1 / 24

Large-Scale Copy Number Polymorphism in the Human Genome J. Sebat et al. Science, 305 :525

Large-Scale Copy Number Polymorphism in the Human Genome J. Sebat et al. Science, 305 :525. Luana Á vila MedG 505 Feb. 24 th 2005. 1/2 4. Outline. Background Method Results Discussion Future applications. 2/ 24. Background. Common genetic variation.

daxia
Download Presentation

Large-Scale Copy Number Polymorphism in the Human Genome J. Sebat et al. Science, 305 :525

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Large-Scale Copy Number Polymorphism in the Human GenomeJ. Sebat et al. Science, 305:525 Luana Ávila MedG 505 Feb. 24th 2005 1/24

  2. Outline • Background • Method • Results • Discussion • Future applications 2/24

  3. Background Common genetic variation Differences between people are given by genetic variations that can exist in a few forms: • Allelic differences • Single nucleotide differences – SNPs • Copy number differences - CNPs 3/24

  4. Background Copy Number Polymorphism (CNP) “A normal variation in DNA due to variation in the number of copies of a sequence within the DNA. Large-scale copy number polymorphisms are common and widely distributed in the human genome.” http://www.medterms.com/script/main/art.asp?articlekey=34373 4/24

  5. Background How do different copy numbers arise? • Gene duplication - gene conversion events - mRNA reverse transcript insertion • Genome duplication - cell cleavage error in mitosis - polyspermy - non-disjunction and non-reduction 5/24

  6. Background Large-scale rearrangements  responsible for many of the genetic differences between humans and other primates Rearrangements can drive evolution but can also alter cell function:  dosage dependent gene regulation  concentration imbalance of protein subunits  chromosome instability 6/24

  7. Background Large-scale rearrangements Large-scale copy number differences are found in cancer due to genomic instability Used ROMA to detect differences between normal and cancer tissues: - found CNPs in cancer cells expected due to genomic instability (Lucito et al.) 7/24

  8. Background Large-scale rearrangements - test ‘normal to normal’ control comparisons Found: CNPs are present in normal samples (Sebat et al.) 8/24

  9. Frequently detected large (100 kb to 1 Mb) chromosomal deletions and duplications in normal DNA samples  Therefore, to correctly interpret data need to to be able to distinguish normal CNPs from abnormal genetic lesions Used ROMA to find normal CNPs in Human Genome Background Large-scale rearrangements 9/24

  10. Method Using ROMA ROMA = Representational Oligonucleotide Microarray Analysis • It is an array-based comparative genomic hybridization • Genomic DNA is digested with restriction enzyme Bgl II, Hind III 10/24

  11. Method Using ROMA • Bgl II fragments (200–1200 bp) are ligated with PCR adapters – amplify genomic representational fragments • Probes are designed in silico from the Human genome project • Use microarray to compare hybridization from unrelated individuals • Further analysis with hidden Markov Model 11/24

  12. Profiling Genetics of Cancer using ROMA C N Identify New Cancer Genes 12/24 http://www.cshl.edu/public/releases/revealing.html

  13. Method ROMA features: • Reduces complexity of the genome • Detect loss of a single allele • Resolution of 1 probe every 35kb of the genome • Lower signal to background ratio • Probes have fewer repetitive sequences in DNA sampled 13/24

  14. Method How did they do it?  Whole blood, lymphoblastoids and sperm samples from 20 people and extracted genomic DNA from each tissue sample Germline CNP Somatic CNP 14/24

  15. Results Probe ratio Genome Order Detection of germline CNP 15/24

  16. Results Detection of Somatic difference 16/24

  17. Verification of Results by FISH ROMA FISH 17/24

  18. Results What did they find? • Identified 221 germline CNPs in 20 people • 76 non-overlapping CNPs (71 Bgl II + 5 Hind III) • Cover 44 Mb of genome 14/25

  19. Results What did they find? • Average CNP length = 465 kb • Average of 11 CNPs between 2 people • 5 CNPs had been described before – Identified 71 novel CNPs • Some CNPs previously reported by McLean (1997) and Townson(2002) were not detected in this study • Estimate that any given experiment may miss up to 30% of CNPs (calculated false negative rate = 33%) 19/24

  20. Results What did they find? 20/24

  21. Discussion What is the relevance of it all? • Large-scale CNPs were found throughout the human genome – in all chromosomes but 18, 20, X and Y - Some CNPs occur in clusters: Hotspots? • CNPs may reflect the genomic regions of instability. • Considerable genome structural variation among humans – responsible for genetic diversity? 21/24

  22. How many of such polymorphisms are commonly present in the population? Discussion Questions:  Which genes/ chromosomal regions are more frequently affected?  Can such variations or "copy number polymorphisms" among individuals underlie many human traits, including heritable predisposition or resistance to disease? 22/24

  23. Discussion Genes content of CNPs Gene symbol Gene name Location Function CNP

  24. Future Applications: • Further development of ROMA • Increase sample size and type – more subjects and different tissues • Investigate selective pressure on CNPs • - mechanism? • compare rate of synonymous vs. non-synonymous substitutions • Use ROMA in cytogenetic diagnosis? (Jobanputra et al., Feb 2005) 24/24

More Related