CNV Analysis Service

Overview

CNV (Copy Number Variation) Analysis Service is a sophisticated genomic research tool focused on the precise detection, detailed characterization, and meaningful interpretation of copy number variations across genomes. CNVs, which involve gains or losses of DNA segments ranging from kilobases to megabases, are pivotal in shaping genetic diversity, influencing disease susceptibility, driving evolutionary adaptations, and contributing to phenotypic variability. These variations can affect gene dosage, disrupt gene regulation, and alter chromosomal structure, thereby playing a significant role in both normal biological processes and pathological conditions. Our service leverages cutting-edge technologies and bioinformatics approaches to provide comprehensive insights into CNV landscapes, enhancing our understanding of genomic architecture and its implications for health and disease.

Our CNV Analysis Service Offers:

• High-Sensitivity CNV Detection: Utilize advanced NGS and array-based comparative genomic hybridization (aCGH) technologies to accurately identify CNVs, even in regions with high sequence complexity or low copy number.
• Detailed CNV Characterization: Employ specialized algorithms and software (e.g., PennCNV, QuantiSNP) to precisely define CNV boundaries, estimate copy number states, and classify CNVs into deletions, duplications, or more complex rearrangements.
• Functional Consequence Prediction: Assess the potential impact of CNVs on gene expression, protein function, and regulatory elements using in silico tools and integrate these predictions with functional genomics data to understand their biological relevance.
• Pathway Enrichment Analysis: Link identified CNVs to biological pathways and gene networks using databases like KEGG and GO, revealing their involvement in disease mechanisms, cellular signaling, or developmental pathways.
• Population Genetics Insights: Analyze CNV frequencies and distributions across populations to explore patterns of genetic variation, demographic history, and potential adaptive significance, employing statistical methods for population structure analysis.
• Disease-Association Studies: Conduct association analyses to identify CNVs linked to specific diseases or traits, utilizing case-control designs or family-based studies to enhance discovery power and uncover novel genetic risk factors.
• Cancer-Specific CNV Analysis: Implement specialized pipelines for cancer genomics, detecting oncogenic CNVs, tumor suppressor gene losses, and chromosomal instability patterns to advance research into tumor biology, tumor evolution, and candidate molecular targets.
• Customized Reporting and Data Visualization: Provide detailed, easy-to-interpret reports summarizing CNV findings, along with interactive visualizations (e.g., heatmaps, genomic plots) to facilitate data exploration, hypothesis testing, and result communication.

What Is CNV Analysis

CNV Analysis integrates diverse genomic data to comprehensively assess how copy number variations impact gene dosage, regulatory elements, and genomic stability. This multifaceted approach allows researchers to elucidate the molecular mechanisms by which CNs contribute to phenotypic traits and disease susceptibility. By examining the prevalence and patterns of CNVs across populations, CNV Analysis Service reveals their role in shaping genetic diversity, evolutionary adaptation, and the genetic basis of complex disorders, such as autism, schizophrenia, and cardiovascular diseases. It also provides insights into how CNVs drive tumor progression and resistance mechanisms in cancer.

How to Measure

1. Sample Collection and Preparation

Sample Type:

• Human: Blood, saliva, buccal swabs, or formalin-fixed tissues (FFPE).
• Plants/Animals: Leaves, seeds, hair follicles, or muscle/liver biopsies.
• Microorganisms: Environmental metagenomic samples (soil, water) or cultured isolates.

Genotyping/Sequencing Technologies for CNV Detection:

Table 1: CNV Detection Methods and Their Characteristics

Data Quality Control (QC):

• Sample-level: Exclude samples with low DNA integrity, contamination, or insufficient coverage.
• Data-level: Filter out low-confidence CNV calls (e.g., <50 kb, <10 supporting reads).

2. Statistical Analysis Workflow for CNVs

CNV Calling and Segmentation:

• Tools: PennCNV, QuantiSNP, Control-FREEC (for NGS data).
• Approach: Use hidden Markov models (HMMs) or circular binary segmentation (CBS) to identify CNV boundaries.

Population-Level CNV Analysis:

• Frequency Estimation: Calculate CNV allele frequencies across populations.
• Burden Testing: Assess whether CNVs are enriched in cases vs. controls (e.g., SKAT, PLINK).

Functional Annotation of CNVs:

• Gene Overlap: Identify CNVs overlapping exons, promoters, or regulatory regions (using ANNOVAR, VEP).
• Pathway Enrichment: Test if CNV-affected genes cluster in disease-related pathways (e.g., DAVID, GSEA).

3. Visualization and Reporting of CNVs

Visualization Tools:

• Circos Plots: Display CNVs across the genome (e.g., using R/Bioconductor).
• Heatmap of CNV Frequencies: Compare CNV distributions between groups (e.g., ComplexHeatmap in R).
• IGV (Integrative Genomics Viewer): Manually inspect CNV breakpoints in NGS data.

Significance Thresholds:

• CNV Size: Typically ≥50 kb for array-based detection, ≥1 kb for NGS.
• Statistical Significance: p < 1×10⁻⁵ for burden testing (Bonferroni-corrected).
• Database-Based Prioritization: CNVs overlapping known disease loci (e.g., ClinVar, DGV) may be prioritized for research interpretation.

Figure 1: CNV Analysis

What Can We do

Comprehensive Genomic Coverage: We employ a combination of whole-genome sequencing (WGS) alongside exome sequencing or targeted approaches like RAD-seq and GBS. This strategy ensures the detection of a broad range of CNVs, from small duplications/deletions to large-scale chromosomal alterations, providing a holistic view of genomic structural variations.

Our Advantages

Applications

1. Cancer Genomics Research

CNV analysis plays a pivotal role in cancer genomics by revealing genomic amplifications or deletions that contribute to tumorigenesis. Identifying CNVs in oncogenes or tumor suppressor genes helps elucidate the molecular mechanisms driving cancer progression and supports discovery research on candidate molecular targets.

2. Neurodevelopmental Disorder Research

CNVs are frequently implicated in neurodevelopmental disorders such as autism spectrum disorder and intellectual disability. Through CNV analysis, researchers can detect copy number changes associated with these conditions, providing insights for association studies and hypothesis generation and informing research questions; not for clinical diagnosis or treatment decisions.

3. Pharmacogenomics and Drug Response Prediction

CNVs can influence drug metabolism and response by altering the dosage or expression levels of drug-metabolizing enzymes or transporters. CNV analysis enables the identification of genetic variants that may be associated with inter-individual variability in drug response in research cohorts.

4. Population Genetics and Evolutionary Studies

CNV analysis contributes to our understanding of human population genetics and evolutionary history by revealing patterns of genetic variation and adaptation. By comparing CNV profiles across different populations, researchers can infer demographic events, migration patterns, and selective pressures that have shaped human genetic diversity.

5. Agricultural Genomics and Crop Improvement

In agricultural genomics, CNV analysis helps identify genomic regions associated with important agronomic traits such as yield, disease resistance, and stress tolerance. By leveraging CNV information, breeders can develop more resilient and productive crop varieties through marker-assisted selection or genomic editing techniques.

Demo

Figure 2: Enriched pathways of differentially expressed CNV genes. (Yang, 2021)

Case Study

FAQs