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Printable Handouts
Navigable Slide Index
- Introduction
- Genetic epidemiology, 2005 onwards
- Genome-wide association analyses, 2005 onwards
- The advantages of genome-wide association
- Genome-wide association studies (GWAS)
- Published genome-wide associations
- Cumulative number of obesity susceptibility loci
- Results of latest GWAS for BMI
- GWAS results for WHRadjBMI
- Further insight into GWAS for BMI
- Effect size of 32 BMI loci (white adult Europeans)
- ‘Adult’ BMI loci do not influence birth weight
- ‘Adult’ BMI loci influence childhood BMI
- Life course effects of the FTO and near MC4R loci
- Cumulative effects attenuated by healthy lifestyle
- Explained variation and predictive value is limited
- From common loci to causal/functional gene/variant
- BMI-associated loci; food intake regulation
- WHRadjBMI-associated loci; peripheral pathways
- Is FTO the obesity gene? (1)
- Is FTO the obesity gene? (2)
- GWAS for body fat percentage
- Body fat percentage accurately assesses adiposity
- Near-IRS1 locus & measures of body composition
- The near-IRS1 locus and disease risk
- The near-IRS1 locus and fat distribution (CT data)
- The near-IRS1 locus & functional implications
- The near-IRS1 locus (summary)
- Conclusion: GWAS
- Future research: low-frequency rare variants
- Overall conclusion and summary
Topics Covered
- Obesity and genome-wide association (GWAS)
- GWAS for body-mass index
- The obesity gene
- GWAS for body-fat percentage
- The near-IRS1 locus
- Future research
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Talk Citation
Loos, R. (2015, November 30). Genetic epidemiology of obesity 2 [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved October 8, 2024, from https://doi.org/10.69645/NQMZ6130.Export Citation (RIS)
Publication History
Financial Disclosures
- Prof. Ruth Loos has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
Genetic epidemiology of obesity 2
Published on November 30, 2015
37 min
Other Talks in the Series: Obesity: Science, Medicine and Society
Transcript
Please wait while the transcript is being prepared...
0:04
Moving on to a new era,
it's 2005.
So in 2005
there was a major change.
And again, that change
was driven through
advances in technology.
0:16
In 2005,
the genome-wide association
approach was introduced
and genome-wide association
as oppose to
genome-wide linkage
allows you to identify
more exact chromosomal locations
because you can screen
a genome with higher resolution.
0:34
Genome-wide association
is also hypothesis generating
but it has a much
higher resolution
than genome-wide linkage.
It's always designed
as a two-stage design
as opposed to
genome-wide linkage.
It has a discovery stage
and whatever is discovered
needs to be replicated
within the same study.
And I'll come back to that
in the next slide.
And genome-wide
association studies
turn out to have
large sample sizes.
And the reason for that
is that very early on
genome-wide association
studies were very expensive.
The chips that where designed
to do these genotyping
were extremely expensive,
so people realized that
to get a return
on their investment
they needed to collaborate.
And that has lead to currently
very large collaborations
in large consortiums.
Genome-wide
association is the result
of advances in technology.
Predominately the advances
in the genotyping technology
which allowed to generate
catalogues
of genetic variation.
And I'm showing you
a few pictures
of the Human Genome Project,
the HapMap project,
the 1000 genomes project.
These are projects that
have generated catalogues
of genetic variations
throughout the year.
So the Human genome project
was the oldest
and then international HapMap
and 1000 genomes are more recent
and more complete ones.
But these catalogues
have allowed companies
to develop these high-throughput
genotyping SNP chips.
You see these pictures
of these chips
that allow you to genotype
hundreds, thousands,
up to millions of variants
in a very short time period.