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New Technology for Human Genetics

New Technology for Human Genetics. Stanley F. Nelson, MD Professor Department of Human Genetics David Geffen School of Medicine at UCLA. Common Diseases.

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New Technology for Human Genetics

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  1. New Technology for Human Genetics Stanley F. Nelson, MD Professor Department of Human Genetics David Geffen School of Medicine at UCLA

  2. Common Diseases • Inherited combinations of common alleles each with a modest effect combine in that are unique and lead to increased risk of disease: likely with environmental factors • Millions of differences Between any two unrelated Humans: which ones Change risk?

  3. Genome-wide SNP analysis A:60% B:40% A:50% B:50%

  4. Can now look everywhere in the genome for many different diseases

  5. 100K 300K 500K 1M

  6. Direct to consumer genetic testing Disclosure: S. Nelson is on the Scientific Advisory Board of 23andme

  7. Personal ‘risk’ profile at the type 2 diabetes genes

  8. What is good • More and personally relevant information provided: changes perspective of individual risk • Motivation to see world of potential diseases and possible behavioral changes… will this motivate us to take some disease areas more seriously? “This affects me more?” “This disease is common and I ought to watch my weight” • New genetic information can be interpreted: new literature finding will motivate greater attention on genetics. • Broader involvement in genetics will increase our public understanding, appreciation of genetics.

  9. What is missing? • Most of the genetic risk is not currently known: typical estimates of 2-25% • Identified genes have small risk • How do we combine the risk alleles • Much of the disease risk is environmental • How do we combine the environmental information with the genetic information

  10. Autism Example • Is not one disease but a group of diseases each with a rare and mostly genetic cause • Progress is impeded by lack of sufficiently large samples

  11. Can we obtain a sufficient Autismsample (>20,000)? • Possible now with Direct saliva DNA Collection • Web based patient assessment • Cost effective and feasible to genotype tens of thousands of affected children to identify the root causes • Register at www.ianproject.org

  12. Cancer example • Genetic disease (100%) • Most is somatic– mutations occurring in brain cells that lead to brain cancer, but does not increase chances of brain cancer in your children • Complete genome sequencing permits identification of mutations in all genes

  13. Spectrum of mutations is highly diverse in human cancers Cancer Types genes

  14. Even within brain cancers: pattern is very diverse Genes Brain Tumor Samples

  15. Some Brain tumors mutate many genes Genes Brain Tumor Samples

  16. Some genes are more commonly mutated in brain cancers Genes Brain Tumor Samples

  17. Whole genome sequencing prospects • Human Genome Project: Used ABI capillary sequencers capable of 2.8 million bases per day. Community set up hundreds of machines and in about one year generated most of the data that is now known as the reference sequence of the human genome: estimated costs to sequence next genome: ~$30 million, 30 machines, 1-2 years. • “Next generation sequencing” Illumina/ABI: Single machine generates up to 3 BILLION bases per day. 1000x speed and much reduced costs: ~$30,000, one machine in 6 weeks.

  18. Whole genome sequencing prospects

  19. Whole genome sequencing Will whole genome individual sequencing be possible? • Yes: Further improvements reasonable to expect under $5,000 whole genome sequencing possible in under two years. • Will whole genome individual sequencing be cost effective? • Depends on who is paying. • Will whole genome sequencing be worthwhile? • Yes: fantastic resource for gene function discovery • Incidental findings: previously unknown serious disease risk discovered: Good if preventive care.

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