Copy Number Variants Calls Research Articles

Abstract 15501: Rare Genomic Copy Number Variants Implicate New Candidate Genes for Bicuspid Aortic Valve

Introduction: Bicuspid Aortic Valve (BAV), the most common adult congenital heart defect, is a major cause of aortic insufficiency or stenosis requiring valve replacement and thoracic aortic aneurysms predisposing to acute aortic dissections. The spectrum of BAV ranges from early onset valve and aortic complications to sporadic late onset disease. We determined the frequency and gene content of rare genomic copy number variants (CNVs) in isolated BAV cases. Methods: We performed genome-wide SNP microarray analysis of familial BAV cases with early onset complications in the UTHealth BAV Research Registry (EBAV, n=394) and elderly probands from the International BAV Consortium (BAVCON, n=4216). CNVs were detected in Illumina genotypes using the PennCNV, QuantiSNP and cnvPartition algorithms. BAV cases were compared to 16,576 controls without known cardiovascular disease from the Database of Genotypes and Phenotypes. Only CNV calls with >6 consecutive variants, >20 kb in length, identified by at least two algorithms and intersected with known genes were included. For comparison, we assessed a cohort with left ventricular outflow tract obstructive lesions including BAV (LVOTO, n=1561) using identical methods. CNV burden and associations were tested using PLINK. Results: We identified 84 large recurrent CNVs in BAV cases that are absent or rare (<0.1%) in controls. 34 rare CNVs overlap between BAV and LVOTO cases and involve candidate genes that interact with each other during heart development. The largest and most prevalent of the recurrent CNVs are 8p23 duplications involving GATA4 (OR 340, CI: 42-2700). CNVs of genes that cause BAV when mutated were significantly enriched in cases (n=135, P =1x10 -27 ). The overall burden of rare genic CNVs, specifically large deletions, was also increased in BAV cases (empiric P < 1x10 -5 ) and was higher in younger EBAV cases than in older BAVCON cases. Conclusion: We identified likely pathogenic CNVs in more than 10% of BAV cases, implicating alterations of candidate genes at these loci in the pathogenesis of BAV.

Analysis of copy number variation in men with non-obstructive azoospermia.

Recent findings demonstrate that single nucleotide variants can cause non-obstructive azoospermia (NOA). In contrast, copy number variants (CNVs) were only analysed in few studies in infertile men. Some have reported a higher prevalence of CNVs in infertile versus fertile men. This study aimed to elucidate if CNVs are associated with NOA. We performed array-based comparative genomic hybridisation (aCGH) in 37 men with meiotic arrest, 194 men with Sertoli cell-only phenotype, and 21 control men. We filtered our data for deletions affecting genes and prioritised the affected genes according to the literature search. Prevalence of CNVs was compared between all groups. Exome data of 2,030 men were screened to detect further genetic variants in prioritised genes. Modelling was performed for the protein encoded by the novel candidate gene TEKT5 and we stained for TEKT5 in human testicular tissue. We determined the cause of infertility in two individuals with homozygous deletions of SYCE1 and in one individual with a heterozygous deletion of SYCE1 combined with a likely pathogenic missense variant on the second allele. We detected heterozygous deletions affecting MLH3, EIF2B2, SLX4, CLPP and TEKT5, in one subject each. CNVs were not detected more frequently in infertile men compared with controls. While SYCE1 and MLH3 encode known meiosis-specific proteins, much less is known about the proteins encoded by the other identified candidate genes, warranting further analyses. We were able to identify the cause of infertility in one out of the 231 infertile men by aCGH and in two men by using exome sequencing data. As aCGH and exome sequencing are both expensive methods, combining both in a clinical routine is not an effective strategy. Instead, using CNV calling from exome data has recently become more precise, potentially making aCGH dispensable.

Exome sequencing as a first-tier test for copy number variant detection: retrospective evaluation and prospective screening in 2418 cases

BackgroundDespite the availability of whole exome (WES) and genome sequencing (WGS), chromosomal microarray (CMA) remains the first-line diagnostic test in most rare disorders diagnostic workup, looking for copy number variations...

Accurate in silico confirmation of rare copy number variant calls from exome sequencing data using transfer learning.

Exome sequencing is widely used in genetic studies of human diseases and clinical genetic diagnosis. Accurate detection of copy number variants (CNVs) is important to fully utilize exome sequencing data. However, exome data are noisy. None of the existing methods alone can achieve both high precision and recall rate. A common practice is to perform heuristic filtration followed by manual inspection of read depth of putative CNVs. This approach does not scale in large studies. To address this issue, we developed a transfer learning method, CNV-espresso, for in silico confirming rare CNVs from exome sequencing data. CNV-espresso encodes candidate CNVs from exome data as images and uses pretrained convolutional neural network models to classify copy number states. We trained CNV-espresso using an offspring-parents trio exome sequencing dataset, with inherited CNVs as positives and CNVs with Mendelian errors as negatives. We evaluated the performance using additional samples that have both exome and whole-genome sequencing (WGS) data. Assuming the CNVs detected from WGS data as a proxy of ground truth, CNV-espresso significantly improves precision while keeping recall almost intact, especially for CNVs that span a small number of exons. CNV-espresso can effectively replace manual inspection of CNVs in large-scale exome sequencing studies.

Identification and Characterization of Copy Number Variations Regions in West African Taurine Cattle.

Simple SummaryNative West African (WA) taurine (Bos taurus) cattle are trypanotolerant; i.e., they have the ability to suppress the establishment of trypanosomes. Cattle trypanotolerance is a heritable trait—partially additive in nature—that can share mechanisms with tick resistance. Copy Number Variations (CNVs) are genomic alterations affecting gene structure, gene expression, and performance. However, the relationship between CNV regions and trypanotolerance and tick resistance in WA taurine cattle has not been approached thusfar. Here, we identify and characterize CNV regions in a sample of WA taurine cattle using WA zebu as an outgroup. The CNV regions identified spanned genes that are mainly involved in innate immunity. However, their relationship with genomic regions, previously reported to be associated with trypanotolerance and tick resistance, was not clear. Therefore, CNV alterations may not be the main genomic features underlying such traits.A total of 106 West African taurine cattle belonging to the Lagunaire breed of Benin (33), the N’Dama population of Burkina Faso (48), and N’Dama cattle sampled in Congo (25) were analyzed for Copy Number Variations (CNVs) using the BovineHDBeadChip of Illumina and two different CNV calling programs: PennCNV and QuantiSNP. Furthermore, 89 West African zebu samples (Bororo cattle of Mali and Zebu Peul sampled in Benin and Burkina Faso) were used as an outgroup to ensure that analyses reflect the taurine cattle genomic background. Analyses identified 307 taurine-specific CNV regions (CNVRs), covering about 56 Mb on all bovine autosomes. Gene annotation enrichment analysis identified a total of 840 candidate genes on 168 taurine-specific CNVRs. Three different statistically significant functional term annotation clusters (from ACt1 to ACt3) involved in the immune function were identified: ACt1 includes genes encoding lipocalins, proteins involved in the modulation of immune response and allergy; ACt2 includes genes encoding coding B-box-type zinc finger proteins and butyrophilins, involved in innate immune processes; and Act3 includes genes encoding lectin receptors, involved in the inflammatory responses to pathogens and B- and T-cell differentiation. The overlap between taurine-specific CNVRs and QTL regions associated with trypanotolerant response and tick-resistance was relatively low, suggesting that the mechanisms underlying such traits may not be determined by CNV alterations. However, four taurine-specific CNVRs overlapped with QTL regions associated with both traits on BTA23, therefore suggesting that CNV alterations in major histocompatibility complex (MHC) genes can partially explain the existence of genetic mechanisms shared between trypanotolerance and tick resistance in cattle. This research contributes to the understanding of the genomic features of West African taurine cattle.

ClearCNV: CNV calling from NGS panel data in the presence of ambiguity and noise.

While the identification of small variants in panel sequencing data can be considered a solved problem, the identification of larger, multi-exon copy number variants (CNVs) still poses a considerable challenge. Thus, CNV calling has not been established in all laboratories performing panel sequencing. At the same time, such laboratories have accumulated large datasets and thus have the need to identify CNVs on their data to close the diagnostic gap. In this article, we present our method clearCNV that addresses this need in two ways. First, it helps laboratories to properly assign datasets to enrichment kits. Based on homogeneous subsets of data, clearCNV identifies CNVs affecting the targeted regions. Using real-world datasets and validation, we show that our method is highly competitive with previous methods and preferable in terms of specificity. The software is available for free under a permissible license at https://github.com/bihealth/clear-cnv. Supplementary data are available at Bioinformatics online.

WAVECNV: A New Approach for Detecting Copy Number Variation by Wavelet Clustering

Copy number variation (CNV) detection based on second-generation sequencing technology is the basis of much gene research, but the read depth is affected by mapping errors, repeated reads, and GC bias. The existing methods have low sensitivity to variation regions with a short length and small variation range. Therefore, it is necessary to improve the sensitivity of algorithms to short-variation fragments. This study proposes a new CNV-detection method named WAVECNV to solve this issue. The algorithm uses wavelet clustering to process the read depth and determine the normal cluster and abnormal cluster according to the size of the cluster. Then, according to the distance between genome bins and normal clusters, the outlier of each genome bin is evaluated. Finally, a statistical model is established, and the p-value test is used for calling CNVs. Through this method, the information of the short variation region is retained. WAVECNV was tested and compared with peer methods in terms of simulated data and real cancer-sequencing data. The results show that the sensitivity of WAVECNV is better than the existing methods. It also has high precision in data with low purity and coverage. In real data experiments, WAVECNV can detect more cancer genes than existing methods. Therefore, this method can be regarded as a conventional method in the field of genomic mutation analysis of cancer samples.

Abstract 2497: Inherited copy number variants that increase risk of developing pediatric germ cell tumors with a particular focus on the X chromosome

Abstract Pediatric malignant germ cell tumors (GCTs) are heterogeneous but are grouped together due to the presumed common cell of origin, the primordial germ cell (PGC). Little is known about etiology; however, evidence supporting in utero origins of pediatric and adult GCT suggests that disruptions in normal germ cell development are likely to be highly relevant. Germline copy number variants (CNVs) are a plausible source of inherited genetic variation and have not been thoroughly evaluated to date. CNVs on the X chromosome are of particular interest because individuals with both Klinefelter syndrome (47, XXY) and Turner syndrome (45, X) are at increased risk of pediatric GCT, suggesting that alterations in X chromosome dosage contribute to etiology. A family study of adult testicular cancer where at least one member had bilateral cancer identified a significant linkage peak at Xq27 (hLOD=4.7). In addition, somatic copy number gains of chromosome X are frequently seen in adult testicular germ cell tumors. Given these findings in adult GCT, similar aberrations may also be relevant risk factors for the pediatric group. Pediatric GCT samples were obtained from a Children’s Oncology Group study and state biobank programs in Michigan and California. Genotyping array data were generated using the Illumina HumanCoreExome Beadchip (Illumina, San Diego). CNV calls were made for 2,002 GCT cases and 1,402 controls with high quality intensity data using Genvisis, which has specialized algorithms to make CNV calls on the X chromosome. Klinefelter syndrome cases (n=27) were excluded from the analyses. No study sample had classic Turner syndrome; however, one female case had a giant 51Mb deletion of Xq. Analyses were performed for females-only, males-only, and everyone together. We identified two regions that are study-wide significant on chromosome X at Xq27.1 and Xp11.2 and one nominally significant near Xq28. The most significant finding was identified in females-only and is located within a region that encodes genes within the SPANX family. Evidence suggests these genes affect germ cells; however, supporting data is reported in spermatozoa rather than oocytes. The events in our study were large (240kb), had similar breakpoints, and appear to be flanked by repetitive DNA that might lead to recurrent events at the same region. The variant was present in the control samples within gnomAD at a low allele frequency (0.18%). In our study, both deletions and duplications were noted: 11 female cases (1 or 2 expected based on gnomAD allele frequencies) and 0 female controls (0-1 expected). The finding was not replicated in males (6 male cases and 5 male controls harbored similar CNVs). We are in the process of validating these in silico and potentially via quantitative PCR. Findings may shed light on disruptions in normal development that may increase risk of GCT. Citation Format: Shannon S. Cigan, John J. Meredith, John Zaharick, Erica Langer, Anthony J. Hooten, John A. Lane, Nathan Pankratz, Jenny N. Poynter. Inherited copy number variants that increase risk of developing pediatric germ cell tumors with a particular focus on the X chromosome [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2497.

Gliosarcoma: The Distinct Genomic Alterations Identified by Comprehensive Analysis of Copy Number Variations.

Gliosarcoma (GSM), a histologic variant of glioblastoma (GBM), carries a poor prognosis with less than one year of median survival. Though GSM is similar with GBM in most clinical and pathological symptoms, GBM has unique molecular and histological features. However, as the rarity of GSM samples, the genetic information of this tumor is still lacking. Here, we take a comprehensive analysis of DNA copy number variations (CNV) in GBM and GSM. Whole genome sequencing was performed on 21 cases of GBM and 15 cases of GSM. CNVKIT is used for CNV calling. Our data showed that chromosomes 7, 8, 9, and 10 were the regions where CNV frequently happened in both GBM and GSM. There was a distinct CNV signal in chromosome 2 especially in GSM. The pathway enrichment of genes with CNV was suggested that the GBM and GSM shared the similar mechanism of tumor development. However, the CNV of some screened genes displayed a disparate form between GBM and GSM, such as AMP, BEND2, HDAC6, FOXP3, ZBTB33, TFE3, and VEGFD. It meant that GSM was a distinct subgroup possessing typical biomarkers. The pathways and copy number alterations detected in this study may represent key drivers in gliosarcoma oncogenesis and may provide a starting point toward targeted oncologic analysis with therapeutic potential.

8. Optical genome mapping analysis of FMR1 expansions in fragile X syndrome

Fragile X syndrome (FXS) is associated with cognitive impairment and intellectual disability. Nearly all cases of FXS are associated with an expansion of a CGG triplet repeat in the FMR1 gene. Phenotype severity is often correlated with the expansion size; thus, accurate sizing of the CCG repeat array is crucial. The repetitive nature of these regions presents difficulties: 1. PCR is unable to traverse through long repeats; 2. sequencing has limited read lengths; 3. Southern blot may be inaccurate, time consuming, and expensive. Optical genome mapping (OGM) has the potential to address some of these shortcomings. OGM utilizes images of ultra-long DNA molecules, labeled at specific sequence motifs linearized in nanochannel arrays and can be used for structural and copy number variation calling and absence of heterozygosity analysis. Bionano Genomics has developed a targeted analysis workflow for analysis of FMR1. To evaluate the capability of measuring repeat arrays consistent with normal (<45 repeats), premutation (55-200 repeats), and full mutation (>200 repeats), we analyzed the FMR1 repeats in 75 samples. We also analyzed the repeat region on 20 phenotypically normal control subjects. We observed alleles consistent with annotation across the entire range of repeat counts. Analytical sensitivity was measured at 97% with 100% PPV for expansions >200 repeats. The largest expansion we detected was ∼1000 repeats. In the control samples, we measured repeat counts below the full mutation cutoff in all cases. Repeatability studies were carried out to show analytical consistency and to establish minimum molecule quality requirements. EnFocus™ analysis report provides pass/fail for QC metrics for input data quality as well as analytic measurement quality using internal control regions of the genome on each autosome. OGM performance for FMR1 repeat lengths analyzed here show a much higher dynamic range compared to PCR and higher precision compared to Southern blot.

Polishing copy number variant calls on exome sequencing data via deep learning.

Accurate and efficient detection of copy number variants (CNVs) is of critical importance owing to their significant association with complex genetic diseases. Although algorithms that use whole-genome sequencing (WGS) data provide stable results with mostly valid statistical assumptions, copy number detection on whole-exome sequencing (WES) data shows comparatively lower accuracy. This is unfortunate as WES data are cost-efficient, compact, and relatively ubiquitous. The bottleneck is primarily due to the noncontiguous nature of the targeted capture: biases in targeted genomic hybridization, GC content, targeting probes, and sample batching during sequencing. Here, we present a novel deep learning model, DECoNT, which uses the matched WES and WGS data, and learns to correct the copy number variations reported by any off-the-shelf WES-based germline CNV caller. We train DECoNT on the 1000 Genomes Project data, and we show that we can efficiently triple the duplication call precision and double the deletion call precision of the state-of-the-art algorithms. We also show that our model consistently improves the performance independent of (1) sequencing technology, (2) exome capture kit, and (3) CNV caller. Using DECoNT as a universal exome CNV call polisher has the potential to improve the reliability of germline CNV detection on WES data sets.

Polishing Copy Number Variant Calls on Exome Sequencing Data via Deep Learning

Polishing Copy Number Variant Calls on Exome Sequencing Data via Deep Learning

SavvyCNV: Genome-wide CNV calling from off-targetreads.

Identifying copy number variants (CNVs) can provide diagnoses to patients and provide important biological insights into human health and disease. Current exome and targeted sequencing approaches cannot detect clinically and biologically-relevant CNVs outside their target area. We present SavvyCNV, a tool which uses off-target read data from exome and targeted sequencing data to call germline CNVs genome-wide. Up to 70% of sequencing reads from exome and targeted sequencing fall outside the targeted regions. We have developed a new tool, SavvyCNV, to exploit this 'free data' to call CNVs across the genome. We benchmarked SavvyCNV against five state-of-the-art CNV callers using truth sets generated from genome sequencing data and Multiplex Ligation-dependent Probe Amplification assays. SavvyCNV called CNVs with high precision and recall, outperforming the five other tools at calling CNVs genome-wide, using off-target or on-target reads from targeted panel and exome sequencing. We then applied SavvyCNV to clinical samples sequenced using a targeted panel and were able to call previously undetected clinically-relevant CNVs, highlighting the utility of this tool within the diagnostic setting. SavvyCNV outperforms existing tools for calling CNVs from off-target reads. It can call CNVs genome-wide from targeted panel and exome data, increasing the utility and diagnostic yield of these tests. SavvyCNV is freely available at https://github.com/rdemolgen/SavvySuite.

CNVind: an open source cloud-based pipeline for rare CNVs detection in whole exome sequencing data based on the depth of coverage

BackgroundA typical Copy Number Variations (CNVs) detection process based on the depth of coverage in the Whole Exome Sequencing (WES) data consists of several steps: (I) calculating the depth of coverage in sequencing regions, (II) quality control, (III) normalizing the depth of coverage, (IV) calling CNVs. Previous tools performed one normalization process for each chromosome—all the coverage depths in the sequencing regions from a given chromosome were normalized in a single run.MethodsHerein, we present the new CNVind tool for calling CNVs, where the normalization process is conducted separately for each of the sequencing regions. The total number of normalizations is equal to the number of sequencing regions in the investigated dataset. For example, when analyzing a dataset composed of n sequencing regions, CNVind performs n independent depth of coverage normalizations. Before each normalization, the application selects the k most correlated sequencing regions with the depth of coverage Pearson’s Correlation as distance metric. Then, the resulting subgroup of k+1 sequencing regions is normalized, the results of all n independent normalizations are combined; finally, the segmentation and CNV calling process is performed on the resultant dataset.Results and conclusionsWe used WES data from the 1000 Genomes project to evaluate the impact of independent normalization on CNV calling performance and compared the results with state-of-the-art tools: CODEX and exomeCopy. The results proved that independent normalization allows to improve the rare CNVs detection specificity significantly. For example, for the investigated dataset, we reduced the number of FP calls from over 15,000 to around 5000 while maintaining a constant number of TP calls equal to about 150 CNVs. However, independent normalization of each sequencing region is a computationally expensive process, therefore our pipeline is customized and can be easily run in the cloud computing environment, on the computer cluster, or the single CPU server. To our knowledge, the presented application is the first attempt to implement an innovative approach to independent normalization of the depth of WES data coverage.

Copy number variants calling from WES data through eXome hidden Markov model (XHMM) identifies additional 2.5% pathogenic genomic imbalances smaller than 30 kb undetected by array‐CGH

It has been estimated that Copy Number Variants (CNVs) account for 10%-20% of patients affected by Developmental Disorder (DD)/Intellectual Disability (ID). Although array comparative genomic hybridization (array-CGH) represents the gold-standard for the detection of genomic imbalances, common Agilent array-CGH 4×180 kb arrays fail to detect CNVs smaller than 30 kb. Whole Exome sequencing (WES) is becoming the reference application for the detection of gene variants and makes it possible also to infer genomic imbalances at single exon resolution. However, the contribution of small CNVs in DD/ID is still underinvestigated. We made use of the eXome Hidden Markov Model (XHMM) software, a tool utilized by the ExAC consortium, to detect CNVs from whole exome sequencing data, in a cohort of 200 unsolved DD/DI patients after array-CGH and WES-based single nucleotide/indel variant analyses. In five out of 200 patients (2.5%), we identified pathogenic CNV(s) smaller than 30kb, ranging from one to six exons. They included two heterozygous deletions in TCF4 and STXBP1 and three homozygous deletions in PPT1, CLCN2, and PIGN. After reverse phenotyping, all variants were reported as causative. This study shows the interest in applying sequencing-based CNV detection, from available WES data, to reduce the diagnostic odyssey of additional patients unsolved DD/DI patients and compare the CNV-detection yield of Agilent array-CGH 4×180kb versus whole exome sequencing.

Clinical Characterization of Copy Number Variants Associated With Neurodevelopmental Disorders in a Large-scale Multiancestry Biobank

Past studies identified rare copy number variants (CNVs) as risk factors for neurodevelopmental disorders (NDDs), including autism spectrum disorder and schizophrenia. However, the clinical characterization of NDD CNVs is understudied in population cohorts unselected for neuropsychiatric disorders and in cohorts of diverse ancestry. To identify individuals harboring NDD CNVs in a multiancestry biobank and to query their enrichment for select neuropsychiatric disorders as well as association with multiple medical disorders. In a series of phenotypic enrichment and association analyses, NDD CNVs were clinically characterized among 24 877 participants in the BioMe biobank, an electronic health record-linked biobank derived from the Mount Sinai Health System, New York, New York. Participants were recruited into the biobank since September 2007 across diverse ancestry and medical and neuropsychiatric specialties. For the current analyses, electronic health record data were analyzed from May 2004 through May 2019. NDD CNVs were identified using a consensus of 2 CNV calling algorithms, based on whole-exome sequencing and genotype array data, followed by novel in-silico clinical assessments. Of 24 877 participants, 14 586 (58.7%) were female; self-reported ancestry categories included 5965 (24.0%) who were of African ancestry, 7892 (31.7%) who were of European ancestry, and 8536 (34.3%) who were of Hispanic ancestry; and the mean (SD) age was 50.5 (17.3) years. Among 24 877 individuals, the prevalence of 64 NDD CNVs was 2.5% (n = 627), with prevalence varying by locus, corroborating the presence of some relatively highly prevalent NDD CNVs (eg, 15q11.2 deletion/duplication). An aggregate set of NDD CNVs were enriched for congenital disorders (odds ratio, 2.0; 95% CI, 1.1-3.5; P = .01) and major depressive disorder (odds ratio, 1.5; 95% CI, 1.1-2.0; P = .01). In a meta-analysis of medical diagnoses (n = 195 hierarchically clustered diagnostic codes), NDD CNVs were significantly associated with several medical outcomes, including essential hypertension (z score = 3.6; P = 2.8 × 10-4), kidney failure (z score = 3.3; P = 1.1 × 10-3), and obstructive sleep apnea (z score = 3.4; P = 8.1 × 10-4) and, in another analysis, morbid obesity (z score = 3.8; P = 1.3 × 10-4). Further, NDD CNVs were associated with increased body mass index in a multiancestry analysis (β = 0.19; 95% CI, 0.10-0.31; P = .003). For 36 common serum tests, there was no association with NDD CNVs. Clinical features of individuals harboring NDD CNVs were elucidated in a large-scale, multiancestry biobank, identifying enrichments for congenital disorders and major depressive disorder as well as associations with several medical outcomes, including hypertension, kidney failure, and obesity and obesity-related phenotypes, specifically obstructive sleep apnea and increased body mass index. The association between NDD CNVs and obesity outcomes indicate further potential pleiotropy of NDD CNVs beyond neurodevelopmental outcomes previously reported. Future clinical genetic investigations may lead to insights of at-risk individuals and therapeutic strategies targeting specific genetic variants. The importance of diverse inclusion within biobanks and considering the effect of rare genetic variants in a multiancestry context is evident.

JAX-CNV: A Whole-genome Sequencing-based Algorithm for Copy Number Detection at Clinical Grade Level

We aimed to develop a whole-genome sequencing (WGS)-based copy number variant (CNV) calling algorithm with the potential of replacing chromosomal microarray assay (CMA) for clinical diagnosis. JAX-CNV is thus developed for CNV detection from WGS data. The performance of this CNV calling algorithm was evaluated in a blinded manner on 31 samples and compared to the 112 CNVs reported by clinically validated CMAs for these 31 samples. The result showed that JAX-CNV recalled 100% of these CNVs. Besides, JAX-CNV identified an average of 30 CNVs per individual, respresenting an approximately seven-fold increase compared to calls of clinically validated CMAs. Experimental validation of 24 randomly selected CNVs showed one false positive, i.e., a false discovery rate (FDR) of 4.17%. A robustness test on lower-coverage data revealed a 100% sensitivity for CNVs larger than 300 kb (the current threshold for College of American Pathologists) down to 10× coverage. For CNVs larger than 50 kb, sensitivities were 100% for coverages deeper than 20×, 97% for 15×, and 95% for 10×. We developed a WGS-based CNV pipeline, including this newly developed CNV caller JAX-CNV, and found it capable of detecting CMA-reported CNVs at a sensitivity of 100% with about a FDR of 4%. We propose that JAX-CNV could be further examined in a multi-institutional study to justify the transition of first-tier genetic testing from CMAs to WGS. JAX-CNV is available at https://github.com/TheJacksonLaboratory/JAX-CNV.

MethylMasteR: A Comparison and Customization of Methylation-Based Copy Number Variation Calling Software in Cancers Harboring Large Scale Chromosomal Deletions.

DNA methylation-based copy number variation (CNV) calling software offers the advantages of providing both genetic (copy-number) and epigenetic (methylation) state information from a single genomic library. This method is advantageous when looking at large-scale chromosomal rearrangements such as the loss of the short arm of chromosome 3 (3p) in renal cell carcinoma and the codeletion of the short arm of chromosome 1 and the long arm of chromosome 19 (1p/19q) commonly seen in histologically defined oligodendrogliomas. Herein, we present MethylMasteR: a software framework that facilitates the standardization and customization of methylation-based CNV calling algorithms in a single R package deployed using the Docker software framework. This framework allows for the easy comparison of the performance and the large-scale CNV event identification capability of four common methylation-based CNV callers. Additionally, we incorporated our custom routine, which was among the best performing routines. We employed the Affymetrix 6.0 SNP Chip results as a gold standard against which to compare large-scale event recall. As there are disparities within the software calling algorithms themselves, no single software is likely to perform best for all samples and all combinations of parameters. The employment of a standardized software framework via creating a Docker image and its subsequent deployment as a Docker container allows researchers to efficiently compare algorithms and lends itself to the development of modified workflows such as the custom workflow we have developed. Researchers can now use the MethylMasteR software for their methylation-based CNV calling needs and follow our software deployment framework. We will continue to refine our methodology in the future with a specific focus on identifying large-scale chromosomal rearrangements in cancer methylation data.

Sensitivity to copy number variation analysis in single cell genomics.

While previous studies have given some guidance, the sensitivity of copy number calling in single-cell genomics is still not comprehensive. We studied the impact of sequencing depth and other factors on single-cell copy number analysis. Sequencing Data from 26 datasets were retrieved, and 2946 single cells passed the filter. Thirty-eight single cells were independently downscaled to evaluate copy number variation (CNV) detection sensitivity at different bin sizes. The sensitivity of whole genome amplification (WGA) approaches and cell types to CNV calling were evaluated using downsampling of 101 and 70 cells. Cluster analysis based on t-SNE was executed to evaluate CNV calling performance. Our results suggest 0.75× sequencing depth with moderate resolution (250kb bin size) may be a practical guideline considering both sequencing cost and performance of copy number calling, which can be appropriately optimized based on amplification approach, cell type, and sample complexity.

Abstract PO-202: Copy number variation (CNV) analysis identifies variants in 1p36 in African American and Caucasian hereditary prostate cancer cases

Abstract Prostate cancer (PCa) is a common malignancy which affects 1 in 8 men. There is a significant racial disparity and African American (AA) males are more at risk for developing such cancers, at a rate of almost double compared to the males of European ancestry (EA). So far, not much is known about the role of germline copy number variations (CNVs) in this health disparity. Our previous work has shown several genetic regions with CNVs in both AA and EA hereditary prostate cancer (HPC) cases. The goal of this project was to detect germline CNVs in the targeted resequencing data spanning 9 Mb region in 1p36, that was previously identified by microarray and whole exome sequencing analyses. For this study, a total of 50 individuals were used, 25 AA and 25 EA men from HPC families. We have used three CNV calling algorithms: XHMM, CANOES, and GATK4. First, we focused on four PCa associated genes: NBPF1, NBL1, SRSF10, and RHD, that were previously identified in our study. In the current CNV analysis, XHMM identified deletions in NBPF1 in several samples in both AA and EA cases. GATK4 was unable to call any CNVs in this gene. NBL1 had no identified deletions in any tool, despite previous microarray data to the contrary. XHMM identified full deletions of SRSF10 in most of the cases, while GATK4 identified partial deletion in several cases from both ancestries, but more frequently in AA cases. Finally, a deletion was detected in RHD in only a few cases, and the deletion was confirmed by both XHMM and GATK4 algorithms. Deletion in RHD was more common in the EA population than the AA population. CANOES was unable to identify any variants within our regions of interest. We then expanded our search to other identified variants to identify regions commonly detected by the CNV calling algorithms. A region of deletion was detected in the CELA3A gene (reported to be downregulated in pancreatic and prostate cancer) and a region between AKR7L and AKR7A3 both of which are found to be frequently mutated in cancer cells. XHMM called the deletion of both of these regions at a much higher rate than GATK4: nearly in all our cases from the two ancestries, compared to only a few in GATK4 and CANOES. The few PCa cases who had deletions across all tools were always in the AA population. Altogether, this new analytical strategy establishes the usefulness of applying multiple CNV callers in identifying regions of potential interest, as well as verifying the results from previous studies of PCa. Results from this study may hold valuable information in finding potential biomarkers to address PCa health disparity in the future. Further validation of the identified variants is ongoing. Citation Format: Alan F. Williams, Kirsten W. Termine, John Waldron, Oliver Sartor, Joan Bailey-Wilson, Diptasri Mandal. Copy number variation (CNV) analysis identifies variants in 1p36 in African American and Caucasian hereditary prostate cancer cases [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-202.