Download 2012-07-09_GEUVADIS_RNA_UNIGE_Tuuli

Geuvadis RNAseq analysis @ UNIGE
Genetic regulatory variants
Tuuli Lappalainen
University of Geneva
Geuvadis Analysis meeting II, July 11, 2012
C
T
Works very well in cis. Difficult in trans
Expression level
Expression quantitative trait loci (eQTLs)
Genotypes
The same principle can be applied to any quantitative phenotype with a genomic
locus
Statistical power only for common variants
QC – mRNA quantifications
miRNA QC
POP
LAB
eQTLs in Geuvadis
Pop
N
Genes with eQTL
(FDR)
Best eQTL indel (null
8.9%)
CEU+GBR
161
2608 (5.1%)
375 (14.4%)
TSI
92
1748 (7.7%)
242 (13.8%)
FIN
89
1822 (7.3%)
255 (14.0%)
YRI
77
2138 (6.3%)
242 (11.3%)
EUR union
342
3898
NA
ALL union
419
4895
NA
Trans-analysis of large deletions didn’t yield much…
TODO:
Some methodological improvements
Combine Europeans with a PC correction of pop structure
Test exon versus transcript quantification
Splicing QTLs (sQTLs) in Geuvadis
Pop
N
Genes with sQTL
– transcript ratio
Genes with sQTL – links
CEU+GBR
161
121 (FDR 9.1%)
1251 forward (FDR 5.6%)
1077 reverse (FDR 6.6%)
nonredundant: 1949
ALL union
419
274
NA
E1
E2
FRE1-E2 = 5 (RE1-E2) / 5 (RE1-E2) + 3 (RE1-E3) = 0.625
E3
links or junctions?
counts or fractions?
FRE1-E3 = 3 (RE1-E3) / 5 (RE1-E2) + 3 (RE1-E3) = 0.375
ALTRANS method by Halit Ongen
Integrating transcriptome QTLs
eQTLs for mRNA and miRNA
exon/miRNA_quantification ~ snp + covariates
sQTLs
link/junction_ratio ~ snp + covariates
link/junction quantification ~ snp + exon_quantification + covariates
multiple tQTLs: for the same gene
exon_quantification ~ snp2 + exon_eQTL_snp1 + covariates
link/junction ratio ~ snp2 + exon_eQTL_snp1 + covariates
targeted trans analysis
exon quantification ~ mi(eQTL)_snp + covariates
link/junction_ratio ~ mieQTL_snp + covariates
Functional annotation of eQTLs
TODO:
Direction of effect
TF motifs, PWM scores
Different eQTL frequencies
Other tQTLs
What’s the best way to tell if we have the causal variant or not? And how often do we
seem to find it?
Allele specific expression
cis eQTL*
coding SNP
G
T
A
C
mRNA-sequencing
T
T
T
T
T
C
C
Statistical
testing for ASE
What is the
allelic ratio?
Significantly
different from
50-50?
*or an epigenetic reason for higher expression of only one homolog in the
studied cell population (e.g. imprinting)
Rare variants have higher effect sizes
ASE analysis
power in eQTL analysis
~ REGULATORY VARIANT FREQUENCY
eQTL analysis – expected result
0
0.5
1
derived allele frequency
Proper quantification of the effect?
Quantifying genetic effects to individual
differences
TODO:
More work on the ASE difference analysis
Variation within/between populations
Rare variant ASE mapping
Can we predict functional effects of genetic
variants?
 How likely is an unknown variant to have regulatory effects
based on known priors?
 Gene expression ~ variant’s : distance from TSS + position
in gene + functional annotation + allele frequency +
conservation score + variant type…
 “gene expression” could be e.g. exon quantification or link ratio
(Gaffney et al. 2012 Genome Biology)
 Does anyone have good experience of this type of
modeling?
Acknowledgements
The FunPopGen lab
Stanford School of Medicine
Stephen Montgomery
Manolis Dermitzakis
Analysis
Alfonso Buil
Thomas Giger
Halit Ongen
Data processing
Ismael Padioleau
Alisa Yurovsky
Technicians
Deborah Bielsen
Emilie Falconnet
Alexandra Planchon
Luciana Romano
The 1000 Genomes Consortium
Functional Interpretation Group
FUNDING
European Union
National Institute of Health
Louis-Jeantet Foundation
Academy of Finland
Emil Aaltonen Foundation
Swiss National Science Foundation
NCCR