Skip to main content
Plant Breeding Consortium

Plant Breeding Graduate Courses

Academic Courses for Training Plant Breeding
Graduate Students at North Carolina State University

This list of courses shows the breadth and depth of academic courses available to graduate students in plant breeding at North Carolina State University. Most of the courses listed are included in the training programs of at least one or more of the plant breeding graduate students at NC State.

Core Plant Breeding Courses

For MS and PhD degree candidates

CS, GN, HS 541 – Plant breeding methods (3 cr)

Instructors: Jeff Dunne
Prerequisite: ST 511, Co-requisite: ST 512

Overview of plant breeding methods for advanced undergraduate and beginning graduate students. Covers principles and concepts of inheritance, germplasm resources, pollen control, measurement of genetic variances, and heterosis. Special topics include heritability, genotype-environment interaction, disease resistance, and polyploidy. In-depth coverage on methods for breeding cross-pollinated and self-pollinated crops. Prepares students for advanced plant breeding courses.

PP 755 – Plant Disease Resistance: Mechanisms and Applications (3 cr)

Instructor: Alejandra Huerta
Prerequisite: Basic Undergraduate Level Genetics Class (GN 311) and an Introductory Plant Pathology Course (PP315, PP318 or PP501) or equivalent course.

Objectives: In this course, we will discuss the genetic and biochemical concepts underlying plant disease resistance and the tools and techniques used to introduce desirable levels of disease resistance into new crop cultivars, including conventional and modern breeding techniques and genetic engineering. We discuss responses of plant pathogen populations to the host resistance, and strategies to maximize the durability of resistance. Lastly, methods of breeding for disease resistance will be discussed.

HS 703 – Breeding asexually propagated crops (1 cr)

Instructors: Craig Yencho
Prerequisite: CS 413

Principles and problems associated with breeding clonally propagated crops and techniques used in overcoming these problems. Taught third five weeks of semester. Drop date is by last day of 3rd week of min-course.

CS, GN, HS 720 – Molecular biology in plant breeding (3 cr)

Instructor: Wusheng Liu
Prerequisite: CS 211, GN 311, PB 421

Theory and principles of molecular biology applied to plant breeding. Understanding of the relationship between genes and crop traits. Principles and molecular mechanisms of crop traits, and their applications to solve breeding problems and improve crop traits, which include heterosis, male/female sterility, self-incompatibility, polyploidy, double haploid, protoplast fusion, random mutagenesis, plant regeneration, transgenic breeding, advanced genome editing for breeding, gene silencing, gene activation, gene drive, plant synthetic biology, metabolic engineering, epigenetics for trait improvement, gene stacking, decoy and R genes, and bioconfinement.

For PhD degree candidates

CS, GN, HS 746 – Cytogenetics in Plant Breeding (2 cr)

Instructor: Vasu Kuraparthy

Prerequisites: For CS 746, students are expected to be familiar with basic concepts and principles of eukaryotic genetics including Mendelian inheritance, linkage and mapping, chromosomal mutation and variation, population genetics and evolution, DNA structure and function, mutation, and gene regulation.

Objectives: This course is intended to provide an introduction to plant genetics and cytogenetics, aneuploidy, polyploidy, haploidy, and recombination to relate the cytogenetics techniques and research discoveries to plant breeding methods, including wide hybridizations and an introduction to linkage mapping and DNA based markers.

CS, GN, HS 860 – Plant breeding laboratory (1 cr)

Instructor: Carlos Iglesias
Prerequisite: CS (GN, HS) 741

Visitation of plant breeding projects in the Depts. of CS and HS at NC State, along with commercial seed companies. Discussion and viewing of breeding objectives, methods and equipment and teaching and practice of hybridization methods.

CS, GN, HS 861 – Plant breeding laboratory (1 cr)

Instructor: Carlos Iglesias
Prerequisite: CS (GN, HS) 860

Continuation of CS, GN, HS, 860. Visitation of plant breeding projects in the Departments of CS and HS at NC State, along with commercial seed companies. Discussion and viewing of breeding objectives, methods and equipment and teaching and practice of hybridization methods.

Quantitative Genetics Graduate Courses

FOR 728 – Quantitative Forest Genetic Methods (3 cr)

Instructors: Juan Acosta, Gary Hodge, Fikret Isik
Prerequisite: ST 511.

Individual students or groups of students, under the direction of a faculty member, may explore topics of special interest not covered by existing courses. The format may consist of readings and independent study, problems, or research not related to the thesis. Also used to develop and test new 700-level courses.

ANS/CS/FOR 726  – Advanced Topics In Quantitative Genetics and Breeding (3 cr)

Instructors: Christian Maltecca, Fikret Isik
Prerequisite: ST 512.

Topics in genetics pertinent to population improvement for quantitative and categorical traits with special applications to plant and animal breeding. DNA markers – phenotype associations. The theory and application of linear mixed models, BLUP, and genomic selection using maximum likelihood and Bayesian approaches. Pedigree and construction of genomic relationships matrices from DNA markers and application in breeding. The concepts and practical applications of genetic data analysis in plant and animal breeding.

GN 703 – Population and Quantitative Genetics (3 cr)

Instructor: Dahlia Nielsen
Prerequisite: GN 311 and ST 512.

Mutation and origin of genetic variation. Measuring genetic variation in natural populations. Gene and genotype frequencies. Hardy-Weinberg equilibrium. Values, means, genetic and environmental variance, heritability of quantitative traits. Random genetic drift and inbreeding. Natural and artificial selection. Theory and tests of models of maintenance of genetic variation. Molecular evolution of genes and proteins. Genome evolution.

ST 757 – Quantitative Genetics Theory and Methods (3 cr)

Instructor: Zhao-Bang Zeng
Prerequisite: ST 511

The essence of quantitative genetics is to study multiple genes and their relationship to phenotypes. How to study and interpret the relationship between phenotypes and whole genome genotypes in a cohesive framework is the focus of this course. We discuss how to use genomic tools to map quantitative trait loci, how to study epistasis, how to study genetic correlations and genotype-by-environment interactions. We put special emphasis in using genomic data to study and interpret general biological problems, such as adaptation and heterosis. The course is targeted for advanced graduate students interested in using genomic information to study a variety of problems in quantitative genetics.

ANS 713 – Quantitative Genetics and Breeding (3 cr)

Instructor: Christian Maltecca
Prerequisite: GN 509, ST 512

Quantitative and population genetic theory of breeding problems; partitioning of genetic variance, maternal effects, genotype by environment interaction and genetic correlation; selection indexes; design and analysis of selection experiments; marker-assisted selection.

Data Analytics Graduate Courses

ST 511 – Statistical Methods for Researchers I  (3 cr)

Prerequisite: Graduate Training

Basic concepts of statistical models and use of samples; variation, statistical measures, distributions, tests of significance, analysis of variance, and elementary experimental design, regression and correlation, chi-square.

ST 512 – Experimental Statistics for Biological Sciences II (3 cr)

Prerequisite: ST 507 and ST 511

Covariance, multiple regression, curvilinear regression, concepts of experimental design, factorial experiments, confounded factorials, individual degrees of freedom, and split-plot experiments. Computing laboratory addressing computational issues and the use of statistical software.

ST 503 – Fundamentals of Linear Models and Regression (3 cr)

Prerequisite: ST 501 and MA 405

Estimation and testing in full and non-full rank linear models. Normal theory distributional properties. Least squares principle and the Gauss-Markov theorem. Estimability, analysis of variance, and covariance in a unified manner. Practical model-building in linear regression including residual analysis, regression diagnostics, and variable selection. Emphasis on the use of the computer to apply methods with data sets. Credit not given for both ST 552 and ST 503.

ST 590 – Bioinformatics I (3 cr)

Instructors: Gavin Conant
Prerequisite: Graduate status

Almost every aspect of modern biology involves large-scale datasets and computational analyses.  In this course, we will cover some of the basic theoretical and practical background needed to understand and use computational tools for biological analyses. The course will feature a mixture of lecture, activity-based, and hand-on computational analyses using the LINUX operating system. Among other topics, students will learn to: a)  Explain the different ways in which computing is used in modern biology; b)  Differentiate between computing approaches that automate tasks, perform statistical analyses, and make evolutionary inferences, c) Define biological homology, orthologs, and paralogy, d)  Explain the factors that make genome assembly a challenging problem, e) Explain the basic algorithm and assumptions of pairwise sequence alignment,  f) Discuss various methods of phylogenetic analysis, and g) Understand the concept of a biology network and explain why this concept represents an abstraction. From a practical perspective, students will learn a) The operation of basic sequence assembly software, b) How to perform sequence database searches with BLAST, c) how to calculate diversity indices including evolutionary distances and measures of nonsynonymous and synonymous divergence in protein-coding sequences d) Read mapping of RNASeq datasets to a reference genome and e)  Limited script creation in Perl.

GGS 771 – Data Science for Genetics & Genomics (3 cr)

Instructor:  Joe Gage

This graduate course is part of a first-year training program for Genetics & Genomics Scholars and provides a broad understanding of how to apply principles of data science to large multi-faceted datasets that are central to modern-day genetics and genomics. The students will focus on the application of these principles in the analysis of genetics and genomic data. Students will develop basic skills for reproducible research, including project organization, version control and test-evaluate-diagnose development. While exploring the universe of genetic and genomics analysis packages, the students will focus on the R data-science platform. They will develop their skills in common genetics and genomics analyses, including RNA-seq differential expression and population genetics statistics. The final product for the course will be a collaborative, small group project consisting of a defined analysis of a genetic or genomic dataset of their choice.

CS 590 – Special Topics (Programming and Data Science for Applied Research) (1 cr)

Instructor: Jeff Dunne
Prerequisite: None

This graduate-level course is designed to provide students with an introductory and advanced programming foundation, data manipulation and visualization skills, and a brief overview and understanding of machine learning algorithms and predictive analytics within the R and Python programming languages. Topics covered within the framework of the course include data types and structures, matrix/dataframe operations, importing and exporting database files, conditional programming (loops and functions), data science for R and Python (Tidyverse, Numpy, Pandas, etc.), data visualizations for R and Python (ggplot2, matplotlib, seaborn, plotly, cufflinks, etc.) and machine learning algorithms including, but not limited to linear and logistic regression; K nearest neighbor; decision trees and random forests; principal components and K-means clustering; and neural networks. In addition to these topics, students are exposed to Anaconda (R and Python) and environment image rendering in Binder/Docker; developing and maintaining GitHub repositories; and data visualizations in Tableau.

Other Courses of Interest