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Applied quantitative genetics in a genomics world
Selective Breeding
&
cDNA Microarrays
Toni Reverter
Bioinformatics Group
CSIRO Livestock Industries
Queensland Bioscience Precinct
306 Carmody Rd., St. Lucia, QLD 4067, Australia
Bribie Island – 26-27 July 2004
Applied quantitative genetics in a genomics world
Selective Breeding & cDNA Microarray
The Process
Tissue Samples
Treat A
Treat B
Analysis
mRNA Extraction & Amplification
+
cDNA “A” Cy5
cDNA “B” Cy3
Image Capture
Laser 1
Hybridization
Laser 2
Optical Scanner
Bribie Island – 26-27 July 2004
Applied quantitative genetics in a genomics world
Selective Breeding & cDNA Microarray
The Possibilities
 Determine genes which are differentially expressed (DE).
 Connect DE genes to sequence databases to search for
common upstream regions.
 Overlay DE genes on pathway diagrams.
 Relate expression levels to other information on cells, e.g.
tumor types.
 Identify temporal and spatial trends in gene expression.
 Seek roles of genes based on patterns of co-regulation.
 …Applications to Selective Breeding Schemes?
Bribie Island – 26-27 July 2004
Applied quantitative genetics in a genomics world
Selective Breeding & cDNA Microarray
3 Types of Data
Phenotype Phenotype
+ Pedigree + Marker
Gene
Expression
How to relate them?
Bribie Island – 26-27 July 2004
Applied quantitative genetics in a genomics world
Selective Breeding & cDNA Microarray
Predict Future Performance
Mixed-Inheritance Model
y  Xβ  Z1u  Z 2q  e
Wang, Fernando & Grossman, 1998
Many authors and many species
NB: Segregation Variance Issues
Infinitesimal Model
y  Xβ  Z1u  e
Var(u)  A u2
ANOVA Model
Phenotype Phenotype y  Xβ  Z2q  e
+ Pedigree + Marker Many authors and many species
Henderson, 1975
Dimension Reduction
y  Xβ  Wα  e
T
ˆ 2  X
ˆΛ
W   K


Cov( X)  KΛΛ T
1
Gene
Expression
Genetical Genomics
y  Xβ  Z 2q  e
Jansen and Nap, 2001 (arabidopsis)
Brem et al, 2002 (yeast)
Schadt et al., 2003 (mice)
Chiaromonte & Matinelli, 2002
(leukemia, humans)
ANOVA Model
y  Xβ  Z3g  e
Cui and Churchill, 2003
Bribie Island – 26-27 July 2004
Applied quantitative genetics in a genomics world
Selective Breeding & cDNA Microarray
Genetical Genomics
Use arrays to identify genes that are DE in relevant tissues of individuals
sorted by QTL genotype. If those DE genes map the chromosome region
Of interest, they would become very strong candidates for QTL.
Source: Jansen and Nap, 2001
Bribie Island – 26-27 July 2004
Applied quantitative genetics in a genomics world
Selective Breeding & cDNA Microarray
Genetical Genomics
Use arrays to identify genes that are DE in relevant tissues of individuals
sorted by QTL genotype. If those DE genes map the chromosome region
Of interest, they would become very strong candidates for QTL.
For lots of $, this will find lots of genes affecting a trait of interest.
…….……Selective Breeding Needs Additivity:
High EBV
Low EBV
1
0
2
2
GeneStar
Marbling
Genotype
3
7
1
1
8
4
(N Stars/Alleles)
5
2
6
0
Bribie Island – 26-27 July 2004
Applied quantitative genetics in a genomics world
Selective Breeding & cDNA Microarray
Genetical Genomics
Use arrays to identify genes that are DE in relevant tissues of individuals
sorted by QTL genotype. If those DE genes map the chromosome region
Of interest, they would become very strong candidates for QTL.
Never enough! …not greed but algebra:
Vq  2 pq 2
…………particularly useful for:
  a  d q  p 
1. Speed up and enhance power to finding New QTL
2. Developing “Diagnostic Kits”
3. Deciphering the genetics of Complex Traits
Ability to score individuals rapidly (and
cheaply) at a very large number of loci.
A trait that is affected by many, often
interacting, environmental and genetic
factors such that no factor is completely
sufficient nor are all factors necessary.
(Andersson and Georges, 2004)
Bribie Island – 26-27 July 2004
Applied quantitative genetics in a genomics world
Selective Breeding & cDNA Microarray
Final Thoughts
Where does this leave us (Quantitative Geneticists)?
Where does this leave Phenotypes (the need to measure)?
Very well, ………I’m afraid
Quantitative Geneticists:
 Never enough QTL
 Association studies
 Study of variation
 When QTL not additive, the
individual is needed but not so with
BLUP
Phenotypes:
 Mutation is continuously
generating new variation
 Selective breeding on genotypes
reduces effective population size
 Integration of the 3 types of data
Bribie Island – 26-27 July 2004
Applied quantitative genetics in a genomics world
Selective Breeding & cDNA Microarray
References
Andersson, L. and Georges (2004) Domestic-animal genomics: deciphering the
genetics of complex traits. Nature Reviews 5:202-212.
Brem, R.B., G. Yvert, R. Clinton, and L. Kruglyak. (2002) Genetic dissection of
transcriptional regulation in budding yeast. Science 296:752-755.
Chiaromonte, F., and Martinelli, J. (2002) Dimension reduction strategies for
analysing global gene expression data with a response. Math. Biosciences, 176:123-144.
Cui, X., and G. A. Churchill. (2003) Statistical tests for differential expression in
cDNA microarray experiments. Genome Biol., 4:210.
Henderson, C.R. (1975) Best linear unbiased estimation and prediction under a
selection model. Biometrics, 31:423.
Jansen, R.C. and J.P. Nap (2001) Genetical genomics: the added value from
segregation. Trend Genet., 17:388-391.
Schadt, E.E., Monks, S.A., Drake, T.A., et al. (2003) Genetics of gene expression
surveyed in maize, mouse and man. Nature 422:297-302.
Wang, T., R.L. Fernando, and M. Grossman (1998) Genetic evaluation by best linear
unbiased prediction using marker and trait information in a multibreed population.
Genetics, 148:507-515.
Bribie Island – 26-27 July 2004