1. Uses of microarrays and related methodologies in animal breeding
Bruce Walsh,
University of Arizona
(Depts. of Ecology & Evolutionary Biology, Molecular & Cellular Biology, Plant Sciences, Animal Sciences, and Epidemology & Biostatistics)

2. The basic idea behind gene expression arrays
With a complete (or partial) genome sequence in hand, one can array sequences from genes of interest on small chip, glass slide, or a membrane
mRNA is extracted from cells of interest and hybridized to the array
Genes showing different levels of mRNA can be detected

3. Types of microarrays Synthetic oligonucleotide arrays
Chemically synthesize oligonucleotide sequences directly on slide/chip/membrane (e.g., using photolithography)
Affymetrix, Agilent
Spotted cDNA arrays
PCR products from clones of genes of interest are spotted on a glass slide using a robot
Extracted cellular mRNA is reverse-transcribed into cDNAs for hybridization

5. Cell type 2
Cell
Type 1
The color of the spot
corresponds to the
relative concentrations
of mRNAs for that gene
in the two cell types

6. mRNAs for these
genes more abundant
in cell type 2
mRNAs from these
Genes of roughly equal
Abundance in both cell
types
mRNAs from these
genes more abundant
in cell type 1

7. Analysis of microarray data
Image processing and normalization
Detecting significant changes in expression
Clustering and classification
Clustering: detecting groups of co-expressed genes
Classification: finding those genes at which changes in mRNA expression level predict phenotype

8. Significance testing-- GLM
Interaction between
gene i and treatment j
Yklijk = u + Ak +Rkl + Ti + Gj + TGij +elkijk
Replicate l
in array k
k-th spotting of gene j under
treatment i on replicate l of array k
Array k
Treatment i
Gene j

9. Problem of very many tests (genes) vs. few actual data vectors
Expectation: A large number of the GxT interactions will be significant
Controlling experiment-wide p value is very overly conservative (further, tests may be strongly correlated)
Generating a reduced set of genes for future consideration (data mining)
FDR (false discovery rate)
PFP (proportion of false positives)
Empirical Bayes approaches

10. Which loci control array-detected changes in mRNA expression?
Cis-acting factors
Control regions immediately adjacent to the gene
Trans-acting factors
Diffusable factors unlinked (or loosely linked) to the gene of interest
Global (Master) regulators
Trans-acting factors that influence a large number of genes

11. David Treadgill’s (UNC) mouse experiment
Recombinant Inbred lines from a cross of DBA/2J and C57BL
The level of mRNA expression (measured by array analysis) is treated as a quantitative trait and QTL analysis performed for each gene in the array

12. Distribution of >12,000 gene interactions
TRANS-modifiers
MASTER modifiers
CIS-modifiers
Distribution
of >12,000
gene
interactions
Genomic location of genes on array
Genomic location of mRNA level modifiers

13. Candidate loci : Differences in Gene Expression between lines
Correlate differences in levels of expression with trait levels
Map factors underlying changes in expression
These are (very) often trans-acting factors
Difference between structural alleles and regulatory alleles

14. Expanded selection opportunities offered by microarrays
G x E
Candidate genes may be suggested by examining levels of mRNA expression over different major environments
With candidates in hand, potential for selection of genes showing reduced variance in expression over critical environments
Breaking (or at least reducing) potentially deleterious genetic correlations
Look for variation in genes that have little (if any) trans-acting effects on other genes

15. Towards the future
Selection decisions using information on gene networks / pathways
Microarrays are one tool for reconstructing gene networks
Tools for examining protein-protein interactions
Two hybrid screens
FRET & FRAP

16. Analysis and Exploitation of Gene and Metabolic Networks
Graph theory
Most estimation and statistical issues unresolved
Major (current) analytic tool: Kascer-Burns Sensitivity Analysis

17. Gene networks are graphs

19. Kascer-Burns Sensitivity Analysis (aka. Metabolic Control Analysis)
“No theory should fit all the facts because some of
the facts are wrong” (N. Bohr)
“All models are wrong, although some models are
Useful” (Box)

20. Flux = production rate of a
particular product, here F
Perhaps we increase the concentration of e1
However, it may be more efficient
To increase the concentration of e4
The flux control coefficient, introduced by
Kascer and Burns, provides a quantitative solution
to this problem
How best to increase the flux through this
pathway?

21. Flux Control Coefficients,
The control coefficient for the flux at step j in
a pathway associated with enzyme j,
Roughly speaking, the control coefficient is
the percentage change in flux divided by
percentage change in enzyme activity

22. Why many mutations are recessive: a 50%
reduction in activity (the heterozygote)
results in only a very small change in the flux
When the activity of E is large,
C is close to zero
When the activity of E is near zero,
C is close to 1

23. Kacser-Burns Flux summation theorem:
• Coefficients are not intrinsic properties
of an enzyme, but rather a (local) system
property
• If a control coefficient is greatly
increased in value, this decreases the
values of other control coefficients
• While most values of C for proteins are positive,
negative regulators (repressors) give negative values,
allowing for C values > 1.
• Truly rate-limiting steps are rare

24. “rate-limiting” steps in pathways
Small-Kacser theorem: the factor f by which flux is
increased by an r-fold increase in activity of E is
Hence, the limiting increase in f is

25. Using estimated Control Coefficients as selection aids
Loci with larger C values should respond faster to selection
Such loci are obvious targets for screens of natural variation (candidate loci)
Selection with reduced correlations
Tallis or Kempthorne - Nordskog restricted selection index
Select on loci with large C for flux of interest, smallest C for other fluxes not of concern
Positive selection on C for flux of interest, selection to reduce flux changes in other pathways

26. We wish this flux to
remain unchanged
The initial approach might be to
try either e3 or e4, rather than e1 or e2
Flux we wish to increase
A more correct approach, however is to
Pick the step(s) that maximize CF while minimizing CH

27. Index selection on pathways
The elements of selection include both phenotype and C, and (possibly) marker markers as well
Problems:
C is a local estimate, changing as the pathway evolves
Still have all the standard concerns with a selection index (e.g., stability of inverse of genetic covariance matrix)
These are important caveats to consider even under the rosy scenaro where all C’s are know

28. What to call it? MAS = Marker Assisted Selection
CAS = Control Coefficient Assisted Selection
CASH $ = Control Activity Selection Helper

29. Summary Microarray analysis = data mining
Potential (immediate) useage:
Suggesting candidate loci
More efficient use of G X E
Reducing/breaking deleterious correlations
Cis (easy) vs. trans (hard) control of expression levels
Future = analysis of pathways
Index selection (and all its problems)

30. Farewell from the “desert”
U of A Campus