genetic variation and rna-seq · 2016. 4. 7. · research topics c. elegans genetics arabidopsis...
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Genetic variation and RNA-seq
Applications of RNA-seq in Genetical Genomics
L. Basten Snoek
7-10-2013 Laboratory of Nematology; Kammenga lab
Power of RNA-seq; Course
Research topics
Genetics C. elegans
Arabidopsis
QTL mapping
Genetical genomics
Network/Systems biology
Micro-arrays
DNA- and RNA-seq
Image analysis
High-throughput phenotyping
Natural variation
Gene expression
Lifespan
Development
Environment
Stress resistance
Adaptation
G x E
What How
Genetical genomics • Gene expression variation linked to genetic
polymorphisms
• expression Quantitative Trait Locus (eQTL).
Gene expression
expression QTL
• How do we find an eQTL?
• Genetic variation
– Alleles
• Different combinations of alleles
Quantitative Trait Locus
Gene X Allele 1: ~CATGGACTG~ Allele 2: ~CATGAACTG~
Gene X1 :: Gene Y1 :: Gene Z1 Gene X2 :: Gene Y2 :: Gene Z2
Gene X1 :: Gene Y2 :: Gene Z1
Gene X1 :: Gene Y2 :: Gene Z2
Gene X2 :: Gene Y1 :: 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
• How do we find an eQTL?
• Search for a link between a genetic locus and the phenotypic distribution
Quantitative Trait Locus High
Low
expression QTL
• How do we find an eQTL?
• Use a statistical test to test for a significant linkage.
• Identify eQTL!
Quantitative Trait Locus
expression QTL
• What does this eQTL tell us?
– A polymorphism affecting the variation in gene expression can be found at this locus
Gene Gene expression
variation
Polymorphic regulator
QTL
Quantitative Trait Locus
Genetic variation
expression QTLs
• What do these eQTLs tell us?
– Genome wide scan of polymorphic regulators
Quantitative Trait Locus
Essential info per gene: Position - Genomic - eQTL peak(s)
expression QTLs
• What do these eQTLs tell us?
– Genome wide scan of polymorphic regulators
– Co-regulated genes
Quantitative Trait Locus
Polymorphic Regulator
Targets
eQTLs Regulation
expression QTLs
• What do these eQTLs tell us?
– Genome wide scan of polymorphic regulators
– Co-regulated genes
Quantitative Trait Locus
Polymorphic Regulator
Targets
eQTLs Regulation
Phenotypic variation
expression QTLs in context
• What do these eQTLs tell us? – Genome wide scan of polymorphic regulators – Co-regulated genes – Context dependent regulators
Viñuela & Snoek etal 2010
Juvenile Reproducing Old
Quantitative Trait Locus
expression QTLs in context
• What do these eQTLs tell us? – Genome wide scan of polymorphic regulators – Co-regulated genes – Context dependent regulators
Polymorphic Regulator
Targets
eQTLs Regulation
Phenotypic variation
Environmental variation
Snoek, Elvin, Rodriquez etal, in prep; Snoek & Sterken etal , in prep
Quantitative Trait Locus
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
Polymorphic Regulator
Targets
eQTLs Regulation
Phenotypic variation
RNA-seq and genetic variation
• Mapping population
– Crossing two genetically different individuals
N2, Bristol CB4856, Hawaii
1 SNP per ~800 bp
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
RNA-seq and genetic variation Example: F08A8.2, variation in exon usage
1 2 3 4 5 6
02
04
06
08
0
F08A8.2
exon
ave
rag
e c
ove
rag
e
N2CB
Genetic variation • Mapping population
– Crossing two genetically different individuals
N2, Bristol
CB4856, Hawaii
Polymorphic Regulator
Targets
eQTLs Regulation
Phenotypic variation
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!
Mapping population
Individuals/Lines Different: Genotypes,
Phenotypes
RNA-seq
Per Genotype: I) Gene expression II) Polymorphisms
Genetic variation
Gene expression variation
eQTLs
Regulators
Targets
Polymorphic Regulator
Targets
eQTLs Regulation
Phenotypic variation
Data
• Storage, Raw, Processed
0
5000000
10000000
15000000
20000000
2004 2006 2008 2010 2012 2014 2016
Raw data-points
Data • Storage, Raw, Processed
• Data points, accessibility, visualization
0
5000000
10000000
15000000
20000000
2004 2006 2008 2010 2012 2014 2016
0
50000000
100000000
150000000
200000000
250000000
300000000
350000000
400000000
450000000
2004 2006 2008 2010 2012 2014 2016
Raw data-points Processed data-points
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