Geuvadis RNAseq analysis @ UNIGE Genetic regulatory variants Tuuli Lappalainen University of Geneva Geuvadis Analysis meeting II, July 11, 2012

Expression quantitative trait loci (eQTLs) Expression level T Genotypes Works very well in cis. Difficult in trans 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 Trans-analysis of large deletions didn’t yield much… 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 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 E3 FRE1-E2 = 5 (RE1-E2) / 5 (RE1-E2) + 3 (RE1-E3) = 0.625 FRE1-E3 = 3 (RE1-E3) / 5 (RE1-E2) + 3 (RE1-E3) = 0.375 links or junctions? counts or fractions? 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 Statistical testing for ASE What is the allelic ratio? Significantly different from 50-50? cis eQTL* coding SNP mRNA-sequencing T T G T T T T A C C C *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 ~ REGULATORY VARIANT FREQUENCY 0 0.5 1 derived allele frequency power in eQTL analysis eQTL analysis – expected result 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 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