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www.genome.gov/gwastudies/
DNA Sequencing
DNA Sequencing
Elevated Risk
Name                               Confidence   Your Risk   Avg. Risk   Compared to Average

Atrial Fibrillation                               33.9%      27.2%      1.25x

Type 2 Diabetes                                   29.9%      23.7%      1.26x

Prostate Cancer                                   22.6%      17.8%      1.27x

Age-related Macular Degeneration                  11.3%        7.0%     1.61x

Restless Legs Syndrome                             2.5%        2.0%     1.25x

Exfoliation Glaucoma                               2.2%        0.7%     2.90x

Multiple Sclerosis                                 0.5%        0.3%     1.37x

Esophageal Squamous Cell
                                                   0.4%        0.4%     1.21x
Carcinoma (ESCC)
Stomach Cancer (Gastric Cardia
                                                   0.3%        0.2%     1.22x
Adenocarcinoma)

Bipolar Disorder                                   0.2%        0.1%     1.44x
Elevated Risk
Name                               Confidence   Your Risk   Avg. Risk   Compared to Average

Atrial Fibrillation                               33.9%      27.2%      1.25x

Type 2 Diabetes                                   29.9%      23.7%      1.26x

Prostate Cancer                                   22.6%      17.8%      1.27x

Age-related Macular Degeneration                  11.3%        7.0%     1.61x

Restless Legs Syndrome                             2.5%        2.0%     1.25x

Exfoliation Glaucoma                               2.2%        0.7%     2.90x

Multiple Sclerosis                                 0.5%        0.3%     1.37x

Esophageal Squamous Cell
                                                   0.4%        0.4%     1.21x
Carcinoma (ESCC)
Stomach Cancer (Gastric Cardia
                                                   0.3%        0.2%     1.22x
Adenocarcinoma)

Bipolar Disorder                                   0.2%        0.1%     1.44x
Specific genetic risk factors for diabetes
Universal Flu Vaccine
HIV
Prevention
Nicotine
Vaccine
Deaths from Tobacco Use
        as percentage of total deaths among men and women over 35
                          2000 regional estimates

                   Over 25%                     10%–14%
                   20%–24%                      5%–9%
                   15%–19%                      Under 5%




           Men




Women
Clinical trial participant
Leslie Cook, after 4 years:

“I haven't smoked a
   cigarette since.
 I don't want one.”

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Preventing and Treating Chronic Disease - Francis Collins

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  • 21. Elevated Risk Name Confidence Your Risk Avg. Risk Compared to Average Atrial Fibrillation 33.9% 27.2% 1.25x Type 2 Diabetes 29.9% 23.7% 1.26x Prostate Cancer 22.6% 17.8% 1.27x Age-related Macular Degeneration 11.3% 7.0% 1.61x Restless Legs Syndrome 2.5% 2.0% 1.25x Exfoliation Glaucoma 2.2% 0.7% 2.90x Multiple Sclerosis 0.5% 0.3% 1.37x Esophageal Squamous Cell 0.4% 0.4% 1.21x Carcinoma (ESCC) Stomach Cancer (Gastric Cardia 0.3% 0.2% 1.22x Adenocarcinoma) Bipolar Disorder 0.2% 0.1% 1.44x
  • 22. Elevated Risk Name Confidence Your Risk Avg. Risk Compared to Average Atrial Fibrillation 33.9% 27.2% 1.25x Type 2 Diabetes 29.9% 23.7% 1.26x Prostate Cancer 22.6% 17.8% 1.27x Age-related Macular Degeneration 11.3% 7.0% 1.61x Restless Legs Syndrome 2.5% 2.0% 1.25x Exfoliation Glaucoma 2.2% 0.7% 2.90x Multiple Sclerosis 0.5% 0.3% 1.37x Esophageal Squamous Cell 0.4% 0.4% 1.21x Carcinoma (ESCC) Stomach Cancer (Gastric Cardia 0.3% 0.2% 1.22x Adenocarcinoma) Bipolar Disorder 0.2% 0.1% 1.44x
  • 23. Specific genetic risk factors for diabetes
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  • 33. Deaths from Tobacco Use as percentage of total deaths among men and women over 35 2000 regional estimates Over 25% 10%–14% 20%–24% 5%–9% 15%–19% Under 5% Men Women
  • 34. Clinical trial participant Leslie Cook, after 4 years: “I haven't smoked a cigarette since. I don't want one.”

Editor's Notes

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  3. Readout from state-of-the-art, high-throughput genotyping (Illumina 5085, blueclusters) technologies Example of a typical readout from a genome-wide association (GWAS) study, which utilize data generated by high-throughput, discovery initiatives like the HapMap and 1000 Genomes Projects Readout from state-of-the-art, high-throughput genotyping (Illumina 5085) technologies Genotyping chip (Illumina SNP Chip) 2X zoom readout from state-of-the-art, high-throughput sequencing (454Data) technologies
  4. Readout from state-of-the-art, high-throughput genotyping (Illumina 5085, blueclusters) technologies Example of a typical readout from a genome-wide association (GWAS) study, which utilize data generated by high-throughput, discovery initiatives like the HapMap and 1000 Genomes Projects Readout from state-of-the-art, high-throughput genotyping (Illumina 5085) technologies Genotyping chip (Illumina SNP Chip) 2X zoom readout from state-of-the-art, high-throughput sequencing (454Data) technologies
  5. Aleksei Aksimentiev, Assistant Professor of Physics, University of Illinois Transport across cell membranes The ability of membrane channels to sort single molecules is of great interest in bioengineering and, as a result, membrane channels like alpha-hemolysin have been adopted for in vitro devices or used as an inspiration for manufacturing artificial channels. Thus, suspended in a lipid bilayer, an alpha-hemolysin channel becomes a stochastic sensor when a molecular adapter is placed inside its genetically re-engineered transmembrane pore, reporting via modulation of the transmembrane ionic current the type and the concentration of analytes entering the channel. The transmembrane pore of alpha-hemolysin can conduct not only small solutes, but also rather big (tens of kDa) linear macromolecules, including DNA and RNA strands. Using alpha-hemolysin as a prototypical beta-barrel membrane channel, Professor Aksimentiev is investigating transport of ions, nucleic acids and proteins across the cells boundaries. In collaboration with leading experimental groups, he has been conducting large scale molecular dynamics simulation of nucleic acids transport. In the future, he will apply this computational methodology to investigate translocation of proteins through membrane pores. http://physics.illinois.edu/people/profile.asp?aksiment
  6. This negative-stained transmission electron micrograph (TEM) depicts the ultrastructural details of an influenza virus particle, or “virion”. A member of the taxonomic family Orthomyxoviridae, the influenza virus is a single-stranded RNA organism. http://phil.cdc.gov/phil/details.asp 3D graphical representation of a generic influenza virion’s ultrastructure. A portion of the virion’s outer protein coat has been cut away, which reveals the virus’ contents. Exterior: Light blue on a stem: Hemagglutinin Dark blue on a stem: Neuraminidase Light blue donuts: M2 Ion Channel Interior: Coiled structure: RNP http://phil.cdc.gov/phil/details.asp
  7. Influenza viral spike (HA): Molecular model of the influenza virus spike and the site of the highly conserved region of vulnerability in the stem region. The spike, composed of a trimeric HA, consists of an upper (head) and lower (stem) region that mediates attachment and entry of influenza into cells of the respiratory tract. The sites of amino acid variability among influenza strains (red, <98% conserved) and the location of the highly conserved stem antibody site (yellow) are highlighted. (Nabel and Fauci, Nature Medicine , 12/2010)
  8. HIV Tiles: HIV virions budding and releasing from an infected cell. Credit: NIAID, NIH HIV virus (CDC) HIV culture (CDC) ? Center: Scanning electron micrograph of HIV particles infecting a human T cell. Credit: NIAID, NIH
  9. iPrEx images from study “infographic” (on website)
  10. iPrEx images from study “infographic” (on website)
  11. Arrow modified by speeches to reflect adherence statistics
  12. NicVAX image from NABI Biopharmaceuticals
  13. from the WHO Tobacco Atlas
  14. Update from Leslie Cook (to RK, 12/23/10): “The answer is yes – Dr. Collins may use me as an example of the fact that NicVax is a great tool to help people quit once and for all.  I look forward to the day when it is widely available to the general public in the hopes that others like me will finally be able to rid themselves of a lethal addiction.”
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