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Single gene and gene interaction effects on fertilization and embryonic survival rates in cattle

H. Khatib^*,1, W. Huang^*, X. Wang, A. H. Tran^*, A. B. Bindrim^*, V. Schutzkus^*, R. L. Monson and B. S. Yandell

^* Department of Dairy Science, University of Wisconsin-Madison, Madison 53706
College of Animal Science and Technology, Northwest A&F University, Yangling, Shaanxi, 712100, P. R. China
Department of Animal Sciences, University of Wisconsin-Madison, Madison 53706
Departments of Statistics, Horticulture, and Biostatistics & Medical Informatics, University of Wisconsin-Madison, Madison 53706

¹ Corresponding author: hkhatib{at}wisc.edu

	ABSTRACT

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGMENTS REFERENCES

 
Decrease in fertility and conception rates is a major cause of economic loss and cow culling in dairy herds. Conception rate is the product of fertilization rate and embryonic survival rate. Identification of genetic factors that cause the death of embryos is the first step in eliminating this problem from the population and thereby increasing reproductive efficiency. A candidate pathway approach was used to identify candidate genes affecting fertilization and embryo survival rates using an in vitro fertilization experimental system. A total of 7,413 in vitro fertilizations were performed using oocytes from 504 ovaries and semen samples from 10 different bulls. Fertilization rate was calculated as the number of cleaved embryos 48 h postfertilization out of the total number of oocytes exposed to sperm. Survival rate of embryos was calculated as the number of blastocysts on d 7 of development out of the number of total embryos cultured. All ovaries were genotyped for 8 genes in the POU1F1 signaling pathway. Single-gene analysis revealed significant associations of GHR, PRLR, STAT5A, and UTMP with survival rate and of POU1F1, GHR, STAT5A, and OPN with fertilization rate. To further characterize the contribution of the entire integrated POU1F1 pathway to fertilization and early embryonic survival, a model selection procedure was applied. Comparisons among the different models showed that interactions between adjacent genes in the pathway revealed a significant contribution to the variation in fertility traits compared with other models that analyzed only bull information or only genes without interactions. Moreover, some genes that were not significant in the single-gene analysis showed significant effects in the interaction analysis. Thus, we propose that single genes as well as an entire pathway can be used in selection programs to improve reproduction performance in dairy cattle.

Key Words: survival rate • fertilization rate • candidate pathway • candidate gene

	INTRODUCTION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGMENTS REFERENCES

 
The concentrated focus on selection for production traits in Holstein and other breeds in the past may have led to genotypes in dairy cattle that are suboptimal for reproductive competence (Royal et al., 2000; Lucy, 2001). Recently, however, reproductive performance has been included in the selection indices of many breeding organizations (Miglior et al., 2005). There appear to be about 50 QTL affecting milk production traits (Bagnato et al., 2008; Lipkin et al., 2008). Restructuring of the dairy cattle genome over the past 30 yr caused by intense selection for production traits may have resulted in a hitchhiking effect on a large number of loci affecting fertilization rate and embryonic survival. The decrease in dairy cattle fertility is a worldwide problem and a major cause of economic loss and cow culling in the global dairy herd. Many reasons account for this reduced reproductive efficiency, but the most important component seems to be a reduction in embryonic survival and fertilization rates (Santos et al., 2004). There appears to be an important genetic basis for this decline (Veerkamp and Beerda, 2007), therefore genetic approaches may help alleviate this problem. As such, there is an urgent need to identify the genetic factors responsible for the decline in embryo survival rate. Identifying these factors would enable reduction in the frequency of the deleterious alleles at these loci by marker- or gene-assisted selection, preventing further decline or even improving reproductive status of the global dairy herd.

In previous studies, the effectiveness of the candidate pathway approach in choosing candidate genes affecting milk production traits has been demonstrated (Khatib et al., 2008a; Wang et al., 2008). In the candidate pathway-based approach, candidate genes affecting quantitative traits can be identified by tracking their biological action through effector pathways. It is readily recognized that phenotypic variation of quantitative traits results from combined effects of many genes. Therefore, there is an increased interest in the use of multigenic pathway-based approach in which gene interactions can be successfully identified (Horikawa et al., 2008). Also, recently an in vitro fertilization (IVF) experimental system in cattle has been developed that enables the association of SNP in candidate genes with fertilization rate and embryonic survival (Khatib et al., 2008a,b). Using this system, 2 genes, fibroblast growth factor 2 (FGF2) and signal transducer and activator of transcription 5 (STAT5A), were found to be significantly associated with variation in fertilization and embryonic survival rates (Khatib et al., 2008a,b). The FGF2 gene was chosen from the interferon-tau (IFNT) pathway and STAT5A was chosen from the POU1F1 signal transduction pathway using the candidate pathway approach. The IFNT pathway plays a key role in regulating the expression of genes involved in initiation of pregnancy in ruminants, in embryo implantation, and in protection of the conceptus against maternal rejection (Martal et al., 1997). In previous studies, it has been shown that members of the POU1F1 pathway—osteopontin, uterine milk protein, STAT5A, POU1F1—are associated with milk production and health traits (Leonard et al., 2005; Khatib et al., 2007a, b, 2008a; Huang et al., 2008). The objectives of this study were 1) to investigate additional genes in the POU1F1 pathway for individual associations, 2) to analyze the effects of gene-gene interactions on fertilization and embryonic survival rates, and 3) to investigate the relationship between milk production and fertility at the gene level.

	MATERIALS AND METHODS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGMENTS REFERENCES

 
Gene Selection and Genotyping
The genes POU1F1, GH, PRL, GHR, PRLR, STAT5A, OPN, and UTMP (Figure 1) were chosen for association tests with fertility traits because they are members of the POU1F1 pathway. Table 1 shows SNP information and references for these genes. Genotyping of genes was performed as described in the literature (Table 1) except for GHR, for which we used primers, GHR-F ctttggaatacttgggctagcagtgaca"a"tat and GHR-R gtctctctgtggacacaaca, that amplify a 230-bp genomic fragment. The original T nucleotide at position –4 of the SNP was mutated to an A nucleotide in the forward primer to create an SspI recognition site. Restriction enzyme digestions were carried out according to the manufacturer’s instructions.

View larger version (10K): [in this window] [in a new window]   Figure 1. Genes of the POU1F1 pathway investigated for association with fertility traits. Each arrow represents a biological interaction and direction of arrowhead indicates the direction of the signaling cascade. Genes are as described in Table 1.

Statistical Analysis
Association of Individual Genes with Fertilization and Survival Rates.
Associations of individual genes in the POU1F1 pathway (Figure 1) with fertilization and survival rates were analyzed using the following logistic regression model:

where log[p/(1 – p)]_i (i = 1, 2, ..., n) is the natural logarithm of odds of survival rate or fertilization rate, β₀ is a general constant, β_1j is the fixed effect associated with the jth bull (Bull_j); and β_2k is the ovary genotype effect associated with the kth genotype (Genotype_k) of the gene analyzed. This model was fitted by maximum likelihood approach. Association between the gene and survival/fertilization rate was tested using a likelihood ratio test (LRT) by comparing to a reduced model without the genotype effect.

Model Selection for Genes in the Pathway With or Without Interaction.
Our goal is to select the models that best explain survival and fertilization rates by polymorphisms in genes in the pathway. A stepwise model selection procedure based on Akaike’s information criterion (AIC) was employed. To select for the interactions that can best fit the data, interaction between each pair of genes was parameterized as follows: 1) Code each gene by 3 variables, "additive" (AA = 0, AB = 1, BB = 2); "Adom" (AA = 1, AB = 1, BB = 0) and "Bdom" (AA = 0, AB = 1, BB = 1); these 3 variables are redundant, and so are the interactions, but they won’t enter the model simultaneously; 2) The interaction variables were obtained by the cross products between parameters of 2 genes; and 3) These interaction variables were subjected to an AIC-based stepwise selection. There are indeed many other types of parameterizations, but this type of partitioning effect gives the best interpretability of the selected model. The stepwise selection procedure started with a simple model. At each step, the selection may happen in both directions, either adding one variable not in the model or removing one variable already in the model, depending on which change produces the smallest AIC. This step is repeated until no variable can be removed or added to arrive at an optimal model. To keep hierarchy, all single genes were forced to be in the model when selecting for interactions. The final tuning of the selected candidate model involved removing insignificant interactions and main effects that did not have interaction terms in the model, thus retaining the hierarchy. Likelihood ratio tests were then applied to successively compare selected models with or without interactions. The null model (model 2) that has only bull effect was

where the notations are as defined for model 1.

To test whether individual interaction between adjacent genes in the pathway was significant, a reduced additive model

was compared with a full interactive model

In the full model (model 4), there are variables representing bull information (β_1j for jth bull), genotype information (β_2mp and β_3nq are the genotypic effects of the pth genotype of the mth gene and of the qth genotype of the nth gene, respectively), and interaction between genes (β_4mnpq is the effect for interaction between the pth genotype of the mth gene and the qth genotype of the nth gene). In contrast, there was no interaction term in the reduced model (model 3). Likelihood ratio testing was performed to test for significance of the interaction term. All significances were declared after idák adjustment (adjusted P-value = 1 – (1 – p)^1/n; n = number of tests) where applicable. All statistical analysis was implemented in R, version 2.7.2 (www.r-project.org) using functions from packages "stats" and "MASS."

	RESULTS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGMENTS REFERENCES

 
Association of Individual Genes with Fertilization Rate and Embryonic Survival
The GHR, PRLR, STAT5A, and UTMP genes showed significant associations with survival rate (Table 2). For GHR, the survival rate of embryos produced from TT ovaries was 9% higher than that of embryos produced from AA ovaries. For STAT5A, CC ovaries showed 9 and 8% higher survival rates than that of GG and GC ovaries, respectively. The PRLR and UTMP genes showed 4 and 6% survival rate differences between their genotypes (Table 2). Table 3 shows the association of individual genes with fertilization rate. Ovaries carrying the TT genotype of OPN showed a 70% fertilization rate versus a 62% rate for ovaries carrying the CC genotype. The CC genotype of STAT5A, which was also associated with high survival rate, showed significant association with fertilization rate versus the GC and GG genotypes. Similarly, although less statistically significant, GHR (P = 0.0647) and POU1F1 (P = 0.0516) also showed associations with fertilization rate.

	DISCUSSION

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGMENTS REFERENCES

Single Gene Effects
Single gene association analysis revealed significant associations of STAT5A, UTMP, and OPN and, to a lesser extent POU1F1, with fertilization rate, and of GHR, PRLR, and STAT5A with survival rate. The causative mutations responsible for these associations have not been identified; therefore we assume that the observed effects are due to linkage disequilibrium between the typed polymorphisms and causative mutations affecting fertilization and early embryonic survival. Given that these effects were discovered at early stages of embryonic development, we questioned whether genes associated with fertilization are expressed in eggs and sperm and whether genes associated with survival are expressed in the blastocyst or in prior stages of development. Indeed, several reports have demonstrated the involvement of these genes in the fertilization process and in early embryonic development. Nakasato et al. (2006) have shown that STAT5A is expressed in oocytes at the metaphase II stage (before fertilization) and in the 2-cell, 4-cell, morula, and blastocyst stages. These results are consistent with effects of STAT5A on fertilization and embryonic development found in our study. The OPN protein contains the arginine-glycine-aspartic acid peptide sequence which is known for its ability to block fertilization (Campbell et al., 2000). Recently, it was reported that fertilization in cows was blocked as a result of OPN-antibody binding in both spermatozoa and oocytes (Gonçalves et al., 2007, 2009). The authors concluded that OPN has a potential role in sperm-egg binding and in early embryonic development. For UTMP, which was found to be associated with fertilization rate in this study, there are no reports on its expression in the developing embryo. However, in a previous study, we reported the predominant expression of this gene in the bovine reproductive tissues endometrium and ovary which suggests an important role for UTMP in reproductive success (Khatib et al., 2007a). Although not statistically significant, GHR (P = 0.0647) and POU1F1 (P = 0.0516) showed association with fertilization rate. These results are consistent with recent reports on the expression of these genes in oocytes and on their importance in embryonic development. Izadyar et al. (2000) showed that GHR is expressed in bovine oocytes and embryos in different developmental stages. Joudrey et al. (2003) reported the expression of the bovine GH and POU1F1 in both immature and mature oocytes and in the early embryo from fertilization to the blastocyst stage. Also, PRLR was reported to be highly expressed in metaphase II-stage oocytes and in lower expression levels between the 4-cell and blastocyst stages (Nakasato et al., 2004).

Given that GH and PRL play important roles in cell differentiation and proliferation, that POU1F1 is a transcription factor that binds and activates promoters of GH and PRL, and that all these genes were found be associated with fertilization and early embryo survival in our study, these results suggest an important role of the integrated pathway in regulating embryo development.

Interaction Effects in the POU1F1 Pathway
To further understand the contribution of the entire integrated POU1F1 pathway to the variation in fertilization and embryonic survival, a model comparison procedure was applied. First, a simple model (model 2) with only bull information was fitted, representing the situation in which bull information and fertilization and survival rate alone are available. Second, this simplest model (model 2) was compared with a more complex model (model 3), which has all 8 genes in the pathway acting additively. The individual genes additively in model 3 clearly improved the fit for both survival and fertilization rate. Finally, a more complicated model (model 4), which contained all 8 individual genes and all interactions between adjacent genes in the pathway, revealed a significant improvement in explaining the variation in both fertilization and survival rates. Thus, genes of this pathway, individually and in interactions with other genes, clearly influence embryo fertilization/survival rates.

One of the drawbacks of the conventional methods of gene mapping is that they focus on the average genetic effects of the genotypes of a QTL or individual genes, not taking into account the possibility that these effects can be influenced by other loci (Carlborg and Haley, 2004). Indeed, recent studies in human, mouse, plants, and Drosophila indicate that epistatic interaction contributes to the regulation of quantitative traits. There are different reports in which disease occurs not through any single gene, but only through a combination (interaction) of 2 or more genes. For example, the combination of the KIR3DS1 allele of the killer immunoglobulin-like receptor gene and the HLA-BW4–80ile allele is associated with delayed progression of AIDS in individuals infected with HIV-1 (Martin et al., 2002). The authors found that in the absence of KIR3DS1 alleles, the HLA-BW4-ile80 allele did not show any significant effects on any of the AIDS traits. In our study, single gene analysis showed no significant associations between POU1F1 and PRL and survival rate. In contrast, the 2-way interaction analysis showed that some genotypic combinations of these genes were found to be associated with significant effects on early embryonic survival (Table 6). Likewise, the genotypic combination of AT/CC of STAT5A/GHR was associated with 45% survival rate versus 25% for the combination of AT/GC (Table 6). Taken together, these results suggest that genes in the pathway are important genetically for fertilization or early embryonic survival and that genotypic combinations should not be ignored in marker-assisted selection programs.

Our results suggest that both polymorphisms in genes and gene interactions are important in determining the phenotypic variation in fertility traits. It should be noted that these results do not rule out the possibility of involvement of many other genes in controlling embryonic survival and fertilization, which are both complex traits. Instead, the results of this study testify to the power of the IVF experimental design and to the efficiency of including gene information and their interactions in the statistical analysis. Additionally, these results emphasize the effectiveness of the candidate pathway approach in choosing candidate genes. Genes are chosen based on their biological functions in the metabolic pathway. When one gene of a pathway affects our target traits, other genes of the same pathway are likely to do so as well. Recent studies show the power of epistatic models in identification of novel QTL, in identification of QTL without individual effects, and in detection of QTL with small effects which may be responsible for a large portion of trait variation (Kroymann and Mitchell-Olds, 2005). Indeed, our results show that gene interactions of POU1F1 and PRL were found to be associated with embryonic survival rate, whereas single-gene analysis of these genes did not show significant associations.

Milk Production and Fertility in Dairy Cattle
It is widely accepted that there is a strong association between high milk production and low fertility in dairy herds. Washburn et al. (2002) analyzed the relationship of conception rate and milk production over more than a 20-yr time period (1976–1999) in dairy herds in the southeastern United States. It was clear that conception rates decreased from about 55% to about 35% during this time period as milk production dramatically increased. López-Gatius (2003) reported that each 1,000-kg increase in milk yield per cow was associated with a decrease of 3.2 to 6% in pregnancy rate, 4.4 to 7.6% in the number of cycling cows, and with a 4.6 to 8% increase in the incidence of inactive ovaries. In a recent review of the literature, Dobson et al. (2008) concluded that over the past 30 to 50 yr, intensive selection to milk yield traits has led to a reduction in first-service pregnancy rate from 70 to 40%.

The genes investigated in this study were chosen for association tests with fertility traits because they are members of the POU1F1 signaling pathway and because of previous studies relating some members of this pathway with fertilization success and embryonic survival. Moreover, all investigated genes have been reported to be associated with milk production traits in dairy cattle. Therefore, for this fertility study, we chose the same polymorphisms reported in milk production association studies to test whether or not they are also associated with fertility traits.

The antagonistic relationship between high milk production and fertility that has been observed for many years in dairy cattle (López-Gatius, 2003; Dobson et al., 2008) was detected for some of the genes investigated in this study. In the Viitala et al. (2006) study, the AA and AG genotypes of PRLR were reported to be associated with significantly higher protein, fat, and milk yields versus the GG genotype. In our study, the AA and AG genotypes of PRLR were associated with a lower survival rate versus the GG genotype. In a previous study (Khatib et al., 2007a), we reported the association of the G allele of the UTMP gene with longer productive life in 2 different Holstein dairy cattle populations. In this study, the GG genotype of UTMP was associated with a significantly lower embryo survival rate. For GHR, the AA genotype of GHR has been reported to be associated with higher milk yield (Blott et al., 2003), while in this study, AA was associated with a lower survival rate. The G allele of STAT5A was associated with lower milk fat and protein percentages (Khatib et al., 2008a) and with lower fertilization and survival rates in this study. Given that milk fat and protein percentages are negatively correlated with milk yield traits, the interpretations of STAT5A regarding the antagonistic relationship between milk production and fertility are consistent with those of GHR, PRLR, and UTMP. However, for OPN, where the C allele has been reported to be associated with significant increases in fat and protein percentages (Leonard et al., 2005), the CC genotype in this study was associated with a decrease in fertilization rate. This result for OPN is inconsistent with the other 4 genes investigated in this study. The OPN protein is a glycophosphoprotein cytokine that has multiple functions including cell migration and adhesion, tissue remodeling and wound healing, promoting differentiation of precursor cells to become osteoclasts and enhancing the activity of osteoblasts, acting in inflammation by attracting macrophages and T cells and accelerating early acute immune responses, and functioning in cell survival and proliferation (reviewed in Johnston et al., 2008). Because of these multiple functions, it is unlikely that OPN has been under selection pressure similar to other milk production genes.

	CONCLUSIONS

TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGMENTS REFERENCES

 
In summary, in this study we demonstrated the effectiveness of the candidate pathway strategy and the usefulness of the experimental IVF population developed in our laboratory in identifying candidate genes affecting fertility traits. To test the effect of the integrated POU1F1 pathway on the variation of the investigated traits, interaction effects were estimated using a model comparison procedure. Some genes that were not significant using single gene analysis (e.g., POU1F1 and PRL for survival rate) showed significant effects in the interaction analysis. Moreover, the analysis that contained all 8 individual genes and all interactions between adjacent genes in the pathway revealed a significant improvement in explaining the variation in both early embryonic survival and fertilization rates. Nonetheless, there is a need for a field test to confirm and validate the in vitro results obtained in this study. Validation of the field test will provide the platform for an extensive future research on the genomic basis of reduced embryonic survival and on improving reproductive efficiency in dairy cattle using modern molecular genetic techniques of gene-assisted and pathway-assisted selection.

	ACKNOWLEDGMENTS

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This study was supported by USDA Hatch grant No. WIS-04895 from the University of Wisconsin-Madison.

Received for publication September 30, 2008. Accepted for publication December 20, 2008.

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TOP ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION CONCLUSIONS ACKNOWLEDGMENTS REFERENCES

 


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