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Printable Handouts
Navigable Slide Index
- Introduction
- Accidental individuality (1)
- Partitioning phenotypic variation
- Road map for exploring developmental plasticity
- Undetected genetics, unmeasured environments
- Saturation of the conventional genetic model
- Twins, identical versus non-identical, raised together or apart
- Minnesota Study of Twins Raised Apart (MISTRA): Personality questionnaire
- MISTRA: Metabolic traits
- Unobserved epigenetics
- The apparent immutability of chance
- Coat color and ‘developmental irregularities’
- An example of chance, depending on G and E
- Quote
- Chance
- Analytical issues
- Issues: The meanness of biology
- Analytical issues
- Variability
- Analysis of variance
- A model for developmental plasticity
- Probabilistic developmental epigenetic model
- Small random epigenetic changes lead to developmental variability
- Model: Probabilistic developmental epigenetics
- Questions about the three dimensions
- Testing the model
- Diet-induced obesity
- A measure of noise, MAD
- Mean and MAD: Ggenes, diet, interactions
- Can we dissect the biology of Chance
- Chromosome Substitution Strains (CSSs)
- Mean and MAD among CSSs
- Summary for a 2.3 Mb segment of Chr6
- Early lessons for the 3rd dimension
- Why is plasticity important?
- Precision medicine – predict outcomes?
- Accidental individuality (2)
- Acknowledgements
Topics Covered
- Road map for exploring developmental plasticity
- Undetected genetics and unmeasured environments
- Unobserved epigenetics
- Analytical issues
- Variability
- Analysis of variance
- A model for developmental plasticity
- Probabilistic developmental epigenetic model
- Testing the model
- The importance of plasticity
Talk Citation
Nadeau, J. (2022, July 31). Developmental plasticity: the 3rd dimension of phenotypic variation and disease risk [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 6, 2024, from https://doi.org/10.69645/EGUF2000.Export Citation (RIS)
Publication History
Financial Disclosures
- Dr. Joseph Nadeau has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
Developmental plasticity: the 3rd dimension of phenotypic variation and disease risk
Published on July 31, 2022
46 min
A selection of talks on Methods
Transcript
Please wait while the transcript is being prepared...
0:00
I'm Joe Nadeau from the
Maine Medical Center
Research Institute
in Portland, Maine.
The topic of this presentation
is developmental plasticity,
the 3rd dimension of phenotypic
variation and disease risk.
0:14
What if our understanding
of human biology
is missing a primary
determinant of who we are?
We want to understand how we came to be the
way we are- our form, and our function.
Both our shared features
that define us as humans,
and the differences that
make us individuals.
Similar questions can be
asked not only for us
but also for all lifeforms;
even bacteria show
individuality.
I will argue this in
this presentation
that the chance events
and accidents occurring
early in development put
our lives on distinct,
and seemingly
unpredictable trajectories
towards alternative
phenotypic outcomes.
I will discuss the
background of this problem.
The evidence that the
conventional models
are limited; the evidence
for this 3rd dimension,
a model for how this
dimension might
act in concert with genes
and the environment.
Then, the first principles
of what we've discovered in
experimental model systems that
are relevant to common
human conditions,
where mechanisms can be defined,
and models tested
experimentally.
1:16
The pioneering work
of Sir Ronald Fisher,
more than a century ago
established the principles
that have guided genetic studies
in all species since then.
He argued that our
phenotypes, P,
are the result of the
combined action of genes, G,
and environment, E.
And then e,
which is a measure of the
error in our measurement;
either measurement error or the inherent
noise of the biological system.
This can also represent
the phenotypic variation,
the V is the variance,
which is equal to the genetic variants plus the
environmental variance, plus the error variance.
The problems with this model
are that it's additive,
it's linear, and
it's deterministic.
It's a good model to start as
an initial focus to develop
theories, and model systems.
But it probably doesn't reflect
the full repertoire of the
complexity in biologic systems.
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