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Interpretation of microarray data: Trudging out of the abyss towards elucidation of biological significance¹

G. W. Smith^*,2 and G. J. M. Rosa

^* Laboratory of Mammalian Reproductive Biology and Genomics, Departments of Animal Science and Physiology, Michigan State University, East Lansing 48824-1225; and and Department of Dairy Science, University of Wisconsin, Madison 53706

² Corresponding author: smithge7{at}msu.edu

The recent development of tools for expression profiling in livestock has availed reproductive biologists of new opportunities to examine global changes in gene expression during key developmental events, in response to hormonal or other treatments, and as a tool for phenotyping or predicting developmental potential. Such experiments often yield lists of tens to thousands of modulated genes, transcripts of interest, or both. Some argue that such technological advances signal a move from hypothesis-driven research to descriptive discovery research, resulting in information overload at the expense of biological significance. One can easily spend hours staring into the abyss, wondering if the results are real and what they mean. However, microarrays can be more than a high throughput and expensive screening tool. Many factors contribute to the success of expression profiling experiments and the yield of interpretable data, including the nature of the hypothesis or objective of the study, the microarray platform, the complexity of the tissue of interest, the experimental design, and the incorporation of the best available strategies for data analysis and interpretation of the biological themes. Although challenging due to the lack of extensive annotation or ontology classification for genes in livestock species, functional categories of coregulated genes and gene pathways can be determined, and hypotheses about common regulatory elements or the functional significance can be formulated. We have applied cDNA microarray technology to studies of follicular growth, oocyte quality, and the periovulatory period in cattle. Lessons learned from such experiments and a review of the available literature form the basis for the strategies described to facilitate successful application of microarray technology to studies of reproductive biology of livestock species.

Key Words: microarray • data analysis • false discovery rate • biological theme • cattle