Identification Of Copy Number Variations Research Articles

Reanalysis of Chromosomal Microarray Data Using a Smaller Copy Number Variant Call Threshold Identifies Four Cases with Heterozygous Multiexon Deletions of ARID1B, EHMT1, and FOXP1 Genes

Introduction: Chromosomal microarray (CMA) is a highly accurate and established method for detecting copy number variations (CNVs) in clinical genetic testing. CNVs are important etiological factors for disorders such as intellectual disability, developmental delay, and multiple congenital anomalies. Recently developed analytical methods have facilitated the identification of smaller CNVs. Therefore, reanalyzing CMA data using a smaller CNV calling threshold may yield useful information. However, this method was left to the discretion of each institution. Methods: We reanalyzed the CMA data of 131 patients using a smaller CNV call threshold: 50 kb 50 probes for gain and 25 kb 25 probes for loss. We interpreted the reanalyzed CNVs based on the most recently available information. In the reanalysis, we filtered the data using the Clinical Genome Resource dosage sensitivity gene list as an index to quickly and efficiently check morbid genes. Results: The number of copy number loss was approximately 20 times greater, and copy number gain was approximately three times greater compared to those in the previous analysis. We detected new likely pathogenic CNVs in four participants: a 236.5 kb loss within ARID1B, a 50.6 kb loss including EHMT1, a 46.5 kb loss including EHMT1, and an 89.1 kb loss within the FOXP1 gene. Conclusion: The method employed in this study is simple and effective for CMA data reanalysis using a smaller CNV call threshold. Thus, this method is efficient for both ongoing and repeated analyses. This study may stimulate further discussion of reanalysis methodology in clinical laboratories.

A comprehensive analysis of copy number variations in diverse apple populations

BackgroundAs an important source of genetic variation, copy number variation (CNV) can alter the dosage of DNA segments, which in turn may affect gene expression level and phenotype. However, our knowledge of CNV in apple is still limited. Here, we obtained high-confidence CNVs and investigated their functional impact based on genome resequencing data of two apple populations, cultivars and wild relatives.ResultsIn this study, we identified 914,610 CNVs comprising 14,839 CNV regions (CNVRs) from 346 apple accessions, including 289 cultivars and 57 wild relatives. CNVRs summed to 71.19 Mb, accounting for 10.03% of the apple genome. Under the low linkage disequilibrium (LD) with nearby SNPs, they could also accurately reflect the population structure of apple independent of SNPs. Furthermore, A total of 3,621 genes were covered by CNVRs and functionally involved in biological processes such as defense response, reproduction and metabolic processes. In addition, the population differentiation index ({V}_{st}) analysis between cultivars and wild relatives revealed 127 CN-differentiated genes, which may contribute to trait differences in these two populations.ConclusionsThis study was based on identification of CNVs from 346 diverse apple accessions, which to our knowledge was the largest dataset for CNV analysis in apple. Our work presented the first comprehensive CNV map and provided valuable resources for understanding genomic variations in apple.

Diagnostic yield and clinical impact of chromosomal microarray analysis in autism spectrum disorder.

Autism spectrum disorder (ASD) is characterized by high heritability estimates and recurrence rates; its genetic underpinnings are very heterogeneous and include variable combinations of common and rare variants. Array-comparative genomic hybridization (aCGH) offers significant sensitivity for the identification of copy number variants (CNVs), which can act as susceptibility or causal factors for ASD. The aim of this study was to evaluate both diagnostic yield and clinical impact of aCGH in 329 ASD patients of Italian descent. Pathogenic/likely pathogenic CNVs were identified in 50/329 (15.2%) patients, whereas 89/329 (27.1%) carry variants of uncertain significance. The 10 most enriched gene sets identified by Gene Ontology Enrichment Analysis are primarily involved in neuronal function and synaptic connectivity. In 13/50 (26.0%) patients with pathogenic/likely pathogenic CNVs, the outcome of array-CGH led to the request of 25 additional medical exams which would not have otherwise been prescribed, mainly including brain MRI, EEG, EKG, and/or cardiac ultrasound. A positive outcome was obtained in 12/25 (48.0%) of these additional tests. This study confirms the satisfactory diagnostic yield of aCGH, underscoring its potential for better, more in-depth care of children with autism when genetic results are analyzed also with a focus on patient management.

Identification of copy number variations in the genome of Dairy Gir cattle.

Studying structural variants that can control complex traits is relevant for dairy cattle production, especially for animals that are tolerant to breeding conditions in the tropics, such as the Dairy Gir cattle. This study identified and characterized high confidence copy number variation regions (CNVR) in the Gir breed genome. A total of 38 animals were whole-genome sequenced, and 566 individuals were genotyped with a high-density SNP panel, among which 36 animals had both sequencing and SNP genotyping data available. Two sets of high confidence CNVR were established: one based on common CNV identified in the studied population (CNVR_POP), and another with CNV identified in sires with both sequence and SNP genotyping data available (CNVR_ANI). We found 10 CNVR_POP and 45 CNVR_ANI, which covered 1.05 Mb and 4.4 Mb of the bovine genome, respectively. Merging these CNV sets for functional analysis resulted in 48 unique high confidence CNVR. The overlapping genes were previously related to embryonic mortality, environmental adaptation, evolutionary process, immune response, longevity, mammary gland, resistance to gastrointestinal parasites, and stimuli recognition, among others. Our results contribute to a better understanding of the Gir breed genome. Moreover, the CNV identified in this study can potentially affect genes related to complex traits, such as production, health, and reproduction.

Identification of Extremely Rare Pathogenic CNVs by Array CGH in Saudi Children with Developmental Delay, Congenital Malformations, and Intellectual Disability

Chromosomal imbalance is implicated in developmental delay (DD), congenital malformations (CM), and intellectual disability (ID), and, thus, precise identification of copy number variations (CNVs) is essential. We therefore aimed to investigate the genetic heterogeneity in Saudi children with DD/CM/ID. High-resolution array comparative genomic hybridization (array CGH) was used to detect disease-associated CNVs in 63 patients. Quantitative PCR was done to confirm the detected CNVs. Giemsa banding-based karyotyping was also performed. Array CGH identified chromosomal abnormalities in 24 patients; distinct pathogenic and/or variants of uncertain significance CNVs were found in 19 patients, and aneuploidy was found in 5 patients including 47,XXY (n = 2), 45,X (n = 2) and a patient with trisomy 18 who carried a balanced Robertsonian translocation. CNVs including 9p24p13, 16p13p11, 18p11 had gains/duplications and CNVs, including 3p23p14, 10q26, 11p15, 11q24q25, 13q21.1q32.1, 16p13.3p11.2, and 20q11.1q13.2, had losses/deletions only, while CNVs including 8q24, 11q12, 15q25q26, 16q21q23, and 22q11q13 were found with both gains or losses in different individuals. In contrast, standard karyotyping detected chromosomal abnormalities in ten patients. The diagnosis rate of array CGH (28%, 18/63 patients) was around two-fold higher than that of conventional karyotyping (15.87%, 10/63 patients). We herein report, for the first time, the extremely rare pathogenic CNVs in Saudi children with DD/CM/ID. The reported prevalence of CNVs in Saudi Arabia adds value to clinical cytogenetics.

Identification of Copy Number Variations in Four Horse Breed Populations in South Korea.

In this study, genome-wide CNVs were identified using a total of 469 horses from four horse populations (Jeju horses, Thoroughbreds, Jeju riding horses, and Hanla horses). We detected a total of 843 CNVRs throughout all autosomes: 281, 30, 301, and 310 CNVRs for Jeju horses, Thoroughbreds, Jeju riding horses, and Hanla horses, respectively. Of the total CNVRs, copy number losses were found to be the most abundant (48.99%), while gains and mixed CNVRs accounted for 41.04% and 9.96% of the total CNVRs, respectively. The length of the CNVRs ranged from 0.39 kb to 2.8 Mb, while approximately 7.2% of the reference horse genome assembly was covered by the total CNVRs. By comparing the CNVRs among the populations, we found a significant portion of the CNVRs (30.13%) overlapped; the highest number of shared CNVRs was between Hanla horses and Jeju riding horses. When compared with the horse CNVRs of previous studies, 26.8% of CNVRs were found to be uniquely detected in this study. The CNVRs were not randomly distributed throughout the genome; in particular, the Equus caballus autosome (ECA) 7 comprised the largest proportion of its genome (16.3%), while ECA 24 comprised the smallest (0.7%). Furthermore, functional analysis was applied to CNVRs that overlapped with genes (genic-CNVRs); these overlapping areas may be potentially associated with the olfactory pathway and nervous system. A racing performance QTL was detected in a CNVR of Thoroughbreds, Jeju riding horses, and Hanla horses, and the CNVR value was mixed for three breeds.

Isolation of single circulating trophoblasts from maternal circulation for noninvasive fetal copy number variant profiling.

To develop a multi-step workflow for the isolation of circulating extravillous trophoblasts (cEVTs) by describing the key steps enabling a semi-automated process, including a proprietary algorithm for fetal cell origin genetic confirmation and copy number variant (CNV) detection. Determination of the limit of detection (LoD) for submicroscopic CNV was performed by serial experiments with genomic DNA and single cells from Coriell cell line biobank with known imbalances of different sizes. A pregnancy population of 372 women was prospectively enrolled and blindly analyzed to evaluate the current workflow. An LoD of 800 Kb was demonstrated with Coriell cell lines. This level of resolution was confirmed in the clinical cohort with the identification of a pathogenic CNV of 800 Kb, also detected by chromosomal microarray. The mean number of recovered cEVTs was 3.5 cells per sample with a significant reverse linear trend between gestational age and cEVT recovery rate and number of recovered cEVTs. In twin pregnanices, evaluation of zygosity, fetal sex and copy number profiling was performed in each individual cell. Our semi-automated methodology for the isolation and single-cell analysis of cEVTS supports the feasibility of a cell-based noninvasive prenatal test for fetal genomic profiling.

VarAmpliCNV: analyzing variance of amplicons to detect CNVs in targeted NGS data.

Computational identification of copy number variants (CNVs) in sequencing data is a challenging task. Existing CNV-detection methods account for various sources of variation and perform different normalization strategies. However, their applicability and predictions are restricted to specific enrichment protocols. Here, we introduce a novel tool named varAmpliCNV, specifically designed for CNV-detection in amplicon-based targeted resequencing data (Haloplex™ enrichment protocol) in the absence of matched controls. VarAmpliCNV utilizes principal component analysis (PCA) and/or metric dimensional scaling (MDS) to control variances of amplicon associated read counts enabling effective detection of CNV signals. Performance of VarAmpliCNV was compared against three existing methods (ConVaDING, ONCOCNV and DECoN) on data of 167 samples run with an aortic aneurysm gene panel (n = 30), including 9 positive control samples. Additionally, we validated the performance on a large deafness gene panel (n = 145) run on 138 samples, containing 4 positive controls. VarAmpliCNV achieved higher sensitivity (100%) and specificity (99.78%) in comparison to competing methods. In addition, unsupervised clustering of CNV segments and visualization plots of amplicons spanning these regions are included as a downstream strategy to filter out false positives. The tool is freely available through galaxy toolshed and at: https://hub.docker.com/r/cmgantwerpen/varamplicnv. Supplementary Data File S1: https://tinyurl.com/2yzswyhh; Supplementary Data File S2: https://tinyurl.com/ycyf2fb4. Supplementary data are available at Bioinformatics online.

Identification of copy number variations and candidate genes for reproduction traits in Finnish pig populations

Animal breeding programs can be improved by genetic markers associated with production and reproduction traits.Reproduction traits are important for economic success of pig production and therefore development of genetictools for selection is of high interest to pig breeding. In this study our objective was to identify genomic regions associatedwith fertility traits in two Finnish pig breeds using large scale SNP genotyping and genome wide associationanalysis and characterization of copy number variations (CNV). Since CNVs are structural variations of the genomethey potentially have a large effect on gene expression and protein function. We analyzed 1265 genotyped boarsfor nine different reproduction traits and identified 46 CNV regions encompassing 13 genes. 11 of the CNV regionswere shared between the breeds, 20 were unique to the Finnish Yorkshire and 15 to the Finnish Landrace. The genomewide association (GWAS) analysis identified zero to five reproduction associated genomic regions per trait.Furthermore, we identified 23 genomic regions with 20 candidate genes associated with fertility traits using GWASanalysis. The identified CNV regions were compared against GWAS regions to detect candidate regions with an effecton reproduction traits. This study reports candidate genes and genomic regions within two Finnish pig breedsfor reproduction traits, which can be utilized in breeding programs.

Copy Number Variants in Two Northernmost Cattle Breeds Are Related to Their Adaptive Phenotypes.

Copy number variations (CNVs) are genomic structural variants with potential functional and evolutionary effects on phenotypes. In this study, we report the identification and characterization of CNVs from the whole-genome resequencing data of two northernmost cattle breeds from Russia: the Yakut and Kholmogory cattle and their phylogenetically most related breeds, Hanwoo and Holstein, respectively. Comparisons of the CNV regions (CNVRs) among the breeds led to the identification of breed-specific CNVRs shared by cold-adapted Kholmogory and Yakut cattle. An investigation of their overlap with genes, regulatory domains, conserved non-coding elements (CNEs), enhancers, and quantitative trait loci (QTLs) was performed to further explore breed-specific biology and adaptations. We found CNVRs enriched for gene ontology terms related to adaptation to environments in both the Kholmogory and Yakut breeds and related to thermoregulation specifically in Yakut cattle. Interestingly, the latter has also been supported when exploring the enrichment of breed-specific CNVRs in the regulatory domains and enhancers, CNEs, and QTLs implying the potential contribution of CNVR to the Yakut and Kholmogory cattle breeds’ adaptation to a harsh environment.

FISH improves risk stratification in acute leukemia by identifying KMT2A abnormal copy number and rearrangements

Most cases of acute leukemia (AL) with KMT2A rearrangement (KMT2A-r) have a dismal prognosis. Detection of this aberration in Chinese adult patients relies on reverse transcription polymerase chain reaction (RT-PCR) and chromosome banding analysis (CBA). The fluorescence in situ hybridization (FISH) probe for KMT2A detects KMT2A-r and copy number variation (CNV) but is not routinely used as a detection technique. This study investigated the potential value of FISH in the treatment of AL by performing FISH along with CBA and RT-PCR in 269 de novo cases of AL. The three detection techniques were compared in identification of KMT2A-r, and the applicability of FISH for detecting KMT2A CNV was evaluated. Twenty-three samples were identified as positive for KMT2A-r (20 using FISH, 15 using RT-PCR, 16 using CBA, and eight according to all three). FISH also identified 17 KMT2A CNV, 15 with gains and two with deletions. Ten patients with acute myeloid leukemia (AML) harboring KMT2A CNV had a complex karyotype, a negative prognostic factor in AML. Adding FISH of KMT2A to routine detection leads to more accurate detection of KMT2A-r and improved identification of KMT2A CNV, which would benefit patients by improving the risk stratification in AL.

Syndromic and Nonsyndromic Obesity: Underlying Genetic Causes in Humans.

Growing evidence supports syndromic and nonsyndromic causes of obesity, including genome-wide association studies, candidate gene analysis, advanced genetic technology using next-generation sequencing (NGS), and identification of copy number variants. Identification of susceptibility genes impacts mechanistic understanding and informs precision medicine. The cause of obesity is heterogeneous with complex biological processes playing a role by controlling peptides involved in regulating appetite and food intake, cellular energy, and metabolism. Evidence for heritability shows genetic components contributing to 40%-70% of obesity. Monogenic causes and obesity-related syndromes are discussed and illustrated as well as biological pathways, gene interactions, and factors contributing to the obesity phenotype. Over 550obesity-related single genes have been identified and summarized in tabular form with approximately 20% of these genes have been added to obesity gene panels for testing by commercially available laboratories. Early studies show that about 10% of patients with severe obesity using NGS testing have a pathogenic gene variant. Discussion to help characterize gene-gene interactions and disease mechanisms for early diagnosis, treatment, and risk factors contributing to disease is incorporated in this review.

5. Comparative benchmarking of optical genome mapping to chromosomal microarray reveals high technological concordance in CNV identification and additional structural variant refinement

Children with developmental delays and/or congenital anomalies with suspected genetic etiology are often tested for structural variation (SVs) by chromosomal microarray (CMA). Because copy number variants (CNVs) are often found to be causative, CMA is recommended as a first-tier testing for many indications. Optical genome mapping (OGM) is an accurate technique for enabling the detection of CNVs similar to a CMA, but with added benefit of detecting balanced rearrangements and repeat array analysis.

Chromosomal Microarray Analysis in Fetuses Detected with Isolated Cardiovascular Malformation: A Multicenter Study, Systematic Review of the Literature and Meta-Analysis.

Cardiovascular malformations (CVM) represent the most common structural anomalies, occurring in 0.7% of live births. The CVM prenatal suspicion should prompt an accurate investigation with fetal echocardiography and the assessment through genetic counseling and testing. In particular, chromosomal microarray analysis (CMA) allows the identification of copy number variations. We performed a systematic review and meta-analysis of the literature, studying the incremental diagnostic yield of CMA in fetal isolated CVM, scoring yields for each category of heart disease, with the aim of guiding genetic counseling and prenatal management. At the same time, we report 59 fetuses with isolated CVM with normal karyotype who underwent CMA. The incremental CMA diagnostic yield in fetuses with isolated CVM was 5.79% (CI 5.54–6.04), with conotruncal malformations showing the higher detection rate (15.93%). The yields for ventricular septal defects and aberrant right subclavian artery were the lowest (2.64% and 0.66%). Other CVM ranged from 4.42% to 6.67%. In the retrospective cohort, the diagnostic yield was consistent with literature data, with an overall CMA diagnostic yield of 3.38%. CMA in the prenatal setting was confirmed as a valuable tool for investigating the causes of fetal cardiovascular malformations.

Genome-wide identification of copy number variation and association with fat deposition in thin and fat-tailed sheep breeds

Copy number variants (CNVs) are a type of genetic polymorphism which contribute to phenotypic variation in several species, including livestock. In this study, we used genomic data of 192 animals from 3 Iranian sheep breeds including 96 Baluchi sheep and 47 Lori-Bakhtiari sheep as fat-tailed breeds and 47 Zel sheep as thin-tailed sheep breed genotyped with Illumina OvineSNP50K Beadchip arrays. Also, for association test, 70 samples of Valle del Belice sheep were added to the association test as thin-tailed sheep breed. PennCNV and CNVRuler software were, respectively, used to study the copy number variation and genomic association analyses. We detected 573 and 242 CNVs in the fat and thin tailed breeds, respectively. In terms of CNV regions (CNVRs), these represented 328 and 187 CNVRs that were within or overlapping with 790 known Ovine genes. The CNVRs covered approximately 73.85 Mb of the sheep genome with average length 146.88 kb, and corresponded to 2.6% of the autosomal genome sequence. Five CNVRs were randomly chosen for validation, of which 4 were experimentally confirmed using Real time qPCR. Functional enrichment analysis showed that genes harbouring CNVs in thin-tailed sheep were involved in the adaptive immune response, regulation of reactive oxygen species biosynthetic process and response to starvation. In fat-tailed breeds these genes were involved in cellular protein modification process, regulation of heart rate, intestinal absorption, olfactory receptor activity and ATP binding. Association test identified one copy gained CNVR on chromosomes 6 harbouring two protein-coding genes HGFAC and LRPAP1. Our findings provide information about genomic structural changes and their association to the interested traits including fat deposition and environmental compatibility in sheep.

Genomic architecture of fetal central nervous system anomalies using whole-genome sequencing

Structural anomalies of the central nervous system (CNS) are one of the most common fetal anomalies found during prenatal imaging. However, the genomic architecture of prenatal imaging phenotypes has not yet been systematically studied in a large cohort. Patients diagnosed with fetal CNS anomalies were identified from medical records and images. Fetal samples were subjected to low-pass and deep whole-genome sequencing (WGS) for aneuploid, copy number variation (CNV), single-nucleotide variant (SNV, including insertions/deletions (indels)), and small CNV identification. The clinical significance of variants was interpreted based on a candidate gene list constructed from ultrasound phenotypes. In total, 162 fetuses with 11 common CNS anomalies were enrolled in this study. Primary diagnosis was achieved in 62 cases, with an overall diagnostic rate of 38.3%. Causative variants included 18 aneuploids, 17 CNVs, three small CNVs, and 24 SNVs. Among the 24 SNVs, 15 were novel mutations not reported previously. Furthermore, 29 key genes of diagnostic variants and critical genes of pathogenic CNVs were identified, including five recurrent genes: i.e., TUBA1A, KAT6B, CC2D2A, PDHA1, and NF1. Diagnostic variants were present in 34 (70.8%) out of 48 fetuses with both CNS and non-CNS malformations, and in 28 (24.6%) out of 114 fetuses with CNS anomalies only. Hypoplasia of the cerebellum (including the cerebellar vermis) and holoprosencephaly had the highest primary diagnosis yields (>70%), while only four (11.8%) out of 34 neural tube defects achieved genetic diagnosis. Compared with the control group, rare singleton loss-of-function variants (SLoFVs) were significantly accumulated in the patient cohort.

Detection of copy number variations from NGS data by using an adaptive kernel density estimation-based outlier factor

Copy number variation (CNV) is a prevalent type of genetic structural variation and is the origin of numerous hereditary diseases. Thorough identification and classification of CNVs are fundamental to provide a whole perspective of human genome and to discover diseased genes. Next generation sequencing (NGS) has provided an abundance of data which has accelerated the revolution of algorithm design to identify CNVs at base-pair resolution. Nonetheless, certain functions are often influenced by several factors which include sequencing artifacts, GC bias, and interrelations among neighboring positions within CNVs. Though a number of peer strategies have coped with a few of the aforementioned artifacts by modeling their approaches, precise identification of CNVs of low amplitudes remains a difficult task. In this paper, we propose an alternative computational method CNV-KOF, to accurately detect CNVs of whole-range amplitudes based on NGS data. The approach adopts an adaptive kernel density estimation (KDE)-based strategy and assigns a KDE-based outlier factor (KOF) to each genomic segment. Along with the outlier factor profile, CNV-KOF adopts a box plot strategy to detect CNVs without depending on distribution assumptions. We have tested CNV-KOF on simulated and real datasets compared to several peer methods. Simulation and real sequencing data experiments demonstrate that the proposed method outperforms the peer methods in respect to F1-score, sensitivity, and precision. Thus, CNV-KOF is expected to become a complementary tool for detecting CNVs even in scenarios of low-level coverage and tumor purity.

EP312: Exon level array utility in follow up to identification of copy number variants on expanded carrier screening

Many of the clinically available expanded carrier screening panels include copy number variant (CNV) analysis through next-generation sequencing (NGS) and/or other methodologies. Identified CNVs may be restricted to the gene in question or represent a larger gain or loss of genetic material that can only be partially detected by the technology utilized for traditional carrier screening. When a CNV extends beyond the gene identified on carrier screening, additional disease genes may be involved, and this can have significant clinical implications.

EP317: Comparative benchmarking of optical genome mapping and chromosomal microarray reveals high technological concordance in CNV identification and structural variant refinement

Children with developmental delays and/or congenital anomalies with suspected genetic etiology are often tested for structural variations (SVs) by chromosomal microarray (CMA), karyotype, fragile X testing, and sequencing. Because copy number variation (CNV) and structural variation are often found to be causative, CMA is recommended as first tier testing for many indications. Optical genome mapping (OGM) is an accurate technique for enabling the detection of CNVs similar to a high-resolution CMA, and also balanced rearrangements which cannot be detected by CMA.

EP400: Utility of genome sequencing in CNV identification in an immune disorders cohort

Chromosomal microarray (CMA) enables the detection of copy number changes within exon or gene-level resolution depending on probe density and gene coverage. Genome sequencing (GS) can circumvent these resolution issues through detection of smaller copy number changes enabled by uniformity of coverage that is not possible by exome sequencing (ES). Copy number variants (CNVs) can add to the diagnostic yield of inborn errors of immunity (IEI); however, the role of genome sequencing in identifying CNVs in IEI is not well understood.