1 Geuvadis RNAseq analysis @ UNIGE Genetic regulatory variants Tuuli LappalainenUniversity of GenevaGeuvadis Analysis meeting II, July 11, 2012
2 Expression quantitative trait loci (eQTLs) Expression levelTGenotypesWorks very well in cis. Difficult in transThe same principle can be applied to any quantitative phenotype with a genomic locusStatistical power only for common variants
5 eQTLs in Geuvadis Trans-analysis of large deletions didn’t yield much… PopNGenes with eQTL (FDR)Best eQTL indel (null 8.9%)CEU+GBR1612608 (5.1%)375 (14.4%)TSI921748 (7.7%)242 (13.8%)FIN891822 (7.3%)255 (14.0%)YRI772138 (6.3%)242 (11.3%)EUR union3423898NAALL union4194895Trans-analysis of large deletions didn’t yield much…TODO:Some methodological improvementsCombine Europeans with a PC correction of pop structureTest exon versus transcript quantification
6 Splicing QTLs (sQTLs) in Geuvadis PopNGenes with sQTL – transcript ratioGenes with sQTL – linksCEU+GBR161121 (FDR 9.1%)1251 forward (FDR 5.6%)1077 reverse (FDR 6.6%)nonredundant: 1949ALL union419274NAE1E2E3FRE1-E2 = 5 (RE1-E2) / 5 (RE1-E2) + 3 (RE1-E3) = 0.625FRE1-E3 = 3 (RE1-E3) / 5 (RE1-E2) + 3 (RE1-E3) = 0.375links or junctions?counts or fractions?ALTRANS method by Halit Ongen
7 Integrating transcriptome QTLs eQTLs for mRNA and miRNAexon/miRNA_quantification ~ snp + covariatessQTLslink/junction_ratio ~ snp + covariateslink/junction quantification ~ snp + exon_quantification + covariatesmultiple tQTLs: for the same geneexon_quantification ~ snp2 + exon_eQTL_snp1 + covariateslink/junction ratio ~ snp2 + exon_eQTL_snp1 + covariatestargeted trans analysisexon quantification ~ mi(eQTL)_snp + covariateslink/junction_ratio ~ mieQTL_snp + covariates
8 Functional annotation of eQTLs TODO:Direction of effectTF motifs, PWM scoresDifferent eQTL frequenciesOther tQTLsWhat’s the best way to tell if we have the causal variant or not? And how often do we seem to find it?
9 Allele specific expression Statistical testing for ASEWhat is the allelic ratio? Significantly different from 50-50?cis eQTL*coding SNPmRNA-sequencingTTGTTTTACCC*or an epigenetic reason for higher expression of only one homolog in the studied cell population (e.g. imprinting)
10 Rare variants have higher effect sizes ASE analysis~ REGULATORY VARIANT FREQUENCYderived allele frequencypower in eQTL analysiseQTL analysis – expected resultProper quantification of the effect?
11 Quantifying genetic effects to individual differences TODO:More work on the ASE difference analysisVariation within/between populationsRare variant ASE mapping
12 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 Genome Biology)Does anyone have good experience of this type of modeling?
13 AcknowledgementsThe FunPopGen lab Manolis Dermitzakis Analysis Alfonso Buil Thomas Giger Halit Ongen Data processing Ismael Padioleau Alisa Yurovsky Technicians Deborah Bielsen Emilie Falconnet Alexandra Planchon Luciana Romano Stanford School of Medicine Stephen Montgomery 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