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 the 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


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.

BIT 815 - Advanced Special Topics (Deep Sequencing Data Analysis) (3 cr)

Instructor:  Ross Whetten Prerequisite: BIT510 This course is designed to introduce biologists to the Linux command-line computing environment, to cloud computing, and to open-source software for analysis of next-generation sequencing data. The importance of cloud and cluster computing is emphasized, due to the increasing demands for RAM and storage space required for analysis and storage of high-throughput DNA sequencing data. Applications of sequencing discussed include genome sequencing (both de-novo and resequencing), transcriptome analysis, the discovery of sequence and structural variations, ChIP-seq methods for mapping DNA-protein interactions, and genotyping by sequencing (GBS and RAD-seq methods).

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.  

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