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Oct 1, 2013 (Vol. 33, No. 17)

CNV Strategies Get a Rethink

  • CNVs as Risk Factors

    “In psychiatry, people have been interested in CNVs because they represent important risk factors,” says Judith L. Rapoport, M.D., senior investigator and chief of the Child Psychiatry Branch at the National Institute of Mental Health. While looking at a very rare, childhood-onset form of schizophrenia, Dr. Rapoport and colleagues found that a pediatric group of schizophrenia patients exhibits a higher prevalence of the 22q11 microdeletion, of approximately 5%, than any adult-onset population with schizophrenia that was previously studied.

    The finding that the same 22q11 microdeletion is also present in children with other neurodevelopmental conditions promises to make the interpretation of sequencing data more challenging. “Approximately 80% of the children born with this deletion have some kind of severe disorder, whether it is a mood disorder, autism, obsessive-compulsive disorder, or delayed language development, and the possibility of conducting prenatal screening has enormous implications,” says Dr. Rapoport.

    The 22q11 microdeletion, which involves an approximately 3 Mb chromosomal region, contains many genes. The large number of genes, together with the microdeletion’s involvement in several neuropsychiatric conditions, makes the identification of the microdeletion’s specific role in disease, and the elucidation of the molecular basis of pathogenesis, more challenging. “There are investigators currently exploring the effects that CNVs from our patients have on neurons grown by cellular reprogramming,” says Dr. Rapoport.

    The identification and characterization of pathological CNVs has ramifications in terms of prenatal screening, particularly since many common structural variants are found in patients who appear to be clinically unaffected. While approximately 26% of the individuals harboring the 22q11 microdeletion will develop schizophrenia, a disorder for which few risk factors are known, approximately 75% of these individuals will not develop this condition. “But having an identical twin with schizophrenia confers a 50% risk, and this deletion represents, therefore, the second largest risk factor that currently exists,” explains Dr. Rapoport.

  • Pathogenic CNVs

    “We developed an algorithm that helps determine the probability that a CNV is pathogenic,” says Ian D. Krantz, M.D., professor of pediatrics at the Children’s Hospital of Philadelphia. One of the most challenging aspects related to interpreting CNVs is that in many instances, they occur in apparently healthy individuals.

    PECONPI software identifies pathogenic CNVs based on their gene content and frequency, and researchers in Dr. Krantz’s lab tested its ability to identify pathogenic CNVs on two genetically heterogeneous cohorts, one with sensorineural hearing loss and the other one with congenital heart defects.

    “This adaptable software allows investigators to incorporate the parameters they want to use, and comb through hundreds of CNVs as they study complex traits or rare individual traits in birth defects,” says Dr. Krantz. Based on the variable parameters that investigators can select, the algorithm automatically ranks CNVs by priority.

    As part of this work, Dr. Krantz and colleagues explored the possibility of evaluating recessive disorders by combining the analysis of pathological CNVs on one allele with next-generation sequencing of the other allele. “After finding a CNV, we are trying to incorporate an additional step to sequence the other allele and search for point mutations that are unmasked in recessive conditions,” says Dr. Krantz.

  • Probabilistic Methods

    “Our background is in mathematics, and after learning from collaborators about the challenges in this area of biology, we wanted to apply probabilistic methods to improve the precision of CNV detection,” says Saman K. Halgamuge, Ph.D., professor and associate dean of the School of Engineering at the University of Melbourne. “Applying expertise from mathematics, computer sciences, and engineering would benefit many areas of biology, and researchers from these fields need to increasingly provide their input in helping solve problems, as this would exert a huge impact on society.”

    A new method developed by Dr. Halgamuge and colleagues in collaboration with Dr. Jason Li of the Peter MacCallum Cancer Research Institute estimates CNV from whole exome sequencing datasets, based on the ratio of average read depths from tumor and normal tissue samples collected from patients. “Exome analysis provides a more specific way to differentiate between normal and tumor samples, and the data is more targeted,” says Dr. Halgamuge.

    A key feature of the method is the use of discrete wavelet transform smoothing to reduce experimental noise from the sequencing data. “After this step, the Hidden Markov Model, a probability-based tool used in mathematics, is applied to detect copy number gains and losses,” explains Dr. Halgamuge.

    A comparison between the proposed method and several other existing methods revealed that it outperforms them in terms of precision, but one of its shortcomings was the detection of small CNVs as noise. Addressing this shortcoming will require additional improvements. “Another challenge, and the next step in our efforts, will be to distinguish driver structural variations, which are more dangerous, from passenger ones, which are not causing the cancer,” says Kaushalya C. Amarasinghe, doctoral student and first author of the study.

  • Improved CNV Detection and Analysis

    As significant contributors to genomic diversity, CNVs are thought to collectively account for up to three times more base pairs than all single nucleotide polymorphisms combined. The recent expansion in experimental genome-scanning technologies, along with the development of novel computational and biotechnological tools, promise to more reliably unveil and characterize structural variation in the human genome. These advances are marking a transformative moment in research and clinical medicine, and are bound to fill important gaps in our understanding of development, disease, and evolution.

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