Epidemiology 217 Molecular and Genetic Epidemiology I Course Director: John Witte Professor of Epidemiology & Biostatistics

Outline I. Class Details II. Introduction to Molecular and Genetic Epidemiology: an example III. Notes on Human Genetics IV. Determining the genetic component to disease before looking at DNA V. Experiment

I. Details Classes: 10 Tuesdays, 1:15-2:45 pm Classes: 10 Tuesdays, 1:15-2:45 pm Office hours: by appointment Office hours: by appointment Course Director: John S. Witte, PhD Phone: Course Director: John S. Witte, PhD Phone: S. Witte, S. Witte, Teaching Assistant: Nerissa Ko, MD Phone: Teaching Assistant: Nerissa Ko, MD Phone: Course website: Course website:

Goals Learn about: Learn about: common molecular and genetic measures common molecular and genetic measures determining the genetic contribution to disease determining the genetic contribution to disease searching for disease-causing genes, searching for disease-causing genes, and their interaction with environmental factors and their interaction with environmental factors pharmacogenomics; proteomics; and bioinformatics. pharmacogenomics; proteomics; and bioinformatics. Main goal: develop a framework for interpreting, assessing, and incorporating molecular and genetic measures in your own research. Main goal: develop a framework for interpreting, assessing, and incorporating molecular and genetic measures in your own research.

Syllabus DateLecturer Title / Content 1/2 Eric Jorgenson Introduction to Molecular and Genetic Epidemiology 1/9 Joe Wiemels Molecular and Genetic Measures 1/16 John Witte Candidate Gene Studies I: Design 1/23 John Witte Candidate Gene Studies II: Analysis 1/30 Eric Jorgenson Genome-Wide Studies: Linkage 2/6 Eric Jorgenson Genome-Wide Studies: Association 2/13 Neil Risch Ethnicity and Race in Genetic Epidemiology 2/20 John Witte Proteomics and Bioinformatics 2/27 Kathy Giacomini Pharmacogenomics 3/6 Joe Wiemels John Witte Incorporating Molecular and Genetic Measures; Putting it all Together

Homework Assignments Count for 70% of grade (30% for final exam). Count for 70% of grade (30% for final exam). Weekly readings and / or brief problem sets. Weekly readings and / or brief problem sets. Readings give important background information, and should be completed before the start of the corresponding lecture. Readings give important background information, and should be completed before the start of the corresponding lecture. Problem sets are due at 8 pm on Mondays, so we can discuss the following day. Problem sets are due at 8 pm on Mondays, so we can discuss the following day. Late assignments are not accepted. Late assignments are not accepted.

Web Resources Video from UAB Short course on statistical genetics: Video from UAB Short course on statistical genetics: Dorak’s notes on genetics: Dorak’s notes on genetics: Strachan & Read’s Human Molecular Genetics: Strachan & Read’s Human Molecular Genetics:

II. Introduction: how was lunch? ? Impact of folate, B12, and homocysteine on cognitive function?

How can we measure these factors? Problems?

Can we improve our measurements? Look at circulating levels in plasma

What else will impact these levels? Methylene tetrahydrofolate reductase (MTHFR): Methylene tetrahydrofolate reductase (MTHFR): e.g., catalyzes the last step in conversion of folic acid to its active form, 5-methyltetrahydrofolate (5MTHF).

MTHFR gene Single nucleotide polymorphisms (SNPs) in MTHFR: Single nucleotide polymorphisms (SNPs) in MTHFR:C677T (C and T are alleles; CC, CT, TT are genotypes) A1298C (A and C are alleles; AA, AC, CC are genotypes) E.g., if an individual is homozygous for the 677TT SNP, MTHFR enzymatic activity can decrease by 50%. E.g., if an individual is homozygous for the 677TT SNP, MTHFR enzymatic activity can decrease by 50%. This may in turn reduce cognitive function. This may in turn reduce cognitive function. Locus

Diet, plasma, & genotype interaction Look at how these work in conjunction with each other to affect cognitive functioning! Look at how these work in conjunction with each other to affect cognitive functioning!

III: Notes on Human Genetics: DNA

Human Chromosomes

Human Chromosome 21 TelomeresCentromere p stands for petit q stands for grand 21q22.1 is pronounced twenty-one q two two point one

Human Genome Statistics 3,253,037,807 basepairs3,253,037,807 basepairs 21,774 known genes21,774 known genes 1,036 novel genes1,036 novel genes 1,069 pseudogenes1,069 pseudogenes 3,976 RNA genes3,976 RNA genes 270,661 exons270,661 exons

Transcription and Translation

Potentially Functional Regions of a Gene cis regulator Amino acid coding RNA processing Transcription regulation promoter

Human Genome Variation 3,253,037,807 basepairs3,253,037,807 basepairs Mutation rate ≈ per bp per generationMutation rate ≈ per bp per generation 65 new mutations expected in each person65 new mutations expected in each person Compare two copies in any one personCompare two copies in any one person 1 variant per 1,331 basepairs1 variant per 1,331 basepairs 2,444,055 variants2,444,055 variants Most variants are oldMost variants are old

Mutation and Meiosis

IV. How can we tell that a disease is genetic? Without looking at DNA… Without looking at DNA… 1. Ecologic Studies (Migrant Studies) 2. Familial Aggregation: Family Studies Family Studies Twin Studies Twin Studies 3. Segregation analyses

Break: How about you? Research interests? Research interests? Background / training in molecular / genetics? Background / training in molecular / genetics? Ever used or considered using molecular or genetic measures in clinical research? Ever used or considered using molecular or genetic measures in clinical research?

IV.1 Ecologic Studies (Migrant) Weeks, Population. 7 th ed. London: Wadsworth Publishing Co 1999

Example: Cancer (SMRs) Not U.S. U.S.BornU.S.Cauc. CancerJapanBorn Stomach (M) Intestine (F) Breast (F) (MacMahon B, Pugh TF. Epidemiology: Principles and Methods. Boston: Little, Brown and Co, 1970:178.)

IV.2 Familial Aggregation Does disease tend to run in families? Does disease tend to run in families? Example: Men who have a brother or father with prostate cancer have 2-3 times the risk of men without a family history. Example: Men who have a brother or father with prostate cancer have 2-3 times the risk of men without a family history. Possible study designs: Possible study designs: 1. Case-control: compare the family history between cases versus controls. 2. Cohort: view the family members of the cases and controls as two cohorts, one exposed (i.e., to a case), the other not exposed.

Twin Studies

MZ Twins (Identical) Twin 1 Twin 2 Both alleles are shared identical by descent (IBD)

DZ Twins (Fraternal) Twin Twin 2 can be any of the four IBD can be 2, 1, or 0

DZ Twins (Fraternal) Twin 1 100% 50% 50%0% Average sharing is 50%

Twin Studies Compare the disease concordance rates Compare the disease concordance rates of MZ (identical) and DZ (fraternal) twins. of MZ (identical) and DZ (fraternal) twins. DiseaseYesNo YesAB NoCD Twin 1 Twin 2 Then one can estimate heritability (the proportion of the variance of an underlying disease liability due to common genes), and environmentality. Concordance = 2A/(2A+B+C)

Example of Twin Study: Prostate Cancer Twin Concordant pairs (A) Discordant pairs (B+C) Concordance MZ DZ Heritability: 0.42 ( ) Non-shared Environment: 0.58 ( ) Lichtenstein et al NEJM ;343: Twin registry (Sweden, Denmark, and Finland) 7,231 MZ and 13,769 DZ Twins (male)

IV.3 Segregation Analysis Evaluate whether the pattern of disease among relatives is compatible with a single major gene, polygenes, or simply shared environment. Evaluate whether the pattern of disease among relatives is compatible with a single major gene, polygenes, or simply shared environment. Fit formal genetic models to data on disease phenotypes of family members. Fit formal genetic models to data on disease phenotypes of family members. The parameters of the model are generally fitted finding the values that maximize the probability (likelihood) of the observed data. The parameters of the model are generally fitted finding the values that maximize the probability (likelihood) of the observed data. If there appears to be a single major gene, then one can estimate its dominance, penetrance, and allele frequency. If there appears to be a single major gene, then one can estimate its dominance, penetrance, and allele frequency.

Taste Test

Bimodal Distribution of PTC

Recessive trait

First PTC Family Study L. H. Snyder Science 1931

Summary Molecular measures can improve upon conventional questionnaire-based measurements. Molecular measures can improve upon conventional questionnaire-based measurements. Genetics can impact many exposures and diseases. Genetics can impact many exposures and diseases. We can assess the heritability with studies of populations and families, including: We can assess the heritability with studies of populations and families, including: 1. Migrant studies 2. Familial aggregation studies 3. Family/Twin studies 4. Segregation analyses