Selective Breeding

1
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
2
Applied quantitative genetics in a genomics
world Selective Breeding cDNA Microarray
The Process
Bribie Island 26-27 July 2004
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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
4
Applied quantitative genetics in a genomics
world Selective Breeding cDNA Microarray
3 Types of Data
Phenotype Pedigree
Phenotype Marker
Gene Expression
Bribie Island 26-27 July 2004
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Applied quantitative genetics in a genomics
world Selective Breeding cDNA Microarray
Predict Future Performance
Phenotype Pedigree
Phenotype Marker
Gene Expression
Bribie Island 26-27 July 2004
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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
7
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
Bribie Island 26-27 July 2004
8
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.

• particularly useful for
• Speed up and enhance power to finding New QTL
• Developing Diagnostic Kits
• Deciphering the genetics of Complex Traits

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
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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, Im 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
10
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 5202-212.
Brem, R.B., G. Yvert, R. Clinton, and L.
Kruglyak. (2002) Genetic dissection
of transcriptional regulation in budding yeast.
Science 296752-755.
Chiaromonte, F., and Martinelli, J. (2002)
Dimension reduction strategies for analysing
global gene expression data with a response.
Math. Biosciences, 176123-144.
Cui, X., and G. A. Churchill. (2003) Statistical
tests for differential expression in cDNA
microarray experiments. Genome Biol., 4210.
Henderson, C.R. (1975) Best linear unbiased
estimation and prediction under a selection
model. Biometrics, 31423.
Jansen, R.C. and J.P. Nap (2001) Genetical
genomics the added value from segregation. Trend
Genet., 17388-391.
Schadt, E.E., Monks, S.A., Drake, T.A., et al.
(2003) Genetics of gene expression surveyed in
maize, mouse and man. Nature 422297-302.
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, 148507-515.
Bribie Island 26-27 July 2004