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Printable Handouts
Navigable Slide Index
- Introduction
- Overview
- G→P models II
- G→P models, Classical phenotypic selection
- G→P models (for main effect genotype)
- Modelling mean & VCOV for MET data
- Two-way fixed genotype x environment interaction
- Models for (genetic) variances & correlations
- Modeling mean & VCOV for GxE data
- QTL mapping as example G-P mapping
- Basic building block of QTL mapping - IBD
- A regression model for QTL detection
- QTL mapping, two problems
- Marker regression, SIM & CIM
- Multi-environment QTL mapping & prediction
- GxE model: Finlay Wilkinson regression
- Finlay Wilkinson: regression on the mean
- From ANOVA to Finlay Wilkinson model
- Interpretation of FW model parameters
- Barley example
- Genotype-specific reaction norm curves
- FW analysis of variance
- Conclusions barley example
- Predicting with the FW model
- Predictions from FW model
- Example of MET QTL mapping
- Multi environment trial QTL analysis CIMMYT
- Maize phenotypic data & genetic predictors
- Numerical & graphical summaries per env.
- Correlation structure between environments
- CIMMYT: QTL+QTLxE analysis for yield
- Maize data: environmental characterizations
- Regression of QTLxE on min. temperature
- QTLxE example
- Summary
Topics Covered
- G→P models II
- QTL mapping as an example of a G→P model
- Example of GxE analysis: Finlay Wilkinson regression
- Example of MET QTL mapping
Links
Series:
Categories:
Talk Citation
van Eeuwijk, F. (2016, April 27). Introductory statistical genetics for plant breeding 2 [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 22, 2024, from https://doi.org/10.69645/VMEJ9032.Export Citation (RIS)
Publication History
Financial Disclosures
- Prof. Fred van Eeuwijk has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
Introductory statistical genetics for plant breeding 2
Published on April 27, 2016
62 min
Other Talks in the Series: Statistical Genetics
Transcript
Please wait while the transcript is being prepared...
0:04
An overview of topics
that we will be dealing with,
we continue in deeper view
on what genotype
to phenotype models are,
and then we get to examples
of these genotype
to phenotype models.
We will talk about QTL mapping,
QTL mapping strategies
as an example
of how to use genotype
to phenotype models.
We will look at an analysis
of genotype-environment
interaction
via a model that is called
Finlay Wilkinson model,
Finlay Wilkinson regression.
And then we will finish
with an example
of what we call multi
environment trial QTL mapping
in which we use rather
a advanced statistical model
to find out which g10 QTLs
are driving
our phenotypic variation.
0:45
Okay, let's start
with our next section,
in a sense this is the most
important section,
I think, of the talk.
I'm going to talk in more detail
about a number of classes
of genotype to phenotype models.
0:58
So on this slide, at the bottom,
we see a model formula
in which the genotypic
main effect
is covered in red
and the other terms refer,
let's say, to the response
that we can observe
for a particular plot
in an environment j
or trial j or a genotype i.
And then at each
of these trials,
we also have a randomized
complete block design,
so we have an extra term called
bk(j) in which, let's say,
there is a block
that is nested within a trial.
So if we take the whole
of this formula,
we have an interceptor Mu,
we have
the environmental main effect,
let's say, a trial intercept,
we have an effect
for the block inside the trial,
we have genetic effect,
and we have a final error term.
And for breeding purposes,
it's specially this genetic
main effect which is important.
Now in this case, on purpose,
I did not include
a specific term
for the genotype-environment
interaction,
so idea is here that we have
a number of trials
that will allow us to make
selection out
of a number of genotypes.
And we focus, in this case,
on the average effect
across those different trials.
The formula is also stated
in words,
you can read at yourself.
Phenotype = Environment + Block
within Environment
+ Genotype + Error.
And so typically, if breeders
perform a set of trials,
there after selecting
the genotype
with the best underlying
genetic effect,
this is also called
the breeding value
in many contexts.