PLNT*6160 Advanced Plant Breeding II

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The following description is for the course offering in Winter 2023 and is subject to change. It is provided for information only. The course outline distributed to the class at the beginning of the semester describes the course content and delivery, and defines the methods and criteria to be used in establishing the final grades for the course. Offered in odd-number years.
 

Fundamentals of quantitative genetics. Topics include gene and genotype frequencies means, variances, covariances and resemblance among relatives. Lecture topics are expanded through discussion of classic and current papers.

The course is designed to familiarize plant breeding graduate students with the theory and application of quantitative genetics in plant breeding, help them understand the fundamentals of population genetics, and explore how quantitative genetics principles and modern tools can help a plant breeder design and implement a breeding program to improve quantitative traits and to study complex traits. The course is intended to provide opportunities for continued learning, critical questioning and discussion of research findings and communication skills. Emphasis is placed on gaining hands-on experience data management and analysis that resembles real-life plant breeding and quantitative genetic problems and situations.

Instructors:

Lewis Lukens

Teaching Assistant:

Credit Weight:

0.50

Course Level:

  • Graduate

Academic Department (or campus):

Department of Plant Agriculture

Campus:

Guelph

Semester Offering:

  • Winter

Class Schedule and Location:

Please refer to WebAdvisor for class schedule and location.

Learning outcomes:

At the end of this course, students should be able to:

  1. Have had hands-on experience with quantitative plant data manipulation, visualization, and analysis using R and R tidyverse.

  2. Understand the fundamentals of quantitative genetics theory and understand how this theory applies to plant breeding situations.

  3. Understand, with hands-on experience, the application of genomics data in the analysis of plant genetics data.

Lecture Content:

Lecture Content:

Course overview and introduction to data exploration and visualization in tidyverse.

  • Introduction.
  • Using tidyverse and ggplot to manage and visualize data.
  • Estimating genotypic values and their importance and stability

Using linear models to estimate the effects of factors that affect plant traits.

  • Best Linear Unbiased Estimators and Best Linear Unbiased Predictors.
  • Estimating genotypic values and variance component analyses.
  • Visualizing genotypic differences.
  • Genotype x environment interaction

The components of genotypic variation

  • Understanding additive, dominance, and epistatic components of genetic variances.
  • Estimating genetic variance components.
  • Defining and calculating narrow sense heritabilities.

Selection

  • Predicting how selection affects trait values and variability.
  • Explore the theoretical and empirical consequences of different selection schemes in plant breeding.

Molecular bases and molecular prediction of trait variation

  • Identifying loci that control trait variation in biparental and diverse populations.
  • Calculating genomic predictions and the consequences of genomic selection.
Labs & Seminars:
Labs:

There is no lab for this course. Nonetheless, hands-on data analysis outside of class is key. Assignments will encourage data analysis skill development.

Course Assignments and Tests:

Assessment Details

Discussion (20%)
Class Participation is 20% of the final mark. Students are expected to actively participate in class learning. I may ask for students to present certain concepts to the class.

Assignments (50%)
Assignments are 50% of the mark. Three assignments will be given. For each assignment, students will be supplied with a data set and questions. Students are asked to analyze the data and write a report, in which they will follow the format Introduction, Materials and Methods, Results (including tables and Figures as appropriate), Discussion, and References. We will discuss the structure of assignments more as a class.

Quiz (30%)
Quizzes/ exercise completions will be 30% of the mark. Students will write short Quizzes that are open book and outside of class time. They are intended for students to practice data analysis and interpretation. Quizzes should be submitted via email. We will discuss assignment and quiz due dates in class. At least 20% of the course grade will be returned to students by the 40th class day, March 10.

Final examination:

There is no final examination scheduled for this course.

Course Resources:

Required Texts:

N/A

Recommended Texts:

Readings will be assigned. The following texts are good resources overall:

  1. Bernardo, R. 2002. Breeding for quantitative traits in plants. Stemma Press, Woodbury, MN
  2. Falconer, D.S. and T.F.C. Mackay. 1996. Introduction to Quantitative Genetics. 4th edition Pearson Prantice Hall. Essex, England.
  3. Isik, F., Holland, J. and Maltecca, C., 2017. Genetic data analysis for plant and animal breeding(Vol. 400). Cham, Switzerland: Springer International Publishing. Wickham, H. and G. Grolemund. 2017. R for Data Science. Oreilly Inc. Sebastopol, California.

An introductory statistics book that uses R such as Statistics: An Introduction Using R, Crawley, M. Wiley. West Sussex, England.

Other Resources:

N/A

Field Trips:

N/A

Additional Costs:

N/A

Course Policies:

Grading Policies:

Students helping others is strongly encouraged. Nonetheless, each person must write their own code and report. Copying the work of others is academic dishonesty.

Course Policy on Group Work:

N/A

Course Policy regarding use of electronic devices and recording of lectures:

Electronic recording of classes is expressly forbidden without consent of the instructor.  When recordings are permitted they are solely for the use of the authorized student and may not be reproduced, or transmitted to others, without the express written consent of the instructor.

Other Course Information:

University Policies

Academic Consideration

When you find yourself unable to meet an in-course requirement because of illness or compassionate reasons, please advise the course instructor in writing, with your name, id#, and e-mail contact. See the academic calendar for information on regulations and procedures for Academic Consideration:

Academic Misconduct

The University of Guelph is committed to upholding the highest standards of academic integrity and it is the responsibility of all members of the University community, faculty, staff, and students  to be aware of what constitutes academic misconduct and to do as much as possible to prevent academic offences from occurring.

University of Guelph students have the responsibility of abiding by the University's policy on academic misconduct regardless of their location of study; faculty, staff and students have the responsibility of supporting an environment that discourages misconduct. Students need to remain aware that instructors have access to and the right to use electronic and other means of detection. Please note: Whether or not a student intended to commit academic misconduct is not relevant for a finding of guilt. Hurried or careless submission of assignments does not excuse students from responsibility for verifying the academic integrity of their work before submitting it. Students who are in any doubt as to whether an action on their part could be construed as an academic offence should consult with a faculty member or faculty advisor.

The Academic Misconduct Policy is detailed in the University Calenders:

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For more information, contact CSD at 519-824-4120 ext. 56208 or email sas@uoguelph.ca or visit the Student Accessibility Services website: http://www.uoguelph.ca/csd/.

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