🧬Computational Genomics Unit 7 – Structural Variation & Copy Number Analysis

Introduction to Structural Variation

• Structural variations (SVs) represent large-scale changes in the genome structure
• Includes deletions, duplications, insertions, inversions, and translocations of DNA segments
• SVs can range in size from 50 base pairs to several megabases
• Play a significant role in genetic diversity and disease susceptibility
• Contribute to phenotypic differences between individuals
• SVs can disrupt gene function, alter gene dosage, or create fusion genes
• Studying SVs helps understand genome evolution and disease mechanisms

Types of Structural Variants

• Deletions involve the loss of a DNA segment from a chromosome
  • Can range from a few base pairs to large chromosomal regions
  • May result in loss of genetic material and altered gene function
• Duplications occur when a DNA segment is copied one or more times
  • Leads to an increase in the number of copies of a particular gene or genomic region
  • Can potentially increase gene dosage and alter gene expression levels
• Insertions introduce additional DNA sequences into a chromosome
  • Can originate from the same chromosome or a different chromosome
  • May disrupt gene function or create novel fusion genes
• Inversions happen when a DNA segment is flipped 180 degrees within a chromosome
  • Can alter gene orientation and disrupt regulatory elements
  • May lead to changes in gene expression patterns
• Translocations involve the exchange of DNA segments between non-homologous chromosomes
  • Can create fusion genes or disrupt gene function at the breakpoints
  • Balanced translocations do not result in net gain or loss of genetic material
  • Unbalanced translocations lead to gain or loss of genetic material

Copy Number Variations (CNVs)

• CNVs are a type of structural variation involving changes in the number of copies of a particular DNA segment
• Can include deletions (fewer copies) or duplications (more copies) of a genomic region
• CNVs can range in size from a few kilobases to several megabases
• Contribute significantly to genetic diversity and disease susceptibility
• Can influence gene dosage and alter gene expression levels
• Some CNVs are associated with specific genetic disorders (Charcot-Marie-Tooth disease)
• CNVs can also be benign and present in healthy individuals
• Studying CNVs helps understand the genetic basis of complex traits and diseases

Detection Methods for Structural Variants

• Microarray-based methods detect SVs by comparing DNA hybridization patterns
  • Comparative genomic hybridization (CGH) arrays compare test and reference samples
  • Single nucleotide polymorphism (SNP) arrays identify SVs based on SNP genotypes
• Next-generation sequencing (NGS) technologies enable high-resolution SV detection
  • Whole-genome sequencing (WGS) provides comprehensive coverage of the entire genome
  • Targeted sequencing focuses on specific genomic regions of interest
• Read-depth analysis infers copy number changes based on the depth of sequencing reads
• Split-read mapping identifies SVs by aligning reads spanning SV breakpoints
• Paired-end mapping detects SVs by analyzing discordant read pairs
• Long-read sequencing technologies (PacBio, Oxford Nanopore) improve SV detection accuracy
• Optical mapping generates high-resolution physical maps to identify large-scale SVs

Bioinformatics Tools for SV Analysis

• Alignment tools map sequencing reads to a reference genome (BWA, Bowtie)
• SV callers identify SVs from aligned sequencing data (Manta, Lumpy, BreakDancer)
• CNV detection tools analyze read depth and identify copy number changes (CNVnator, FREEC)
• Annotation tools provide functional and clinical interpretation of SVs (ANNOVAR, VEP)
• Visualization tools display SV data and facilitate interpretation (IGV, Circos)
• Data management and integration platforms handle large-scale SV datasets (Galaxy, DNAnexus)
• Quality control tools assess the quality and reliability of SV calls (SVQual, SVScore)
• Benchmarking and validation datasets help evaluate the performance of SV detection methods

Interpreting Structural Variant Data

• Assess the quality and reliability of SV calls using quality metrics and filtering criteria
• Annotate SVs with functional and clinical information using databases and annotation tools
• Determine the potential impact of SVs on gene function and disease risk
• Consider the frequency and population distribution of SVs using population databases (gnomAD)
• Evaluate the inheritance pattern and segregation of SVs in families
• Integrate SV data with other types of genomic data (gene expression, epigenetic modifications)
• Validate SV calls using orthogonal methods (PCR, Sanger sequencing) for critical findings
• Interpret SVs in the context of the individual's clinical presentation and family history

Clinical Implications and Applications

• SVs can contribute to the development of various genetic disorders and complex diseases
• Deletions and duplications can cause genomic disorders (DiGeorge syndrome, Williams syndrome)
• SVs can disrupt tumor suppressor genes or activate oncogenes in cancer
• CNVs are associated with neurodevelopmental disorders (autism, schizophrenia)
• SVs can influence pharmacogenomic traits and drug response
• SV analysis is important in prenatal and postnatal genetic testing
• SVs can be used as diagnostic and prognostic biomarkers for certain diseases
• Understanding SVs helps guide personalized treatment and management strategies

Challenges and Future Directions

• Improving the accuracy and sensitivity of SV detection methods, particularly for complex SVs
• Developing standardized protocols and guidelines for SV analysis and reporting
• Integrating SV data with other omics data to gain a comprehensive understanding of disease mechanisms
• Establishing large-scale SV databases and resources for research and clinical applications
• Addressing the challenges of interpreting SVs in non-coding regions of the genome
• Investigating the role of SVs in complex traits and common diseases
• Developing targeted therapies and interventions based on SV information
• Exploring the use of long-read sequencing technologies for improved SV detection and characterization