LECTURERS           DR F N MHLANGA [COORDINATOR]
                                 DR S M MAKUZA
                                 DR K DZAMA
                                 DR E BHEBHE

 COURSE OBJECTIVES

The course aims to expose students to aspects of Advanced Quantitative Genetics applied to animal breeding. By the end of this course, students should be able to i) construct animal models for analysis of animal breeding data, ii) estimate an animal's breeding value from field data, iii) design appropriate breed improvement strategies and iv) evaluate
different breed improvement strategies.

 PRE-REQUISITES:

        Crop Science and Genetics (CR101)
        Biometry (CR206)
        Livestock Improvement (AS 305)
        (equivalents of these courses are acceptable)

 COURSE ASSESSMENT:

 Continuous Assessment:                    30 %

This includes, quizzes, take home exams, in class tests, practicals
projects and seminars

Final Exam (3 hrs):                        70 %

The exam will be written at the end of the year and will consist of Eve  compulsory questions.
SUMMARY OF COURSE OUTLINE

  A. Review       4  h
  B. Linear Models               36 h
  C. Estimation of Variance Components          14 h
  D. Selection Index Theory                     12 h
  E. Quantitative Genetics                      12 h
  F. Molecular Genetics                         12 h
  G. Research Techniques in animal breeding     10 h

  Course Outline

The time allocated to this course is 100 hrs; 60 hrs of lectures and 40 hrs of practicals. Practicals include working on problems in class, computer labs, field trips and class presentations.

  A:       REVIEW       [4 h]          Dr F N Mhlanga

  i.      Mendelian Genetics
  ii.     Population Genetics
  iii.    Quantitative Genetics
  iv.     Livestock Improvement

  B:       LINEAR MODELS:

  1.      MATRIX ALGEBRA [6 h] Dr F N Mhlanga
Scalars, vectors, Matrix Dimensions, Square Matrices, Diagonal matrices, Symmetric matrioes, idempotent matrices,
transposition, matrix comformability. Matrix addition, subtraction, multiplication A, inversion, singular k non-singular matrices.

  2.      LINEAR MODELS: [36 h] [3 Projects]

  2.1     Model Building [4 h]         Dr F N Mhlanga
           True models, ideal models, operational models,
           Model Factors (fixed Sc random) and variables,     distributions, expectations

  2.2     Regression Models [8 h]
            Regression models, Normal equations, Unique Inverses, Best   Linear Unbiased Estimates
            (BLUE), estimability, test for   estimability, (co) variance of BLUE, test of hypotheses.

  2.3     Fixed Effects Models [8 h]
fixed classification models, covariate models, generalized inverses, biasedness, multifactor models with interaction, non-estimability of main sects, test of hypotheses
 

  2.4     Mixed Models         [8 h]                   Dr K Dzama
           Predictors - BP, BLP, BLUP
           Application of BLUP-Single 8c Multiple traits

  2.5     Animal Models [8 h]
           Individual Records, genetic relationships, relatives'    Numerator relationship matrix, Sire models, Reduced Animal   Model information
 

  C.      ESTIMATION OF VARIANCE COMPONENTS [14 H] Dr S M Makuza

  1.1     Henderson's Methods 1,2 3 A, 4
  1.2      Maximum Likelihood (ML)
  1.3      Restricted Maximum Likelihood(REML)
  1.4      MIVQUE, MINQUE
  1.5      Relationships among ML, REML, MIVQUE
  1.6      Convergence for iterative methods

  D.      SELECTION INDEX THEORY [12 h]  Dr S M Makuza [1 project]

  1.       True additve genetic merit (T), selection index (I), economic weights & derivation, genetic coeeficients, methods of predicting expected breeding values and genetic progress, comparison of selection programs, breeding goals, breeding plans, strategies, evaluation of breeding plans

  E.      QUANTITATIVE GENETICS [10 h] Dr F N Mhlanga / Dr SM Makuza
          [1 project]

  1.1     Large populations
          Random mating, Single k infinite loci genetic models
          Estimation of breeding values, estimation of genetic variance

  1.2     Small populations
          Random Mating, inbreeding, drift, fixation, disequilibrium,
          estimation of breeding values

  1.3     Selection
          sects on disequilibrium, inbreeding, fixation, estimation of      breeding values, estimation of genetic variances, response to
     selection.

  1.4  Heterosis

       - Non-additive genetic variation
       - Dominance deviation
       - Crossbreeding strategies and estimation of heterosis

  F.   MOLECULAR GENETICS  [12 h]      Dr E Bhebhe [1 project]

       Quantitative trait loci
       Major Genes
       Types of markers - RFLPs, RAPDS, VNTRs/Minisatellites,
           Microsatellites, AFLPs
       Marker Assisted Selection, Marker Introgression techniques,
           transgenesis
       Design of QTL studies - Daughter Designs, Granddaughter designs
       Analysis of QTL data- application of animal models and regression
           models.
       Laboratory methods: DNA Gel Electrophoresis

  G.   RESEARCH TECHNIQUES  [10 h] Dr F N Mhlanga / Dr S M Makuza
       [1 project]

       1. Handling Messy data
       2. Computing strategies
       3. Computer programs