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Endocrinology Vol. 140, No. 2 556-561
Copyright © 1999 by The Endocrine Society

ARTICLES

Interacting Quantitative Trait Loci Control Phenotypic Variation in Murine Estradiol-Regulated Responses1

Randall J. Roper, John S. Griffith, C. Richard Lyttle, R. W. Doerge, Andrew W. McNabb, Robert E. Broadbent and Cory Teuscher

Department of Veterinary Pathobiology (R.J.R., C.T.) and University Laboratory High School (A.W.M., R.E.B.), University of Illinois at Urbana-Champaign, Urbana, Illinois 61802; the Department of Microbiology, Brigham Young University (J.S.G.), Provo, Utah 84602; the Department of Obstetrics and Gynecology, University of Pennsylvania School of Medicine (C.R.L.), Philadelphia, Pennsylvania 19104; and the Departments of Agronomy and Statistics, Purdue University (R.W.D.), West Lafayette, Indiana 47907

Address all correspondence and requests for reprints to: Dr. Cory Teuscher, Department of Veterinary Pathobiology, 2001 South Lincoln Avenue, Urbana, Illinois 61802. E-mail: cteusche{at}staff.uiuc.edu


   Abstract
Top
 Abstract
Introduction
 Materials and Methods
 Results and Discussion
 References
 
The steroid hormone estradiol (E2) elicits a spectrum of systemic and uterotropic responses in vivo. For example, E2 treatment of ovariectomized adult and sexually immature rodents leads to uterine leukocytic infiltration, cell proliferation, and organ growth. E2-regulated growth is also associated with a variety of normal and pathological phenotypes. Historically, the uterine growth response has been used as the key model to understand the molecular and biochemical mechanisms underlying E2-dependent growth. In this study, genome exclusion mapping identified two quantitative trait loci (QTL) in the mouse, Est2 and Est3 on chromosomes 5 and 11, respectively, that control the phenotypic variation in uterine wet weight. Both QTL are linked to a variety of E2-regulated genes, suggesting that they may represent loci within conserved gene complexes that play fundamental roles in mediating the effects of E2. Interaction and multiple trait analyses using the uterine leukocyte response and wet weight suggest that Est4, a QTL on chromosome 10, may encode an interacting factor that influences the quantitative variation in both responses. Our results show that E2-dependent responses can be genetically controlled and that a genetic basis may underlie the variation observed in many E2-dependent phenotypes.


   Introduction
Top
 Abstract
 Introduction
Materials and Methods
 Results and Discussion
 References
 
THE BIOLOGICAL actions of estradiol (E2) are exceedingly diverse and are associated with a variety of normal and pathological phenotypes (1, 2, 3, 4, 5). However, the primary role of E2 in mammals is in regulating reproductive processes (2). For example, E2 elicits both genomic and uterotropic responses that effect dramatic changes. These changes include organ growth, increased vascular permeability, water imbibition, and an increase in the expression of hormonally regulated gene products. E2 also regulates the infiltration of leukocytes into the uterus, the most striking of which is an increase in the number of eosinophils (6). Thus, the E2 response is pleiotropic. Several methods, including differential display, have been used to identify new genes that are estrogen regulated (7, 8, 9).

Recently, we showed that the number of eosinophils infiltrating the uterus of ovariectomized (Ovx) E2-treated mice is a genetically controlled phenotype (6). Estrogen-dependent pituitary growth has also been shown to be genetically controlled in rats (10). The extent of eosinophilic infiltration into the uterus is governed by Est1, a quantitative trait locus (QTL) that maps to chromosome 4 and two minor loci on chromosomes 10 and 16 (6). Given that the E2-regulated uterine inflammatory response is genetically controlled, we undertook the present study to address whether uterine growth, as determined by wet weight, is also a genetically controlled phenotype. Using a whole genome scan on a (C57BL/6J x C3H/HeJ) x C3H/HeJ backcross population segregating for the high and low responder wet weight phenotypes, we report the identification of two QTL, Est2 on chromosome 5 and Est3 on chromosome 11, controlling E2-regulated uterine growth. Additionally, we present evidence for genetic interaction between loci involved in the genetic response to E2 and identified an interaction locus, Est4, on chromosome 10. Characterization of the genes at the molecular level will undoubtedly provide greater insight into the complex mechanisms by which E2 regulates growth in both normal and pathological states.


   Materials and Methods
Top
 Abstract
 Introduction
 Materials and Methods
Results and Discussion
 References
 
Animals
Five- to 6-week-old female C57BL/6J mice, female (C57BL/6J x C3H/HeJ)F1 (B6C3) hybrid mice, and female and male C3H/HeJ mice were purchased from The Jackson Laboratory (Bar Harbor, ME). (C57BL/6J x C3H/HeJ) x C3H/HeJ backcross (BC1) mice were generated and maintained in the animal facilities at Brigham Young University (Provo, UT) on a diet of Purina mouse pellets (Ralston Purina Co., St. Louis, MO) and water ad libitum.

Ovariectomies (Ovx) and hormonal stimulation
Parental, F1 hybrid, and BC1 female mice were Ovx (6) at 5–7 weeks of age. After Ovx, all animals were rested for 1 week, at which time they underwent E2 stimulation. All mice received 40.0 µg/kg BW 17ß-estradiol, sc, in 0.1 ml saline containing 20% (vol/vol) ethanol on days 0 and 1. All animals were killed 24 h after the second injection. The uteri were removed, cleaned of fat and adventitia, blotted to remove luminal fluid, and weighed.

Genotyping
Genomic DNA was isolated from liver tissue, as previously described (6). The genotype of each of the 94 BC1 animals was determined at 178 microsatellite marker loci (11, 12, 13) that distinguished the C57BL/6J and C3H/HeJ parental strains. These markers were spaced 20 centimorgans (cM) apart across the entire genome and more densely in regions of association with the phenotype. PCR parameters were optimized as previously described (11, 12, 13, 14).

Linkage analysis
A linkage map of the microsatellite markers was estimated using the Kosambi map function of the MAPMAKER/EXP computer package (15, 16). Likelihood ratio tests (LRT) were used to test association of single markers or marker intervals with the uterine wet weight phenotype in QTL Cartographer (17, 18, 19). When significant linkage was indicated by the LRT value, a new QTL was proposed (17, 20). Single marker analysis used each of the markers to detect QTL linked to the phenotype (17, 19). Additionally, interval mapping was used to identify and locate single QTL across the genome in 2-cM intervals (18, 19). Multiple trait analysis was performed using interval mapping based on the phenotypes of uterine wet weight and eosinophilic infiltration (19, 21).

Permutation-derived critical values
Significant linkage of QTL to genetic loci for all analyses was determined by permutation threshold theory (17, 20, 22). This method of analysis takes into account the specifics of the experimental situation, such as the number of animals used, genome size, and missing data, and also satisfies the multiple testing issues implicit in genome scans (marker density, number of tests used, and independence of tests) (20). For this experiment, 1000 permutations of the actual data were generated to provide the sampling distribution of test statistics under a null hypothesis of no linkage. Each permutation was performed by randomly shuffling and reassigning phenotypes from the BC1 animals to one of the specific genotypes of the population as defined by the microsatellite markers. Linkage analysis was then performed for each permuted set of data, and new test statistics (LRT) were generated for that permutation. A distribution of test statistics from the 1000 permutations was created, and significant linkage was declared according to the values of the pertinent distribution. Experimentwise threshold values included all data points in the genome scan and were obtained by using a distribution of the maximum test statistic from each of the 1000 permutations of the data. Comparisonwise thresholds used all of the permutation test statistics at a single marker and were identified using a distribution composed of the test statistics from 1000 permutations for the single marker (20, 23). Using 1000 permutations of the data, we are able to accurately define 90% ({alpha} = 0.10) and 95% ({alpha} = 0.05) thresholds of linkage, whereas higher thresholds would be estimated with substantially more permutations (20).

Interaction analysis
Interaction between QTL was investigated by using a general linear model in SAS (PROC GLM) (10, 24). No additional genetic map information was incorporated because all QTL tested were found on separate chromosomes. Models were constructed and assumptions were verified for each model. Interaction between Est2 and Est3 was tested using a model containing Est2, Est3, the interaction term between these QTL, and the dependent variable of uterine wet weight.

Models with QTL for both the uterine weight and number of infiltrating eosinophils and their interactions were constructed using either of the two phenotypes as the dependent variable. Independent variables representing QTL linked to D4Mit6 (Est1), D5Mit296 (Est2), D11Mit67 (Est3), D10Mit180 (Est4), and D16Mit144 (6) as well as all possible interactions among these variables were included in the model. The independent variables were selected in the following manner. The two known markers linked to the QTL for uterine weight, D5Mit296 (Est2) and D11Mit67 (Est3), or number of eosinophils [D4Mit6 (Est1), D10Mit180 (Est4), and D16Mit144] respectively, remained in the model while the other independent variables were selected using stepwise regression in SAS (24).


   Results and Discussion
Top
 Abstract
 Introduction
 Materials and Methods
 Results and Discussion
References
 
The uterine wet weights for control and E2-treated C57BL/6J, C3H/HeJ, and B6C3 F1 hybrid mice are presented in Table 1Go. C57BL/6J, C3H/HeJ, and B6C3 F1 hybrid mice, responded with a significant increase in uterine wet weight after E2 treatment compared with vehicle-treated mice. However, C57BL/6J and B6C3 F1 hybrid mice responded with a greater increase in wet weight than did C3H/HeJ mice ({alpha} < 0.05). In the carrier-treated animals, no significant difference in uterine wet weight was observed among the three groups. These results are consistent with the E2-regulated high responder uterine growth as seen in C57BL/6J mice being a genetically controlled, dominant trait. Thus, although all strains displayed a response to E2 stimulation, it was clear that the magnitude of the response was genetically determined. This finding, as in other genetically controlled phenotypes, can lead to the identification of loci regulating such responses.


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  		Table 1. Uterine wet weights of Ovx C57BL/6J, C3H/HeJ, and (C57BL/6J X C3H/HeJ) F1 hybrid mice after treatment with E2

 
To map the genes controlling uterine growth, we generated a linkage map using 178 microsatellite markers on 94 (C57BL/6J x C3H/HeJ) x C3H/HeJ phenotyped BC1 mice. Significant associations between the genotypic markers and the phenotypic values for the animals were examined. Based on 1000 permutations of the original data, single marker analysis exhibited significant experimentwise linkage ({alpha} = 0.05; threshold value = 12.85) on chromosome 5 at D5Mit296, D5Mit148, and D5Mit388 and on chromosome 11 at D11Mit67 and D11Mit132. Significant comparisonwise linkage ({alpha} = 0.05) was detected on chromosome 5 for markers D5Mit387-D5Mit80 and on chromosome 11 for markers D11Mit86-D11Mit61 (Table 2Go). Therefore, genetic control of uterine wet weight was shown to be linked to marker loci on chromosomes 5 and 11.


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  		Table 2. Linkage maps of mouse chromosomes 5 and 11 and association of marker loci to uterine weight

 
Additionally, interval mapping using 1000 permutations of the original data showed significant experimentwise linkage to chromosomes 5 and 11. On proximal chromosome 5, the 2.39-cM region that encompasses D5Mit296 and D5Mit388 is significant ({alpha} = 0.05; threshold value = 13.39; see Fig. 1Go). Similar to the single marker analysis, interval mapping shows a small region on the distal end of chromosome 11 encompassing D11Mit67 and D11Mit132 that is significant ({alpha} = 0.10; threshold value = 11.67; see Fig. 2Go). These results support the single marker linkage analysis and establish the existence of QTL on chromosomes 5 and 11 controlling uterine wet weight after E2 stimulation. Based on the results from single marker and interval mapping analysis, we have designated the D5Mit296 locus on chromosome 5 as Est2 and the D11Mit67 locus on chromosome 11 as Est3.



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  		Figure 1. Interval map showing linkage of markers on mouse chromosome 5 with the E2-dependent quantitative trait uterine weight. Est2 is defined by the permutation threshold at {alpha} = 0.05.

 


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  		Figure 2. Interval map showing linkage of markers on mouse chromosome 11 with the E2-dependent quantitative trait uterine weight. Est3 is defined by the permutation threshold at {alpha} = 0.10.

 
As Est2 and Est3 both govern E2-induced uterine wet weight, we wanted to examine whether animals that are heterozygous at both loci have a greater uterine wet weight than those animals that are heterozygous at only one of the loci. Table 3Go presents the mean uterine wet weights for the possible genotype combinations at Est2 and Est3. Mice that are homozygous for C3H/HeJ alleles at both Est2 and Est3, as expected, are the lowest responders (31.0 ± 2.7). The presence of C57BL/6J alleles at either Est2 (38.8 ± 2.6) or Est3 (37.5 ± 3.1) results in a minor increase in average uterine wet weight when they are the only major contributing alleles. However, when C57BL/6J alleles at Est2 and Est3 are present together, a nearly 2-fold increase in uterine wet weight is seen (52.0 ± 2.5). When the means of the allelic combinations were tested using Tukey’s multiple comparison test, the animals that were heterozygous at both Est2 and Est3 (C/B, C/B) had means significantly different ({alpha} = 0.05) from those of the other loci combinations. No differences between other loci combinations, (C/C, C/C), (C/B, C/C), and (C/C, C/B), were statistically significant. This suggests epistasis or interaction between Est2 and Est3.


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  		Table 3. Correlation between genotype and uterine wet weight

 
To directly test the interaction between Est2 and Est3, a general linear model was used. A model containing Est2 and Est3 explained 27.3% of the variance and was statistically significant (F = 16.52; P = 0.0001). When the interaction term between Est2 and Est3 was added to the model, the variance accounted for increased to 28.5%. Yet, the addition of this interaction term to the model already containing the single independent variables was not statistically significant (F = 1.51; P = 0.2224). Therefore, the data collected provided no evidence for significant interaction between Est2 and Est3.

Multiple interaction factors in the signaling pathways leading to the E2-regulated responses have been suggested (25, 26, 27, 28). We hypothesized that the QTL controlling eosinophilic infiltration in the uterus after E2 stimulation may also be involved in the uterine growth phenotype and that these QTL may interact to control these E2-dependent phenotypes. Our rational for creating new interactive independent variables was that some of the interaction terms may encode transcription factors, activation factors, or coactivators (25, 26, 27, 28) that are common to multiple pathways in E2 responses. Most simply, these interactions may represent heterodimers that function as a single entity and therefore are represented effectively as new, independent interaction terms.

For uterine weight, a model including the independent variables D5Mit296 (Est2) and D11Mit67 (Est3) as well as the interaction term between D11Mit67 (Est3) and D10Mit180 was significant in explaining the quantitative trait (F = 14.256; P < 0.0001). The model with these three independent variables explained 33.0% of the variation in uterine weight, and when adjusted for the number of parameters in the model, the explained variation was reduced to 30.7%. A model based on linear multiple regression with uterine eosinophils counted in the stroma (6) as the dependent variable found the independent variables D4Mit6 (Est1), D10Mit180, and D16Mit144 as well as the interaction terms between D10Mit180 and D4Mit6 (Est1) and between D10Mit180 and D5Mit296 (Est3) to be significant (F = 9.026; P < 0.0001). The model with these three independent variables explained 34.7% of the variation in uterine weight, and when adjusted for the number of parameters in the model, the explained variation was reduced to 30.9%. The interaction analysis supports our hypothesis of interaction between QTL controlling E2-regulated responses.

To understand the overall genetic control of the E2-regulated uterine response, we performed multiple trait analysis (21) for both quantitative traits (uterine weight and number of infiltrating eosinophils) together (6). Linkage was found to marker loci D4Mit6 (Est1; LRT = 15.05), D5Mit296 (Est2; LRT = 17.84), D10Mit180 (LRT = 16.02), and D11Mit67 (Est3; LRT = 14.38; {alpha} = 0.10 and {alpha} = 0.05 thresholds equal 14.60 and 16.72, respectively). Based on the significance of D10Mit180 in eosinophil infiltration, the significant interaction of D10Mit180 in both E2-regulated responses, and the significance of this locus in multiple trait analysis, we have designated the QTL linked to D10Mit180 as Est4. These results corroborate the conclusions of the interaction analysis.

Interestingly, each of the QTL identified in this study controlling E2-regulated uterine growth maps to a region known to encode other E2-regulated genes or loci that may potentially influence E2-regulated responses (www.informatics.jax.org/locus.html). On chromosome 5 these include serotonin receptor 5a (Htr5a) (29) and interleukin-6 (Il6) (30, 31, 32). Genes linked to Est3 on chromosome 11 include procollagen, type I, {alpha} 1 (Cola1) (33), integrin {alpha}3 (Itga3) (34, 35), granulocyte colony-stimulating factor (Csfg) (36), retinoic acid receptor-{alpha} (Rara) (37, 38, 39), thyroid hormone receptor-{alpha} (Thra) (38), and one of the genes linked to human familial breast cancer (Brca1). Although the interval encoding each QTL may contain several known E2-regulated genes, the exact role of any of these candidate genes in the hormonal response can only be suggested by the current data.

The colocalization of Brca1 with Est3 is, however, worth noting given the current status of the role of E2 in the regulation of Brca1 (40). Human Brca1 is known to contain E2 response elements, but the murine form of the gene has no homolog to these Alu sequences (27). It has been postulated that the expression of Brca1 in numerous tissues, including the ovary of both humans and mice, is controlled by E2 (25, 28, 41), yet the current consensus is that Brca1 is not directly influenced by E2. Rather, E2 is thought to indirectly influence the expression of Brca1 and is under complex control (26, 27, 42, 43, 44).

The control of the expression of Brca1 is interesting in light of the interaction analysis in this study. When uterine weight was used as a dependent variable, the interaction between Est3 and Est4 was significant. Moreover, Est4 was also involved in interactions controlling uterine eosinophilic infiltration (6). We hypothesize that Est4 may encode a transcription factor, activation factor, or coactivation factor that is important in the overall genetic response to E2 treatment and that only in combination with additional factors is quantitative variation observed in the phenotype (25, 26, 27, 28). As such, Est4 may encode the putative factor involved in the E2-regulated expression of Brca1. It is also interesting that neither Est3 nor Est4 interacts with D5Mit169, the closest marker linked to Brca2 in our model (data not shown; www.genome.wi.mit.edu/cgi-bin/mouse/index). This suggests independent estrogen regulation of Brca1 and Brca2 (44). We have shown that interaction and multiple trait analysis may prove useful to identify additional QTL, provide a model to describe the phenotypic effect, and elucidate mechanisms of biochemical interaction.

Similar mapping approaches have identified QTL on chromosomes 2, 3, 5, 9, and 14 controlling diethylstilbestrol-dependent pituitary tumor growth in the rat (10). From the current comparative map of mouse and rat (www.informatics.jax.org/homology.html), it appears that Est2, Est3, and Est4 are not represented among these five QTL. Est2 on mouse chromosome 5 is syntenic with rat chromosome 4, whereas Est3 on mouse chromosome 11 is syntenic with rat chromosome 10 (www.informatics.jax.org/homology.html). However, Est1, the locus involved in controlling the E2-regulated uterine eosinophilic inflammatory response is probably syntenic with Edpm5 on rat chromosome 5 (www.informatics.jax.org/homology.html). It is worth noting that in all three models epistasis plays a role in the genetic control. Additionally, none of the QTL mapped colocalize with estrogen receptor-{alpha} (Estra; chromosome 10 at 12 cM) or estrogen receptor-ß (Estrb; chromosome 12 at 33 cM; www.informatics.jax.org/locus.html), suggesting that polymorphisms in these two E2 receptor genes do not underlie the phenotypic variations observed.

Taken together, our results suggest that the regions of chromosomes 5 and 11 encoding Est2 and Est3 contain a variety of E2-regulated genes and that many of these genes have the clear potential to diversify or amplify E2-regulated responses. Thus, it is conceivable that Est2 and Est3 may represent single genes or loci within conserved gene complexes that play fundamental roles in mediating the effects of E2. These genes or loci may also be involved in the general reproductive performance and characteristics of mice. Interestingly, C57BL/6J mice have larger litter sizes, more litters in a lifetime, and greater relative fecundity than C3H/HeJ mice. C57BL/6J mice also have a high response to superovulation, whereas C3H/HeJ mice are low responders (45). Characterization of the Est loci at the molecular level will undoubtedly lead to a greater understanding of the genetic components underlying both genomic and uterotropic responses elicited by E2. Finally, our results suggest that other E2-regulated/dependent responses, such as the immunological (46, 47), carcinogenic (3), skeletal (4), and developmental (5), may also have a genetic component that plays a role in the phenotypic spectra observed within these response groups, and that mapping studies can be used for the eventual identification of genes involved in hormonally regulated responses.


   Acknowledgments
 
We thank Julie Teuscher for her expert technical assistance. We also thank Christopher J. Basten for valuable comments and technical assistance.


   Footnotes
 
1 This work was supported by NIH Grants HD-21926 (to C.T.), HD-27275 (to C.T.), AI-40712 (to C.T.), and NS-36526 (to C.T.); National Multiple Sclerosis Society Grant RG-2659 (to C.T.); and a traineeship on NIH Grant T32-GM-07283 (to R.J.R.). Back

Received May 28, 1998.


   References
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 Abstract
 Introduction
 Materials and Methods
 Results and Discussion
 References
 

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