Incorporating Physiological Genomics into the Medical Student Curriculum IUPS Refresher Course Integrating Genomics into Physiology Courses: A New Paradigm.

Slides:



Advertisements
Similar presentations
Linkage and Genetic Mapping
Advertisements

Lecture 45 Prof Duncan Shaw. Applications - finding genes Currently much interest in medical research, in finding the genes causing disease Sometimes.
The next generation Chapters 9, 10, 17 in the course textbook, especially pages , ,
Lecture 2 Strachan and Read Chapter 13
Applications of genome sequencing projects 1) Molecular Medicine 2) Energy sources and environmental applications 3) Risk assessment 4) Bioarchaeology,
applications of genome sequencing projects
Note that the genetic map is different for men and women Recombination frequency is higher in meiosis in women.
Genetic research designs in the real world Vishwajit L Nimgaonkar MD, PhD University of Pittsburgh
Tutorial #1 by Ma’ayan Fishelson
SNP Applications statwww.epfl.ch/davison/teaching/Microarrays/snp.ppt.
Genetic Analysis in Human Disease
Mapping Genes for SLE: A Paradigm for Human Disease? Stephen S. Rich, Ph.D. Department of Public Health Sciences Wake Forest University School of Medicine.
Polymorphisms: Clinical Implications By Amr S. Moustafa, M.D.; Ph.D. Assistant Prof. & Consultant, Medical Biochemistry Dept. College of Medicine, KSU.
Dr. Almut Nebel Dept. of Human Genetics University of the Witwatersrand Johannesburg South Africa Significance of SNPs for human disease.
Human Genetics Chapter 14. DNA fingerprinting Every cell that has a nucleus contains the DNA fingerprint for that individual. Only two to four percent.
Introduction to Medical Genetics Fadel A. Sharif.
Genetic Epidemiology Lecture 13 PS Timiras. A Few Definitions GENOME: THE COMPLETE SET OF GENES OF AN ORGANISM GENOTYPE: THE GENETIC CONSTITUTION OF.
Introduction to Linkage Analysis March Stages of Genetic Mapping Are there genes influencing this trait? Epidemiological studies Where are those.
Something related to genetics? Dr. Lars Eijssen. Bioinformatics to understand studies in genomics – São Paulo – June Image:
Introduction of Cancer Molecular Epidemiology Zuo-Feng Zhang, MD, PhD University of California Los Angeles.
Restriction Fragment Length Polymorphisms (RFLPs) By Amr S. Moustafa, M.D.; Ph.D. Assistant Prof. & Consultant, Medical Biochemistry Dept. College of.
RFLP DNA molecular testing and DNA Typing
Computational Molecular Biology Biochem 218 – BioMedical Informatics Simple Nucleotide.
Introduction Basic Genetic Mechanisms Eukaryotic Gene Regulation The Human Genome Project Test 1 Genome I - Genes Genome II – Repetitive DNA Genome III.
1 Modern Genetics Chapter 4. 2 Human Inheritance Some human traits are controlled by single genes with two alleles, and others by single genes with multiple.
Copyright © 2010 Pearson Education Inc.
DR. ERNEST K. ADJEI FRCPath. DEPARTMENT OF PATHOLOGY SMS-KATH
Standardization of Pedigree Collection. Genetics of Alzheimer’s Disease Alzheimer’s Disease Gene 1 Gene 2 Environmental Factor 1 Environmental Factor.
Biotechnology SB2.f – Examine the use of DNA technology in forensics, medicine and agriculture.
Methods of Genome Mapping linkage maps, physical maps, QTL analysis The focus of the course should be on analytical (bioinformatic) tools for genome mapping,
Introduction to BST775: Statistical Methods for Genetic Analysis I Course master: Degui Zhi, Ph.D. Assistant professor Section on Statistical Genetics.
The medical relevance of genome variability Gabor T. Marth, D.Sc. Department of Biology, Boston College
Analyzing DNA Differences PHAR 308 March 2009 Dr. Tim Bloom.
SNPs Daniel Fernandez Alejandro Quiroz Zárate. A SNP is defined as a single base change in a DNA sequence that occurs in a significant proportion (more.
The Complexities of Data Analysis in Human Genetics Marylyn DeRiggi Ritchie, Ph.D. Center for Human Genetics Research Vanderbilt University Nashville,
The medical relevance of genome variability Gabor T. Marth, D.Sc. Department of Biology, Boston College Medical Genomics Course – Debrecen,
CS177 Lecture 10 SNPs and Human Genetic Variation
Gene Hunting: Linkage and Association
A basic review of genetics Dr. Danny Chan Associate Professor Assistant Dean (Faculty of Medicine) Department of Biochemistry Department of Biochemistry.
Genome-Wide Association Study (GWAS)
Experimental Design and Data Structure Supplement to Lecture 8 Fall
Quantitative Genetics
ABC for the AEA Basic biological concepts for genetic epidemiology Martin Kennedy Department of Pathology Christchurch School of Medicine.
Lecture 6. Functional Genomics: DNA microarrays and re-sequencing individual genomes by hybridization.
KEY CONCEPT Biotechnology relies on cutting DNA at specific places.
Class 22 DNA Polymorphisms Based on Chapter 10 Recombinant DNA Technology Copyright © 2010 Pearson Education Inc.
An quick overview of human genetic linkage analysis
In The Name of GOD Genetic Polymorphism M.Dianatpour MLD,PHD.
An quick overview of human genetic linkage analysis Terry Speed Genetics & Bioinformatics, WEHI Statistics, UCB NWO/IOP Genomics Winterschool Mathematics.
Simple-Sequence Length Polymorphisms SSLPs Short tandemly repeated DNA sequences that are present in variable copy numbers at a given locus. Scattered.
Computational Biology and Genomics at Boston College Biology Gabor T. Marth Department of Biology, Boston College
GENETICS Dr. Samar Saleh Assiss. Lecturer Mosul Medical College Pathology3 rd year.
From: Scheinfeld A (1965) Your heredity and environment. JB Lippincott Company, Philadelphia Phenotypic variation among humans is enormous.
Different microarray applications Rita Holdhus Introduction to microarrays September 2010 microarray.no Aim of lecture: To get some basic knowledge about.
1 Finding disease genes: A challenge for Medicine, Mathematics and Computer Science Andrew Collins, Professor of Genetic Epidemiology and Bioinformatics.
Restriction Fragment Length Polymorphism. Definition The variation in the length of DNA fragments produced by a restriction endonuclease that cuts at.
Simple-Sequence Length Polymorphisms
Single Nucleotide Polymorphisms (SNPs
Biotechnology.
Genomic Analysis: GWAS
MOLECULAR MARKERS.
Monogenic Disorders Genetic Counselling
The human genome Contains all the genetic material of an individual
DNA Marker Lecture 10 BY Ms. Shumaila Azam
Introduction to bioinformatics lecture 11 SNP by Ms.Shumaila Azam
California Standard and Learning Objectives
Chapter 7 Multifactorial Traits
Medical genomics BI420 Department of Biology, Boston College
Medical genomics BI420 Department of Biology, Boston College
Genomics, genetic epidemiology, and genomic medicine
Presentation transcript:

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

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

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

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

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

Introduction to Genomic Tools and Technology

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

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)

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

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

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

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

Linkage and Association

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

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

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

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

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

Example of Linked Marker

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

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

Applications of Physiological Genomics

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

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

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

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!

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

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

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

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

See Figure 1 from Lifton et al. Cell 104: , &issue=4 Free access

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

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

Comparative Genomics Tying Phenotype and Genotype Across Species

Comparative Genomics and Gene Identification

PKD Linkage – Human 6Linkage – Rat 9 Human and Rat ARPKD Ward, et al, Nature Genetics, 30: (free access)

PKD Gene Mutation PKDH1 Gene in Human and Rat

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

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]

Genomics to Proteomics

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

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)

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