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Personalized Medicine

Personalized Medicine. William Schwarz. Background. Goal: To measure the risk an individual has of contracting a disease based on their genetic makeup. How is this done? Using genotyping technology an individual’s genome can be analyzed against a general population to determine differences.

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Personalized Medicine

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  1. Personalized Medicine William Schwarz

  2. Background • Goal: To measure the risk an individual has of contracting a disease based on their genetic makeup. • How is this done? • Using genotyping technology an individual’s genome can be analyzed against a general population to determine differences. • These differences can then be compared to association studies of various diseases to determine if this individual has an increased risk due to variation in their genetic structure.

  3. Existing Companies • 23 and me • deCODEme • Navigenics • Etc.. • These companies utilize SNP mapping chips that have probes that bind to particular SNP versions. Fluorescent markers glow so that the chips can be visually analyzed for SNP variations. • 23 and me analyzes 550,000 SNPs • deCODEme analyzes 1 million (using Illumina Human 1M Beadchip)

  4. Abdominal aneurysm Alzheimer's disease Atrial fibrillation Brain aneurysm Breast cancer Celiac disease Colon cancer Crohn's disease Deep vein thrombosis Diabetes, type 2 Glaucoma Graves' disease Heart attack Hemochromatosis Lactose intolerance Lung cancer Lupus Macular degeneration Melanoma Multiple sclerosis Obesity Osteoarthritis Prostate cancer Psoriasis Restless legs syndrome Rheumatoid arthritis Sarcoidosis Stomach cancer, diffuse From Navigenics.com Criteria for disease selection These diseases have causal SNPs verified by multiple studies The diseases must be possible to act on. Other companies’ policies may vary. Diseases

  5. Assumptions • From project description: • Use 2 disease mutations each increasing an individuals risk by 20% • Assume a 5% frequency for the disease in the population. • Other assumptions • Assume only these two SNP mutations affect the disease risk. • Assume MAF of 25%. • Risk Factors behave independently (joint conditional probabilities can be represented as products).

  6. Using our Assumptions • RR(x1) = 1.20 • F = 0.05 • p = 0.25 • The risk to someone who has the SNP is RR*F = 0.06 or 6% of contracting the disease • With MAF of 0.25, 25% of the population has the allele. • How do these values change when analyzing multiple SNPs?

  7. Measuring Worth • Is this SNP worth testing? • General Rules for choosing a SNP to test. • Prevalence of risk allele • Low p value correlation (< 0.001 lower the better) • High relative risk • Validated studies showing correlation • We can pick a lower bound to represent a SNP being worth testing • >5% prevalence in the population • RR > 2.0

  8. Easy Project • Compute risks for multiple disease mutations assuming independence. • Combining risk from multiple markers is done as the product of the estimates from the individual markers. • RR(x1,x2) = RR(x1)RR(x2) • (Note) This is assuming independence • So Pr(A|x1, x2) = Pr(A|x1)Pr(A|x2)/Pr(A) and Pr(x1, x2) = Pr(x1)Pr(x2)

  9. Results • Now that we know the relative risk for a specific genotype we can take what an individual has and analyze. • Going back to the original assumptions • RR(x1) = 1.20, RR(x2) = 1.20 • RR(x1,x2) = RR(x1)RR(x2) • Overall risk = 1.20 * 1.20 = 1.44 • 1.44*F (avg. population risk) = Individuals risk • 1.44*0.05 = 0.072 or 7.2% chance of having the disease.

  10. Results (cont.) • Lets look at another example • SNP A(x1, x2) and SNP B(y1,y2) • A has MAF 0.20, RR 2.0(per instance) • B has MAF 0.40, RR 1.5(per instance)

  11. Thoughts • Having multiple mutations has a multiplicative instead of additive effect. • This means that the more mutations that can be tested will give a better understanding and a higher risk value. • In our previous examples it would require 4 SNPs with RR of 1.2 to meet the overall RR of 2.0. • However, the more SNPs we analyze the lower chance of an individual having all of them. • For the 2 SNPs in the example the prob of having both is 0.0625 so we are above the threshold but if we add another SNP we would drop to 0.015625 which is below our threshold • SNPs that we definitely want to test for have high RR and a relatively small MAF. • These SNPs have a high impact on the total risk and are the major SNPs in a group of SNPs that are associated with a disease.

  12. Future Work • Analyze risk using different models. • This analysis was done using the multiplicative model. • Dominant model • Recessive model • Estimate the variance of the risk.

  13. Individual Markers • Sample Analysis (Using the multiplicative model) • Given • Rs1799950 CEU pop SNP freq AA-0.9167 GG-0.0000 AG-0.0833 A-0.9583 G-0.0417 • Odds ratio calculated at 1.72 (Breast Cancer) • G is the risk allele • Analysis • p = Pr(G), q = Pr(A) • Pr(GG) = p^2 = 0.00, Pr(AG) = 2pq = 0.08, Pr(AA) = q^2 = 0.92 • R(GG) = 1.72^2 = 2.9584, R(AG) = 1.72, R(AA) = 1.0 • R = 0*2.9584 + 0.08*1.72 + 1*0.92 = 1.0576 (baseline risk) • RR(GG) = 2.9584/1.0576 = 2.80, RR(AG) = 1.72/1.0576 = 1.63, RR(AA) = 1/1.0576 = 0.95

  14. Resources • SNPedia (http://www.snpedia.com) • SNP Nexus (http://snp-nexus.org) Using sites like these an individual can calculate their own risks if they receive their genetic information.

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