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2016 Dal Human Genetics - Genomics in Medicine Lecture
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.
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2016 Dal Human Genetics - Genomics in Medicine Lecture
- 1. GENOMICS IN MEDICINE The Futureof Healthcare
- 2. Goal of GenomicMedicine 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 Innovationin Healthcare Healthcare Research Innovation
- 4. Human Genome Project1st Draft
- 5. Personalized Medicine: Expectationsand 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 GenomicInnovation in Medicine? Cost Knowledge Utility
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- 14. Total Cost ofSequencing • Whole Genome: • Approximately $5000 - $10,000 • Technically $1000 Genome is “here” with Illumina 10X
- 15. Total Cost ofSequencing • Whole Genome: • Approximately $5000 - $10,000 • Technically $1000 Genome is “here” with Illumina 10X • Analysis cost >> Sequencing Cost
- 16. Total Cost ofSequencing • 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 theHuman Genome
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- 20. What Drives GenomicInnovation in Medicine? Cost Knowledge Utility
- 21. Bioinformatics Roles • Support/MaintainComputational Infrastructure • Raw data -> Genome/Exome • Identify genetic variation • Annotate genetic variation • Quality Control • Report to Stake Holders (Clinicians, Fellow Scientists)
- 22. Typical Bioinformatics Workflow QCof Raw Data Map to Reference QC Find Variants QC Annotate Filter
- 23. It Sounds simplebut… • For every stage there are multiple programs available and published in the literature
- 24. It Sounds simplebut… • 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 simplebut… • 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 simplebut… • 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
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- 28. Typical Bioinformatics Workflow QCof Raw Data Map to Reference QC Find Variants QC Annotate Filter
- 29. Clinical Genomics: IdentifyClinically Relevant Genetic Variation
- 30. Discovering Disease-Causing GeneticVariants 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 problemcannot 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
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- 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 PipelineExample
- 39. Variant Annotation PipelineExample
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- 45. Potential Pitfalls withAnnotation 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 GenomicInnovation in Medicine? Cost Knowledge Utility
- 47. Genomic Medicine: Inthe Clinic • Rapid diagnosis of genetic disease in NICU cases • Quicker and cheaper than sequential genetic testing (traditional method) • 50 hour diagnosis
- 48. Genomic Medicine: Inthe Clinic
- 49. Genomic Medicine: Inthe Clinic
- 50. Genomic Medicine: Inthe Clinic
- 51. Genomic Medicine: Inthe Clinic
- 52. Genomic Medicine: Inthe Clinic
- 53. Types of Next-GenerationSequencing Experiments • DNA-Seq • RNA-Seq • Methyl-Seq • ChIP-Seq • CLIP-Seq
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- 61. The Missing Pieces? Exon1 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 WeGoing?
- 63. Where Are WeGoing? 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 WeGoing?
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- 66. New Technologies: OxfordNanopore
- 68. Summary of KeyPoints • 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