- Genetics & Plant Breedingbharsar Students Fall
- Genetic Breeding Techniques
- Genetics & Plant Breedingbharsar Students List
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Plant Breeding Graduate Courses
Academic Courses for Training Plant BreedingGraduate 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
- For MS and PhD degree candidates
- CS,GN,HS 541 Plant breeding methods
- HS 703 Breeding asexually propagated crops
- CS,GN,HS 720 Molecular biology in plant breeding
- For PhD degree candidates
- CS,GN,HS 746 Breeding methods
- CS,GN,HS 860 Plant breeding laboratory
- CS,GN,HS 861 Plant breeding laboratory
- PP,CS,GN,HS 590D/790D Plant disease resistance mechanisms and breeding
Quantitative Genetics Graduate Courses
FOR 728 - Quantitative Forest Genetic Methods (3 cr)
Instructors: Juan Acosta, Gary Hodge, Fikret IsikPrerequisite: 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 IsikPrerequisite: 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 NielsenPrerequisite: 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 ZengPrerequisite: ST 511The 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 MalteccaPrerequisite: GN 509, ST 512Quantitative 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 TrainingBasic 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 511Covariance, 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 405Estimation 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 ConantPrerequisite: Graduate statusAlmost 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 WhettenPrerequisite: BIT510This 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 DunnePrerequisite: NoneThis 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.Recommended Plant Breeding Courses
- Animal Science (ANS course list)
- Biotechnology (BIT course list)
- BIT 510 Core technologies in molecular and cellular biology
- BIT 562 Microarrays
- BIT 565 Real Time PCR techniques
- BIT 567 PCR techniques / DNA fingerprinting
- BIT 568 Genome Mapping
- BIT 581 Plant tissue culture & transformation.
- Crop Science (CS course list)
- CS 685/CS 885 MS/PhD supervised teaching
- CS 690/CS 890 MS final/PhD preliminary examination
- CS 695/CS 895 MS thesis/PhD dissertation research
- Entomology (ENT course list)
- ENT 762 Insect pest management in agricultural crops
- ENT 791R Insects and plants
- Forestry and Environmental Resources (FOR course list)
- FOR,GN 725 Forest genetics
- FOR 727 Tree Improvement Research Techniques
- Genetics (GN course list)
- GN 513 Advanced genetics
- GN 701 Molecular genetics
- GN 702 Cellular and developmental genetics
- GN 756 Computational molecular evolution
- Horticultural Science (HS course list)
- HS 685/HS 885 MS/PhD supervised teaching
- HS 690/HS 890 MS final/PhD preliminary examination
- HS 695/HS 895 MS thesis/PhD dissertation research
- Plant Pathology (PP course list)
- PP 500 Plant disease: principles, diagnosis and management
- PP,PB,MB 501 Biology of plant pathogens
- PP,CS,HS 502 Plant disease: methods and diagnosis
- PP 504 Plant nematology
- PP 506 Epidemiology and plant disease control
- PP 507 Plant-microbe interactions
- PP 715 Applied evolutionary analysis of population genetic data
- PP 730 Fungal genetics & physiology
Other Courses of Interest
- Agricultural and Human Sciences (AEE course list)
- AEE 735 Effective teaching in agricultural and life sciences
- Biological & Agricultural Engineering (BAE course list)
- BAE 590 Biomass to renewable energy processes
- Crop and Soil Sciences (CS course list)
- CS 620B/820B Issues in Bioethics
- CS 713 Physiological aspects of crop production
- CS 714 Crop physiology
- English (ENG course list)
- ENG 610A Advanced writing for academic research
- Horticultural Science (HS course list)
- HS 601 Seminar techniques and technology
- HS 701 Carborhydrate metabolism
- HS 702 Plant hormones
- HS 703 Breeding asexually propagated crops
- HS 704 Plant nomenclature
- HS 705 Physiology of flowering
- HS 706 Postharvest physiology
- HS 707 Environmental stress physiology
- HS 717 Weed management systems
- HS 790A Plant hormone biology
- HS 790C Plant metabolism
- Business Management (BUS course list)
- BUS 562 Research Methods in Marketing
- BUS 564 Project Management
- BUS 590 Business management for non-majors
- BUS 590G Leadership & Ethics
- Plant and Microbial Biology (PB course list)
- PB 580 Introduction to Plant Biotechnology
- PB 733 Plant growth and development
- PB 751 Advanced plant physiology I
- PB 752 Advanced plant physiology II
- PB 754 Lab for advanced plant physiology II
- PB 780 Plant molecular biology
- PB 795F Plant form and function
- PB 795G Plant form and function lab
- Graduate Student Handbook (revised Spring 2018/19)
- Course Schedule - updated October, 2019
- Doctoral Program Coursework
Master's Program Coursework
Degree Program Requirements
M.S. students must have 30 credit hours to graduate comprised of:
21 hours of coursework, 12 of which are graduate level courses only (not from split undergraduate/graduate courses, e.g., 4100/6100). On the Program of Study form, please use * to designate 6000/7000 courses open only to graduate students.
9 hours of research (6) and thesis writing (3)
The following courses are required for graduation:
- one course of Research Seminar (PBGG/CRSS/HORT 8861)
- one course of Plant Breeding (PBGG/CRSS/HORT 6140)
- one course of Plant Breeding Practicum (PBGG/CRSS/HORT 6000)
- one Statistics course (STAT 6220 or STAT 6315 or FANR 6750 or higher)
M.S. students can only transfer 6 graduate course credits from another institution. No more than 6 hours of research (PBGG 7000) will be counted as hours on your program of study.
Core Courses
A list of our core courses are below along with suggested electives. Students will work with their Major Professor and Advisory Committee to determine the best program of study.
Plant Breeding Courses:
Genetics and Cytogenetics Courses:
Plants and Their Environment Courses:
Biometry and Bioinformatics Courses:
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- STAT 6315. Statistical Methods for Researchers
Biochemical and Molecular Genetics Courses:
Electives for professional development:
- PBIO(PBGG) 8020. Essential skills for graduate students
- 2 Classes in August & Classes from October till end of semester
Ph.D. Program Requirements
Ph.D. (M.S. already earned) – students must have 30 credit hoursto graduate comprised of:
16 hours of coursework (a minimum of 16 hours of 8000-9000 level credits) excluding 9000/9005/9300
3 hours of dissertation writing (9300)
6 hours of research (PBGG 9000) and the remainder in research or relevant lower level courses
Genetics & Plant Breedingbharsar Students Fall
The following courses are required for graduation:
- two credits of Graduate Seminar (PBGG/CRSS/HORT 8860 & PBGG/CRSS/HORT 8861)
- Advanced Plant Breeding (PBGG/CRSS/HORT 8140)
- Plant Breeding Practicum (PBGG/CRSS/HORT 6000)
- Plant Genetics (PBIO 6500 or PBIO 8100 or PBGG 8890 or comparable)
- Statistics (PBGG 8010 or STAT 8200 or comparable)
Ph.D. students can only transfer 9 graduate course credits from another institution after beginning at UGA.
Straight Ph.D. (no M.S. required) – students must have 36 credit hoursto graduate comprised of:
27 hours of coursework (a minimum of 16 hours of 8000-9000 level credits), and including MS/PhD requirements
9 hours (minimum) of research (6h) and writing(3h)
The following courses are required for graduation:
- two credits of Graduate Seminar (PBGG/CRSS/HORT 8860 & PBGG/CRSS/HORT 8861)
- Plant Breeding (PBGG/CRSS/HORT 6140)
- Plant Breeding Practicum (PBGG/CRSS/HORT 6000)
- Advanced Plant Breeding (PBGG/CRSS/HORT 8140)
- Plant Genetics (PBIO 6500 or PBIO 8100 or PBGG 8890 or comparable)
- Statistics (STAT6220 or STAT 6315 or FANR 6750 or higher)
- Statistics (PBGG 8010 or STAT 8200 or comparable)
Core Courses
A list of our core courses are below along with suggested electives. Students will work with their Major Professor and Advisory Committee to determine the best program of study.
Genetic Breeding Techniques
Plant Breeding Courses:
- PBGG (CRSS) (HORT) (PBIO) 8871. Genome Analysis and Comparative Mapping
- Weeks 6-10 Spring Semester Even Years
- PBGG (CRSS) (HORT) (PBIO) 8872. QTL Mapping and Discovery
- Weeks 11-15 Spring Semester Even Years
- PBGG (CRSS) (HORT) 8873. Transgenic Breeding
- Weeks 1-5 Fall Semester Odd Years
- PBGG (CRSS) (HORT) 8874. Genomic Selection (PENDING APPROVAL)
- PBGG (CRSS) (HORT) (PBIO) 8875. Genome-wide Association in Plants (PENDING APPROVAL)
- PBGG 9980. Graduate Internship in Plant Breeding, Genetics & Genomics (PENDING APPROVAL)
Genetics and Cytogenetics Courses:
Plants and Their Environment Courses:
Biometry and Bioinformatics Courses:
Biochemical and Molecular Genetics Courses:
Electives for professional development:
- GRSC 8550. Responsible Conduct of Research
- PBIO(PBGG) 8020. Essential skills for graduate students
- 2 Classes in August & Classes from October till end of semester
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