1. Uses of microarrays and related methodologies in animal breeding Bruce Walsh, jbwalsh@u.arizona.edu 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. The color of the spot corresponds to the relative concentrations of mRNAs for that gene in the two cell types Cell Type 1 Cell type 2

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

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 predicts phenotype

8. Significance testing-- GLM Y klijk = u + A k +R kl + T i + G j + TG ij +e lkijk Array k Replicate l in array k Treatment i Gene j Interaction between gene i and treatment j k-th spotting of gene j under treatment i on replicate l of array k

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

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 –Different structural alleles may go undetected on an array analysis

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 Additional tools for examining protein- protein interactions –Two hybrid screens –FRET & FRAP –2D Protein gels

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, some models are useful” (Box)

20. Flux = production rate of a particular product, here F How best to increase the flux through this pathway? Perhaps we increase the concentration of e 1 However, it may be more efficient To increase the concentration of e 4 The flux control coefficient, introduced by Kascer and Burns, provides a quantitative solution to this problem

21. Flux Control Coefficients, The control coefficient for the flux at step i 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. When the activity of E is near zero, C is close to 1 When the activity of E is large, C is close to zero Why many mutations are recessive: a 50% reduction in activity (the heterozygote) results in only a very small change in the flux

23. Kacser-Burns Flux summation theorem: Truly rate-limiting steps are rare 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.

24. “rate-limiting” steps in pathways Hence, the limiting increase in f is Small-Kacser theorem: the factor f by which flux is increased by an r-fold increase in activity of E 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. Flux we wish to increase We wish this flux to remain unchanged The initial approach might be to try either e 3 or e 4, rather than e 1 or e 2 A more correct approach, however is to Pick the step(s) that maximize C F while minimizing C H

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 known

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. U of A Campus Farewell from the “desert”