Detection and characterisation of copy number variants from exome sequencing in the DDD study

Purpose Structural variants such as multi-exon deletions and duplications are an important cause of disease, but are often overlooked in standard exome/genome sequencing analysis. We aimed to evaluate the detection of copy number variants (CNVs) from exome sequencing (ES) in comparison to genome-wide low-resolution and exon-resolution chromosomal microarrays (CMA), and to characterise the properties of de novo CNVs in a large clinical cohort. Methods We performed CNV detection using ES of 13,462 parent-offspring trios in the Deciphering Developmental Disorders (DDD) study, and compared them to CNVs detected from exon-resolution array comparative genomic hybridization (aCGH) in 5,197 probands from the DDD study. Results Integrating calls from multiple ES-based CNV algorithms using random forest machine learning generated a higher quality dataset than using individual algorithms. Both ES- and aCGH-based approaches had the same sensitivity of 89% and detected the same number of unique pathogenic CNVs not called by the other approach. Of DDD probands pre-screened with low resolution CMA, 2.6% had a pathogenic CNV detected by higher resolution assays. De novo CNVs were strongly enriched in known DD-associated genes and exhibited no bias in parental age or sex. Conclusion ES-based CNV calling has higher sensitivity than low-resolution CMAs currently in clinical use, and comparable sensitivity to exon-resolution CMA. With sufficient investment in bioinformatic analysis, exome-based CNV detection could replace low-resolution CMA for detecting pathogenic CNVs.

[1]  V. Govorun,et al.  Benchmarking germline CNV calling tools from exome sequencing data , 2021, Scientific Reports.

[2]  James C. Wright,et al.  GENCODE 2021 , 2020, Nucleic Acids Res..

[3]  M. Hurles,et al.  Non-coding variants upstream of MEF2C cause severe developmental disorder through three distinct loss-of-function mechanisms , 2020, medRxiv.

[4]  Patrick J. Short,et al.  Evidence for 28 genetic disorders discovered by combining healthcare and research data , 2020, Nature.

[5]  Eric S. Lander,et al.  Mapping and characterization of structural variation in 17,795 human genomes , 2020, Nature.

[6]  Ryan L. Collins,et al.  The mutational constraint spectrum quantified from variation in 141,456 humans , 2020, Nature.

[7]  Tariq Ahmad,et al.  A structural variation reference for medical and population genetics , 2020, Nature.

[8]  Allison H Seiden,et al.  Elucidation of de novo small insertion/deletion biology with parent‐of‐origin phasing , 2020, Human mutation.

[9]  J. Rosenfeld,et al.  De novo copy number variants and parental age: Is there an association? , 2019, European journal of medical genetics.

[10]  S. South,et al.  Technical standards for the interpretation and reporting of constitutional copy number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen) , 2019, Genetics in Medicine.

[11]  Patrick J. Short,et al.  Integrating healthcare and research genetic data empowers the discovery of 49 novel developmental disorders , 2019, bioRxiv.

[12]  Lisa T. Emrick,et al.  Disruptive mutations in TANC2 define a neurodevelopmental syndrome associated with psychiatric disorders , 2019, Nature Communications.

[13]  Matthew E Hurles,et al.  Exome-wide assessment of the functional impact and pathogenicity of multinucleotide mutations , 2019, Genome research.

[14]  Y. Kamatani,et al.  Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing , 2019, Genome Biology.

[15]  P. Stankiewicz,et al.  Copy number variant and runs of homozygosity detection by microarrays enabled more precise molecular diagnoses in 11,020 clinical exome cases , 2019, Genome Medicine.

[16]  S. Girirajan,et al.  A machine-learning approach for accurate detection of copy number variants from exome sequencing , 2018, bioRxiv.

[17]  Patrick J. Short,et al.  De novo mutations in regulatory elements in neurodevelopmental disorders , 2018, Nature.

[18]  G. Kirov,et al.  Medical consequences of pathogenic CNVs in adults: analysis of the UK Biobank , 2018, Journal of Medical Genetics.

[19]  Jing Guo,et al.  A clear bias in parental origin of de novo pathogenic CNVs related to intellectual disability, developmental delay and multiple congenital anomalies , 2017, Scientific Reports.

[20]  Joan,et al.  Prevalence and architecture of de novo mutations in developmental disorders , 2017, Nature.

[21]  L. Vissers,et al.  Detection of clinically relevant copy-number variants by exome sequencing in a large cohort of genetic disorders , 2016, Genetics in Medicine.

[22]  Xuegong Zhang,et al.  CNV analysis in Chinese children of mental retardation highlights a sex differentiation in parental contribution to de novo and inherited mutational burdens , 2016, Scientific Reports.

[23]  S. Archibald,et al.  Optimizing Array Design and Content for the Modern Cytogenetic Research Lab , 2016 .

[24]  Bradley P. Coe,et al.  Maternal Modifiers and Parent-of-Origin Bias of the Autism-Associated 16p11.2 CNV. , 2016, American journal of human genetics.

[25]  Gabor T. Marth,et al.  An integrated map of structural variation in 2,504 human genomes , 2015, Nature.

[26]  Frederick E. Dewey,et al.  CLAMMS: a scalable algorithm for calling common and rare copy number variants from exome sequencing data , 2015, Bioinform..

[27]  Andrew Collins,et al.  Exome sequence read depth methods for identifying copy number changes , 2015, Briefings Bioinform..

[28]  Alejandro Sifrim,et al.  Genetic diagnosis of developmental disorders in the DDD study: a scalable analysis of genome-wide research data , 2015, The Lancet.

[29]  Tomas W. Fitzgerald,et al.  Large-scale discovery of novel genetic causes of developmental disorders , 2014, Nature.

[30]  Yufeng Shen,et al.  CANOES: detecting rare copy number variants from whole exome sequencing data , 2014, Nucleic acids research.

[31]  Lars Feuk,et al.  The Database of Genomic Variants: a curated collection of structural variation in the human genome , 2013, Nucleic Acids Res..

[32]  B. V. van Bon,et al.  Identification of pathogenic gene variants in small families with intellectually disabled siblings by exome sequencing , 2013, Journal of Medical Genetics.

[33]  E. Banks,et al.  Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth. , 2012, American journal of human genetics.

[34]  S. Steinberg,et al.  Rate of de novo mutations and the importance of father’s age to disease risk , 2012, Nature.

[35]  L. Vissers,et al.  De novo copy number variants associated with intellectual disability have a paternal origin and age bias , 2011, Journal of Medical Genetics.

[36]  Gregory M. Cooper,et al.  A Copy Number Variation Morbidity Map of Developmental Delay , 2011, Nature Genetics.

[37]  Leslie G Biesecker,et al.  Consensus statement: chromosomal microarray is a first-tier clinical diagnostic test for individuals with developmental disabilities or congenital anomalies. , 2010, American journal of human genetics.

[38]  Richard Durbin,et al.  Fast and accurate long-read alignment with Burrows–Wheeler transform , 2010, Bioinform..

[39]  A. Forabosco,et al.  Incidence of non-age-dependent chromosomal abnormalities: a population-based study on 88965 amniocenteses , 2009, European Journal of Human Genetics.

[40]  Manuel Corpas,et al.  DECIPHER: Database of Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources. , 2009, American journal of human genetics.

[41]  C. Shaw-Smith,et al.  Array CGH in patients with learning disability (mental retardation) and congenital anomalies: updated systematic review and meta-analysis of 19 studies and 13,926 subjects , 2009, Genetics in Medicine.

[42]  Sergio Cocozza,et al.  Spastic Paraplegia and OXPHOS Impairment Caused by Mutations in Paraplegin, a Nuclear-Encoded Mitochondrial Metalloprotease , 1998, Cell.

[43]  C. Laprise,et al.  Impact of Paternal Age at Conception on Human Health. , 2019, Clinical chemistry.

[44]  Large-scale discovery of novel genetic causes of developmental disorders , 2018 .