Quantitative Methods in Human Genetics Syllabus 2016

Sessions:

- Two 75-minute lecture classes per week
- One 75-minute recitation per week

Location:

- 12 Waverly Place, Room L120

Texts:

- Introduction to Genetic Analysis, 10th Edition

Griffiths, Wessler, Carrol, Doebley

- The Analysis of Biological Data

Whitlock & Schluter

These texts are references only. I do not recommend purchasing the texts.

Instructor: Professor David Gresham

Teaching Assistant: Darach Miller

Course Aims: To provide an integrated study of the biological basis of human heredity and statistical methods for studying human inheritance in families and populations. Students will learn the fundamentals of statistical analysis and computer programming using R.

This course fulfills the “Quantitative Skills” requirement for Biology Majors

Prerequisites:

- Principles of Biology I
- Principles of Biology II
- Molecular and Cellular Biology I
- Molecular and Cellular Biology II

Grading:

- Weekly assignments: 50%
- Quizzes: 10%
- Midterm: 15%
- Final Exam: 20%
- Attendance/Participation: 5%

The quizzes are intended to encourage retention of key formula, facts and concepts. They will be held during the first five minutes of Thursday’s class.

Assignments are problem based and require the use of R. With each assignment you should submit a .html file containing the computer code used to generate your results and the results generated using RMarkdown. The document should include comments describing what each step in the code is doing.

Course Description:

Deciphering the information encoded in the human genome is one of the greatest (and most exciting) challenges of the 21st century. This course will provide an introduction to studying and interpreting the human genome with a focus on the statistical methods required for its study. Fundamental concepts in human genetics will be introduced including inheritance of mendelian disease, population genetics, multifactorial disease and functional genomics. Accompanying each topic will be an introduction to the statistical concepts and tools that are required to study inheritance, genes and gene function. These include probability, hypothesis testing, ANOVA, regression, correlation and likelihood. Hands on experience will be provided through weekly assignments using the statistical programming language, R. Prior experience with statistics and genetics is not required.

Policy on missed tests:

Exams will be excused only for medical or family emergencies. I need to be notified by phone or email before the exam time. An unexcused absence from an exam will be calculated as 0% for that particular test! If you miss an exam and present a legitimate excuse, a make-up test will be made available to you.

Assignments:

Must be handed in on time. Late assignments will be penalized 25% per day.

Each class will address a topic in human genetics (Genetics) and statistics (Statistics). The recommended texts provide material complementary to that covered in class. However, the texts are not required and it is the student’s responsibility to find the appropriate section in the texts. Occasional additional readings will be provided in class.

Week 0

Class 1: Tuesday January 26th (Darach Miller)

- An introduction to using R for statistical computing

Class 2: Thursday January 28th (Darach Miller)

- Using R and reproducible research

Week 1

Lecture 1: Tuesday February 2nd

- Genetics: Distributions of human phenotypes
- Statistics: Descriptive Statistics

Lecture 2: Thursday February 4th

- Genetics: Samples from populations
- Statistics: Uncertainty, sampling distributions, standard error

Week 2

Lecture 3: Tuesday February 9th

- Genetics: Mendel’s experiments and expected phenotypic proportions
- Statistics: Probability

Lecture 4: Thursday February 11th

- Genetics: Independent assortment, introduction to linkage
- Statistics: chi square, contingency tables, hypothesis testing

Week 3

Lecture 5: Tuesday February 16th

- Genetics: Mendelian inheritance in humans I, penetrance
- Statistics: Conditional probability, Relative risk

Lecture 6: Thursday February 18th

- Genetics: Mendelian inheritance in humans II, Segregation ratios
- Statistics: Binomial distribution

Week 4

Lecture 7: Tuesday February 23rd

- Genetics: Linkage, recombination, three factor cross
- Statistics: Probability

Lecture 8: Thursday February 25th

- Genetics: Interference, Genetic variation in humans, Mapping functions
- Statistics: Poisson distribution

Week 5

Lecture 9: Tuesday March 1st

- Genetics: Linkage analysis in human pedigrees
- Statistics: Likelihood

Lecture 10: Thursday March 3rd

- Genetics: Human Linkage Analysis, Refined genetic mapping
- Statistics: Likelihood, LOD scores

Week 6

Lecture 11: Tuesday March 8th

- Genetics: Genetic testing
- Statistics: Law of total probability, conditional probability; Bayes Theorem

Midterm exam: Thursday March 10th

Week 7

Lecture 12: Thursday March 22nd

- Genetics: Gene frequencies in populations
- Statistics: Hardy-Weinberg equilibrium

Lecture 13: Tuesday March 24th

- Genetics: Inbreeding
- Statistics: Recursive calculations

Week 8

Lecture 14: Tuesday March 29th

- Genetics: Genetic drift, Selection
- Statistics: Binomial sampling, simulation

Lecture 15: Thursday March 31st

- Genetics: Genetic diversity, Linkage disequilibrium, mutation-drift equilibrium
- Statistics: Computational simulation

Week 9

Lecture 16: Tuesday April 5th

- Genetics: Distribution of quantitative traits
- Statistics: Normal distribution, z-scores, Central limit theorem

Lecture 17: Thursday April 7th

- Genetics: Sampling quantitative phenotypes
- Statistics: Student’s t-test, confidence intervals

Week 10

Lecture 18: Tuesday April 12th

- Genetics: Comparison of quantitative phenotypes between two populations
- Statistics: Two sample t-test

Lecture 19: Thursday April 14th

- Genetics: Comparison of quantitative traits in more than two populations
- Statistics: ANOVA

Week 11

Lecture 20: Tuesday April 19th

- Genetics: Broad sense heritability
- Statistics: Covariance, correlation

Lecture 21: Thursday April 21st

- Genetics: Narrow sense heritability
- Statistics: Linear regression I

Week 12

Lecture 22: Tuesday April 26th

- Genetics: Narrow sense heritability and prediction
- Statistics: Linear regression II: significance and variance explained

Lecture 23: Thursday April 28th

- Genetics: Genome-wide expression analysis
- Statistics: Non-parametric methods

Week 13

Lecture 24: Tuesday May 3rd

- Genetics: eQTL mapping
- Statistics: Randomization and Bootstrapping

Lecture 25: Thursday May 5th

- Genetics: Genome-wide association studies
- Statistics: Odds ratio, multiple hypothesis testing

END OF SEMESTER