1. Incorporating Physiological Genomics into the Medical Student Curriculum IUPS Refresher Course Integrating Genomics into Physiology Courses: A New Paradigm or Just More Information? Anne Kwitek, Ph.D. Human and Molecular Genetics Center Medical College of Wisconsin

2. Integration of Physiological Genomics  Separate Course  Incorporate within Medical Physiology Course Independent genetics series Information integrated throughout the Course

3. Challenges  Teaching both introductory genetics AND how it fits with basic physiology  Seemingly disparate information – how to make physiological genomics ‘fit’ a basic physiology course

4. What to Cover  Introduction to genomics and genetics Basic tools and technology Linkage  Monogenic disease  Complex disease Expression  Examples using topics covered in class

5. Goals of Genetics Lectures  Introduction Become familiar with the concepts and technologies behind genomics and genetics  Applications Applications of genetics and genomics toward the understanding of human monogenic disease Applications of genetics and genomics toward the understanding of human complex disease

6. Introduction to Genomic Tools and Technology

7. Genomics vs. Genetics  Genomics: Structural aspects of the genome  Genetics: The use of transmission of genetic material

8. Genetic Markers to Locate Disease  Simple Sequence Repeat (SSR) microsatellite CA repeat Short Tandem Repeat Polymorphism (STRP) Simple Sequence Length Polymorphism (SSLP)  Single Nucleotide Polymorphism (SNP)

9. Simple Sequence Repeat (SSR) Momtctttgggactg cacacacacaca tcagaatccggag tctttgggactg cacacacacacaca tcagaatccggag Dadtctttgggactg cacacacacacacaca tcagaatccggag tctttgggactg cacacacacacacacaca tcagaatccggag Child 12341234

10. Single Nucleotide Polymorphisms (SNPs)  We are 99.9% identical at the genome level (1/1000 bp differences)  Will use sequence variants (SNPs) as a form of diagnosis  Different outcomes of variation Coding  Synonymous changes  Non-synonymous changes Non-coding  Changes in gene expression/protein levels

11. Compare expression in tissues between disease and normal states Compare expression in tissues before/after drug treatment Evaluate many thousands of genes at the same time Genes turned up or down in disease state may lead to understanding of mechanism Lead to a diagnostic fingerprint Expression Profiling

12. See Figure 3.9 from A Primer of Genome Science, Second Edition Greg Gibson and Spencer V. Muse Sunderland, MA: Sinauer Associates, 2004

13. Linkage and Association

14. Disease Traits  Qualitative Trait that is either present or absent e.g. Cystic fibrosis  Quantitative Trait with a continuous distribution of measurement e.g. height, weight  Clinical definition of disease E.g. Hypertension

15. Monogenic (Mendelian) Disease  Simple inheritance patterns within families Autosomal Dominant Autosomal Recessive X-linked  Caused by a mutation in a single gene  Relatively rare  Powerful for identifying genes by linkage analysis and positional cloning

16. Complex (Common) Disease  No clear pattern of Mendelian inheritance  A mix of genetic and environmental factors  Incomplete penetrance  Phenocopies  Heterogeneity  High frequency of disease-causing allele

17. Gene Mapping Strategies  Linkage Analysis within Pedigrees  Allele Sharing within Relative (Sib) Pairs  Association Study

18. Linkage Analysis Within Pedigrees  Tests for the likelihood of recombination between assumed disease and marker alleles.  Great for single gene disorders  Limitation for common/multifactorial diseases frequency of disease locus heterogeneity penetrance of the disease

19. Example of Linked Marker 1234 23241234 1223 2222 22 2213 23 12

20. Association Study  Correlation of different SNPs in this region with disease.  Family-based and case-control based

21. Association Studies  Advantages Ease of collecting subjects to study, i.e. cases and controls More powerful to detect genes Analysis methodology similar to standard case- control methods  Disadvantages Most assumption-laden Spurious Associations – far exceed true associations Ascertainment Bias/Allele frequencies

22. Applications of Physiological Genomics

23. Why Study Monogenic Disease  Advantages Clear genetic inheritance Single gene mutation Hopefully lead to better understanding of mechanism of more common forms of disease  Disadvantages Rare Not causing most common disease

24. Linkage Studies of Hypertrophic Cardiomyopathy (HCM)  One of the most common inherited cardiac disorders  Prevalence in young adults of 1 in 500  Autosomal dominant  Variable expressivity  Etiological heterogeneity  Environmental and genetic modifiers

25. Linkage Studies on Monogenic HCM 1/2 3/4 1/41/31/4 2/31/1 1/21/3

26. Linkage Results to Gene Mutation  Linkage of a marker to a disease does not mean a gene is found!  Fine-mapping  Positional Candidate Genes Look for obvious biological candidates within the region of linkage Screen for mutations in this gene in disease families = SEQUENCING Successful for HCM!

27. Mutations in Monogenic Disease  Mutations are often causal  Mutations are often ‘severe’, i.e. destroy protein function Non-sense mutations Missense mutations Insertions/deletions

28. Understanding Pathways through Monogenic Disease  Other mutations related to common disease? Not complete loss of function mutations Interactions with other genes/environment  May not be gene involved in common forms, but part of the pathway

29. Hypertension and the Kidney  Linkage in monogenic forms of severe hypertension and hypotension Gitelman Syndrome GRA Aldosterone Synthase Deficiency Liddle Syndrome PHA1 Bartter syndrome AME Hydroxylase deficiency Hypertension exacerbated by pregnancy

30. Hypertension and the Kidney  17 genes cloned 8 for hypertension 9 for hypotension  All genes involving sodium handling in the nephron  All Monogenic forms of hypertension/hypotension

31. See Figure 1 from Lifton et al. Cell 104:545-556, 2001 http://www.cell.com/content/issue?volume= 104&issue=4 Free access

32. Genes in Complex Disease  Multiple genes, each with additive effect  Genes interacting with one another  Genes interacting with environment

33. Hypertension  Complex  Many different subtypes  Animal models offer advantages for finding genes for complex disease Inbred Controlled breeding Controlled environment

34. Comparative Genomics Tying Phenotype and Genotype Across Species

36. Comparative Genomics and Gene Identification

37. PKD Linkage – Human 6Linkage – Rat 9 Human and Rat ARPKD Ward, et al, Nature Genetics, 30:259-269 http://www.nature.com/ng/journal/v30/n3/full/ng833.html (free access) http://www.nature.com/ng/journal/v30/n3/full/ng833.html

38. PKD Gene Mutation PKDH1 Gene in Human and Rat

39. Subdividing Cancer through Gene Expression Profiling  Classify cancers based on their gene expression profiles  Compare different cancer types to identify ‘fingerprint’ gene expression  Provide diagnostic tool

40. See Figure on gene expression profiles of mesenchymal, leukemia, epithelial, and melanoma cells along with 3 probability graphs comparing overall survival of patients with GC B-like vs. activated B-like [from A Primer of Genome Science, Second Edition by Greg Gibson and Spencer V. Muse. Sunderland, MA: Sinauer Associates, 2004]

41. Genomics to Proteomics

42. Finding Genes for Disease  We know the blueprint  Technology makes possible large-scale testing that will likely become the norm in your practice Diagnostics Therapy

43. The Basics About Genetic Testing  To find out if a person is a carrier for a certain disease  To learn if a person has an inherited predisposition to a certain disease, like breast or ovarian cancer (also known as susceptibility testing)  To help expecting parents know whether their unborn child will have a genetic disease or disorder (prenatal testing)  To confirm diagnosis of certain diseases or disorders (for example, Alzheimer's disease)

44. Goals of Personalized Medicine  Match the right drug/treatment with the right patient  Predisposition testing  Preventative medicine