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Posts Tagged ‘Copy-number variation’


Copy Number Variants (CNV) Alleles to be Detected by a Complete Recessive Carrier Screening Diagnostics

Reporter: Aviva Lev-Ari, PhD, RN

 

Using array comparative genomic hybridization data for 21,470 individuals, Baylor College of Medicine‘s James Lupski and colleagues considered the frequency with which deletions or other disruptive copy number variants appear in genes known for roles in recessive disease. As they report in Genome Research, the investigators unearthed more than 3,200 instances in which deletions affected one allele of a recessive disease gene, affecting 419 different recessive disease genes in all. The CNVs — which render individuals potential carriers of recessive disease — tended to occur in long genes and genes falling far from those contributing to dominant disease risk, study authors note. Based on their findings, they argue that “a complete recessive carrier screening method or diagnostic test should detect CNV alleles.”

Deletions of recessive disease genes: CNV contribution to carrier states and disease-causing alleles

Abstract

Over 1,200 recessive disease genes have been described in humans. The prevalence, allelic architecture, and per-genome load of pathogenic alleles in these genes remain to be fully elucidated, as does the contribution of DNA copy-number variants (CNVs) to carrier status and recessive disease. We mined CNV data from 21,470 individuals obtained by array comparative genomic hybridization in a clinical diagnostic setting to identify deletions encompassing or disrupting recessive disease genes. We identified 3,212 heterozygous potential carrier deletions affecting 419 unique recessive disease genes. Deletion frequency of these genes ranged from one occurrence to 1.5%. When compared with recessive disease genes never deleted in our cohort, the 419 recessive disease genes affected by at least one carrier deletion were longer and were located farther from known dominant disease genes, suggesting that the formation and/or prevalence of carrier CNVs may be affected by both local and adjacent genomic features and by selection. Some subjects had multiple carrier CNVs (307 subjects) and/or carrier deletions encompassing more than one recessive disease gene (206 deletions). Heterozygous deletions spanning multiple recessive disease genes may confer carrier status for multiple single-gene disorders, for complex syndromes resulting from the combination of two or more recessive conditions, or may potentially cause clinical phenotypes due to a multiply heterozygous state. In addition to carrier mutations, we identified homozygous and hemizygous deletions potentially causative for recessive disease. We provide further evidence that CNVs contribute to the allelic architecture of both carrier and recessive disease-causing mutations. Thus, a complete recessive carrier screening method or diagnostic test should detect CNV alleles.

  • Received February 7, 2013.
  • Accepted May 6, 2013.

© 2013, Published by Cold Spring Harbor Laboratory Press

 

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Genome-Wide Detection of Single-Nucleotide and Copy-Number Variation of a Single Human Cell(1)

Reporter, Writer: Stephen J. Williams, Ph.D.

Most tumors exhibit a level of diversity, at the cellular, histologic, and even genetic level (2).  This genetic heterogeneity within a tumor has been a focus of recent research efforts to analyze the characteristics, expression patterns, and genetic differences between individual tumor cells.  This genetic diversity is usually manifested as single nucleotide variations (SNV) and copy number variations (CNV), both of which provide selection pressures in both cancer and evolution.

As cancer research and personalized medicine is focused on analyzing this tumor heterogeneity it has become pertinent view the tumor as a heterogeneous population of cells instead of as a homogenous mass.  In, fact, studies have suggested that cancer cell lines growing on plastic in culture, even though thought of as clonogenic, can actually display a varied degree of expression differences between neighboring cells growing on the same dish.  Indeed, cancer stem cells show an asynchronous cell division, for example a parent CD133-positive cell will divide into a CD133-positive and a CD133-negative cell(3). In addition, the discovery that circulating tumor cells (a rare population of circulating cells in the blood) can be prognostic of outcome in cancer such as inflammatory breast cancer(4), it is ever more important to develop methods to analyze single cell populations.

Harvard University researchers, Dr. Chenghang Zong, Sijia Lu, Alec Chapman and Sunney Xie developed a new amplification method utilizing multiple annealing and looping-based amplification cycles (MALBAC)(1).   A quasilinear preamplification process is used on pictograms of DNA genomic fragments (form 10 to 100 kb) isolated from a single cell.   This is performed to reduce the bias associated with nonlinear DNA amplification.  A series of random primers (which the authors termed MALBAC primers, constructed with a common sequence tags) are annealed at low temperature (0 °C). PCR rounds produce semiamplicons.  Further rounds of amplification, after a step of looping the amplicons, result in full amplicons with complementary ends.  When the two ends hybridize to form the looped DNA, this prevents use of this loop structure as a template, therefore leading to a close-to–linear amplification.    The process allows for a higher fidelity of DNA replication and the ability to amplify a whole genome.  The amplicons are then sequenced either by whole-genome sequencing methods using Sanger-sequencing to verify any single nucleotide polymorphisms.  This procedure of MALBAC-amplification resulted in coverage of 85-93% of the genome of a single cell.

As proof of principle, the authors used MALBAC to amplify the DNA of single SW480 cancer cells (picked from a clonally expanded population of a heterogeneous population (the bulk DNA).  Comparison of the MALBAC method versus the MDA method revealed copy number variations (CNV) between three individual cells, which had been picked from the clonally expanded pool. Their results were in agreement with karyotyping studies on the SW480 cell line.  Meticulous quality controls were performed to limit contamination, high false positive rates of SNV detection due to amplification bias, and false positives due to amplification or sequencing errors.

Interestingly, the authors found 35 unique single nucleotide variations which h had occurred from 20 cell divisions from a single SW480 cancer cell.  This resulted in an estimated 49 mutations which occurred in 20 generations, yielding a mutation rate of 2.5 nucleotides per generation.  In addition, the authors were able to map some of these mutations on various chromosomes and perform next-gen sequencing (deep sequencing) to verify the nucleotide mutations and found an unusually high purine-pyrimidine exchange rate.

In a subsequent paper, investigators from the same group at Harvard used this technology to sequence 99 sperm cells from a single individual to study genetic diversity created during meiotic recombination, a mechanism involved in evolution and development(5).

Reference:

1.            Zong, C., Lu, S., Chapman, A. R., and Xie, X. S. (2012) Science 338, 1622-1626

2.            Cooke, S. L., Temple, J., Macarthur, S., Zahra, M. A., Tan, L. T., Crawford, R. A., Ng, C. K., Jimenez-Linan, M., Sala, E., and Brenton, J. D. (2011) British journal of cancer 104, 361-368

3.            Guo, R., Wu, Q., Liu, F., and Wang, Y. (2011) Oncology reports 25, 141-146

4.            Giuliano, M., Giordano, A., Jackson, S., Hess, K. R., De Giorgi, U., Mego, M., Handy, B. C., Ueno, N. T., Alvarez, R. H., De Laurentiis, M., De Placido, S., Valero, V., Hortobagyi, G. N., Reuben, J. M., and Cristofanilli, M. (2011) Breast cancer research : BCR 13, R67

5.            Lu, S., Zong, C., Fan, W., Yang, M., Li, J., Chapman, A. R., Zhu, P., Hu, X., Xu, L., Yan, L., Bai, F., Qiao, J., Tang, F., Li, R., and Xie, X. S. (2012) Science 338, 1627-1630

Other related posts on this website regarding Cancer and Genomics include:

Cancer Genomics – Leading the Way by Cancer Genomics Program at UC Santa Cruz

Identifying Aggressive Breast Cancers by Interpreting the Mathematical Patterns in the Cancer Genome

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Harvard Group Using Bio-Rad Digital PCR System as Part of NHGRI-Funded Study of Multi-Allelic CNV

 

Reporter: Aviva Lev-Ari, PhD, RN

August 23, 2012

Researchers in the Department of Genetics at the Harvard University Medical School have been awarded $500,000 by the National Institutes of Health for the first year of a four-year project to study multi-allelic copy number variation in the human genome.

As part of the research, the Harvard team is using a Bio-Rad QX100 Droplet Digital PCR system as one of two methods to analyze multi-allelic CNVs in human cohorts. The researchers are also using a computational method that compares available whole-genome sequencing data.

Steven McCarroll, a professor of genetics at Harvard Med and director of genetics at the Stanley Center for Psychiatric Research at the Broad Institute, is principal investigator on the grant, which is being administered by NIH’s National Human Genome Research Institute.

According to a recently published grant abstract, McCarroll and colleagues seek to analyze multi-allelic CNVs, which involve genes and other functional elements for which three or more segregating alleles give rise to a wide range of copy numbers — between two and 10 — per diploid human genome.

These multi-allelic CNVs have been “refractory to widely used analysis methods and are not assessed in the genome-scale molecular or statistical approaches used to study genetically complex phenotypes in humans,” the researchers wrote.

The project builds on research that McCarroll’s group previously conducted on characterizing multi-allelic duplication CNVs of a megabase-long inversion polymorphism in a particular locus of chromosome 17 called 17q21.31, which contains markers previously associated with female fertility, female meiotic recombination, and neurological disease.

As part of that research, published in the August 2012 issue of Nature Genetics, the group analyzed read depth in the locus by applying an algorithm called Genome Structure in Populations, or Genome STRiP, to whole-genome sequencing data from 946 unrelated individuals sampled as part of the 1000 Genomes Project; and used droplet-based digital PCR to analyze 120 parent-offspring trios from HapMap.

http://www.nature.com/ng/journal/v44/n8/full/ng.2334.html

They found that their measurements of integer copy number varied from two to eight, and were 99.1 percent concordant across 234 genotypes in overlapping samples, thus validating both the computational and digital PCR methods.

More specifically, for the digital PCR assay, the group designed a pair of PCR primers and a dual-labeled fluorescence-FRET oligonucleotide probe to both the CNV locus and a two-copy control locus. Then they used a droplet generator from QuantaLife to compartmentalize the PCR reaction into uniform 1-nanoliter emulsion-based droplets containing zero, one, or very few template molecules for each locus; and a droplet reader from QuantaLife to count the number of positive and negative droplets, comparing the droplet counts of the CNV locus to the control locus to determine absolute copy number.

QuantaLife originally developed the droplet-based digital PCR system, but was acquired in October by Bio-Rad, which rebranded the platform as the QX100 Droplet Digital PCR system (PCR Insider, 10/6/2011).

Annette Tumolo, director of the digital biology center at Bio-Rad, told PCR Insider this week that McCarroll has access to two such platforms, one of which is in use at Harvard and was obtained from QuantaLife, and one of which Bio-Rad sold to the Broad Institute.

Tumolo said that Bio-Rad maintains “an active and positive relationship” with the McCarroll lab. “They’ve gotten great results [with the QX100], and were able to rapidly publish the Nature Genetics paper,” Tumolo said.

Under the new NHGRI grant, McCarroll and colleagues plan to “accurately analyze mCNVs in reference populations” using both the computational and digital PCR approach, the researchers wrote in their grant abstract.

“By analyzing these data in a statistical framework that incorporates information about genotypes, allele frequencies, inheritance, and haplotypes, we will place mCNV alleles onto the haplotype maps created by HapMap and 1000 Genomes, and render mCNVs accessible to genotype imputation to the fullest extent possible,” the grant abstract states.

In addition, McCarroll’s group hopes to “deeply characterize mCNVs at 10 biomedically important loci, to understand these polymorphisms at the levels of population genetics, mutational rates and histories, and relationships to clinical phenotypes. Finally, we will pilot inexpensive in silico genome-wide association studies for mCNVs based on statistical imputation into existing GWAS data sets.”

The end goal of the project is to discover relationships between disease risk and gene dosage, which will help reveal the molecular etiology of human disease, the researchers wrote.

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Ben Butkus is senior editor of GenomeWeb’s premium content and the editor of PCR Insider. He covers technologies and trends in PCR, qPCR, nucleic acid amplification, and sample prep. E-mail him here or follow his GenomeWeb Twitter account at@PCRInsider.

 

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