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a Section of Neurobiology, Physiology and Behavior, University of California at Davis, Davis, California 95616
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The largest genetic differences in ovulation rate are found in response to exogenous gonadotropins. A 6-fold difference in HIOR between mouse strains A/J (8–9 eggs in cumulus) and C57BL/6J (40–54 eggs in cumulus) segregates in a F2 cross as though controlled by the action of approximately 3–4 loci with major effects [7, 8]. The 6-fold higher HIOR of C57BL/6J (B6 or B) over that of A/J is largely due to a greater induction of follicle maturation and a decreased incidence of atresia in B6 [9]. Interestingly, the higher response of B6 to gonadotropins is associated with higher ovarian aromatase activity and estrogen production [10, 11]. Aromatase activity is extremely important in large antral follicles, and intrafollicular estradiol and the ratio of estradiol to androgen is much higher in healthy than in atretic follicles [2, 12–14]. Variation in intrafollicular aromatase activity could result from differences in serum gonadotropins, or in any step in the mechanism by which gonadotropins induce follicular maturation and aromatase activity [15].
The nature of strain differences in HIOR is complex and cannot be determined simply by using biochemical or physiological approaches, which fail to separate the direct effects of genes that control reproduction from the secondary or pleiotropic effects of these genes. For example, another gene, located elsewhere in the genome, that codes for a different regulatory control point could be responsible for both the differences in aromatase activity and in HIOR. Genes that control differences in a trait will segregate with the differences in the trait while unlinked genes will segregate independently of the trait. Linkage mapping, which is based on Gregor Mendel's law of independent segregation, enables one to map genes that control differences in a trait, i.e., quantitative trait loci (QTL). Significant quantitative trait loci (QTL) are defined as QTL that occur with a P < 0.05, due to chance alone, per genome wide scan [16]. A suggestive QTL is defined as a QTL that occurs with a P < 1.0, due to chance alone, per genome wide scan [16]. QTL linkage analysis have been conducted on a widespread basis in divergent crosses of mice and rats to map QTL that control many traits including intestinal neoplasia [17], hypertension [18], and obesity [19]. While this powerful approach is being conducted on a widespread basis to genetically dissect complex traits, very few studies have used this powerful approach to map genes that control complex reproductive traits [20–24].
A small number of genes controlling differences in ovulation rate have been identified or mapped in mammals. The Booroola fecundity gene (Fecb) increases litter size by increasing ovulation rate and maps to Chr 6 in sheep, which is syntenic to Chr 4 in humans and either Chr 3 or 5 in mice [25]. The Inverdale Fecundity gene (FecXI), which maps to the X chromosome, also increases natural ovulation rate in the heterozygous state. However, homozygous FecXI/FecXI ewes are infertile due to the presence of streak gonads that lack developed follicles [26]. In cattle and swine, suggestive natural ovulation rate QTL have been mapped to bovine Chr (BTA) 7 and swine Chr 8 and 13 [20, 21]. Less-than-suggestive ovulation rate QTL have been mapped to BTA 23 [21] and swine Chr 4 and 15 [20]. While these ovulation rate QTL have yet to be confirmed in cattle, the bovine Chr23 marker (Cyp21, i.e., steroid 21-hydroxylase) maps to the same syntenic region affecting induced ovulation rate in mice [9].
We have used a molecular genetic linkage analysis to identify regions of the mouse genome that control major differences in HIOR. This linkage analysis was performed on (B6xA)xA backcrosses previously scored for major differences in HIOR [8]. Cyp19 (P450 aromatase) was considered a priori as the most likely candidate locus controlling HIOR since the activity of ovarian P450 aromatase plays a critical role in determining whether a follicle matures and ovulates or undergoes atresia [2, 13]. Furthermore, A/J and B6 mice show major differences in aromatase activity and estrogen production [10, 11, 27]. As Cyp19 is tightly linked to Cyp11A on chromosome (Chr) 9 [28], the first locus tested in the present study as an ovulation rate-induced (ORI) QTL (Oriq) was Cyp19. The genotype of a (B6xA)xA backcross population was determined at 232 molecular genetic markers, providing markers within 10 cM of about 95% of the mouse genome. The present data support suggestive ORI QTL on mouse Chr 9, 2, and X, as well as significant ORI QTL on Chr 6 that control the number of eggs in cumulus. Each QTL on mouse Chr 2, 6, and X has subsequently been confirmed in reproductive congenic mouse strains [29]. These data also mapped suggestive ORI QTL to Chr 6, 7, and X, and a significant ORI QTL to Chr 10, which control the number of eggs matured by endogenous gonadotropins and ovulated by eCG.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The 474 (B6xA)xA backcross mice used in the present molecular genetic analysis were previously scored for HIOR. The hormonal treatments, care and breeding of mouse strains and crosses, and results of a segregation analysis of HIOR in this backcross population are presented in a companion paper [8].
Molecular Biology Procedures
Large molecular weight genomic DNA was purified from the liver and spleen of mice using a modification of the procedure of Taylor and Rowe [30]. Southern blots were prepared by separating restriction fragments on 0.8–1% SeaKem LE agarose gels (FMC BioProducts, Rockland, ME) in 0.8-strength TBE, UV nicking at 0.8 KJ/m2, vacuum transferring to Hybond N+ membranes in 0.4 M NaOH, 1.5 M NaCl, neutralizing in double-strength SSPE, pH 7.2, and cross-linking wet in a Stratagene (La Jolla, CA) UV cross-linker at 1.2 KJ/m2 [31–33]. To obtain RFLPs, DNA purified from A/J and B6 was digested with a panel of 6-base and 4-base restriction enzymes, including BamHI, EcoRI, HindIII, HinfI, MspI, PalI, PstI, PvuII, and/or TaqI. Southern blots prepared with these DNAs were used to detect RFLPs.
Polymorphic markers were obtained as follows: Mmtv Env and Mmtv LTR probes were from Dr. Wayne Frankel (Jackson Laboratory, Bar Harbor, ME) [34]; rat LH alpha and rat FSH beta cDNAs were from Dr. William Chen (Harvard Medical School, Boston, MA); rat P450 aromatase and P450 side chain cleavage enzyme cDNAs were from Dr. JoAnne Richards (Baylor College of Medicine, Houston, TX); GST Ya cDNA was from Dr. Violet Daniel (Weizmann Institute, Rehovot, Israel) [35]; minisatellite (Ms) 2 (pYNZ2), Ms4 (pYNH24), and Ms6 (pAW101) were from the American Type Culture Collection (Manassas, VA) [36]. Oligonucleotide JS-5 was synthesized and purified by HPLC, while oligonucleotides JS-4 and JS-6/10 were obtained from Dr. Wayne Frankel [37–39].
Cloned plasmids were purified, and inserts were isolated and random oligonucleotide labeled with [
-32P]dATP [33, 40]. Oligonucleotides JS-4, JS-5, and JS-6+10 (40 ng) were end-labeled with [32P]dATP using terminal deoxynucleotide transferase. Southern blots were hybridized in a custom rotary tube hybridization incubator in 0.5 M Na2HPO4, 7% SDS, 1 mM EDTA, pH 7.2 at 58 to 65°C. Blots were washed at the desired stringency in double-strength to 0.1-strength SSPE, 0.2% SDS at 58–67°C, and autoradiographs were prepared using x-ray film and intensifying screens at -80°C.
The genotype of backcross mice at microsatellite or simple sequence length polymorphism (SSLP) loci was determined by modifications of the procedures of Weber [41] and Dietrich et al. [42]. The SSLP primers were chosen from those polymorphic between A/J and C57BL/6J [42] and were purchased from Research Genetics (Huntsville, AL). Primers for 4–6 different SSLP [42] were multiplex amplified in a MJ Research (Watertown, MA) 96-well thermal cycler using 50 ng of mouse genomic DNA, 0.15 µM of each primer, single-strength Taq polymerase buffer, 100 µM each of dCTP, dGTP, and dTTP, 20 µM dATP, 3 x 105 cpm of [-32P]dATP and either 0.75 mM free MgCl2 with 0.2 U Taq polymerase (Promega, Madison, WI) or 1.0 free mM MgCl2 with 0.6 U Stoffel fragment Taq polymerase (Perkin Elmer, Norwalk, CT) per 10-µl reaction. After denaturing the DNA (2.5 µl) and primers (2.5 µl) under mineral oil at 93°C for 4 min, 5 µl of a double-strength mixture of the remaining Taq buffer components was added at 82°C, and then 25–28 cycles of primer extension were performed by denaturating at 94°C for 50 sec, annealing primers at 54–58°C for 60 sec, and chain extension at 72°C for 90 sec for Taq or 3 min for Stoffel fragment. Following a final extension at 72°C for 8 min, 10 µl of gel loading buffer (95% formamide, 3 mM EDTA, and 0.2% xylene cyanole FF) were added. To facilitate analysis of large numbers of samples and to minimize errors in loading samples, reaction mixtures were denatured at 80°C and loaded directly from 96-well plates onto denaturing 7% polyacrylamide gels with wells at 4.5-mm centers (CBS Scientific, Del Mar, CA) with a custom-made, 7-well syringe pipettor. Following electrophoresis, one glass plate was removed, the gel and remaining plate were covered with plastic wrap, and autoradiographs were prepared with an intensifying screen at -85°C.
Hormonal Induction of Follicle Maturation and Ovulation
Both the number of eggs in cumulus and the number of eggs out of cumulus were examined. The number of eggs in cumulus is a function of the number of follicles that mature in response to eCG and ovulate in response to hCG, and then are counted in cumulus. Thus, the number of eggs in cumulus is an indicator of a female's ability to mature and ovulate follicles in response to exogenous gonadotropins. In contrast, the number of eggs out of cumulus reveals the number of eggs matured by endogenous gonadotropins and ovulated by the initial dose of eCG. Since induced ovulation rate was measured in immature females that were too young to generate an LH surge, the number of eggs out of cumulus corresponds to differences in the number of follicles matured by endogenous gonadotropins and then ovulated by eCG. Therefore, the number of eggs out of cumulus should be an indicator of factors controlling natural or spontaneous follicle maturation and ovulation rate.
Selective Genotyping
Selectively genotyping the individuals with the most extreme phenotype in a backcross or F2 population increases the efficiency of mapping QTL [43]. Indeed, determining the genotype of the most extreme 33% of the population provides 81% of the information. To maximize the chances of detecting reproductive QTL in the (B6xA)xA backcross, we initially determined the molecular genotype of the 79 backcrosses that showed most divergent HIOR at up to 232 loci each throughout the genome. While Lander and Bostein [43] strongly argued in favor of selectively genotyping the extreme individuals in the population, they did not mention that it is essential to also determine the genotype of all individuals in the population at loci flanking suspected QTL. Due to the rationale for selectively genotyping only the extreme individuals, we unfortunately followed their recommendations and saved tissue from all 79 backcross individuals with most extreme HIOR, but only a portion of the individuals with more intermediate HIOR. Nevertheless, after determining the genotype of the extreme individuals at loci throughout the genome, i.e., selective genotyping, molecular genotype was also determined at 48 loci flanking suspected QTL for the 88 remaining backcrosses from which tissue was saved. Thus, molecular genotype was determined on a total of 167 (B6xA)A backcross mice at a total of 19 636 individual mouse x molecular marker combinations (not including the questionable scorings of molecular markers).
Linkage Mapping
To detect errors, all loci were scored by two individuals. Potential double crossovers were rechecked and in some cases experiments repeated to confirm double crossovers. Linkage between loci was determined with the aid of Map Manager V 2.6.5 with backcross statistics [44] and Mapmaker V3.0 [45, 46]. Analyses included single point and multipoint procedures for testing linkage of loci [43]. Two loci were considered linked only if they showed a log of the odds (LOD) score of 3.0 or greater, indicating a 1000:1 probability of being linked relative to the probability of segregating independently.
QTL Linkage Mapping
Reproductive QTL were identified and mapped in a preliminary interval analysis with Mapmaker QTL 1.1b [43, 47, 48] on a Sun Sparc Station II computer (Sun Microsystems, Palo Alto, CA). The effects of individual loci and loci in combination on 1) the number of eggs in cumulus (HIOR), 2) the number of ova out of cumulus, 3) the total number of eggs ovulated, and 4) body weight were considered. Traits were also analyzed following square root transformation to improve the homogeneity of variances. The effect of the B allele at each locus on HIOR, i.e, the difference in HIOR between mice that were AB versus AA at a given locus in the (B6xA)xA backcross population, was determined with Mapmaker QTL 1.1b using the reproductive performance from all 474 (B6xA)xA backcrosses and the molecular genotype of each of the 79 selectively genotyped backcrosses at 232 different loci (data not shown). Following preliminary QTL analysis, molecular genotypes were also determined at 48 loci flanking suspected reproductive QTL in an additional 88 backcrosses with more intermediate HIOR. The final QTL linkage analysis was then conducted on all 167 backcrosses at 165 loci that were most thoroughly genotyped and evenly spaced throughout the mouse genome. The molecular genotypes of the other backcross mice that were not examined in the molecular genetic analysis were included as missing data in analysis with Mapmaker QTL, but had to be omitted for analysis with ZmapQTL and Map Manager QTb23 [43, 44, 49].
QTL analysis was also conducted with ZmapQTL [50] using interval analysis (Zeng model 3). Finally, QTL analysis was conducted with ZmapQTL using composite interval mapping (Zeng model 6) [49]. Composite interval mapping involved an interval analysis at 2-cM intervals combined with correction by linear regression for the 5 loci with largest effects outside a 10-cM window flanking the locus under consideration.
Critical QTL Thresholds
Several approaches have been used to estimate the critical thresholds for establishing the presence of a QTL. When a single candidate locus is tested as a QTL a priori, a single comparison-wise type I error rate or alpha, i.e., the probability of finding a QTL when none exists, has been used to estimate the probability of finding a false positive. However, since many separate tests can be performed in any QTL linkage analysis study (and usually are performed following negative results), the use of a standard single test alpha of P < 0.05, which is equivalent to LOD = 0.83, would result in the detection of several QTL due to chance alone in genome-wide tests and is therefore highly inappropriate [16]. A more conservative approach to reducing type I errors is to use sparse or intermediate marker density-case LOD thresholds or P values [43, 51]. However, the use of interval mapping methods or hierarchical searches (where additional markers are typed in "interesting" regions) often result in type I (false positive) errors more characteristic of dense-case maps. The best and most conservative approach to reducing type I errors is to use dense-case LOD thresholds [43]. A significant dense-case QTL has a genome-wide alpha or false positive rate of (P < 0.05), which requires an LOD score > 3.29 or nominal P < 0.0001 for backcrosses, or an LOD score > 4.3 or a nominal P < 0.000052 for F2 intercrosses [16]. Note that the formula provided by Lander and Botstein [43] for estimating dense-case LOD thresholds contains typographical errors (Corrigendum: Lander and Botstein, Genetics 1994; 136:705).
The stringent standards for obtaining appropriate type I or alpha errors when detecting a QTL greatly increase the probability of type II or beta errors, i.e., failing to detect a QTL when a QTL actually exists. It is very difficult to obtain a genome-wide, alpha error of 0.05 (corresponding to a nominal single test P < 0.0001 in a backcross) for traits with medium to low heritability, especially if they are controlled by several QTL. Standards for publication of QTL previously required LOD scores above 3, which prohibited several reports of genetic analyses of QTL for reproductive and low heritability traits, including portions of the present data. However, molecular markers are most urgently needed for reproductive traits that are among the most difficult to map due to their low heritability and difficulty in measurement, as well as their sex-limited and developmentally specific expression. To minimize type one and type two errors, and to allow extension studies, we used thresholds proposed by Lander and Kruglyak [16] namely that 1) QTL data with point-wise P < 0.05 be published with no claims of linkage to a QTL; 2) QTL data with a genome-wide alpha of P < 1.0, i.e, QTL data obtained under conditions that would result in detection of one spurious QTL per genome be published as "suggestive QTL" (which corresponds to LOD scores > 1.9 and nominal P values < 0.0034 in backcrosses and LOD scores > 2.8 or a nominal P value < 0.0016 for F2 intercrosses); 3) QTL data with a genome-wide alpha of P < 0.05, which correspond to LOD scores above 3.29 for mouse backcrosses, be published as "significant QTL"; and 4) significant QTL that have been confirmed in extension studies with a nominal P < 0.01 be considered as "confirmed QTL" [16].
The above methods for estimating critical thresholds suffer from the fact that such QTL thresholds are dependent on the phenotypic distribution of the population for each trait [52]. Therefore, following analysis of this backcross data set, comparison-wise and experiment-wise P values were also estimated empirically by QTL analysis of 1000–10 000 permutations of random shuffles of the data set using Map Manager QTb23 or ZMapQTL [49, 52, 53].
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RESULTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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As shown in Table 1, RFLPs were detected between A/J and B6 strain mice at several loci and then used as molecular markers in a RFLP linkage analysis. The endogenous proviral Pmv, Mpmv, and Xmv loci were detected on Southern blots prepared from EcoRI and from PvuII digests [37–39]. Julier and colleagues [36] mapped several VNTR loci in BXD RI strains, but they did not report the size of each of the many VNTR alleles. Since we mapped these minisatellites (Ms) in a B6 cross with A/J rather than with DBA/2J, it was not possible to determine which of the loci observed in the present study definitely correspond to the previously reported bands. Therefore, new Cph locus designations were assigned to avoid confusion. The Ms 2 VNTR locus D2Cph29 may or may not be analogous to D2Cph24. Similarly, the Ms 2 VNTR locus D9Cph23 may or may not be analogous to D9Cph9 [36]. Several loci including the Ms 4 VNTR locus D6Cph8 and the Ms 6 VNTR loci D2Cph30 and D6Cph7 have not been reported previously. The majority of SSLP alleles showed amplification products with lengths similar to those of previous reports [42]. However, some microsatellites reported by Dietrich et al. to be polymorphic between A/J and B6 were not polymorphic or showed different allele sizes in the present study.
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The genotype of 167 (B6xA)xA backcross mice was determined at 232 different RFLP, endogenous proviral, VNTR, and SSLP loci. Linkage analysis of these data with Map Manager 2.6.5 and Mapmaker 3.0 resulted in the genetic linkage map shown in Figure 1. This linkage map has molecular genetic markers on each of the 20 mouse chromosomes with molecular markers within approximately 10 cM of about 95% of the mouse genome. Most loci showed recombination frequencies and chromosomal locations similar to those reported previously and to the GBase consensus mouse genomic map [54, 55].
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Type 3 Interval Analysis for QTL That Control Number of Eggs in Cumulus
Table 2 shows the results of interval analysis of the (B6xA)xA backcross population for HIOR using Mapmaker QTL. To minimize beta errors, markers with LOD scores greater than 0.83, which is equivalent to a nominal P < 0.05, are shown. To minimize alpha errors, only markers with LOD scores over 1.9 should be considered as suggestive QTL, and only markers with LOD scores over 3.29 should be considered as significant QTL. As shown in Table 2 and Figure 2, interval analysis with Mapmaker QTL detected suggestive ORI QTL with LOD scores > 1.9 on mouse Chr 2, 6, 9 and X that control the number of eggs in cumulus. However, none of the individual ORI QTL controlling the number of eggs in cumulus were significant on an experiment-wise basis in an interval analysis using Mapmaker QTL.
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QTL Linkage Analysis was also conducted with ZmapQTL and Map Manager QTb23 [44, 49, 50]. Unlike Mapmaker QTL, which estimates genotypic effects based on the full phenotypic distribution, ZmapQTL and Map Manager QT determine the actual mean effect of a B6 allele in the backcross individuals that were genotyped. Since we kept with Lander and Bostein's recommendation of only selectively genotyping the extremes [43], only a portion of the backcrosses with intermediate HIOR could be included in the genotypic analysis. Thus, the genotypic means estimated by Mapmaker QTL for the full population differ from the actual means determined by ZmapQTL and Map Manager QT on genotyped individuals. Nevertheless, ZMapQTL and Map Manager QT allowed composite interval analysis and permutation analysis while Mapmaker QTL did not.
As shown in Table 3, interval analysis with ZmapQTL detected suggestive ORI QTL with LOD scores > 1.9 in the same regions of mouse Chr 2, 9, and X as detected in the full data set by Mapmaker QTL. Using ZmapQTL model 3, these markers showed empirical comparison-wise P values of P < 0.001 to P < 0.004 for HIOR. Permutation analysis with Map Manager QT also detected an empirical genome-wide suggestive QTL near D6Mit316.
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Composite Interval Analysis
The results of composite interval analysis for HIOR using ZmapQTL (Zeng model 6) are shown in Table 4 and Figure 3. Composite interval analysis, i.e., interval analysis combined with correction for the effects of the 5 most significant loci located elsewhere in the genome, revealed that the effects of loci on Chr 6, 9, and X were most significant on HIOR. Composite interval analysis detected a ORI QTL on Chr 6 that was significant (P 
0.02) on a experiment-wise basis, i.e., on a genome-wide scan (LOD = 3.45; likelihood ratio = 15.889). Thus, a significant QTL that controls HIOR maps to Chr 6 in the vicinity of D6Mit316.
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Suggestive HIOR QTL on Chromosome 9: Oriq1
Cyp19 (the P450 aromatase gene) was considered as the most likely candidate locus controlling HIOR. A screen for RFLPs showed polymorphisms between A/J and B6 at Cyp19 using DNAs digested with BamHI, HindIII, TaqI, HaeIII, or HinfI. Southern blots prepared from Hind III digests were used for mapping Cyp19 in the (B6xA)xA backcross population. A screen for RFLPs at Cyp11A (P450 side chain cleavage enzyme) showed polymorphisms between A/J and B6 using DNAs digested with BamHI or with EcoRI. As a result, Southern blots prepared from EcoRI digests were used for mapping Cyp11A. Linkage analysis did not detect recombinants between Cyp19, Cyp11A, and D9Mit4 in any of the 79 backcross mice, which is consistent with earlier reports that these loci are tightly linked on chromosome 9 [28]. Since no recombinants were found between D9Mit4, Cyp19, or Cyp11, we consider D9Mit4 to be a good molecular marker for these endocrine structural genes.
As shown in Table 2 and Figure 2C, analysis with Mapmaker QTL detected a suggestive B6 ORI QTL on Chr 9. The effect of the B6 allele on HIOR as estimated by Mapmaker QTL, i.e., the difference in HIOR between backcross individuals with the AB versus the AA genotype, peaked about 2 cM distal to Cyp19 and D9Mit4 but proximal to Xmv15. Mapmaker QTL estimated that backcross mice with a B6 allele on Chr 9 at Cyp19 ovulated 2.74 more eggs in cumulus (LOD = 2.23); did not differ in number of eggs out of cumulus; and shed 3.95 more total eggs (LOD = 2.401) than mice that were AA at this locus. Given that we planned to test Cyp19 as a ORI QTL a priori, the observed LOD score is highly significant (P = 0.0022) as a single test alpha. However, a more appropriate permutation analysis with Map Manager QT showed that this suggestive ORI QTL had a P value of 0.55 on a genome-wide basis.
Interval analysis using QTL Cartographer (Program in Statistical Genetics, Department of Statistics, North Carolina State University, Raleigh, NC; http://statgen.ncsu.edu/) estimated that a B6 allele in the vicinity of D9Mit4 and Cyp19 on mouse Chr 9 increased HIOR by 5.1–5.5 eggs (likelihood ratio = 9.5 to 9.9; LOD = 2.148 to 2.072; P < 0.001 to P < 0.002), depending on whether the data were corrected for the effect of other loci by regression. These data support the presence of a suggestive ORI QTL in the vicinity of Cyp19 and D9Mit4 on mouse Chr 9.
Suggestive ORI QTL on Chromosome 2: Oriq2
Analysis with Mapmaker QTL detected a suggestive B6 ORI QTL peaking near Pmv7 and D2Mit433 on Chr 2 (Table 2 and Fig. 2A). Mapmaker QTL estimated that mice with a B6 allele at this region of Chr 2 ovulated 2.65 more eggs in cumulus (LOD = 2.084). Square-root transformed data also showed a QTL at Pmv7/D2Mit433 (LOD = 2.382). Genotype at this region of Chr 2 did not affect the number of eggs out of cumulus.
Interval analysis with QTL cartographer estimated that a B6 allele in the vicinity of D2Mit433 on mouse Chr 2 increased HIOR by 5.2 eggs (likelihood ratio = 8.75; LOD = 1.901; empirical P < 0.003). Interval and permutation analysis with Map Manager QT also detected a suggestive ORI QTL at D2Mit433 with a P = 0.35 on a genome-wide basis, as well as a more proximal suggestive ORI QTL between D2Mit1 and D2Mit32. However, as shown in Table 4 and Figure 3A, composite interval mapping with ZMapQTL Zeng Model 6, did not detect a QTL controlling HIOR in these regions of Chr 2.
Significant ORI QTL on Chromosome 6: Oriq3
Depending on the method of analysis, a suggestive-to-significant QTL controlling the number of eggs in and out of cumulus mapped to Chr 6 Near D6Mit316. Analysis with Mapmaker QTL detected a suggestive B6 ORI QTL which peaked near D6Mit316 (Table 2 and Fig. 2B). Mapmaker QTL estimated that mice with a B6 allele on Chr 6 at or near D6Mit316 increased HIOR by 2.6 eggs in cumulus (LOD = 1.98). Mapmaker QTL also estimated that mice with a B6 allele on Chr 6 at or near D6Mit316 ovulated 2.2 more eggs out of cumulus (LOD = 2.089 for square-root transformed data). Mapmaker QTL further estimated that mice with a B6 allele on Chr 6 at or near D6Mit316 ovulated 4.2 more total eggs (LOD = 2.67) than mice that were AA at this locus.
Interval and permutation analysis with Map Manager QT detected a suggestive ORI QTL that controls the number of eggs in cumulus near D6Mit316 (P = 0.35 on a genome-wide basis). As shown in Table 3, interval analysis with QTL Cartographer Zeng Model 3 detected an effect of B6 allele in the vicinity of D6Mit316 on HIOR that was slightly under the suggestive QTL threshold (likelihood ratio = 8.468; LOD = 1.839; P < 0.004). Nevertheless, as shown in Table 4, composite interval analysis using QTL Cartographer (Zeng Model 6) estimated that a B6 allele in the vicinity of D6Mit316 increased HIOR by 8.1 eggs (likelihood ratio = 15.9; LOD = 3.45; P < 0.0001 [Table 4 and Fig. 3B]). In this combined interval analysis, the ORI QTL near D6Mit316 was significant (P < 0.02) on a experiment-wise basis, i.e., in a genome-wide analysis. Furthermore, combined interval analysis also showed that the Oriq near D6Mit316 also resulted in 1.5 more eggs out of cumulus (LOD = 2.4). Thus, these data show that a significant ORI QTL maps to mouse Chr 6 in the vicinity of D6Mit316. This Chr 6 ORI QTL has a significant effect on the number of eggs matured by equine chorionic gonadotropin (eCG) and ovulated by hCG, and a suggestive effect on the number of eggs matured by endogenous gonadotropins and ovulated by eCG.
Suggestive ORI QTL on Chromosome X: Oriq4
QTL analysis with MapMaker QTL revealed a suggestive B6 QTL on Chr X slightly proximal to DXMit22, which increased HIOR by 4.04 eggs (LOD score = 2.743) (Table 2 and Fig. 2D).
Interval analysis with QTL Cartographer estimated that a B6 allele in the vicinity of DXMit22 on mouse Chr X increased HIOR by 6.9–7.1 eggs (likelihood ratio = 10.931–12.036; LOD = 2.37–2.61 [P < 0.001 on a comparison-wise basis; P 0.1 on a genome-wide or experiment-wise basis]), regardless of whether the data were corrected for the effect of other loci by regression (Table 3, Table 4, and Fig. 3D). Interval and permutation analysis with Map Manager QT also detected a suggestive Oriq peaking at DXMit22+ 4 with an effect of 7.6 eggs (likelihood ratio = 12.3) that was suggestive (P = 0.065) on a genome-wide basis. These data support the presence of a suggestive ORI QTL on Chr X near DXMit22.
QTL Linkage Analysis of Eggs Out of Cumulus
The present study also mapped QTL that control the number of eggs out of cumulus, which is indicative of the number of eggs matured by endogenous gonadotropins and ovulated by eCG. Several, but not all, QTL linkage analyses detected a significant QTL controlling eggs out of cumulus on Chr 10, and suggestive ORI QTL controlling this trait on Chr 6, 7, and X.
Permutation analysis with Map Manager QT detected suggestive ORI QTL that control eggs out of cumulus on central Chr 2, on central Chr 5, on Chr 6 near D6Mit4, on Chr 10 near D10Nds1, and on Chr X between DXMit16 and DXMit79. Permutation analysis with correction for the other most significant loci using Map Manager QT detected suggestive QTL that control eggs out of cumulus on Distal Chr 2, on Chr 6 near D6Mit33, and on Chr X between DXMit16 and DXMit79.
Suggestive ORI QTL on Chr 10 Controlling the Number of Ova Matured by Endogenous Gonadotropins and Ovulated by eCG
Depending on the method of analysis, a suggestive-to-significant QTL controlling the number of eggs out of cumulus maps to Chr 10 (Table 2 and Fig. 4A). Untransformed data for the number of eggs out of cumulus showed a peak on Chr 10 between D10Nds1 and Mpmv12 with a less-than-suggestive effect of +3.3 eggs (LOD = 1.45). Interval analysis of the number of ova out of cumulus with ZmapQTL (Zeng model 3) also revealed marginal effects of loci in the vicinity of D10Nds1 on Chr 10 (LOD = 1.453 [comparison-wise P < 0.017]). Nevertheless, permutation analysis with Map Manager QT showed that this region of Chr 10 contained a suggestive QTL that controls eggs out of cumulus. Composite interval analysis using ZMapQTL (Zeng model 6) also detected a suggestive B6 QTL affecting the number of ova out of cumulus in this region of Chr 10 with an effect of 4.4 eggs (LOD = 2.35 [comparison-wise P < 0.001; experiment-wise P < 0.2] [Table 4 and Fig. 4B]). Analysis of square-root transformed number of eggs out of cumulus using Mapmaker QTL detected a significant QTL affecting this trait on Chr 10 between Mpmv12 and D10Mit31. This B6 QTL showed an LOD score of 3.939 and accounted for 61.6% of the variance in square-root transformed eggs out of cumulus. Therefore, we propose Oriq5 as an ORI QTL on mouse Chr 10 that controls the number of eggs matured by endogenous gonadotropins and ovulated by exogenous gonadotropins.
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Suggestive ORI QTL on Chr 7 Controlling the Number of Ova Matured by Endogenous Gonadotropins and Ovulated by eCG
Analysis of the number of ova out of cumulus using Mapmaker QTL revealed a suggestive ORI QTL on mouse Chr 7 (Table 2 and Fig. 4C). A region of Chr 7 showed a marginal effect on the number of eggs out of cumulus that peaked between D7Nds1 and Tyr with an effect of 2.4 eggs (LOD = 1.34). Interval analysis of the number of ova out of cumulus with ZmapQTL (Zeng model 3) revealed marginal effects of loci in the vicinity of Pmv 4 on Chr 7 (LOD = 1.244 [comparison-wise P < 0.015]). Composite interval analysis using ZMapQTL (Zeng model 6) revealed a suggestive B6 QTL affecting the number of ova out of cumulus near Pmv4 with an effect of 3.7 eggs out of cumulus (LOD = 2.32 [comparison-wise P < 0.001; experiment-wise P < 0.2]). We plan to confirm this suggestive reproductive QTL before assigning it a name.
Suggestive ORI QTL on Chr 6 Controlling the Number of Ova Matured by Endogenous Gonadotropins and Ovulated by eCG
Analysis of square-root transformed number of eggs out of cumulus with Mapmaker QTL also revealed a suggestive QTL affecting this trait that peaked near D6Mit316 and D6Mit4 with an LOD of 2.023. Map Manager QT also detected a suggestive ORI QTL that controls the number of eggs out of cumulus in the vicinity of D6Mit316 and D6Mit4 on Chr 6.
Suggestive ORI QTL on Chr X Controlling the Number of Ova Matured by Endogenous Gonadotropins and Ovulated by eCG
Interval analysis and permutation analysis with Map Manager QT also detected a suggestive ORI QTL that controls the number of eggs out of cumulus near DXMit16 on Chr X. This suggestive Chr X ORI QTL had an effect of 3.81 eggs, accounted for 10% of the variance in this trait with an empirical genome-wide P < 0.53. Thus, qualifying it as a suggestive ORI QTL.
Effect of Considering Several QTL Simultaneously
Simultaneous fitting of the effects of several reproductive QTL resulted in a total LOD score that approximated the sum of the LOD scores for the individual QTLs fitted in the model. For example, when considered separately, Mapmaker QTL estimated that a B6 allele at Oriq2 or at Oriq3 increased the number of eggs in cumulus by 2.65 (LOD = 2.07) and 2.64 ova (LOD = 1.95), respectively. Fitting the effect of Oriq2 and Oriq3 together increased the LOD score to 3.93, an increase of 1.98 over that of fitting the effect of Oriq3 alone. Similar results were observed for simultaneous fitting of the other ORI QTL. Since the effects of each QTL are the same whether fitted individually or simultaneously with other QTL, this suggests that there is little epistasis or interaction among these ORI QTL.
Mapping QTL That Control HIOR After Including the Effect of Body Weight
It is well known that differences in body weight affect ovarian responsiveness to gonadotropins [56–59]. As shown in Table 2, interval analysis with Mapmaker QTL detected markers on Chr 1, 5, 8, 11, 15, and 18 with point-wise associations of P < 0.05 with body weight. However, none of these markers reached the significance required for suggestive QTL for body weight. Furthermore, body weight did not differ significantly between mice that were AA versus AB in the vicinity of any of the suggestive or significant ORI QTL.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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We placed markers within 10 cM of about 95% of the mouse genome to reveal some of the first molecular genetic markers for QTL that control major quantitative differences in induced ovulation rate and ovarian responsiveness to gonadotropins. The present results agree with previous map placements of mouse molecular markers with only minor differences in overall map positions. Using a series of molecular genetic linkage analyses, four suggestive QTL that control the number of eggs matured and ovulated by exogenous gonadotropins, were mapped to regions of mouse Chr 9, 2, 6, and X. As recommended by the mouse gene nomenclature committee, these QTL that control HIOR have been assigned the gene name Ovulation Rate Induced QTL and given the gene symbol Oriq. Composite interval analysis (interval analysis combined with correction for the effects of the 5 most significant unlinked loci) showed that the ORI QTL on Chr 6 is indeed a significant ORI QTL. Furthermore, one significant QTL controlling differences in the number of eggs matured by endogenous gonadotropins and ovulated by eCG (scored as eggs out of cumulus) was also mapped to regions of Chr 10. Two other suggestive QTL that control differences in the number of eggs matured by endogenous gonadotropins and ovulated by eCG were also mapped to regions of Chr 6, 7, and X. Given the number of loci tested throughout the genome and the LOD scores observed, these different reproductive QTLs were provisionally mapped with about a 2–55% chance of falsely identifying a single positive QTL somewhere in the genome.
These data reveal one ORI QTL controlling eggs in cumulus and one controlling eggs out of cumulus that were significant on a genome-wide basis. Nevertheless, these results also detected 3 suggestive ORI QTL controlling eggs in cumulus and 2 to 3 suggestive ORI QTL controlling eggs out of cumulus, under conditions where no more than one suggestive QTL per genome-wide scan was expected to be found for each trait due to chance alone. While these markers can be legitimately considered as "suggestive ORI QTL," it seems most likely that all but 1 of these suggestive ORI QTL controlling each HIOR trait are valid QTL.
The present study mapped several suggestive and significant reproductive QTL using the standards and thresholds proposed by Lander and Kruglyak [16] and empirical thresholds proposed by Churchhill and Doerge [52]. Despite these generally accepted significance thresholds, recent studies have reported several peaks as reproductive "QTL" that in fact were only suggestive or less-than-suggestive QTL on a genome-wide basis [20, 21, 60]. Since up to one of the suggestive QTL and many of the less-than-suggestive QTL per data set are likely to be artifacts of sampling, the trend in reporting less-than-significant or less-than-suggestive QTL as QTL should be avoided since it is likely to lead to a loss of confidence in QTL mapping as spurious QTL appear and disappear with additional studies. Nevertheless, it is also important to publish markers with point-wise significance of P < 0.05 with no claims of linkage to a QTL. Even though these suggestive and less-than-suggestive markers have a higher probability of being false positives, they allow additional focused studies to identify minor QTL without having to conduct a full genome-wide analysis. This approach minimizes beta errors by enhancing the confirmational testing of such regions for QTL in another data set.
In contrast to these suggestive and/or significant LOD scores in the 1.9–3.9 range for the observed reproductive QTLs, the same data set shows a highly significant LOD score of 264 for pigmented versus albino coat color, which peaks as expected at tyrosinase (Tyr) on mouse Chr 7. This illustrates the much greater difficulty in mapping multiple QTLs that control a low heritability trait relative to mapping a gene controlling a simple Mendelian trait.
Individual ORI QTLs That Control the Number of Eggs in Cumulus: Suggestive Oriq1 on Chr 9
The present data show that a suggestive ORI QTL maps in the vicinity of D9Mit4 and Cyp19 on Chr 9. These findings in conjunction with physiological studies and the well-defined roles of aromatase and intrafollicular estrogen in promoting follicular maturation and ovulation, as well as of dopamine receptor in regulating gonadotropin secretion, suggest that one of the QTL controlling HIOR maps on Chr 9 in the vicinity of P450 aromatase (Cyp19) and dopamine receptor 2 (Drd2). We therefore propose Oriq1 as a suggestive QTL controlling differences in HIOR that maps at or near Cyp19, Drd2, and D9Mit4 on Chr 9. This region of mouse Chr 9 is syntenic with human Chr 15q21 [55]. While further studies are needed to confirm this suggestive ORI QTL, subsequent to the submission of this manuscript and our previous publication of this suggestive QTL [29, 61–64], a QTL controlling litter size was also mapped to an overlapping region of mouse Chr 9 [22].
Oriq2 on Chr 2
The present results show that a second suggestive ORI QTL maps to mouse Chr 2 in the vicinity of D2Mit433. Interestingly, QTL that control major genetic differences in hormone-induced aromatase activity also map to-proximal-to mid Chr 2 [29, 65]. In addition, a suggestive QTL of similar magnitude controlling differences in HIOR and hormone-induced aromatase activity maps to adjacent regions of Chr 2 in AXB, BXA recombinant inbred (RI) strains of mice (unpublished results). More convincingly, in subsequent studies we confirmed this ORI QTL using Chr 2 congenic strains of mice. S15 x A.B6-Chr 2 congenic testcross mice with B6 alleles on the proximal half of Chr 2 ovulate significantly more eggs than those with A/J alleles [29, 63, 64]. Thus, in three independent data sets, QTL that control differences in ovarian responsiveness to gonadotropins map to Chr 2. Based on this consistency, we propose Oriq2 as a confirmed QTL controlling differences in HIOR that maps to Chr 2.
Although QTL with significant effects on ovarian function map to Chr 2, the number of reproductive QTL in this region is not clear from this data. Interval mapping with correction by regression analysis for the effect of the five most significant loci outside of a 10-cM window surrounding the locus under consideration, eliminated the significance of Oriq2. Nevertheless, this region has a suggestive-to-significant effect on induced ovulation rate in three different experiments to date. Further studies with Chr 2 reproductive congenic strains of mice support the presence of two ovarian response to gonadotropin QTL on Chr 2 [29, 66]. After several abstracts and a review describing the central Chr 2 Hior/ORI QTL appeared [29, 62–64], a QTL controlling litter size was also mapped to the same region of mouse Chr 2 [22].
Ovulation rate and embryo survival are the two main components of litter size, with natural or spontaneous ovulation rate being a function of serum gonadotropin regulation and the ovarian responsiveness to gonadotropins, i.e., induced ovulation rate [64]. The present study mapped genes that control induced ovulation rate while the study by Kirpatrick et al. [22] mapped genes that control litter size. Although different crosses were studied, it is possible that the same Chr 2 QTL that regulates induced ovulation rate also control(s) spontaneous ovulation rate and thereby influences litter size. Examination of these potential relationships should prove interesting.
Significant Oriq3 on Chr 6
A significant ORI QTL controlling differences in the number of eggs in cumulus and the total number of eggs ovulated maps to Chr 6 in the vicinity of D6Mit316. Furthermore, depending on the method of analysis, this region of Chr 6 also had a marginal or suggestive effect on the number of eggs out of cumulus. These data place a reproductive QTL in the vicinity of D6Mit316 that controls the number of eggs in cumulus and the total number of eggs ovulated with an alpha (chance of detecting false positives somewhere in the entire genome) of about 2–10%. Subsequent studies using Chr 6 congenic mouse strains have confirmed the presence of a significant ORI QTL in this region of Chr 6 affecting the number of eggs in and out of cumulus [29]. We therefore propose Oriq3 as a confirmed QTL controlling differences in induced ovulation rate that maps to Chr 6 in the vicinity of D6Mit316. This region of Chr 6 near D6Mit316 is syntenic with human Chr 7p15-p14 [55].
Suggestive Oriq4 on Chr X
The Inverdale Fecundity gene (FecXI) was mapped to the X chromosome in sheep by its differential pattern of transmission from male versus female carriers [26]. Like FecXI, which increases natural ovulation rate in the heterozygous state, mice that are AB at Oriq4 have increased induced ovulation rate. However, unlike homozygous FecXI/FecXI ewes, which are infertile due to the presence of streak gonads [26], mice that are AA or BB at Oriq4 on Chr X have "normal" ovaries and ovarian function [7, 9, 67]. The present study in mice show that a suggestive ORI QTL controlling the number of eggs in cumulus maps to Chr X in the vicinity of DXMit22. The present study also shows that a suggestive ORI QTL controlling the number of eggs matured by endogenous gonadotropins and ovulated by the initial dose of eCG maps to Chr X in a slightly more telomeric region of Chr X. Subsequent studies using Chr X congenic mouse strains have also confirmed the presence of a highly significant ORI QTL in this region of Chr X that affects the number of eggs matured by endogenous gonadotropins and ovulated by exogenous gonadotropins [66]. We therefore propose Oriq4 as a confirmed QTL that controls differences in HIOR and that maps to Chr X in the vicinity of DXMit22. Since the Inverdale fecundity gene has not been reported to map to a specific region of Chr X, it is not clear if Oriq4 is allelic with FecXI. However, the X chromosome is highly conserved across a wide range of mammalian species.
Physiological Basis of Genetic Differences in Induced Ovulation Rate
HIOR is a function of the number of follicles ready to respond to gonadotropins, as well as their ability to respond to these hormones, escape atresia, grow, mature, and ovulate. In the absence of gonadotropin priming, 4-wk-old B6 and A/J mice differ only slightly in the number of follicles matured by endogenous gonadotropins and ovulated by hCG [67]. Following priming with eCG or oFSH, the number of eggs ovulated by hCG remains relatively constant in A/J strain mice but increases dramatically in B6 mice [7, 67]. Thus, B6 and A/J differ dramatically in hormone response genotype, and the observed ORI QTL mainly represent hormone response QTL.
Available data on the kinetics of follicular growth in these strains of mice suggest that most of the differences in HIOR are due to differences in the hormonal induction of follicle maturation and/or atresia. The majority of follicles that are matured by eCG and ovulated by hCG are likely to be recruited by eCG from the early antral types 5a and 5b follicles [9]. Untreated 4-wk-old B6 and A/J mice both have about 70 healthy early antral (types 5a and 5b) follicles per mouse [9]. Yet two days following eCG treatment, B6 mice have matured 55 ± 7 healthy follicles to the type 7 stage of development (preovulatory follicles) while A/J mice have only matured 6 ± 2 follicles to the same preovulatory stage of development [9]. Since B6 mice ovulate 54 ± 2 eggs in cumulus while A/J mice ovulate only 9 ± 1 eggs in response to 5 IU eCG and 5 IU hCG [7], the number of healthy type 7 follicles matured by eCG accounts for essentially all of the follicles that ovulate in response to eCG and hCG in both strains of mice. Note that eggs from these follicles are scored as ova in cumulus.
While untreated B6 mice have about 14 more healthy type 6 follicles than that of A/J [9], most of these follicles are likely to be desensitized and down-regulated by the eCG. In the unlikely event that all of these additional type 6 follicles were matured by eCG and ovulated by hCG, they would still account for less than a third of the increased HIOR of B6 strain mice over that of A/J strain mice.
Although the incidence of follicular atresia is similar in untreated 4-wk-old B6 and A/J mice, two days following eCG treatment, ovaries of B6 mice show a dramatically lower incidence of follicular atresia. Since B6 has more healthy antral follicles than A/J at each stage of development following eCG [9], one or more B6 ORI QTL must act to decrease the incidence of atresia. It is also possible that other B6 ORI QTL increase the induction of follicular maturation by altering as yet to be measured follicular growth rates.
Thus, from a follicular kinetics perspective, the 4- to 6-fold difference in the number of follicles matured and ovulated by exogenous gonadotropins between B6 and A/J mice seems to be due to hormone response genes/ORI QTL controlling 1) the incidence of follicular atresia, 2) perhaps the induction of follicle growth and maturation, and 3) perhaps the number of large follicles supported by endogenous gonadotropins. We propose that B6 Oriq1, Oriq2, Oriq3, and Oriq4 all act to increase HIOR through one or more of these mechanisms. Further studies are needed to determine which QTL mediate which specific physiological-genetic changes as well as the molecular mechanisms by which these QTL act to alter reproductive function.
The induction of follicular growth and maturation as well as the prevention of follicular atresia is associated with elevated intrafollicular aromatase activity and estradiol concentrations in several mammalian species [13, 68–70]. Follicles that fail to maintain a high aromatase activity undergo atresia and fail to ovulate. Ovaries from B6 mice respond to eCG with a 16- to 21-fold greater induction of ovarian P450 aromatase enzyme activity and 6-fold greater production of estrogen in vitro than ovaries from A/J mice [10, 11, 64, 71]. B6 ORI QTL alleles could act to increase aromatase activity by an early step in gonadotropin action or by a direct effect on the expression of the Cyp19 gene, which codes for P450 aromatase. The resulting elevation in aromatase activity in response to gonadotropins could explain the decreased incidence of follicular atresia, increased follicular maturation, and higher ovulation rate of B6 over that of A/J mice.
The present study shows that a suggestive ORI QTL maps to Chr 9 in the vicinity of Cyp19. The validity of this Chr 9 reproductive QTL is supported by the finding of a suggestive litter size QTL in the same region of mouse Chr 9 [22]. Although the present results indicate that the suggestive Oriq1 maps near Cyp19, differences in aromatase activity two days after eCG treatment do not map to Cyp19 ([11], unpublished results). This suggests that if Cyp19 is actually an ORI QTL, this locus may critically alter aromatase activity at a different stage of follicular development. Alternatively, the suggestive Chr 9 Oriq we have identified and perhaps the suggestive Chr 9 OR QTL found by Kirkpatrick et al. [22] may be a genetic variant in another hormonal regulatory gene that maps nearby on Chr 9. A very likely candidate is dopamine receptor 2 (Drd2), which maps about 2 cM more centromeric. Dopamine receptor 2 haplotype in humans has been associated with differences in the regulation of serum gonadotropins in a polycystic ovarian syndrome population [72].
While the structural gene coding for P450 aromatase (Cyp19) maps to Chr 9, genes affecting aromatase activity map to regions of central Chr 2 near Oriq2 in our laboratory ([11], unpublished results). It appears that a B6 Oriq2 allele on central Chr 2 acts to increase HIOR, at least in part, by increasing follicular aromatase activity, which then results in increased intrafollicular estradiol levels leading to stimulation of follicular maturation and decreased follicular atresia, and thereby increased ovulation rate. Since Oriq2 is associated with increased induction of granulosa cell aromatase activity, it appears that Oriq2 is a genetic variant on Chr 2 in either a step in the gonadotropin receptor-cAMP-protein kinase A cascade, or in a trans-acting transcription factor which induces P450 aromatase mRNA.
Synteny of ORI QTL in Mice to QTL Controlling Spontaneous Ovulation Rate and Litter Size in Several Mammalian Species
Similar physiological mechanisms regulate several of the components of ovarian function that control both natural (spontaneous) and induced ovulation rate. HIOR is controlled by endocrine and molecular factors that are likely to be closely associated with some but not all components regulating natural or spontaneous ovulation rate and litter size. Thus, we hypothesize that several ORI QTL mapped in the present study will also be natural ovulation rate and/or litter size QTL. Indeed, the suggestive/significant ORI QTL on mouse Chr 2 and 9, as well as the ORI markers with nominal P < 0.05 on Chr 4 and 11 map to the same chromosomal regions as suggestive/significant QTL that control litter size in a C57Bl/6J x Quackenbush-Swiss mouse cross [22]. The central mouse Chr 2 (Oriq2/Aaiq2) is likely to be syntenic with the less-than-suggestive ovulation rate QTL on Swine Chr 15 [20].
Other potential candidates for these ORI QTL include the Booroola and the Inverdale fecundity genes. The Booroola fecundity gene (Fecb) was recently mapped to a region of Chr 6 in sheep, which is syntenic to Chr 4q in humans and either Chr 3 or 5 in mice [25]. While Fecb may be syntenic to mouse Chr 5, this region had only a marginal effect on the number of eggs out of cumulus in the present study. Since the suggestive and significant ORI QTL we discovered map to chromosomal regions that are not syntenic to Fecb in sheep, Oriq 1 to 5 are unlikely to be allelic with Fecb. Nevertheless, the Ori marker with nominal significance of P < 0.05 on Chr 5 near Pmv5 we report may be syntenic with Fecb in sheep.
FecXI was mapped to the X chromosome in sheep by its differential pattern of transmission from male versus female carriers [26], but the map position on Chr X of this sheep QTL has not reported [73]. Interestingly, Oriq4 on mouse Chr X near DXMit22 may be syntenic with the Inverdale Fecundity gene (FecXI) in sheep. However, despite the dramatic differences in ovarian function between A/J and B6, neither homozygote mouse shows the streak ovaries and complete infertility characteristic of the FecXI/FecXI homozygote sheep. Thus, even though Oriq4 and FecXI map to Chr X and may be syntenic, they are likely to be different alleles or to alter ovarian function and ovulation rate through different physiological-genetic mechanisms.
Physiological Basis of Genetic Differences in Natural Ovulation Rate
The number of follicles matured by endogenous gonadotropins and ovulated by exogenous gonadotropins is controlled by endocrine and molecular factors that are likely to be closely associated with components regulating spontaneous ovulation rate. Endogenous levels of gonadotropins are sufficient to support the growth and maturation of follicles in some immature females, but the mice examined in the present study were generally too young to undergo a natural LH surge, and they rarely ovulate at this age unless injected with exogenous gonadotropins [67]. Previous studies showed that untreated 4-wk-old B6 and A/J females both have about 4 ± 2 healthy type 7 follicles, whereas B6 has 24 ± 5 and A/J has 10 ± 3 healthy type 6 follicles [9]. Yet the number of follicles matured by endogenous gonadotropins and ovulated by eCG alone (and found as eggs out of cumulus after hCG) in A/J, B6, and B6AF1 mice averaged 0.8 ± 0.2, 6.6 ± 0.8, and 11.5 ± 1.3, respectively [8]. The increased number of follicles matured by endogenous gonadotropins and ovulated by eCG in B6 mice over that of A/J could be due to either the increased number of available healthy type 6 antral follicles, and/or an increased induction of ovulation of the larger antral follicles. While it is generally thought that type 6 follicles are not capable of ovulating acutely in response to gonadotropins [9, 74], several reproductive QTL in sheep act at least in part by decreasing the size of follicles capable of ovulating.
The present results show that a significant Oriq controlling the number of eggs out of cumulus maps to Chr 10 (Oriq5) and suggestive Oriq map to Chr 6, 7, and X. These and other Oriq alleles are likely to increase the number of follicles matured by endogenous gonadotropins and ovulated by eCG, as well as natural ovulation rate through a variety of mechanisms including altering 1) estrogen negative feedback on gonadotropin secretion, and/or 2) ovarian follicular growth, 3) FSH and LH receptor induction, and 4) follicular atresia in response to endogenous gonadotropins [58, 64, 75].
Significant Oriq5 on Chr 10
Our data indicate that Oriq5 on proximal Chr 10 is a significant reproductive QTL controlling the number of follicles matured by endogenous gonadotropins and ovulated by eCG (scored as eggs out of cumulus). Oriq5 increased the number of follicles matured by endogenous gonadotropins and ovulated by eCG by 4.4 eggs with a genome-wide alpha of about 2% (LOD = 3.94). Since Mapmaker QTL determined that Oriq5 accounted for 61.6% of the variance in this trait, these data show that this gene has a major effect on the number of eggs matured by endogenous gonadotropins.
After the present work was completed, a QTL controlling litter size in divergent swine crosses was mapped using a candidate gene approach in the vicinity of the estrogen receptor [60]. Unfortunately, this swine litter size QTL was not significant on a genome-wide basis, and flanking markers were not examined to determine if this locus or a linked locus was responsible for the observed differences in litter size. The submission of the present mouse work preceded that of Rothschild et al. [60], but the subsequent need to determine over 3500 additional molecular genotypes and a lack of funding greatly delayed establishing this reproductive QTL in the public domain.
Oriq5, i.e., the ovulation rate QTL controlling the number of follicles matured by endogenous gonadotropins and ovulated by exogenous gonadotropins, maps in the vicinity of the estrogen receptor alpha (Estra) locus on Chr 10. Estra is thus a likely candidate for Oriq5. Oriq5 probably increases the number of follicles matured by endogenous gonadotropins by decreasing the sensitivity of the hypothalamus/pituitary to estrogen negative feedback. Indeed, the observed changes in the number of follicles matured by endogenous gonadotropins occurred in females with a functional hypothalamo-pituitary-ovarian axis sensitive to estrogen negative feedback. Such molecular markers in the vicinity of the Estra locus on Chr 10 are useful markers for genetic differences in follicle maturation and ovulation rate in mice. Estrogen negative feedback on gonadotropin release plays a primary role in the regulation of ovulation rate in a wide array of divergent vertebrate species. Thus, syntenic markers in the vicinity of the Estra locus will also be useful as markers for genetic differences in follicle maturation and ovulation rate in many other vertebrate species, especially mammals.
While confirmation of some QTL is needed, the present study provides some of the first molecular genetic markers that could lead to a more accurate prediction of genetic differences in ovarian responsiveness to gonadotropins, which is an important component controlling ovarian/estrus cyclicity, spontaneous ovulation rate, and litter size. Thus, several of the ORI QTL mapped in the present study are likely to also be estrus cyclicity, spontaneous ovulation rate, and litter size QTL. The parsimonious nature of physiological/genetic mechanisms that control ovarian function and ovulation rate in a wide array of vertebrate species makes it likely that syntenic markers for the ORI QTL mapped in the present study are also reproductive QTL in many other species.
The task of identifying all the reproductive QTL segregating in a backcross population was known a priori to be difficult. Nevertheless, we attempted to map such QTL because molecular markers are needed for genetic differences in reproductive traits. Molecular markers with a low heritability and a relatively large phenotypic variance will be among the most difficult to map due to their low heritability and quantitative inheritance, i.e., control by several loci and environmental effects. Nevertheless, molecular markers for low heritability reproductive QTL are among the most urgently needed. Additional studies are under way to higher-resolution map and determine the biochemical and physiological mechanisms by which the reported mouse ORI QTL alter ovarian responsiveness to gonadotropins and ovulation rate.
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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2 Correspondence: Jimmy Spearow, Section of NPB, Rm. 196 Briggs Hall, University of California at Davis, One Shields Ave., Davis, CA 95616. FAX: 530 752 5582; jlspearow{at}ucdavis.edu
3 Current address: Department of Internal Medicine, University of Colorado, Denver, CO 80262.
4 Current address: Neuroscience Research Institute, University of California, San Diego, CA 94143.
5 Current address: Dept. of Surgery, William Beaumont Army Medical Center, El Paso, TX 79920–5001.
Accepted: June 3, 1999.
Received: January 5, 1995.
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONREFERENCES |
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