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2016 Dal Human Genetics - Genomics in Medicine Lecture

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Undergraduate Guest Lecture on the use of genomics in medicine

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2016 Dal Human Genetics - Genomics in Medicine Lecture

  1. 1. GENOMICS IN MEDICINE The Future of Healthcare
  2. 2. Goal of Genomic Medicine Identify genetic variation that causes or contributes to disease (diagnostic), informs treatment options or patient care (therapeutic/prognostic), or provides other useful clinical information
  3. 3. Research Drives Innovation in Healthcare Healthcare Research Innovation
  4. 4. Human Genome Project 1st Draft
  5. 5. Personalized Medicine: Expectations and Reality
  6. 6. Primary Clinical Applications • Severe childhood genetic disorders • Clinical Exome or Targeted Disease Panel • Cheaper than 4 or 5 sequential gene tests • Cystic Fibrosis Testing • Oncology • Classification • Treatment Guidance • Infectious disease • Epidemiology/Outbreak monitoring • Strain discrimination
  7. 7. What Drives Genomic Innovation in Medicine? Cost Knowledge Utility
  8. 8. The Players
  9. 9. Total Cost of Sequencing • Whole Genome: • Approximately $5000 - $10,000 • Technically $1000 Genome is “here” with Illumina 10X
  10. 10. Total Cost of Sequencing • Whole Genome: • Approximately $5000 - $10,000 • Technically $1000 Genome is “here” with Illumina 10X • Analysis cost >> Sequencing Cost
  11. 11. Total Cost of Sequencing • Whole Genome: • Approximately $5000 - $10,000 • Technically $1000 Genome is “here” with Illumina 10X • Analysis cost >> Sequencing Cost • But, do we need the whole genome?
  12. 12. Composition of the Human Genome
  13. 13. Exome Sequencing
  14. 14. Targeted Sequencing Panels
  15. 15. What Drives Genomic Innovation in Medicine? Cost Knowledge Utility
  16. 16. Bioinformatics Roles • Support/Maintain Computational Infrastructure • Raw data -> Genome/Exome • Identify genetic variation • Annotate genetic variation • Quality Control • Report to Stake Holders (Clinicians, Fellow Scientists)
  17. 17. Typical Bioinformatics Workflow QC of Raw Data Map to Reference QC Find Variants QC Annotate Filter
  18. 18. It Sounds simple but… • For every stage there are multiple programs available and published in the literature
  19. 19. It Sounds simple but… • For every stage there are multiple programs available and published in the literature • For every program there are a wide-variety of parameter values and options. Defaults often “good enough” but not always
  20. 20. It Sounds simple but… • For every stage there are multiple programs available and published in the literature • For every program there are a wide-variety of parameter values and options. Defaults often “good enough” but not always • Best combinations of programs and options not well understood
  21. 21. It Sounds simple but… • For every stage there are multiple programs available and published in the literature • For every program there are a wide-variety of parameter values and options. Defaults often “good enough” but not always • Best combinations of programs and options not well understood • Protocols changing rapidly as new technologies and methods developed
  22. 22. Clinical Bioinformatics Validate, validate, validate!
  23. 23. Typical Bioinformatics Workflow QC of Raw Data Map to Reference QC Find Variants QC Annotate Filter
  24. 24. Clinical Genomics: Identify Clinically Relevant Genetic Variation
  25. 25. Discovering Disease-Causing Genetic Variants 4 million genetic variants 2 million associated with protein-coding genes 10,000 possibly of disease causing type 1500 <1% frequency in population Clinically Relevant Genetic Variants
  26. 26. If a problem cannot be solved, enlarge it. --Dwight D. Eisenhower Supreme Commander Allied Forces: Second World War 34th President of the USA 4 million genetic variants 2 million associated with protein-coding genes 10,000 possibly of disease causing type 1500 <1% frequency in population
  27. 27. Knowledge Required Variant Gene Population Frequency Pathways Functions Tissues Variant Type Impact on Protein
  28. 28. Populations are Important
  29. 29. 2001 – Present: 14 years of Knowledge Building Exome Variant Server Exome Aggregation Consortium
  30. 30. 2001 – Present: 14 years of Knowledge Building
  31. 31. 2001 – Present: 14 years of Knowledge Building
  32. 32. Building Knowledge Take-Away • Clinical utility relies on: • Knowledge of background variation from well sampled populations • Knowledge of function of as much genomic sequence as possible • Well defined workflows • Knowledge of sources of error
  33. 33. Variant Annotation Pipeline Example
  34. 34. Variant Annotation Pipeline Example
  35. 35. Genetic Variant Reporting
  36. 36. Genetic Variant Reporting
  37. 37. Genetic Variation Reporting
  38. 38. Genetic Variation Reporting
  39. 39. Genetic Variation Reporting
  40. 40. Potential Pitfalls with Annotation Sources • Databases often overlap and agree, but there may be disagreements • Source of information: Predicted versus experimental • Incorrect and out-of-date information • Large-scale un-validated versus manually curated datasets
  41. 41. What Drives Genomic Innovation in Medicine? Cost Knowledge Utility
  42. 42. Genomic Medicine: In the Clinic • Rapid diagnosis of genetic disease in NICU cases • Quicker and cheaper than sequential genetic testing (traditional method) • 50 hour diagnosis
  43. 43. Genomic Medicine: In the Clinic
  44. 44. Genomic Medicine: In the Clinic
  45. 45. Genomic Medicine: In the Clinic
  46. 46. Genomic Medicine: In the Clinic
  47. 47. Genomic Medicine: In the Clinic
  48. 48. Types of Next-Generation Sequencing Experiments • DNA-Seq • RNA-Seq • Methyl-Seq • ChIP-Seq • CLIP-Seq
  49. 49. The Missing Pieces?
  50. 50. The Missing Pieces?
  51. 51. The Missing Pieces?
  52. 52. The Missing Pieces?
  53. 53. The Missing Pieces?
  54. 54. The Missing Pieces?
  55. 55. The Missing Pieces?
  56. 56. The Missing Pieces? Exon 1 Intron 1 Exon 2Reference Patient Start TAA StopmRNA coding for protein Exon 1 Intron 1 Exon 2 TAC TyrSplice Site Loss Missense/Frameshift Stop Gain
  57. 57. Where Are We Going?
  58. 58. Where Are We Going? Do whole genome anyway, use bioinformatics to filter down to reportable/actionable information 4 million genetic variants 2 million associated with protein-coding genes 10,000 possibly of disease causing type 1500 <1% frequency in population Clinically Relevant Genetic Variants
  59. 59. Where Are We Going?
  60. 60. Direct-to-Consumer
  61. 61. New Technologies: Oxford Nanopore
  62. 62. Summary of Key Points • Clinical application possible when cost and applicable knowledge reach critical point • Personalized genomic medicine is here already • The genome alone isn’t enough • Large population surveys of healthy individuals • Sample from diverse human populations globally • Large-scale surveys of genes, genetic elements, and their functions • Data, data, and more data required

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