While single-nucleotide polymorphisms were an initial focus, the subsequent discovery of copy-number variation unveiled a new dimension of genomic diversity, and made apparent the need to develop more refined technologies to capture chromosomal rearrangements.
Comparative genomic hybridization (CGH) provided the opportunity to detect chromosomal imbalances in a high-throughput manner, at the genome-wide scale, and with higher resolution than with previously available methods, such as G-banding and fluorescence in situ hybridization. At the same time, the wealth of CGH data generated gave rise to several challenges.
“Identifying the genomic changes that are significant is a challenging aspect of CGH array data analysis and interpretation,” says Kenneth J. Craddock, M.D., a cytogeneticist at Toronto General Hospital.
While copy-number abnormalities can be found in nearly all medical conditions, their significance must be explored individually, particularly because some variants simply reflect normal inter-individual variation, while others may be of unknown significance. “At this time, the findings for which we know the significance are a minority, and this constitutes another huge challenge in the field,” Dr. Craddock says.
With the generation of more complex datasets, the availability of computing power is becoming increasingly important in analyzing and understanding the significance of structural changes in the chromosome.
“Support from informaticians and computer programmers is not very prominent in genetics labs that are testing for various diseases, including cancer, but with the newly emerging technologies, these aspects will need to be addressed,” Dr. Craddock says.
Importantly, this support will be crucial in helping differentiate physiological structural variants from the ones that are pathologically significant. “This will also help connect the data to existing databases and increase automation, something for which most hospitals currently do not have the necessary resources,” he adds.
“While we have a great capacity to sequence genomes in a high-throughput manner at lower costs, we still lack accurate computational algorithms to detect copy-number variations from sequencing data,” according to Santhosh Girirajan, Ph.D, assistant professor of biochemistry and molecular biology at Penn State University. Dr. Girirajan and his colleagues used array CGH to visualize chromosomal rearrangements in several developmental disorders.
For a recent effort, they examined a cohort of more than 2,300 children who had copy-number variants associated with intellectual disability, finding that harboring two large copy-number variants of unknown significance is associated with an over eightfold higher likelihood of developmental delay. This finding suggested that multiple copy-number variants may interact with one another to shape the clinical presentation in complex diseases, and explains previous reports of phenotypic heterogeneity, when dissimilar clinical presentations were described in individuals harboring identical chromosomal abnormalities.
More recently, Dr. Girirajan and colleagues examined the global load of chromosomal deletions and duplications in autism, a heterogeneous disorder that, based on 2013 estimates by the Centers for Disease Control and Prevention, affects one in 50 schoolchildren in the U.S.
This study revealed an approximately sevenfold increase in duplications and a twofold increase in deletions in children with autism, and pointed toward the relationship between an increased genomic load of copy-number variations, particularly duplications, and the risk to develop this condition. “This also points toward the need to understand the impact of deletions or duplications of chromosomal regions harboring tens of genes, as opposed to the more simple genetic mutations that we often talk about in human genetics,” Dr. Girirajan says.
“CGH, a very well understood and accepted test, provides a cost-effective way to find copy-number variants, but interpreting the results and figuring out what the variants mean, is currently the major challenge,” says Robert L. Nussbaum, M.D., professor of medicine and chief of the division of medical genetics at the University of California, San Francisco.