Genomic medicine aims to identify genetic variations that cause disease and inform treatment. While whole genome sequencing is technically possible for $1000, analysis costs remain high. Current clinical applications include diagnosing rare childhood disorders and guiding cancer treatment. Continued cost reductions and expanding biological knowledge databases will drive further innovation, though challenges around data interpretation and reporting remain. Large reference populations and functional studies are still needed to realize genomics' full potential in healthcare.

1. GENOMICS IN
MEDICINE
The Future of Healthcare

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. Research Drives Innovation in Healthcare
Healthcare
Research
Innovation

4. Human Genome Project 1st Draft

5. Personalized Medicine: Expectations and Reality

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. What Drives Genomic Innovation in Medicine?
Cost
Knowledge Utility

11. The Players

14. Total Cost of Sequencing
• Whole Genome:
• Approximately $5000 - $10,000
• Technically $1000 Genome is “here” with Illumina 10X

15. Total Cost of Sequencing
• Whole Genome:
• Approximately $5000 - $10,000
• Technically $1000 Genome is “here” with Illumina 10X
• Analysis cost >> Sequencing Cost

16. 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?

17. Composition of the Human Genome

18. Exome Sequencing

19. Targeted Sequencing Panels

20. What Drives Genomic Innovation in Medicine?
Cost
Knowledge Utility

21. 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)

22. Typical Bioinformatics Workflow
QC of Raw Data
Map to Reference
QC
Find Variants
QC
Annotate
Filter

23. It Sounds simple but…
• For every stage there are multiple programs available and
published in the literature

24. 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

25. 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

26. 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

27. Clinical Bioinformatics
Validate, validate, validate!

28. Typical Bioinformatics Workflow
QC of Raw Data
Map to Reference
QC
Find Variants
QC
Annotate
Filter

29. Clinical Genomics: Identify Clinically Relevant
Genetic Variation

30. 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

31. 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

32. Knowledge Required
Variant
Gene
Population
Frequency
Pathways
Functions
Tissues
Variant
Type
Impact on
Protein

33. Populations are Important

34. 2001 – Present: 14 years of Knowledge Building
Exome Variant Server
Exome Aggregation Consortium

35. 2001 – Present: 14 years of Knowledge Building

36. 2001 – Present: 14 years of Knowledge Building

37. 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

38. Variant Annotation Pipeline Example

39. Variant Annotation Pipeline Example

40. Genetic Variant Reporting

41. Genetic Variant Reporting

42. Genetic Variation Reporting

43. Genetic Variation Reporting

44. Genetic Variation Reporting

45. 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

46. What Drives Genomic Innovation in Medicine?
Cost
Knowledge Utility

47. 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

48. Genomic Medicine: In the Clinic

49. Genomic Medicine: In the Clinic

50. Genomic Medicine: In the Clinic

51. Genomic Medicine: In the Clinic

52. Genomic Medicine: In the Clinic

53. Types of Next-Generation Sequencing
Experiments
• DNA-Seq
• RNA-Seq
• Methyl-Seq
• ChIP-Seq
• CLIP-Seq

54. The Missing Pieces?

55. The Missing Pieces?

56. The Missing Pieces?

57. The Missing Pieces?

58. The Missing Pieces?

59. The Missing Pieces?

60. The Missing Pieces?

61. 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

62. Where Are We Going?

63. 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

64. Where Are We Going?

65. Direct-to-Consumer

66. New Technologies: Oxford Nanopore

68. 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