Quantitative genetics course

Innovation Lab for Crop Improvement - QuickWin project
May 20th - June 10th, 2021

Theoretical section

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The Molecular Basis of Genetics

DNA, central dogma of biology, mitosis, meiosis, and inheritance.


Slides
Suggested reference

Lecture 1
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Population Genetics

Allele frequencies, Hardy-Weinberg equilibrium, chi-squared test, inbreeding.


Slides
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Lecture 2
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The Inheritance of Complex Traits

Quantitative variation, genetic model, heritability.

Slides
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Lecture 3


Week 1: Lectures 1-3



Discussion 1
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Values and Means in a Single Locus

Genetic model, additivity and dominance, population mean, average allele effect.

Slides

Lecture 4
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Quantitative Trait Loci

Methods for mapping QTLs, experimental results, from QTL to gene, pros and cons.


Slides
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Lecture 5
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Association Mapping

Methods for association mapping, fixed and random variables, multiple test corrections, pros and cons.

Slides
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Lecture 6


Week 2: Lectures 4-6



Discussion 2
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Selection with QTL

Steps to use QTL in plant breeding.



Slides

Lecture 7
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Genomic Selection (GS) Part 1

Framework for GS, basic concepts, training and testing populations.

Slides
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Lecture 8
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Genomic Selection (GS) Part 2

GS application, factors affecting predictive ability, GS with major QTL.

Slides
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Lecture 9


Week 3: Lectures 7-9



Discussion 3


Hands-on section (in R)

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Introduction to R and RStudio

RStudio & Data in R, data manipulation, data visualization, get help.


R tutorial
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Single Allele Model

Simulate alleles with additive and dominant effects.

R code

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Multiple Allele Models

Simulate genotypes, phenotypes and QTL in a population.

R code

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Genomic Selection

Estimate prediction accuracy and use fixed QTL.

R code



contact

Instructor
Paulo Izquierdo – Michigan State University
Email: izquier7@msu.edu

Facilitator
Karen Cichy - USDA-ARS, Michigan State University
Email: karen.cichy@usda.gov

Organizers
Clare Mukankusi - The Alliance of Bioversity and CIAT
Email: c.mukankusi@cgiar.org
Stanley Nkalubo - National Agricultural Research Organization
Email: tamusange@gmail.com