Comparative Genomic Hybridization (CGH) is a molecular cytogenetic method for analyzing copy number variations (CNVs) in the DNA content of a genome. By comparing DNA samples from different origins, CGH identifies gains and losses of chromosomal regions without the need for cell culture. This technique is particularly useful in detecting genetic anomalies associated with diseases such as cancer.
The process of CGH involves labeling DNA from test and reference samples with different fluorescent dyes. The labeled DNA is then co-hybridized to a normal metaphase chromosome spread or a microarray. After hybridization, the ratio of the fluorescent signals is measured, revealing regions of DNA that are amplified or deleted in the test sample compared to the reference.
In
epidemiology, CGH is instrumental in understanding the genetic basis of diseases within populations. It helps in:
Cancer Research: CGH can identify specific chromosomal changes associated with different types of cancer, enabling researchers to understand the genetic underpinnings and progression of the disease.
Genetic Disorders: CGH is used to detect CNVs that may be responsible for congenital abnormalities and inherited genetic conditions.
Infectious Diseases: By comparing the genomes of pathogenic strains, CGH can help identify genetic factors that contribute to virulence and antibiotic resistance.
Population Genetics: CGH provides insights into genetic diversity and evolutionary history within and between populations.
CGH offers several advantages:
It does not require cell culture, making it faster and less labor-intensive.
It can detect both large and small chromosomal aberrations.
It provides a genome-wide overview of copy number changes.
It is useful for analyzing archival samples, such as formalin-fixed, paraffin-embedded tissues.
Despite its advantages, CGH has some limitations:
It cannot detect balanced chromosomal rearrangements, such as translocations and inversions.
The resolution of CGH is dependent on the quality and density of the array used.
It may not detect low-level mosaicism due to its reliance on average signal intensities.
Interpretation of results can be complex and requires expertise in genomics and bioinformatics.
While
Next-Generation Sequencing (NGS) and
Polymerase Chain Reaction (PCR) are also used to study genetic variations, CGH is unique in its ability to provide a comprehensive overview of CNVs across the entire genome. NGS offers higher resolution and can detect single nucleotide variations, but it is more expensive and requires more computational resources. PCR is highly specific and sensitive but is limited to targeted regions of the genome.
Future Directions and Developments
The future of CGH in epidemiology looks promising with ongoing advancements in technology. The development of higher density arrays and integration with NGS platforms are enhancing the resolution and accuracy of CGH. Additionally, combining CGH data with clinical and environmental information can provide a more holistic understanding of disease etiology and progression, paving the way for personalized medicine and targeted interventions.
