Dataset Information

A high-resolution copy-number variation resource for clinical and population genetics.

ABSTRACT: Chromosomal microarray analysis to assess copy-number variation has become a first-tier genetic diagnostic test for individuals with unexplained neurodevelopmental disorders or multiple congenital anomalies. More than 100 cytogenetic laboratories worldwide use the new ultra-high resolution Affymetrix CytoScan-HD array to genotype hundreds of thousands of samples per year. Our aim was to develop a copy-number variation resource from a new population sample that would enable more accurate interpretation of clinical genetics data on this microarray platform and others.Genotyping of 1,000 adult volunteers who are broadly representative of the Ontario population (as obtained from the Ontario Population Genomics Platform) was performed with the CytoScan-HD microarray system, which has 2.7 million probes. Four independent algorithms were applied to detect copy-number variations. Reproducibility and validation metrics were quantified using sample replicates and quantitative-polymerase chain reaction, respectively.DNA from 873 individuals passed quality control and we identified 71,178 copy-number variations (81 copy-number variations/individual); 9.8% (6,984) of these copy-number variations were previously unreported. After applying three layers of filtering criteria, from our highest confidence copy-number variation data set we obtained >95% reproducibility and >90% validation rates (73% of these copy-number variations overlapped at least one gene).The genotype data and annotated copy-number variations for this largely Caucasian population will represent a valuable public resource enabling clinical genetics research and diagnostics.

SUBMITTER: Uddin M

PROVIDER: S-EPMC4752593 | biostudies-literature | 2015 Sep

REPOSITORIES: biostudies-literature

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A high-resolution copy-number variation resource for clinical and population genetics.

Uddin Mohammed M Thiruvahindrapuram Bhooma B Walker Susan S Wang Zhuozhi Z Hu Pingzhao P Lamoureux Sylvia S Wei John J MacDonald Jeffrey R JR Pellecchia Giovanna G Lu Chao C Lionel Anath C AC Gazzellone Matthew J MJ McLaughlin John R JR Brown Catherine C Andrulis Irene L IL Knight Julia A JA Herbrick Jo-Anne JA Wintle Richard F RF Ray Peter P Stavropoulos Dimitri J DJ Marshall Christian R CR Scherer Stephen W SW

Genetics in medicine : official journal of the American College of Medical Genetics 20141211 9

<h4>Purpose</h4>Chromosomal microarray analysis to assess copy-number variation has become a first-tier genetic diagnostic test for individuals with unexplained neurodevelopmental disorders or multiple congenital anomalies. More than 100 cytogenetic laboratories worldwide use the new ultra-high resolution Affymetrix CytoScan-HD array to genotype hundreds of thousands of samples per year. Our aim was to develop a copy-number variation resource from a new population sample that would enable more a ...[more]

PMID: 25503493

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Project description:Purpose: Chromosomal microarray analysis (CMA) to assess copy number variation (CNV) content is now used as a first tier genetic diagnostic test for individuals with unexplained neurodevelopmental disorders (NDD) or multiple congenital anomalies (MCA). Over 100 cytogenetic labs worldwide are using the Affymetrix CytoScan HD 2.7M array to genotype >15,000 clinical samples per month. The aim of this study is to develop a CNV resource from a population control cohort that can be used as a community resource for interpretation of clinical and research samples. Methods: We have genotyped a large population control set (1,000 individuals from our Ontario Population Genomics Platform (OPGP)) using the Affymetrix CytoScan HD microarray comprising 2.7 million probes. Four independent algorithms were applied to detect and assess high confidence CNVs. Reproducibility and validations were quantified using sample replicates and Quantitative-PCR (QPCR), respectively. Results: DNA from 873 individuals from the OPGP cohort passed quality control and we have identified 71,178 CNVs (81 CNVs/individual) distributed across 796 different cytogenetic regions in the genome; 9.8% of the CNVs were previously unreported. After applying three layers of filtering criteria, from our high confidence CNVs dataset, we obtained a >95% reproducibility and >90% validation rate. Due to the array's high probe density within genic regions, our high confidence CNV data set show 73% of the detected CNVs overlapped at least one gene. Conclusion: The genotype data and annotated CNVs presented in this study will represent a valuable public resource enabling clinical genetics research and diagnostics. For array quality control, CEL files were processed using modules from the Affymetrix power tools and genotypes were extracted from the CHP file. Samples passing the median of the absolute pairwise differences (MAPD) < 0.20 and waviness-sd < 0.11 were retained for further analysis. After multiple checks, we excluded 52 samples that do not meet quality control (QC) cutoffs. To confirm the sample's self-reported gender, we have matched the sex chromosome information from the array and identified six samples with gender mismatch, which were excluded from the analysis. We also excluded 47 samples due to excessive CNV calls. A final set of 895 samples were used for further analysis. This number included 22 sample replicates (indicated by _1 following the Sample title), which were used to determine reproducibility of the array calls. The CNV data for this study is available from dbVar (NCBI), DGVa (EBI) accession number estd212, and DGV.

Project description:Genetic analysis of copy number variations (CNVs), especially in complex regions, is challenging due to reference bias and ambiguous alignment of Next-Generation Sequencing (NGS) reads to repetitive DNA. Consequently, aggregate copy numbers are typically analyzed, overlooking variation between gene copies. Pangenomes contain diverse sequences of gene copies and enable the study of sequence-resolved CNVs. We developed a method, ctyper, to discover sequence-resolved CNVs in NGS data by leveraging CNV genes from pangenomes. From 118 public assemblies, we constructed a database of 3,351 CNV genes, distinguishing each gene copy as a resolved allele. We used phylogenetic trees to organize alleles into highly similar allele-types that revealed events of linked small variants due to stratification, structural variation, conversion, and duplication. Saturation analysis showed that new samples share an average of 97.8% CNV alleles with the database. The ctyper method traces individual gene copies in NGS data to their nearest alleles in the database and identifies allele-specific copy numbers using multivariate linear regression on k-mer counts and phylogenetic clustering. Applying ctyper to 1000 Genomes Project (1kgp) samples showed Hardy-Weinberg Equilibrium on 99.3% of alleles and a 97.6% F1 score on genotypes based on 641 1kgp trios. Leave-one-out analysis on 39 assemblies matched to 1kgp samples showed that 96.5% of variants in query sequences match the genotyped allele. Genotyping 1kgp data revealed 226 population-specific CNVs, including a conversion on SMN2 to SMN1, potentially impacting Spinal Muscular Atrophy diagnosis in Africans. Our results revealed two models of CNV: recent CNVs due to ongoing duplications and polymorphic CNVs from ancient paralogs missing from the reference. To measure the functional impact of CNVs, after merging allele-types, we conducted genome-wide Quantitative Trait Locus analysis on 451 1kgp samples with Geuvadis rRNA-seqs. Using a linear mixed model, our genotyping enables the inference of relative expression levels of paralogs within a gene family. In a global evolutionary context, 150 out of 1,890 paralogs (7.94%) and 546 out of 16,628 orthologs (3.28%) had significantly different expression levels, suggesting divergent expression from original genes. Specific examples include lower expression on the converted SMN and increased expression on translocated AMY2B (GTEx pancreas data). Our method enables large cohort studies on complex CNVs to uncover hidden health impacts and overcome reference bias.

Project description:Copy number variation (CNV) is a major genetic polymorphism contributing to genetic diversity and human evolution. Clinical application of CNVs for diagnostic purposes largely depends on sufficient population CNV data for accurate interpretation. CNVs from general population in currently available databases help classify CNVs of uncertain clinical significance, and benign CNVs. Earlier studies of CNV distribution in several populations worldwide showed that a significant fraction of CNVs are population specific. In this study, we characterized and analyzed CNVs in 3,017 unrelated Thai individuals genotyped with the Illumina Human610, Illumina HumanOmniexpress, or Illumina HapMap550v3 platform. We employed hidden Markov model and circular binary segmentation methods to identify CNVs, extracted 23,458 CNVs consistently identified by both algorithms, and cataloged these high confident CNVs into our publicly available Thai CNV database. Analysis of CNVs in the Thai population identified a median of eight autosomal CNVs per individual. Most CNVs (96.73%) did not overlap with any known chromosomal imbalance syndromes documented in the DECIPHER database. When compared with CNVs in the 11 HapMap3 populations, CNVs found in the Thai population shared several characteristics with CNVs characterized in HapMap3. Common CNVs in Thais had similar frequencies to those in the HapMap3 populations, and all high frequency CNVs (>20%) found in Thai individuals could also be identified in HapMap3. The majorities of CNVs discovered in the Thai population, however, were of low frequency, or uniquely identified in Thais. When performing hierarchical clustering using CNV frequencies, the CNV data were clustered into Africans, Europeans, and Asians, in line with the clustering performed with single nucleotide polymorphism (SNP) data. As CNV data are specific to origin of population, our population-specific reference database will serve as a valuable addition to the existing resources for the investigation of clinical significance of CNVs in Thais and related ethnicities.

Project description:Olfactory receptors (ORs), which are involved in odorant recognition, form the largest mammalian protein superfamily. The genomic content of OR genes is considerably reduced in humans, as reflected by the relatively small repertoire size and the high fraction ( approximately 55%) of human pseudogenes. Since several recent low-resolution surveys suggested that OR genomic loci are frequently affected by copy-number variants (CNVs), we hypothesized that CNVs may play an important role in the evolution of the human olfactory repertoire. We used high-resolution oligonucleotide tiling microarrays to detect CNVs across 851 OR gene and pseudogene loci. Examining genomic DNA from 25 individuals with ancestry from three populations, we identified 93 OR gene loci and 151 pseudogene loci affected by CNVs, generating a mosaic of OR dosages across persons. Our data suggest that approximately 50% of the CNVs involve more than one OR, with the largest CNV spanning 11 loci. In contrast to earlier reports, we observe that CNVs are more frequent among OR pseudogenes than among intact genes, presumably due to both selective constraints and CNV formation biases. Furthermore, our results show an enrichment of CNVs among ORs with a close human paralog or lacking a one-to-one ortholog in chimpanzee. Interestingly, among the latter we observed an enrichment in CNV losses over gains, a finding potentially related to the known diminution of the human OR repertoire. Quantitative PCR experiments performed for 122 sampled ORs agreed well with the microarray results and uncovered 23 additional CNVs. Importantly, these experiments allowed us to uncover nine common deletion alleles that affect 15 OR genes and five pseudogenes. Comparison to the chimpanzee reference genome revealed that all of the deletion alleles are human derived, therefore indicating a profound effect of human-specific deletions on the individual OR gene content. Furthermore, these deletion alleles may be used in future genetic association studies of olfactory inter-individual differences.

Dataset Information

A high-resolution copy-number variation resource for clinical and population genetics.

Publications

A high-resolution copy-number variation resource for clinical and population genetics.

Similar Datasets

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