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Genome Composition

Genome Composition. Loss-Of-function Variants. Group B1 Greg Benz Leo Espinosa David Caro Rupert Wu. Introduction. Loss-of-Function variants The genetic variants predicted to severely disrupt protein- coding genes

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Genome Composition

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  1. Genome Composition Loss-Of-function Variants Group B1 Greg Benz Leo Espinosa David Caro Rupert Wu

  2. Introduction • Loss-of-Function variants • The genetic variants predicted to severely disrupt protein- coding genes • LoF’s include Single-nucleotide variants (SNVs) and insertion/deletion (indel) variants • Recent studies indicates that LoF variants are not always deleterious • Can exist in healthy individual.

  3. Classify the Lof variants: SNVs and indel Filter the candidate LoF variants into high-confidence , missense and synonymous. Analyze the numbers and the properties of LoF variants in the individual genome High confidence LoF: Mainly include nonsense and frame-shift variants Usually deleterious. Missense LoF: Can not determined. Investigate how LoF variants affect human phenotype and disease risk Filter the candidate LoF variants into high-confidence LoF, missense and synonymous. Some implications of this research: Predict the probability of disease causation for novel variants.

  4. Overview Of study • What would we find if our genome was sequenced? • -LoF Variants • -Helpful, Harmful, or Harmless? • -Do these variants affect phenotype?

  5. How is the effect of genetic variance on phenotype shown? (Quintana-Murci et al. 2012)

  6. Data (figure 1) (MacArthur et al. 2012)

  7. Data (figure 1) (MacArthur et al. 2012)

  8. Data (figure 1) (MacArthur et al. 2012)

  9. Data (figure 2) (MacArthur et al. 2012)

  10. Data (figure 2) (MacArthur et al. 2012)

  11. Data (figure 2) (MacArthur 2012)

  12. Data (figure 3) (MacArthur 2012)

  13. Data (figure 3) (MacArthur 2012)

  14. Data (figure 3) (MacArthur 2012)

  15. Discussion • How does this relate to our Biology class? • Every person has their own unique genome • Result of mutations • Different phenotypes • 3+ billion base pairs in genome

  16. Discussion • Differing base pairs = loss-of-function variants • Single base change = point mutation • Types of point mutations • Silent • Missense • Nonsense • Frameshift

  17. Original DNA Sequence Thr Cys Met Pro CCT ACA TGC ATG

  18. Silent Mutation Thr Cys Met Pro CCT ACG TGC ATG No change in amino acid sequence

  19. Original DNA Sequence Thr Cys Met Pro CCT ACA TGC ATG

  20. Missense Mutation CCT CCA TGC ATG Pro Thr Cys Met Pro Change in one amino acid

  21. Original DNA Sequence Thr Cys Met Pro CCT ACA TGC ATG

  22. Nonsense Mutation CCT ACA TGA ATG Thr Cys STOP Met Pro Change from amino acid to stop codon

  23. Original DNA Sequence Thr Cys Met Pro CCT ACA TGC ATG

  24. Frame Shift Insertion of base C G C C ACA TGC A C T Thr Cys Met Pro Thr Ala Changes in multiple amino acids

  25. DNA Transcription RNA synthesized from DNA template mRNA

  26. Discussion • Translation • RNA delivered to ribosomes • Each codon aligns with its anticodon • Amino acid produced for each codon • Amino acids joined by peptide bonds to form a polypeptide (protein)

  27. Take-Home message • “Everyone’s Got Problems”

  28. references • Macarthur, Daniel G. . "A Systematic Survey of Loss-of-Function Variants in Human Protein-Coding Genes ." Science 335 (2012): 823-828. Print. • Quintana-Murci, Lluis. "Gene Losses in the Human Genome." Science 14 Nov. 2012: 806-807. Print. • Freeman, Scott, and Healy Hamilton. Biological science. 4th ed. Upper Saddle River, N.J.: Pearson Prentice Hall, 2011. Print. • Segalat, Laurent. "Loss-of-function Genetic Diseases and the Concept of Pharmaceutical Targets ." Orphanet 225 (2007): Web. 18 Nov. 2012

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