chromosomal aberration, and in screen-negative pregnancies (cFTS trisomy 21 risk < 1 in 100 and/or trisomy 18/13 risk < 1 in 50), 0.41% (95% CI, 0.38-0.44%) were affected. In screen-negative pregnancies with a typical first-trimester profile (AcFTS < 80%), the risk of chromosomal aberrations was significantly reduced (0.28%) compared with the overall risk. The risk of chromosomal aberrations increased with higher atypicality index to 0.49% (AcFTS [80-90%)), 1.52% (AcFTS [90-99%)) and 4.44% (AcFTS ≥ 99%) and was significantly increased in the two most atypical subgroups. The same applied for the second-trimester atypicality index, with risks of chromosomal aberrations of 0.76% and 4.16% in the two most atypical subgroups (AcFTS + EFW [90-99%) and AcFTS + EFW ≥ 99%, respectively). CONCLUSIONS: As an add-on to cFTS, the atypicality index identifies women with typical measurement profiles, which may provide reassurance, whereas atypical profiles may warrant specialist referral and further investigation. In pregnancies identified as low risk on cFTS but with a highly atypical distribution of NT, PAPP-A and ß-hCG, the risk of a chromosomal aberration is substantially increased. The atypicality index optimizes the interpretation of pre-existing prenatal screening profiles and is not limited to cFTS markers or EFW. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

Combined first-trimester screening and invasive diagnostics for atypical chromosomal aberrations: Danish nationwide study of prenatal profiles and detection compared with NIPT.

OBJECTIVES: Our aim was to examine the prenatal profiles of pregnancies affected by an atypical chromosomal aberration, focusing on pathogenic copy-number variants (pCNVs). We also wanted to quantify the performance of combined first-trimester screening (cFTS) and a second-trimester anomaly scan in detecting these aberrations. Finally, we aimed to estimate the consequences of a policy of using non-invasive prenatal testing (NIPT) rather than invasive testing with chromosomal microarray analysis (CMA) to manage pregnancies identified as high risk by cFTS. METHODS: This was a retrospective review of the Danish Fetal Medicine Database of all pregnant women who underwent cFTS and a risk assessment for trisomy 21 between 1 January 2008 and 31 December 2018. Chromosomal aberrations diagnosed prenatally, postnatally or from fetal tissue following pregnancy loss or termination of pregnancy were identified. Chromosomal aberrations were grouped into one of six categories: triploidy; common trisomy (13, 18 or 21); monosomy X; other sex-chromosome aberration (SCA); pCNV; and rare autosomal trisomy (RAT) or mosaicism. The prevalence of each aberration category was stratified by the individual cFTS markers and trisomy 21 risk estimate, and the size of each pCNV diagnosed by CMA was calculated. RESULTS: We retrieved data on 565 708 pregnancies, of which 3982 (0.70%) were diagnosed with a fetal chromosomal aberration. cFTS identified 87% of the common trisomies, but it also performed well in identifying triploidies (86%), monosomy X (92%), atypical SCAs (58%) and RATs or mosaicisms (70%). pCNVs comprised 27% (n = 1091) of the chromosomal aberrations diagnosed overall, and the prevalence increased during the study period, as prenatal CMA was increasingly being performed. In pregnancies with a maternal age < 30 years, nuchal translucency (NT) thickness ≤ 95th centile, pregnancy-associated plasma protein-A (PAPP-A) ≥ 1 multiple of the median, or trisomy 21 risk of ≤ 1 in 1000, the prevalence of pCNVs exceeded significantly the prevalence of trisomies 21, 18 and 13. Pregnancies affected by a pCNV had significantly increased NT and decreased levels of the maternal biomarkers PAPP-A and ß-human chorionic gonadotropin compared with unaffected pregnancies. However, only 23% of these pregnancies screened positive on cFTS and 51% of pCNVs were not detected until after birth. Among high-risk pregnancies, pCNVs comprised 14% of diagnosed aberrations, and when other atypical aberrations were considered, conventional NIPT (screening for trisomies 21, 18 and 13 and monosomy X) would miss 27% of all pathogenic aberrations diagnosed from invasive testing following a high-risk cFTS result. Thus, 1 in 26 pregnancies at high risk following cFTS would be affected by a chromosomal aberration despite a normal result from conventional NIPT. In a contingent screening model using NIPT for the 'intermediate'-risk group (trisomy 21 risk of 1 in 100-299), 50% of the aberrations would be missed. In our cohort, 79% of the pCNVs diagnosed were < 5Mb and therefore not detectable using current forms of 'genome-wide' NIPT. CONCLUSIONS: As a by-product of screening for trisomies 21, 18 and 13, most triploidies and the majority of atypical SCAs, RATs and mosaicisms are detected before birth. However, only 23% of pCNVs are associated with a high-risk result according to cFTS and only half are diagnosed before birth. Replacing invasive testing with NIPT for high-risk pregnancies would substantially decrease the first-trimester detection of pathogenic chromosomal anomalies. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

High-resolution PGT-A results in incidental identification of patients with small pathogenic copy number variants.

PURPOSE: This study aimed to evaluate whether a high-throughput high-resolution PGT-A method can detect copy number variants (CNVs) that could have clinical implications for patients and their embryos. METHODS: A prospective analysis of PGT-A cases was conducted using a high-resolution SNP microarray platform with over 820,000 probes. Cases where multiple embryos possessed the same segmental imbalance were identified, and preliminary PGT-A reports were issued recommending either parental microarray or conventional karyotyping to identify CNVs or translocations. RESULTS: Analysis of 6080 sequential PGT-A cases led to identification of 41 cases in which incidental findings were observed (0.7%) and parental testing was recommended. All cases, in which parental studies were completed, confirmed the original PGT-A incidental findings. In 2 of the cases, parental studies indicated a pathogenic variant with clinical implications for the associated embryos. In one of these cases, the patient was identified as a carrier of a duplication in chromosome 15q11.2:q11.2 (SNRPN + +), which is associated with autism spectrum disorder. In the second case, the patient was heterozygous positive for an interstitial deletion of 3p26.1:p26.3, which is associated with 3p deletion syndrome and had clinical implications for the patient and associated embryos. In each case, parental studies were concordant with PGT-A findings and revealed the presence of an otherwise unknown CNV. CONCLUSION: High-throughput high-resolution SNP array-based PGT-A has the ability to detect previously unknown and clinically significant parental deletions, duplications, and translocations. The use of cost-effective SNP array-based PGT-A methods may improve the effectiveness of PGT by identifying and preventing previously unknown pathogenic CNVs in children born to patients seeking in vitro fertilization.

Best Practices in Microbial Experimental Evolution: Using Reporters and Long-Read Sequencing to Identify Copy Number Variation in Experimental Evolution.

Copy number variants (CNVs), comprising gene amplifications and deletions, are a pervasive class of heritable variation. CNVs play a key role in rapid adaptation in both natural, and experimental, evolution. However, despite the advent of new DNA sequencing technologies, detection and quantification of CNVs in heterogeneous populations has remained challenging. Here, we summarize recent advances in the use of CNV reporters that provide a facile means of quantifying de novo CNVs at a specific locus in the genome, and nanopore sequencing, for resolving the often complex structures of CNVs. We provide guidance for the engineering and analysis of CNV reporters and practical guidelines for single-cell analysis of CNVs using flow cytometry. We summarize recent advances in nanopore sequencing, discuss the utility of this technology, and provide guidance for the bioinformatic analysis of these data to define the molecular structure of CNVs. The combination of reporter systems for tracking and isolating CNV lineages and long-read DNA sequencing for characterizing CNV structures enables unprecedented resolution of the mechanisms by which CNVs are generated and their evolutionary dynamics.

Simultaneous diagnosis of tuberculous pleurisy and malignant pleural effusion using metagenomic next-generation sequencing (mNGS).

BACKGROUND: Metagenomic next-generation sequencing (mNGS) has become a powerful tool for pathogen detection, but the value of human sequencing reads generated from it is underestimated. METHODS: A total of 138 patients with pleural effusion (PE) were diagnosed with tuberculous pleurisy (TBP, N = 82), malignant pleural effusion (MPE, N = 35), or non-TB infection (N = 21), whose PE samples all underwent mNGS analysis. Clinical TB tests including culture, Acid-Fast Bacillus (AFB) test, Xpert, and T-SPOT, were performed. To utilize mNGS for MPE identification, 25 non-MPE samples (20 TBP and 5 non-TB infection) were randomly selected to set human chromosome copy number baseline and generalized linear modeling was performed using copy number variant (CNV) features of the rest 113 samples (35 MPE and 78 non-MPE). RESULTS: The performance of TB detection was compared among five methods. T-SPOT demonstrated the highest sensitivity (61% vs. culture 32%, AFB 12%, Xpert 35%, and mNGS 49%) but with the highest false-positive rate (10%) as well. In contrast, mNGS was able to detect TB-genome in nearly half (40/82) of the PE samples from TBP subgroup, with 100% specificity. To evaluate the performance of using CNV features of the human genome for MPE prediction, we performed the leave-one-out cross-validation (LOOCV) in the subcohort excluding the 25 non-MPE samples for setting copy number standards, which demonstrated 54.1% sensitivity, 80.8% specificity, 71.7% accuracy, and an AUC of 0.851. CONCLUSION: In summary, we exploited the value of human and non-human sequencing reads generated from mNGS, which showed promising ability in simultaneously detecting TBP and MPE.

Copy number variant analysis for syndromic congenital heart disease in the Chinese population.

BACKGROUND: Syndromic congenital heart disease (CHD) is among the most severe conditions in the pediatric population. Copy number variant (CNV) is an important cause of syndromic CHD, but few studies focused on CNVs related to these patients in China. The present study aimed to identify pathogenic CNVs associated with syndromic CHD in the Chinese population. METHODS: A total of 109 sporadic patients with syndromic CHD were applied chromosomal microarray analysis (CMA). Phenotype spectrum of pathogenic or likely pathogenic CNVs was analyzed. CHD-related genes were prioritized from genes within pathogenic or likely pathogenic CNVs by VarElect, OVA, AMELIE, and ToppGene. RESULTS: Using CMA, we identified 43 candidate CNVs in 37/109 patients. After filtering CNVs present in the general population, 29 pathogenic/likely pathogenic CNVs in 24 patients were identified. The diagnostic yield of CMA for pathogenic/likely pathogenic CNVs was 23.1% (24/104), excluding 5 cases with aneuploidies or gross chromosomal aberrations. The overlapping analysis of CHD-related gene lists from different prioritization tools highlighted 16 CHD candidate genes. CONCLUSION: As the first study focused on CNVs in syndromic CHD from the Chinese population, this study reveals the importance of CMA in exploring the genetic etiology of syndromic CHD and expands our understanding of these complex diseases. The bioinformatic analysis of candidate genes suggests several CHD-related genes for further functional research.

Associations between acute and chronic lifetime stressors and psychosis-risk symptoms in individuals with 22q11.2 copy number variants.

BACKGROUND: The 22q11.2 deletion (22q11Del) is among the strongest known genetic risk factors for psychosis. Stress, a known risk factor for psychosis in the general population, has seldom been studied in 22q11Del. We investigated how lifetime stressors related to symptomatic outcomes in patients with 22q11Del. We also explored this association in individuals with 22q11.2 duplications (22q11Dup), which may be potentially protective against psychosis. METHOD: One hundred individuals (46 with 22q11Del, 30 with 22q11Dup, and 24 healthy controls; Mage = 17.30 years±10.15) were included. Logistic models were used to examine cross-sectional associations between lifetime acute and chronic stressors (severity and count) and the presence (score ⩾3) of positive, negative, and general symptoms, assessed via the Structured Interview for Psychosis-risk Syndromes (SIPS). RESULTS: The 22q11Dup group reported the greatest number and severity of acute lifetime stressors, but did not differ from 22q11Del in chronic stressor count or severity. Lifetime chronic and acute stressors were uniquely associated with positive symptoms in 22q11Del (chronic count: odds ratio [OR] = 2.35, p = 0.02; chronic severity: OR = 1.88, p = 0.03; acute count: OR = 1.78, p = 0.03), but not with negative or general symptoms (ps > 0.05). CONCLUSION: Findings suggest that stress may play a role in psychotic symptoms in 22q1Del, while the 22q11Dup CNV appears protective against psychotic symptoms despite higher rates of stressors. Interventions that mitigate effects of stressors in 22qDel may reduce the odds of psychosis in this group. Prospective longitudinal research is needed to replicate these findings.