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Gene-Environment Interactions in Complex Diseases

Gene-Environment Interactions in Complex Diseases. Jeppe Madura Larsen, MSc , PhD Assistant Professor. Life expectancy increase. Advances. Nutrition, food availability Living conditions, urbanization Universal health care Vaccines: Polio, HepB /A, small pox

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Gene-Environment Interactions in Complex Diseases

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  1. Gene-EnvironmentInteractions in ComplexDiseases Jeppe Madura Larsen, MSc, PhD Assistant Professor

  2. Life expectancy increase

  3. Advances • Nutrition, food availability • Living conditions, urbanization • Universal health care • Vaccines: Polio, HepB/A, small pox • Surgery: Transfusion, transplantation, technology • Medicine: Penicillin, steriods, chemotherapy

  4. On the flip side:what doesn’t kill us... (fast) • Asthma Eder et al, N Engl J Med, 2006

  5. On the flip side:what doesn’t kill us... (fast) • Asthma • Hayfever, eczema Latvala et al, BMJ, 2005

  6. On the flip side:what doesn’t kill us... (fast) • Asthma • Hayfever, eczema • Obesity, T2D Kavey et al, Pediatrics, 2011

  7. On the flip side:what doesn’t kill us... (fast) • Asthma • Hayfever, eczema • Obesity, T2D • Autoimmune diseases: IBD, RA, MS • Cancer

  8. Societal challenges • Patient morbidity • Social-economic impact • Health care expenditures

  9. Disease characteristics

  10. Genetics: GWAS • Define genotypes associated/predictive of disease Manhattan plot

  11. Genetics: GWAS findings • Several disease associated loci found Colitis (McGovern el. al., Nat Gen, 2010)

  12. Genetics: GWAS findings • Several disease associated loci found BMI & T2D (O’Rahilly el. al., Nature, 2009)

  13. Genetics: GWAS findings • Several disease associated loci found • However: • Frequently major alleles associates with disease • Low disease predictive value • In T1D: 30 % heritability explained • In T2D: 1 % heritability explained • No single SNP is clearly associates with disease. However, several SNPs may collectively contribute to disease via a common pathway.

  14. GWAS challenge:Extracting disease “genotypes” • Integrating GWAS data with: • Protein-protein interaction • Protein function • Metabolic pathway • Cell/tissue specificity • Cell interaction • Future • Repetitive DNA • Copy-number variants • Epigenetics: DNA/histonemethylation

  15. The environmental factors • Likely accountable for recent rise in disease prevalence • Act on genetic predisposition

  16. The environmental factorsin childhood asthma • Several diverse factors • Living on a farm/rural area (increased bacterial diversity or microbial products) • Airway microbiota composition • Nutrition (vitamin D, PUFA) • Parental smoking • C-section • Birth order • Siblings in home • Pets • A role for both peri-natal and natal exposures

  17. Overview: Shaping disease risk Renz et. al., Nat Imm, 2011

  18. Translational research Guo & Zakhari, NIAAA

  19. Challenges for the clinic • Disease definitions are likely inadequate • Define disease phenotypes/endotypes: “Endotype—a contraction of endophenotype—is a subtype of disease defined functionally and pathologically by a molecular mechanism or by treatment response. Asthma, like many chronic disorders, is a heterogeneous and genetically complex disease, meaning that many genes (>100 have been identified) are likely to contribute, variably, to its different manifestations. Asthma is likely to have several specific endotypes associated with distinct clinical features, divergent underlying molecular causes, and distinct treatment responses.” (Anderson, Lancet, 2008) • A need for additional objective and quantitative parameters • Standard treatment algorithms/guidelines • Run large cohorts for studies of disease development and preventive intervention

  20. Challenges for basic science • Sampling and measuring environmental factors • Develop/improve HTS methods in-depth genetic and biochemical characterization • Translate human findings into focused disease relevant animal models for pharmacological development

  21. Challenges for systems biology • Develop methods for integration of new datasets • Develop standardized data structures, data handling and pipelines • Data sharing (both in academia and industry) • Model development and validation. Unrestricted of previous disease definitions. To be tested in the clinic and/or animal models.

  22. Litterature • European Science Foundation, rapport 2011. Forward Look: Gene-environment interaction in Chronic Disease. • Renz et. al., JACI, 2011. Gene-environment interaction in Chronic Disease. • Renz et. al., Nat. Imm., 2011. Gene-environment interaction in Chronic Inflammatory Disease.

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