Urzúa et al. BMC Genomics 2010, 11(Suppl 5):S1 http://www.biomedcentral.com/1471-2164/11/S5/S1 which is primarily extracted from the literature, from tumor pathology images submitted by investigators, and from routine animal health screenings of mouse colonies at Jackson Laboratory [9]. Then, is curated with the help of natural language processing tools to cope with increasing amounts of phenotype information in the lit- erature [48]. Similarly, the Mouse Phenome Database (MPD) has developed standards for deposition of pheno- typic data of mice including strain purity, study design, animal age and statistical power. Contributors are requested to provide complete measurement descrip- tions, experimental protocols as well as housing, diet and health status of animals. MPD curates data and computes summary statistics for each measurement in all strains [8]. Results presented here were obtained with a linear regression analysis. However, interplaying gene networks linked to phenotypes may not necessarily follow linear relationships with regard to transcript levels. Recently, a few reports have attempted to identify non-monotone or non-linear phenotype-transcriptome associations. Lin et al. (2008) proposed the coefficient of multiple determi- nation (R2) of a natural cubic spline regression model [49]. In a related work, three correlation methods (Pear- son, Spearman and Hoeffding’s D) were compared to analyze co-expressed genes. Hoeffding’s D dependence measure was found to be the best suited to identify non- linear and non-monotonic associations [50]. These types of analytical approaches are needed to uncover causal phenotype-transcriptome connections that do not follow obvious linear behaviors. Finally, since one of the strains showed a much higher OTF than the other three, we were interested in search for stronger gene links with this phenotype in the SWR strain. A t-test conducted between SWR versus the remaining 3 strains resulted in 530 statistically signifi- cant clones (see Additional file 1, figure 1). Of these, 373 clones were common with the regression test while 280 were coincident with the ANOVA test. The overlap between the 3 tests resulted in a list of 266 clones hav- ing a functional profile that resembled the terms described in Additional file 1, Table 2 for the regression results. On the other hand, 143 clones were exclusive in the t-test. Reduction of the latter subset resulted in 92 unique gene identities which were subjected to a GO analysis summarized in Additional file 1, table 3. The combined 10-terms list suggests the involvement of intracellular vesicle traffic, protein sorting and actin cytoskeleton dynamics in the observed high OTF of SWR strain. Indeed, oocyte meiotic maturation involves events related to spindle assembly. In somatic cells, chromosome segregation errors during mitosis may con- tribute to cancer development and progression [51]. The genes App, Aplp2 and Appbp2, all related to the amyloid Page 8 of 11 beta precursor protein, were recurrent in Additional file 1, table 3. The well known involvement of amyloid beta protein in Alzheimer’s disease pathogenesis may actually be due to a chromosomal instability process [52]. Analo- gous mechanisms may partly explain the high OTF observed in SWR mice. Conclusion This work describes statistically significant variation in ovarian gene expression of four commonly studied mouse strains. We found that over 60% of these differ- ences are linked to the biological variability observed in spontaneous ovarian tumor rates and reproductive para- meters across strains. If NL is equivalent to multiparity, the inverse relationship detected between genes corre- lated to OTF and NL points to a protective effect of successive pregnancies. Post-transcriptional control of ovarian angiogenesis, folliculogenesis and oocyte maturation seems to be major contributors to this effect. Conversely, overexpression of protein folding genes might be considered as a susceptibility factor. These findings, in addition to the lack of association between OTF and LS -a measure of multiple ovulation- support repetitive menstrual cycling instead of repetitive ovula- tions as an important contributor to ovarian tumorigen- esis. Further experimental research as well as development of bioinformatic and statistical tools to uncover complex phenotype-transcriptome associations is needed. Methods Animals, RNA extraction and cDNA labeling Mouse strains BALB/c, C57BL/6, FVB and SWR were maintained at the Laboratory Animal Sciences Program, SAIC-NCI Frederick (Frederick, MD), under protocols of the Institutional Animal Care and Use Committee (IACUC). Adult (8-weeks old) females grown from trio mating-established colonies were euthanised on late metestrus phase by cervical dislocation after gaseous CO2 administration. Whole ovaries from 4-5 animals were removed from surrounding adipose tissue, there- after pooled and immediately frozen in liquid nitrogen. Total RNA was extracted with Trizol (Invitrogen, CA) and directly labeled as Cyanine-3 or Cyanine-5 fluores- cent cDNA using reverse transcription under conditions previously described [11]. Microarray experiments NIA-15K mouse cDNA microarrays were used. This is a curated collection consisting of 15,261 clones derived from expression libraries obtained from pre- and peri- implantation embryos, E12.5 female gonad/mesonephros and newborn ovaries [53]. Microarrays were spotted at the Laboratory of Molecular Technology, SAIC-NCI