A model for state-based monitoring of inherited hemoglobin disorders

see State-based surveillance for selected hemoglobinopathies

Despite widespread newborn screening in the United States for sickle cell disease and thalassemia, there is no national system to monitor the health outcomes of individuals diagnosed with these disorders. To gain a better understanding of prevalence and treatment outcomes, the National Institutes of Health and the Centers for Disease Control and Prevention collaborated on a pilot surveillance system in six states, beginning in 2004. The Registry and Surveillance System for Hemoglobinopathies (RuSH) identified individuals using multiple data sources, including newborn screening records, emergency room records, and Medicaid claims. Researchers collected demographic data, clinical characteristics, and health care–utilization records. Although the program was designed to produce a standardized system, difficulties with access to records in some states made a standardized approach untenable. But it did become clear that creating a comprehensive picture required combining data from many sources because information for individuals found through one data source was not present in others. In addition, the various sources contained different types of information. The research team concluded that combining all these data yielded a more comprehensive picture of the number of individuals living with hemoglobinopathies, as well as how and where they receive health care. A follow-up project—the Public Health Research Epidemiology and Surveillance for Hemoglobinopathies (PHRESH), now being conducted in two of the RuSH states (California and Georgia)—is expected to validate and refine the data collected in RuSH.

Detection of copy-number variants and point mutations using only sequence data

see Improved molecular diagnosis by the detection of exonic deletions with target gene capture and deep sequencing

Copy-number variants (CNVs)—structural changes that result in deletion or duplication of chromosomal segments—are a significant contributor to inherited genetic disease. Currently, clinical testing for CNVs involves exon–targeted array comparative genomic hybridization or other methods involving stepwise probing of genomic regions of interest. But molecular diagnosis of human genetic diseases in clinical settings is rapidly moving toward massively parallel sequencing (MPS) technology. The ability to identify both single-nucleotide alterations and larger deletions and duplications using the same technology would greatly simplify and streamline the clinical workflow. Now, a team from Baylor College of Medicine, Houston, Texas, reports that it has taken a step in that direction using a method that analyzes copy number by comparing the number of sequencing reads of each exon of interest. Using previously collected clinical samples, the investigators correctly identified 11 samples with deletions. However, identification of duplications proved more problematic, with high false-positive rates. Similar to previous reports using sequence data, false-positive findings typically arose from areas of highly repetitive GC sequences. The authors concluded that, with further refinements to the methodology, point mutations and exonic deletions can be detected reliably using MPS.