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6.047/6.878/HST.507 Computational Biology: Genomes, Networks, Evolution. Lecture 15 Regulatory variation and eQTLs. Chris Cotsapas [email protected] Module 4: Population / Evolution / Phylogeny. L15/16: Association mapping for disease and molecular traits

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Lecture 15 regulatory variation and eqtls

6.047/6.878/HST.507Computational Biology: Genomes, Networks, Evolution

Lecture 15Regulatory variation and eQTLs

Chris Cotsapas

[email protected]


Module 4 population evolution phylogeny
Module 4: Population / Evolution / Phylogeny

L15/16: Association mapping for disease and molecular traits

Statistical genetics: disease mapping in populations (Mark Daly)

Quantitative traits and molecular variation: eQTLs, cQTLs

L17/18: Phylogenetics / Phylogenomics

Phylogenetics: Evolutionary models, Tree building, Phylo inference

Phylogenomics: gene/species trees, coalescent models, populations

L19/20: Human history, Missing heritability

Measuring natural selection in human populations

The missing heritability in genome-wide associations

And done! Last pset Nov 11 (no lab), In-class quiz on Nov 20

No lab 4! Then entire focus shifts to projects, Thanksgiving, Frontiers


Today regulatory variation and eqtls
Today: Regulatory variation and eQTLs

  • Quantitative Trait Loci (QTLs), Regulatory Variation

    • Molecular phenotypes as QTs: expression, chromatin…

    • Discretization: a GWAS for each gene. Cis-/Trans-eQTLs

    • Underlying regulatory variation: eQTLs, GWAS, cis-eQTL

  • Finding trans-eQTLs (distal from gene that varies)

    • Challenges: Power, structure, sample size

    • Cross-phenotype analysis: trans QTLs affect many genes

  • Identifying underlying regulatory mechanisms

    • Cis-eQTLs: TSS-distance, cell type specificity

    • eQTLs vs. GWAS: Expression as intermediate trait

  • Population differences, emerging efforts

    • Shared associations, SNP-gene pairs, allelic direction

    • Confound: environment, preparation, batch, ancestry


Quantitative traits weight height anything measurable today gene expression
Quantitative traits- weight, height- anything measurable- today: gene expression

QTLs (QT Loci)

- The loci that control quantitative traits


Regulatory variation
Regulatory variation

  • What do trait-associated variants do?

  • Genetic changes to:

    • Coding sequence **

    • Gene expression levels

    • Splice isomer levels

    • Methylation patterns

    • Chromatin accessibility

    • Transcription factor binding kinetics

    • Cell signaling

    • Protein-protein interactions

Regulatory


Basic concepts

History, eQTL, mQTL, others

Basic Concepts


Within a population
Within a population

  • Damervalet al 1994

  • 42/72 protein levels differ in maize

  • 2D electrophoresis, eyeball spot quantitation

  • Problems:

    • genome coverage

    • quantitation

    • post-translational modifications

  • Solution: use expression levels instead!


Usual mapping tools available
Usual mapping tools available

  • Discretization approach


Whole genome eqtl analysis is an independent gwas for expression of each gene

gene 4

gene 1

Whole-genome eQTL analysis is an independent GWAS for expression of each gene

gene 2

gene 3

gene N

gene 5


Genetics of gene expression eqtl
Genetics of gene expression (eQTL)

  • cis-eQTL

    • The position of the eQTLmaps near the physical position of the gene.

    • Promoter polymorphism?

    • Insertion/Deletion?

    • Methylation, chromatin conformation?

  • trans-eQTL

    • The position of the eQTLdoes not map near the physical position of the gene.

    • Regulator?

    • Direct or indirect?

Modified from Cheung and Spielman 2009 Nat Gen


E qtl the array era

yeast, mouse, maize, human

eqtl – the array era


Yeast
Yeast

  • Brem et al Science 2002

  • Linkage in 40 offspring of lab x wild strain cross

  • 1528/6215 DE between parents

  • 570 map in cross

    • multiple QTLs

    • 32% of 570 have cis linkage

  • 262 not DE in parents also map


Trans hotspots
transhotspots

Brem et al Science 2002


Yvert et al Nat Genet 2003


Mammals i
Mammals I

  • F2 mice on atherogenic diet

  • Expression arrays; WG linkage

Schadt et al

Nature 2003


Mammals ii
Mammals II

10% !!

Chesler et al Nat Genet 2005


Mammals iii
Mammals III

  • No major trans loci in humans

    • Cheung et al Nature2003

    • Monks et al AJHG 2004

    • Stranger et alPLoS Genet 2005, Science 2007


Today regulatory variation and eqtls1
Today: Regulatory variation and eQTLs

  • Quantitative Trait Loci (QTLs), Regulatory Variation

    • Molecular phenotypes as QTs: expression, chromatin…

    • Discretization: a GWAS for each gene. Cis-/Trans-eQTLs

    • Underlying regulatory variation: eQTLs, GWAS, cis-eQTL

  • Finding trans-eQTLs (distal from gene that varies)

    • Challenges: Power, structure, sample size

    • Cross-phenotype analysis: trans QTLs affect many genes

  • Identifying underlying regulatory mechanisms

    • Cis-eQTLs: TSS-distance, cell type specificity

    • eQTLs vs. GWAS: Expression as intermediate trait

  • Population differences, emerging efforts

    • Shared associations, SNP-gene pairs, allelic direction

    • Confound: environment, preparation, batch, ancestry


Where are the trans e qtls

Open question

Where are the trans eQTLS?


Whole genome eqtl analysis is an independent gwas for expression of each gene1

gene 4

gene 1

Whole-genome eQTL analysis is an independent GWAS for expression of each gene

gene 2

gene 3

gene N

gene 5


Issues with trans mapping
Issues with trans mapping

  • Power

    • Genome-wide significance is 5e-8

    • Multiple testing on ~20K genes

    • Sample sizes clearly inadequate

  • Data structure

    • Bias corrections deflate variance

    • Non-normal distributions

  • Sample sizes

    • Far too small


But…

  • Assume that transeQTLs affect many genes…

  • …and you can use cross-trait methods!



Cross phenotype meta analysis
Cross-phenotype meta-analysis

L(data | λ≠1)

SCPMA ~

L(data | λ=1)

Cotsapas et al, PLoS Genetics



Open research questions
Open research questions

  • Do trans effects exist?

    • Yes – heritability estimates suggest so.

    • Can we detect them?

  • Larger cohorts?

    • Most eQTL studies ~50-500 individuals

    • See later, GTEx Project

  • Better methods?

    • Collapsing data?

    • PCA, summary statistics, modeling?


Today regulatory variation and eqtls2
Today: Regulatory variation and eQTLs

  • Quantitative Trait Loci (QTLs), Regulatory Variation

    • Molecular phenotypes as QTs: expression, chromatin…

    • Discretization: a GWAS for each gene. Cis-/Trans-eQTLs

    • Underlying regulatory variation: eQTLs, GWAS, cis-eQTL

  • Finding trans-eQTLs (distal from gene that varies)

    • Challenges: Power, structure, sample size

    • Cross-phenotype analysis: trans QTLs affect many genes

  • Identifying underlying regulatory mechanisms

    • Cis-eQTLs: TSS-distance, cell type specificity

    • eQTLs vs. GWAS: Expression as intermediate trait

  • Population differences, emerging efforts

    • Shared associations, SNP-gene pairs, allelic direction

    • Confound: environment, preparation, batch, ancestry



First let s define the question
First, let’s define the question

  • Can we use genetic perturbations as a way to understand how genes are regulated?

  • In what groups, in which tissues?

  • To what stimuli/signaling events?

  • Do ciseQTLs perturb promoter elements?

  • Do trans perturb TFs? Signaling cascades?


Significant associations are symmetrically distributed around TSS

Most significant SNP per gene

0.001 permutation threshold

Stranger et al., PLoS Gen 2012


69-80% of around TSScis associations are cell type-specific

Cell type-specific and cell type-shared gene associations

(0.001 permutation threshold)

262

268

271

82

73

85

86

86

86

No. of cell types with gene association

cell type

  • cis association sharing increases slightly when significance thresholds are relaxed

  • Cell type specificity verified experimentally for subset of eQTLs

Dimas et al Science 2009

Slide courtesy Antigone Dimas

Dimas et alScience 2009


Open research questions1
Open research questions around TSS

  • Do ciseQTLs perturb functional elements?

    • Given each is independent, how can we know?

  • Do tissue-specific effects correlate with the expression of a gene across tissues? Or a regulator?

    • Perhaps a gene is expressed, but in response to different regulators across tissues?

  • If we ever find trans eQTLs…

    • Common regulators of coregulated genes?

    • Tissue specificity?

    • Mechanisms?



Eqtls as intermediate traits
eQTLs as intermediate traits phenotypes

Schadt et al Nat Genet 2005


cell type not relevant for disease phenotypes

relevant cell type for disease

Exploring eQTLs in the relevant cell type is important for disease association studies

Importance of cataloguing regulatory variation in multiple cell types

Slide courtesy Antigone Dimas

Modified from Nica and Dermitzakis Hum Mol Genet 2008


Barrett phenotypeset al 2008

de Jageret al 2007


Franke phenotypeset al 2010

Anderson et al 2011


Today regulatory variation and eqtls3
Today: Regulatory variation and phenotypeseQTLs

  • Quantitative Trait Loci (QTLs), Regulatory Variation

    • Molecular phenotypes as QTs: expression, chromatin…

    • Discretization: a GWAS for each gene. Cis-/Trans-eQTLs

    • Underlying regulatory variation: eQTLs, GWAS, cis-eQTL

  • Finding trans-eQTLs (distal from gene that varies)

    • Challenges: Power, structure, sample size

    • Cross-phenotype analysis: trans QTLs affect many genes

  • Identifying underlying regulatory mechanisms

    • Cis-eQTLs: TSS-distance, cell type specificity

    • eQTLs vs. GWAS: Expression as intermediate trait

  • Population differences, emerging efforts

    • Shared associations, SNP-gene pairs, allelic direction

    • Confound: environment, preparation, batch, ancestry



Shared association in 8 hapmap populations
Shared association in 8 phenotypesHapMap populations

APOH: apolipoprotein H

Stranger et al., PLoS Gen 2012


Number of genes with phenotypescis-eQTL associations

8 extended HapMap populations

SRC: permutation threshold

Stranger et al., PLoS Gen 2012


Direction of allelic effect same snp gene combination across populations
Direction of allelic effect phenotypessame SNP-gene combination across populations

Population 1

Population 2

AGREEMENT

log2 expression

log2 expression

OPPOSITE

log2 expression

log2 expression

Stranger et al., PLoS Gen 2012


Slide courtesy phenotypesAlkes Price


Population differences could have non genetic basis
Population differences could have non-genetic basis phenotypes

• Differences due to environment? (Idaghdour et al. 2008)‏

• Differences in cell line preparation? (Stranger et al. 2007)‏

• Differences due to batch effects? (Akey et al. 2007)‏

(Reviewed in Gilad et al. 2008)‏

Slide courtesy Alkes Price


Gene expression experiment
Gene expression experiment phenotypes

Does gene expression in 60 CEU + 60 YRI vary with ancestry?

Does gene expression in 89 AA vary with % Eur ancestry?

60 CEU + 60 YRI from HapMap, 89 AA from Coriell HD100AA

Gene expression measurements at 4,197 genes obtained using Affymetrix Focus array

c

Slide courtesy Alkes Price


Gene expression differences in african americans validate ceu yri differences
Gene expression differences in African Americans validate CEU-YRI differences

12% ± 3%

in cis

c = 0.43 (± 0.02)‏

(P-value < 10-25)‏

Slide courtesy Alkes Price


Emerging efforts

RNAseq CEU-YRI differences, GTEx

Emerging efforts


Rnaseq questions
RNAseq CEU-YRI differences questions

  • Standard eQTLs

    • Montgomery et al, Pickrell et al Nature 2010

  • Isoform eQTLs

    • Depth of sequence!

      • Long genes are preferentially sequenced

      • Abundant genes/isoforms ditto

      • Power!?

      • Mapping biases due to SNPs


Strategies for transcript assembly CEU-YRI differences

Garber et al. Nat Methods 8:469 (2011)


GTEx CEU-YRI differences – Genotype-Tissue EXpression

An NIH common fund project

Current: 35 tissues from 50 donors

Scale up: 20K tissues from 900 donors.

Novel methods groups: 5 current + RFA


Rnaseq combined with other techs
RNAseq CEU-YRI differences combined with other techs

  • Regulons: TF gene sets via CHiP/seq

    • Look for trans effects

  • Open chromatin states (Dnase I; methylation)

    • Find active genes

    • Changes in epigenetic marks correlated to RNA

    • Genetic effects

  • RNA/DNA comparisons

    • Simultaneous SNP detection/genotyping

    • RNA editing ???


Today regulatory variation and eqtls4
Today: Regulatory variation and CEU-YRI differenceseQTLs

  • Quantitative Trait Loci (QTLs), Regulatory Variation

    • Molecular phenotypes as QTs: expression, chromatin…

    • Discretization: a GWAS for each gene. Cis-/Trans-eQTLs

    • Underlying regulatory variation: eQTLs, GWAS, cis-eQTL

  • Finding trans-eQTLs (distal from gene that varies)

    • Challenges: Power, structure, sample size

    • Cross-phenotype analysis: trans QTLs affect many genes

  • Identifying underlying regulatory mechanisms

    • Cis-eQTLs: TSS-distance, cell type specificity

    • eQTLs vs. GWAS: Expression as intermediate trait

  • Population differences, emerging efforts

    • Shared associations, SNP-gene pairs, allelic direction

    • Confound: environment, preparation, batch, ancestry


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