Novel dominant distal titinopathy phenotype associated with copy number variation.
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ABSTRACT:
SUBMITTER: Perrin A
PROVIDER: S-EPMC8419403 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Obesity is a multifactorial condition that is highly heritable. There have been ~60 susceptibility loci identified, but they only account for a fraction of cases. As copy number variations (CNVs) have been implicated in the etiology of a multitude of human disorders including obesity, here, we investigated the contribution of rare (<1% population frequency) CNVs in pediatric cases of obesity. We genotyped 67 such individuals, including 22 with co-morbid developmental delay and prioritized rare CNVs at known obesity-associated loci, as well as, those impacting genes involved in energy homeostasis or related processes. We identified clinically relevant or potentially clinically relevant CNVs in 15% (10/67) of individuals. Of these, 4% (3/67) had 16p11.2 microdeletions encompassing the known obesity risk gene SH2B1. Notably, we identified two unrelated probands harboring different 6p22.2 microduplications encompassing SCGN, a potential novel candidate gene for obesity. Further, we identified other biologically relevant candidate genes for pediatric obesity including ARID5B, GPR39, PTPRN2, and HNF4G. We found previously reported candidate loci for obesity, and new ones, suggesting CNV analysis may assist in the diagnosis of pediatric obesity.
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Project description:To determine the role of CYP450 copy number variation (CNV) beyond CYP2D6, 11 CYP450 genes were interrogated by multiplex ligation-dependent probe amplification and quantitative PCR in 542 African-American, Asian, Caucasian, Hispanic and Ashkenazi Jewish individuals. The CYP2A6, CYP2B6 and CYP2E1 combined deletion/duplication allele frequencies ranged from 2 to 10% in these populations. High-resolution microarray-based comparative genomic hybridization (aCGH) localized CYP2A6, CYP2B6 and CYP2E1 breakpoints to directly oriented low-copy repeats. Sequencing localized the CYP2B6 breakpoint to a 529-bp intron 4 region with high homology to CYP2B7P1, resulting in the CYP2B6*29 partial deletion allele and the reciprocal, and novel, CYP2B6/2B7P1 duplicated fusion allele (CYP2B6*30). Together, these data identified novel CYP450 CNV alleles (CYP2B6*30 and CYP2E1*1Cx2) and indicate that common CYP450 CNV formation is likely mediated by non-allelic homologous recombination resulting in both full gene and gene-fusion copy number imbalances. Detection of these CNVs should be considered when interrogating these genes for pharmacogenetic drug selection and dosing.
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Project description:BACKGROUND: Copy number variants (CNVs) account for substantial variation between genomes and are a major source of normal and pathogenic phenotypic differences. The dog is an ideal model to investigate mutational mechanisms that generate CNVs as its genome lacks a functional ortholog of the PRDM9 gene implicated in recombination and CNV formation in humans. Here we comprehensively assay CNVs using high-density array comparative genomic hybridization in 50 dogs from 17 dog breeds and 3 gray wolves. RESULTS: We use a stringent new method to identify a total of 430 high-confidence CNV loci, which range in size from 9 kb to 1.6 Mb and span 26.4 Mb, or 1.08%, of the assayed dog genome, overlapping 413 annotated genes. Of CNVs observed in each breed, 98% are also observed in multiple breeds. CNVs predicted to disrupt gene function are significantly less common than expected by chance. We identify a significant overrepresentation of peaks of GC content, previously shown to be enriched in dog recombination hotspots, in the vicinity of CNV breakpoints. CONCLUSIONS: A number of the CNVs identified by this study are candidates for generating breed-specific phenotypes. Purifying selection seems to be a major factor shaping structural variation in the dog genome, suggesting that many CNVs are deleterious. Localized peaks of GC content appear to be novel sites of CNV formation in the dog genome by non-allelic homologous recombination, potentially activated by the loss of PRDM9. These sequence features may have driven genome instability and chromosomal rearrangements throughout canid evolution.
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Project description:Next-generation sequencing (NGS) technologies offer new opportunities for precise and accurate identification of genomic aberrations, including copy number variations (CNVs). For high-throughput NGS data, using depth of coverage has become a major approach to identify CNVs, especially for whole exome sequencing (WES) data. Due to the high level of noise and biases of read-count data and complexity of the WES data, existing CNV detection tools identify many false CNV segments. Besides, NGS generates a huge amount of data, requiring to use effective and efficient methods. In this work, we propose a novel segmentation algorithm based on the total variation approach to detect CNVs more precisely and efficiently using WES data. The proposed method also filters out outlier read-counts and identifies significant change points to reduce false positives. We used real and simulated data to evaluate the performance of the proposed method and compare its performance with those of other commonly used CNV detection methods. Using simulated and real data, we show that the proposed method outperforms the existing CNV detection methods in terms of accuracy and false discovery rate and has a faster runtime compared to the circular binary segmentation method.
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Project description:BACKGROUND:Neurodevelopmental and neuropsychiatric disorders represent a wide spectrum of heterogeneous yet inter-related disease conditions. The overlapping clinical presentations of these diseases suggest a shared genetic etiology. We aim to identify shared structural variants spanning the spectrum of five neuropsychiatric disorders. METHODS:We investigated copy number variations (CNVs) in five cohorts, including schizophrenia (SCZ), bipolar disease (BD), autism spectrum disorders (ASD), attention deficit hyperactivity disorder (ADHD), and depression, from 7849 cases and 10,799 controls. CNVs were called based on intensity data from genome-wide SNP arrays and CNV frequency was compared between cases and controls in each disease cohort separately. Meta-analysis was performed via a gene-based approach. Quantitative PCR (qPCR) was employed to validate novel significant loci. RESULTS:In our meta-analysis, two genes containing CNVs with exonic overlap reached genome-wide significance threshold of meta P value?<?9.4?×?10-6 for deletions and 7.5?×?10-6 for duplications. We observed significant overlap between risk CNV loci across cohorts. In addition, we identified novel significant associations of DOCK8/KANK1 duplications (meta P value?=?7.5?×?10-7) across all cohorts, and further validated the CNV region with qPCR. CONCLUSIONS:In the first large scale meta-analysis of CNVs across multiple neurodevelopmental/psychiatric diseases, we uncovered novel significant associations of structural variants in the locus of DOCK8/KANK1 shared by five diseases, suggesting common etiology of these clinically distinct neurodevelopmental conditions.
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Project description:Objective: Most methods to detect copy number variation (CNV) have high false positive rates, especially for small CNVs and in real-life samples from clinical studies. In this study, we explored a novel scatterplot-based method to detect CNVs in microarray samples. Methods: Illumina SNP microarray data from 13,254 individuals were analyzed with scatterplots and by PennCNV. The data were analyzed without the prior exclusion of low-quality samples. For CNV scatterplot visualization, the median signal intensity of all SNPs located within a CNV region was plotted against the median signal intensity of the flanking genomic region. Since CNV causes loss or gain of signal intensities, carriers of different CNV alleles pop up in clusters. Moreover, SNPs within a deletion are not heterozygous, whereas heterozygous SNPs within a duplication show typical 1:2 signal distribution between the alleles. Scatterplot-based CNV calls were compared with standard results of PennCNV analysis. All discordant calls as well as a random selection of 100 concordant calls were individually analyzed by visual inspection after noise-reduction. Results: An algorithm for the automated scatterplot visualization of CNVs was developed and used to analyze six known CNV regions. Use of scatterplots and PennCNV yielded 1019 concordant and 108 discordant CNV calls. All concordant calls were evaluated as true CNV-findings. Among the 108 discordant calls, 7 were false positive findings by the scatterplot method, 80 were PennCNV false positives, and 21 were true CNVs detected by the scatterplot method, but missed by PennCNV (i.e., false negative findings). Conclusion: CNV visualization by scatterplots allows for a reliable and rapid detection of CNVs in large studies. This novel method may thus be used both to confirm the results of genome-wide CNV detection software and to identify known CNVs in hitherto untyped samples.
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