Comprehensive characterization of copy number variation (CNV) called from array, long- and short-read data

The impact of damaging epilepsy and cardiac genetic variant burden in sudden death in the young

BACKGROUND

Sudden unexpected death in children is a tragic event. Understanding the genetics of sudden death in the young (SDY) enables family counseling and cascade screening. The objective of this study was to characterize genetic variation in an SDY cohort using whole genome sequencing.

METHODS

The SDY Case Registry is a National Institutes of Health/Centers for Disease Control and Prevention surveillance effort to discern the prevalence, causes, and risk factors for SDY. The SDY Case Registry prospectively collected clinical data and DNA biospecimens from SDY cases < 20 years of age. SDY cases were collected from medical examiner and coroner offices spanning 13 US jurisdictions from 2015 to 2019. The cohort included 211 children (median age 0.33 year; range 0-20 years), determined to have died suddenly and unexpectedly and from whom DNA biospecimens for DNA extractions and next-of-kin consent were ascertained. A control cohort consisted of 211 randomly sampled, sex- and ancestry-matched individuals from the 1000 Genomes Project. Genetic variation was evaluated in epilepsy, cardiomyopathy, and arrhythmia genes in the SDY and control cohorts. American College of Medical Genetics/Genomics guidelines were used to classify variants as pathogenic or likely pathogenic. Additionally, pathogenic and likely pathogenic genetic variation was identified using a Bayesian-based artificial intelligence (AI) tool.

RESULTS

The SDY cohort was 43% European, 29% African, 3% Asian, 16% Hispanic, and 9% with mixed ancestries and 39% female. Six percent of the cohort was found to harbor a pathogenic or likely pathogenic genetic variant in an epilepsy, cardiomyopathy, or arrhythmia gene. The genomes of SDY cases, but not controls, were enriched for rare, potentially damaging variants in epilepsy, cardiomyopathy, and arrhythmia-related genes. A greater number of rare epilepsy genetic variants correlated with younger age at death.

CONCLUSIONS

While damaging cardiomyopathy and arrhythmia genes are recognized contributors to SDY, we also observed an enrichment in epilepsy-related genes in the SDY cohort and a correlation between rare epilepsy variation and younger age at death. These findings emphasize the importance of considering epilepsy genes when evaluating SDY.

Genome-wide investigation to assess copy number variants in the Italian local chicken population

BACKGROUND

Copy number variants (CNV) hold significant functional and evolutionary importance. Numerous ongoing CNV studies aim to elucidate the etiology of human diseases and gain insights into the population structure of livestock. High-density chips have enabled the detection of CNV with increased resolution, leading to the identification of even small CNV. This study aimed to identify CNV in local Italian chicken breeds and investigate their distribution across the genome.

RESULTS

Copy number variants were mainly distributed across the first six chromosomes and primarily associated with loss type CNV. The majority of CNV in the investigated breeds were of types 0 and 1, and the minimum length of CNV was significantly larger than that reported in previous studies. Interestingly, a high proportion of the length of chromosome 16 was covered by copy number variation regions (CNVR), with the major histocompatibility complex being the likely cause. Among the genes identified within CNVR, only those present in at least five animals across breeds (n = 95) were discussed to reduce the focus on redundant CNV. Some of these genes have been associated to functional traits in chickens. Notably, several CNVR on different chromosomes harbor genes related to muscle development, tissue-specific biological processes, heat stress resistance, and immune response. Quantitative trait loci (QTL) were also analyzed to investigate potential overlapping with the identified CNVR: 54 out of the 95 gene-containing regions overlapped with 428 QTL associated to body weight and size, carcass characteristics, egg production, egg components, fat deposition, and feed intake.

CONCLUSIONS

The genomic phenomena reported in this study that can cause changes in the distribution of CNV within the genome over time and the comparison of these differences in CNVR of the local chicken breeds could help in preserving these genetic resources.

Identification of an NF1 Microdeletion with Optical Genome Mapping

Neurofibromatosis type 1 (NF1) is a clinically heterogeneous neurocutaneous disorder inherited in autosomal dominant manner. Approximately 5-10% of the cases are caused by NF1 microdeletions involving the NF1 gene and its flanking regions. Microdeletions, which lead to more severe clinical manifestations, can be subclassified into four different types (type 1, 2, 3 and atypical) according to their size, the genomic location of the breakpoints and the number of genes included within the deletion. Besides the prominent hallmarks of NF1, patients with NF1 microdeletions frequently exhibit specific additional clinical manifestations like dysmorphic facial features, macrocephaly, overgrowth, global developmental delay, cognitive disability and an increased risk of malignancies. It is important to identify the genes co-deleted with NF1, because they are likely to have an effect on the clinical manifestation. Multiplex ligation-dependent probe amplification (MLPA) and microarray analysis are the primary techniques for the investigation of NF1 microdeletions. However, based on previous research, optical genome mapping (OGM) could also serve as an alternative method to identify copy number variations (CNVs). Here, we present a case with NF1 microdeletion identified by means of OGM and demonstrate that this novel technology is a suitable tool for the identification and classification of the NF1 microdeletions.

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.

Enhancing genomic mutation data storage optimization based on the compression of asymmetry of sparsity

Background: With the rapid development of high-throughput sequencing technology and the explosive growth of genomic data, storing, transmitting and processing massive amounts of data has become a new challenge. How to achieve fast lossless compression and decompression according to the characteristics of the data to speed up data transmission and processing requires research on relevant compression algorithms. Methods: In this paper, a compression algorithm for sparse asymmetric gene mutations (CA_SAGM) based on the characteristics of sparse genomic mutation data was proposed. The data was first sorted on a row-first basis so that neighboring non-zero elements were as close as possible to each other. The data were then renumbered using the reverse Cuthill-Mckee sorting technique. Finally the data were compressed into sparse row format (CSR) and stored. We had analyzed and compared the results of the CA_SAGM, coordinate format (COO) and compressed sparse column format (CSC) algorithms for sparse asymmetric genomic data. Nine types of single-nucleotide variation (SNV) data and six types of copy number variation (CNV) data from the TCGA database were used as the subjects of this study. Compression and decompression time, compression and decompression rate, compression memory and compression ratio were used as evaluation metrics. The correlation between each metric and the basic characteristics of the original data was further investigated. Results: The experimental results showed that the COO method had the shortest compression time, the fastest compression rate and the largest compression ratio, and had the best compression performance. CSC compression performance was the worst, and CA_SAGM compression performance was between the two. When decompressing the data, CA_SAGM performed the best, with the shortest decompression time and the fastest decompression rate. COO decompression performance was the worst. With increasing sparsity, the COO, CSC and CA_SAGM algorithms all exhibited longer compression and decompression times, lower compression and decompression rates, larger compression memory and lower compression ratios. When the sparsity was large, the compression memory and compression ratio of the three algorithms showed no difference characteristics, but the rest of the indexes were still different. Conclusion: CA_SAGM was an efficient compression algorithm that combines compression and decompression performance for sparse genomic mutation data.

Targeted nanopore sequencing enables complete characterisation of structural deletions initially identified using exon-based short-read sequencing strategies

BACKGROUND

The widespread adoption of exome sequencing has greatly increased the rate of genetic diagnosis for inherited conditions. However, the detection and validation of large deletions remains challenging. While numerous bioinformatics approaches have been developed to detect deletions from whole - exome sequencing and targeted panels, further work is typically required to define the physical breakpoints or integration sites. Accurate characterisation requires either expensive follow - up whole - genome sequencing or the time - consuming, laborious process of PCR walking, both of which are challenging when dealing with the repeat sequences which frequently intersect deletion breakpoints. The aim of this study was to develop a cost-effective, long-range sequencing method to characterise deletions.

METHODS

Genomic DNA was amplified with primers spanning the deletion using long-range PCR and the products purified. Sequencing was performed on MinION flongle flowcells. The resulting fast5 files were basecalled using Guppy, trimmed using Porechop and aligned using Minimap2. Filtering was performed using NanoFilt. Nanopore sequencing results were verified by Sanger sequencing.

RESULTS

Four cases with deletions detected following comparative read-depth analysis of targeted short-read sequencing were analysed. Nanopore sequencing defined breakpoints at the molecular level in all cases including homozygous breakpoints in EYS, CNGA1 and CNGB1 and a heterozygous deletion in PRPF31. All breakpoints were verified by Sanger sequencing.

CONCLUSIONS

In this study, a quick, accurate and cost - effective method is described to characterise deletions identified from exome, and similar data, using nanopore sequencing.