In just a few years, microarrays have gone from obscurity to being almost ubiquitous in biological research. At the same time, the statistical methodology for microarray analysis has progressed from simple visual assessments of results to a weekly deluge of papers that describe purportedly novel algorithms for analysing changes in gene expression. Although the many procedures that are available might be bewildering to biologists who wish to apply them, statistical geneticists are recognizing commonalities among the different methods. Many are special cases of more general models, and points of consensus are emerging about the general approaches that warrant use and elaboration.

Coexpression patterns of gene expression across many microarray data sets may reveal networks of genes involved in linked processes. To identify factors involved in cellulose biosynthesis, we used a regression method to analyze 408 publicly available Affymetrix Arabidopsis microarrays. Expression of genes previously implicated in cellulose synthesis, as well as several uncharacterized genes, was highly coregulated with expression of cellulose synthase (CESA) genes. Four candidate genes, which were coexpressed with CESA genes implicated in secondary cell wall synthesis, were investigated by mutant analysis. Two mutants exhibited irregular xylem phenotypes similar to those observed in mutants with defects in secondary cellulose synthesis and were designated irx8 and irx13. Thus, the general approach developed here is useful for identification of elements of multicomponent processes.Arabidopsis ͉ cell wall ͉ xylem ͉ coexpression

Conflict of interest statement: No conflicts declared.Freely available online through the PNAS open access option.

Established guidelines for causal inference in epidemiological studies may be inappropriate for genetic associations. A consensus process was used to develop guidance criteria for assessing cumulative epidemiologic evidence in genetic associations. A proposed semi-quantitative index assigns three levels for the amount of evidence, extent of replication, and protection from bias, and also generates a composite assessment of 'strong', 'moderate' or 'weak' epidemiological credibility. In addition, we discuss how additional input and guidance can be derived from biological data. Future empirical research and consensus development are needed to develop an integrated model for combining epidemiological and biological evidence in the rapidly evolving field of investigation of genetic factors.

Annual plants grow vegetatively at early developmental stages and then transition to the reproductive stage, followed by senescence in the same year. In contrast, after successive years of vegetative growth at early ages, woody perennial shoot meristems begin repeated transitions between vegetative and reproductive growth at sexual maturity. However, it is unknown how these repeated transitions occur without a developmental conflict between vegetative and reproductive growth. We report that functionally diverged paralogs FLOWERING LOCUS T1 (FT1) and FLOWERING LOCUS T2 (FT2), products of whole-genome duplication and homologs of Arabidopsis thaliana gene FLOWERING LOCUS T (FT), coordinate the repeated cycles of vegetative and reproductive growth in woody perennial poplar (Populus spp.). Our manipulative physiological and genetic experiments coupled with field studies, expression profiling, and network analysis reveal that reproductive onset is determined by FT1 in response to winter temperatures, whereas vegetative growth and inhibition of bud set are promoted by FT2 in response to warm temperatures and long days in the growing season. The basis for functional differentiation between FT1 and FT2 appears to be expression pattern shifts, changes in proteins, and divergence in gene regulatory networks. Thus, temporal separation of reproductive onset and vegetative growth into different seasons via FT1 and FT2 provides seasonality and demonstrates the evolution of a complex perennial adaptive trait after genome duplication.ife cycles of higher plants display a great diversity in morphological and seasonal adaptation. Annual plants grow, reproduce, and senesce within a growing season, whereas woody perennials display successive years of vegetative growth before reaching sexual maturity (1-3). After this time, shoot meristems begin cyclical transitions between vegetative and reproductive growth. Consequently, shoots may repeatedly form early vegetative buds (Vegetative Zone I), reproductive buds (Floral Zone), and late vegetative buds (Vegetative Zone II) in a sequential manner (3). However, our understanding of the mechanisms underlying such complex phenotypes, and thus variation in growth habits and adaptation, remain rudimentary. In the herbaceous perennial Arabis alpina, repeated transcriptional repression and activation of PERPETUAL FLOWERING 1 (PEP1), an ortholog of the floral repressor FLOWERING LOCUS C (FLC) in annual Arabidopsis thaliana (4), controls recurring seasonal transitions between reproductive and vegetative phases (5). However, a true functional ortholog of FLC has not been reported in trees, nor does phylogenetic analysis point to a clear structural ortholog of FLC in poplar (Populus spp.) (6).Previous results showed that FLOWERING LOCUS T1 (FT1) (7) and FLOWERING LOCUS T2 (FT2) (8) under the cauliflower mosaic virus 35S (CaMV 35S) constitutive overexpression promoter induce early flowering in poplar. Transcript abundance of both genes gradually increases in the growing season as poplar trees mature....

No abstract

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Given the complexity of microarray-based gene expression studies, guidelines encourage transparent design and public data availability. Several journals require public data deposition and several public databases exist. However, not all data are publicly available, and even when available, it is unknown whether the published results are reproducible by independent scientists. Here we evaluated the replication of data analyses in 18 articles on microarray-based gene expression profiling published in Nature Genetics in 2005-2006. One table or figure from each article was independently evaluated by two teams of analysts. We reproduced two analyses in principle and six partially or with some discrepancies; ten could not be reproduced. The main reason for failure to reproduce was data unavailability, and discrepancies were mostly due to incomplete data annotation or specification of data processing and analysis. Repeatability of published microarray studies is apparently limited. More strict publication rules enforcing public data availability and explicit description of data processing and analysis should be considered.