Power of RNA-seq; Course Genetic variation and RNA-seq Applications of RNA-seq in Genetical Genomics Laboratory of Nematology; Kammenga lab L. Basten Snoek 7-10-2013
Research topics C. elegans Genetics What How Arabidopsis Natural variation Gene expression Lifespan Development Environment Stress resistance Adaptation G x E Genetical genomics QTL mapping DNA- and RNA-seq Micro-arrays Image analysis Network/Systems biology High-throughput phenotyping
Genetical genomics Gene expression variation linked to genetic polymorphisms expression Quantitative Trait Locus (eqtl). Gene expression
expression QTL How do we find an eqtl? Quantitative Trait Locus Genetic variation Alleles Gene X Allele 1: ~CATGGACTG~ Allele 2: ~CATGAACTG~ Different combinations of alleles Gene X1 :: Gene Y1 :: Gene Z1 Gene X2 :: Gene Y2 :: Gene Z2 Gene X1 :: Gene Y2 :: Gene Z1 Gene X2 :: Gene Y1 :: Gene Z2 Gene X1 :: Gene Y2 :: Gene Z2 Gene X1 :: Gene Y1 :: Gene Z2 Gene X2 :: Gene Y2 :: Gene Z1 Gene X2 :: Gene Y1 :: Gene Z1
expression QTL How do we find an eqtl? Obtain a mapping population Quantitative Trait Locus
expression QTL How do we find an eqtl? Measure a phenotype Quantitative Trait Locus
expression QTL High Quantitative Trait Locus How do we find an eqtl? Low Search for a link between a genetic locus and the phenotypic distribution
expression QTL Quantitative Trait Locus How do we find an eqtl? Use a statistical test to test for a significant linkage. Identify eqtl!
expression QTL What does this eqtl tell us? Quantitative Trait Locus A polymorphism affecting the variation in gene expression can be found at this locus Gene Genetic variation Polymorphic regulator Gene expression variation QTL
expression QTLs What do these eqtls tell us? Quantitative Trait Locus Genome wide scan of polymorphic regulators Essential info per gene: Position - Genomic - eqtl peak(s)
expression QTLs Quantitative Trait Locus What do these eqtls tell us? Genome wide scan of polymorphic regulators Co-regulated genes Polymorphic Regulator eqtls Regulation Targets
expression QTLs Quantitative Trait Locus What do these eqtls tell us? Genome wide scan of polymorphic regulators Co-regulated genes Phenotypic variation Polymorphic Regulator eqtls Regulation Targets
expression QTLs in context What do these eqtls tell us? Genome wide scan of polymorphic regulators Co-regulated genes Context dependent regulators Juvenile Reproducing Old Quantitative Trait Locus Viñuela & Snoek etal 2010
Snoek, Elvin, Rodriquez etal, in prep; Snoek & Sterken etal, in prep expression QTLs in context What do these eqtls tell us? Genome wide scan of polymorphic regulators Co-regulated genes Context dependent regulators Quantitative Trait Locus Environmental variation Phenotypic variation Polymorphic Regulator eqtls Regulation Targets
Requirements for eqtl detection Mapping population Crossing two genetically different individuals Genetic markers, polymorphisms, SNPs PCR, FLP, AFLP, Micro-satellites Transcript level variation Micro-arrays RNA-seq eqtls Phenotypic variation Polymorphic Regulator Regulation Targets
RNA-seq and genetic variation Mapping population Crossing two genetically different individuals CB4856, Hawaii 1 SNP per ~800 bp N2, Bristol RNA-seq RNA-seq
RNA-seq and genetic variation Gene expression differences
RNA-seq and genetic variation Example: pgp-6, genotype specific transcript levels Viñuela & Snoek etal 2010
RNA-seq and genetic variation Example: pgp-6, new exons Li, Breitling, Snoek, van der Velde, Swertz, Riksen, Jansen & Kammenga, 2010, Genetics
RNA-seq and genetic variation Example: F08A8.2, variation in exon usage
average coverage 0 20 40 60 80 RNA-seq and genetic variation Example: F08A8.2, variation in exon usage F08A8.2 N2 CB 1 2 3 4 5 6 exon
Mapping population Genetic variation Crossing two genetically different individuals CB4856, Hawaii Phenotypic variation Polymorphic Regulator N2, Bristol eqtls Regulation Targets Li & Alvarez etal. 2006; Doroszuk etal 2009; Reddy & Andersen etal 2009; McGrath etal 2009; Rockman etal 2010; Viñuela & Snoek etal 2010; Elvin & Snoek etal 2011; Viñuela & Snoek etal 2012; Rodriguez etal 2012; Green etal 2013 Snoek etal 2013
RNA-seq, two for the price of one Information from RNA-seq Gene expression Transcript level variation Polymorphisms Genetic markers In the same experiment! Phenotypic variation Polymorphic Regulator Individuals/Lines Different: Genotypes, Phenotypes Mapping population RNA-seq Per Genotype: I) Gene expression II) Polymorphisms Genetic variation Gene expression variation eqtls Regulation Targets eqtls Regulators Targets
Data Storage, Raw, Processed 20000000 Raw data-points 15000000 10000000 5000000 0 2004 2006 2008 2010 2012 2014 2016
Data Storage, Raw, Processed Data points, accessibility, visualization 20000000 15000000 10000000 5000000 0 Raw data-points 2004 2006 2008 2010 2012 2014 2016 Processed data-points 450000000 400000000 350000000 300000000 250000000 200000000 150000000 100000000 50000000 0 2004 2006 2008 2010 2012 2014 2016
RNA-seq vs Micro-arrays RNA-seq Micro-arrays Price ~200 (10M reads) <100 (44K spots) Genetic variation ++ +- Gene expression ++ ++ Splice variants ++ +- New genes ++ - Sample preparation + + Feature extract time - +++ PC power Cluster Desktop Non model species +++ +-
Summary RNA-seq is useful for: Non-model species New mapping populations Linking gene expression variation to genetic variation Measuring splicing Micro-arrays are useful for experiments needing many measurements (on model species) Example: time-series on populations
Acknowledgements Wageningen - Kammenga lab - Harm Nijveen Kiel - Schulenburg lab Gent - Braeckman lab Groningen - Jansen lab - Swertz lab Cambridge - Fisher lab - Babu Lab Manchester - Poulin lab Liverpool - Cossins lab Paris - Nechaev lab Zurich - Hajnal lab - Hengartner lab Barcelona - Lehner lab