L32 Functional Human Genetics 2 Flashcards

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1
Q

GWAS hits

A

see onenote

  • Most GWAS hits aren’t causal but are in LD with causal variant
  • majority of GWAS hits are located outside protein coding regions
  • How do we prioritise each variant?
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2
Q

What does rs8050136 do?

A

see onenote

  • strong association and large effect on BMI, weight, T2D
  • located in intron of FTO gene
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3
Q

How do we know the genome works?

A

see onenote

  • Protein code - triplets
  • String of polypeptides
  • But most of the genome is not protein-coding genes - only 1.5% codes for proteins but the rest aren’t junk DNA, they have a function
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4
Q

From genotype to function

A

see onenote slides

  • functional genomics, deciphering how the genome actually works

transcriptomics
- Transcriptomics is also referred to as expression profiling, examines the expression level of RNAs in a given cell population

epigenomics
- Epigenomics is the study of the complete set of epigenetic modifications on the genetic material of a cell, known as the epigenome

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5
Q

Why do we measure RNA?

A
  • Easy to measure at high throughput
  • Close to DNA, which we understand fairly well
  • But RNA may be far removed from the ultimate phenotype - us

Two main technologies today:

  • Microarray
  • RNA seq
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6
Q

Expression microarrays

A

see onenote

  • Start with sample of RNA
  • if RNA molecule matches to known DNA probe, it will bind
  • 25bp DNA probes, we know the sequence of these probes
  • The more RNA that binds, the brighter the signal
  • Microarray is ultimately a fluorescent based technology
  • But you can only go so bright, you lose a bit of definition
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7
Q

RNA-seq

A

see onenote

  • Sequence every single of RNA in that sample
  • Map reads back to the genome
  • How many reads map to the 1st gene, 2nd gene etc.
  • Doesn’t depend on having the right probe, depends on the gene being in your sample
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8
Q

Microarrays vs RNA-seq

A
  • Microarrray
    ○ Shallow, fast, cheap overviews of expression across many individuals
    ○ Straightforward
    ○ Relies on known DNA probes, can miss genes that don’t have probes present
  • RNA seq
    ○ Unbiased, can be used to uncover new biology
    ○ Quantitative, more precision
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9
Q

What can we learn from gene expression levels?

A

see onenote

  • Cell-type specific, foundation of tissue specificity
  • Look at same tissue between healthy people vs patients
  • Tissues within people
  • Tissues within people across time
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10
Q

The human transcriptome

A
  • Fewer protein coding genes than we expected

- but transcriptionally complex, complexity must be somehow encoded in our DNA but how?

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11
Q

Large-scale quantification of human transcription: GTEx

A

see onenote

Genotype-tissue expression project

  • RNA-seq on 40+ human tissues across 400+ (deceased, healthy) humans
  • capture both intra- and inter-individual variation
  • Main lesson: human tissues are transcriptionally complex and fairly distinct
  • Differences between tissues don’t depend on a lot of genes
  • Specificity is encoded by a small number of genes in comparison to house keeping genes which are required is most cells and tissues
  • transcription can vary in association with age, sex, ethnicity etc.
  • Good reference source for QTL identification and GWAS annotation
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12
Q

Quantitative trait loci can impact expression levels

A

QTL - genetic region associated with a particular quantitative phenotype
- by this definition, all GWAS hits are QTLs

  • using RNA-seq we can measure the impact of individual SNPs on nearby gene expression levels, knows as eQTLs
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13
Q

How does eQTL impact expression levels?

A

see onenote

biological process of going from DNA to RNA to protein can be regulated at many different levels

  • eQTL testing requires both genotype and expression measurements in the same individual
  • Gene specific
  • When, where, how long, how many transcripts are made, how quickly are they made

Post-transcriptional regulation
- effects stability and localisation of transcripts

Identifying eQTLs brings us one step closer to understanding the cellular mechanism that underlie differences between individuals

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14
Q

Gene regulation simultaneously occurs at multiple levels

A

see onenote

  1. DNA methylation
  2. chromatin modifications
  3. DNAse 1 hypersensitive sites
  4. TF binding sites
  5. long range regulatory elements
  6. promoter architecture
  7. protein-coding and non-coding transcripts
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15
Q

Chromatin accessibility

A

see onenote

  • DNA coiling around histones => nucleosomes
  • euchromatin
  • heterochromatin, inaccessible
  • accessibility impacts whether a gene can be transcribed
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16
Q

Histone modification

A

see onenote

  • Histone modification of histone tails, different modifications can impact gene expression and chromatin structure
  • different modifications mean different things; some repress, activate, enhance
17
Q

TF binding

A

see onenote

  • for transcription to take place, a complex of proteins must interact with DNA e.g. GTF (RNA pol 2), TF (activators, repressors)
  • TFs tend to act additively or combinatorically
  • TFs can act locally or at a distance (promoters vs enhancers)
  • many TFs have preferred sequences they bind with high affinity
18
Q

ENCODE

A

see onenote slides

  • cataloguing regulatory diversity
  • RNA seq, ChIP seq, Dnase seq, bisulfite seq
19
Q

DNase-1 hypersensitivity

A

see onenote slides

  • DNase-1 will cleave DNA not bound around nucleosomes, DNAse-1 hypersensitive sites (DHS); good markers of open chromatin
  • 1 million of these sites are cell-type specific, much chromatin structure varies by cell type - they are context dependent
  • If DHS overlaps TF binding sites - Dnase won’t cut if DNA is bound to TF (not just if it’s bound to nucleosomes)
  • Can show whether TF is binding to TF binding site in a particular individual in comparison to another individual e.g. if T changes to C => TF doesn’t bind, loses gene expression
20
Q

What is function?

A
  • no widespread definition of function
  • just because something occurs does not mean it has function
  • just becomes some occurs often does not mean it always has function
21
Q

Beyond ENCODE: roadmap epigenomics

A
  • followup effort to characterise regulatory variation and mechanisms
  • profiled a lot of things across a lot of samples with a big focus on histone modifications
22
Q

Epigenetics

A

mean different things to different people

Our definition:
set of chemical modifications on DNA of a cell that are associated with changes in gene expression

23
Q

Regulating expression through fine-tuning chromatin

A

see onenote

  • identified 15 distinct chromatin states beyond traditional open/closed heterochromatin dichotomy
24
Q

Insights into gene regulation

A
  • regulation of gene expression involves interactions between multiple cellular mechanisms, sometimes at great physical distances from one another
  • still mostly correlation, not causation
25
Q

rs8050136

A
  • one of the early successes

- assumed it impacted FTO expression levels somehow since the phenotype made perfect sense

26
Q

FTO impacts BMI, regulatory variants within FTO impact BMI

A

see onenote

problem = variation in FTO expression could never be correlated to variation in genotypes at regulatory region identified by GWAS

rs8050136 turned out to the wrong SNP

rs9930506
- less significant, also not an eQTL for FTO but an eQTL for a nearby gene IRX3 which also impacts BMI