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Printable Handouts
Navigable Slide Index
- Introduction
- Much of the basis of complex traits is noncoding
- GWAS hits due to non-synonymous variants
- eQTLs: expression Quantitative Trait Loci
- How do genetic variants influence expression?
- How do SNPs impact gene regulation?
- One major source of eQTL data is from GTEx
- HapMap cell lines as a model system
- RNA-seq studies identified thousand cis-eQTLs
- Example cis-eQTL from HapMap samples
- Some eQTLs affect individual exons only
- What is the molecular basis for cis-eQTLs?
- Genotype correlates with steady state expression
- Which site is causal
- Most eQTLs lie inside or very near target genes
- eQTLs affect regulators of chromatin function
- DNaseI sequencing
- DNase-seq in 70 HapMap cell lines
- Example dsQTL
- dsQTL due to disruption of an NF-KB binding site
- Binding is virtually eliminated from one haplotype
- dsQTL SNPs function
- Promoter SNP at SNX7 drives chromatin changes
- DNaseI sensitivity at dsQTLs and TF occupancy
- DNaseI sensitivity and nucleosome occupancy
- Causal links from DNA to chromatin function
- Disrupting transcription factor binding sequences
- Do TF affects histones?
- Histone marks
- PWM changes and allele-specific mark changes
- Model: closed and open configurations
- How do dsQTLs affect promoters and expression?
- How a SNP in SLFN5 affects DNaseI sensitivity
- This dsQTL also impacts expression of SLFN5
- dsQTLs drive remote chromatin activation
- SNPs, chromatin architecture and mRNA levels
- eQTLs and their effects on proteins
- Ribosomal profiling and mass spec
- Most eQTLs are preserved at protein level
- The effect sizes are smaller on protein
- Protein-specific QTLs act post-translationally
- Summary
- Acknowledgments
Topics Covered
- Expression Quantitative Trait Loci: linking genetic variation to changes in gene regulation
- How do SNPs impact gene regulation
- HapMap cell lines as a model system for studying expression variation
- eQTL analysis
- dsQTL
- Histone marks
- Protein-specific QTLs
Talk Citation
Pritchard, J. (2015, April 21). Genetic variation in gene regulation [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved December 21, 2024, from https://doi.org/10.69645/RYXB3148.Export Citation (RIS)
Publication History
Financial Disclosures
- Dr. Jonathan Pritchard has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.
Other Talks in the Series: Human Population Genetics II
Transcript
Please wait while the transcript is being prepared...
0:00
My name's Jonathan Pritchard.
I'm at Stanford University.
Today I'm going to be talking about
genetic variation
in gene regulation.
0:08
We now know that a lot of
the genetic basis of complex
traits
is noncoding, and presumably
this is because of variants
that are affecting gene regulation,
as opposed to variants
that are affecting protein
coding sequences.
So just as one example,
the figure here shows the results
of a genome-wide association study
for Crohn's disease.
The dots on the figure
show the strength of signal
for association between individual
SNPs and risk of Crohn's disease.
Down below, you can see
the locations of coding regions
in yellow.
What you can see is that there's
a very significant
region of association
for Crohn's disease;
however, this lies outside
any known genes.
And in a case like this,
presumably what's going on
is that there is a SNP
in this region
that's affecting a regulatory
element that drives
regulation
of one of those genes marked
in yellow
in such a way that it affects
risk for disease.
And so it's become clear
during the last few years
that this is a major mechanism
by which genetic variation
affects complex traits,
and so there's been a great deal
of interest
in trying to understand
how regulatory variants work
and how we detect them
and understand them.
1:23
So what we know now
is that only a minority
of genome-wide association hits
are due to non-synonymous
variants.
This is a figure here
from a paper by Joe Pickrell
in 2014
where he estimates the fraction
of associated SNPs
that are non-synonymous, i.e.,
that they're changing protein
coding sequences.
And you can see that
all of the traits in this study,
approximately between 3% and 20%
of the association hits
that were discovered,
are due to non-synonymous variants,
and this suggests that
the large majority
of genome-wide association hits
are due to regulatory variation.