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J. Dairy Sci. 2008. 91:4312-4322. doi:10.3168/jds.2008-1000
© 2008 American Dairy Science Association ®
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Fine Mapping of Quantitative Trait Loci on Bovine Chromosome 6 Affecting Calving Difficulty

H. G. Olsen*,1, T. H. E. Meuwissen*,{dagger}, H. Nilsen*, M. Svendsen{ddagger} and S. Lien*,{dagger}

* Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, N-1432 Ås, Norway
{dagger} Centre for Integrative Genetics, Norwegian University of Life Sciences, N-1432 Ås, Norway
{ddagger} Geno Breeding and AI Organisation, N-1432 Ås, Norway

1 Corresponding author: hanne-gro.olsen{at}umb.no


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Calving difficulty is an economically and ethically important trait for dairy cattle breeding. The aim of the present paper was to refine the position of a previously detected quantitative trait locus (QTL) affecting calving difficulty (direct effect) in Norwegian Red dairy cows. A granddaughter design consisting of 18 elite sire families and a total of 713 sons was genotyped for 154 markers spanning the QTL region, and the trait data were analyzed by using a combined linkage and linkage disequilibrium approach. A highly significant QTL was detected in a 150-kb interval between the markers LAP3_281 and BTA-114677. Additionally, there were some indications of a second QTL between the markers BTA-75776 and BTA-75780 located less than 500 kb apart. Several candidate genes may be identified close to these QTL. Of these, a cluster of genes expected to affect bone and cartilage formation may be of particular interest for follow-up studies.

Key Words: calving difficulty • cattle • fine mapping • linkage disequilibrium


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Good calving performance is of major importance in dairy cattle breeding, from both an economic and an animal welfare point of view. Veterinary assistance may be needed during a difficult parturition, and the cow may later experience reduced health, fertility, and milk production. A difficult calving may also substantially reduce the calf’s viability, may result in morbidity or mortality, and, in the worst cases, may result in both animals dying or having to be culled.

Calving difficulty, or dystocia, arises because of factors related to the calf, the cow, or both, and is also affected by the environment. The main factors related to the calf are birth weight and viability. Calves lighter or heavier than average tend to have more difficult births (Berger et al., 1992) than average-sized calves, and male calves often experience more difficult births than females because of their larger size at birth (Johanson and Berger, 2003; Steinbock et al., 2003). Factors related to the cow include the shape of the birth canal, the size of the pelvis, and the cow’s ability to nourish the fetus. Environmental factors, such as the cow’s age at parturition (first-parity cows have a greater risk than cows in later parities) and calving season (more difficulties during the winter months), are also important (Johanson and Berger, 2003; Steinbock et al., 2003).

Calving difficulty has been a part of the total merit index used for selection of Norwegian Red sires since 1978 (http://www.geno.no). For calving difficulty, bulls are genetically evaluated as sire of the calf (direct effect, CDdir) and sire of the dam (maternal effect, CDmat). The trait is recorded on a 3-level scale consisting of the categories 1 (easy calving), 2 (slight problems), and 3 (difficult calving). The frequency of calving difficulty is relatively low in the Norwegian Red breed. During the period from 1991 to 2001, the mean frequency of "slight problems" increased from 4 to 7% for first calving, and from 2 to 3% for second and later calvings (Heringstad et al., 2007). The frequency of "difficult calving" was 2 to 3% for heifers and 1% for cows during the same period (Heringstad et al., 2007). Heritability estimates for Norwegian Red vary from 0.02 to 0.03 (Svendsen and Andersen-Ranberg, 2000) to 0.07 for a direct effect and 0.13 for a maternal effect (Heringstad et al., 2007), depending on the model. Heringstad et al. (2007) used a threshold model that accounted for the categorical nature of the data, whereas Svendsen and Andersen-Ranberg (2000) used a linear model.

The scoring system for calving difficulty varies among countries; thus, it is difficult to compare frequencies among cattle populations. However, the frequency found for the Norwegian Red is clearly lower than those for several other breeds. In Sweden, the mean frequency of calving difficulty was 4% for Swedish Red heifers and 8% for Swedish Holstein heifers (Philipsson et al., 2006). In Danish Holsteins, 11.2% of calvings were considered difficult (Hansen et al., 2004). Gevrekçi et al. (2006) reported that in American Holsteins, 13.2% of the parturitions fell in the "needed assistance" category and 13.7% fell in the "considerable force" category. In a study of first-parity Canadian Holstein cows, which included the categories of "hard pull" or "surgery needed," 19% of the male calves and 13% of the female calves were born with difficulty (Luo et al., 1999).

We previously performed a genome scan for QTL affecting calving difficulty in Norwegian dairy cattle (our unpublished results). The scan detected a QTL on bovine chromosome 6 (BTA6) affecting the direct effect of calving difficulty. The most likely position was between the markers FBN13 and BMS470, but the 95% confidence interval for the position spanned almost the entirety of BTA6. In a follow-up study using 399 markers on BTA6 (Nilsen et al., 2008), the QTL position was narrowed to an interval between the markers LAP3_581 and HCAP-G_119 (unpublished results). In the present study, we have constructed a very dense marker map spanning the QTL position, and have aimed to refine the position of this QTL even further by using a combined linkage and linkage disequilibrium (LD) approach.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Data
All animals in the study belonged to the Norwegian Red breed. Sires and sons from 18 families were used. The total number of sons in the study was 713, ranging from 24 sons for the smallest family to 68 sons for the largest family. The total number of daughters was approximately 300,000, with an average of 418 daughters per son. The pedigree of each animal was traced back as far as known. Performance information was obtained in the form of daughter yield deviations for a direct effect (CDdir) and a maternal effect (CDmat) of calving difficulty of the sons of the 18 grandsires. Calving difficulty was subjectively scored on a 3-level scale consisting of 1) "easy calvings," 2) "slight problems," and 3) "difficult calvings." The 2 latter categories were subsequently combined into one group by the breeding organization for genetic evaluations. Only the first calving of each cow was included because incidence of "slight problems" and "difficult calvings" was very sparse for subsequent calvings, and records from multiple births, abortions, or stillbirths more than 20 d before expected calving date were excluded. The model used for evaluation of calving difficulty included the fixed effects of sex of the calf, the cow’s age in months at calving, and month x year of calving, and the random effects of herd x year of calving, sire of the cow, and sire of the calf. The solutions for sire of the cow and sire of the calf effects were transformed into direct and maternal effects according to their expectations (Wilham, 1963; Van Vleck, 1978).

Marker Map
A dense marker map consisting of 154 single nucleotide polymorphisms (SNP) was developed. The map consisted of SNP detected by PCR resequencing of bulls from the Norwegian Red population (Nilsen et al., 2008) or selected from the list of "between breed" SNP produced in the Bovine Genome Sequencing Project (ftp://ftp.hgsc.bcm.tmc.edu/pub/data/Btaurus/). The SNP identification, Reference SNP (rs) numbers (http://www.ncbi.nlm.nih.gov/projects/SNP/), physical positions, and SNP allele frequencies are given in Table 1Go. Because very few recombinations were found between the closely linked markers, genetic distances based on recombination rates could not be obtained. Instead, distances in morgans were approximated by setting 1 cM equal to 1 Mb. Because the QTL mapping methods required some recombination between markers, all small marker distances were increased to 0.0001 M.


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  		Table 1. Marker names, reference single nucleotide polymorphism (rs) numbers, positions in base pairs, Hardy-Weinberg P-value (HWpval), percentage genotyped (%Geno), minor allele frequency (MAF), and alleles
 
Statistical Analyses
Single-QTL Analysis Using Linkage and LD.
The CDdir and CDmat were analyzed separately by using the combined linkage and LD method of Meuwissen et al. (2002). Briefly, the method consists of the following 3 steps. First, the linkage phases of all sires and sons were estimated based on marker information. Second, the identical by descent (IBD) probabilities of pairs of haplotypes were calculated at predefined positions on the basis of the similarity of the marker alleles carried by the haplotypes. Hayes et al. (2003) defined a measure of LD called chromosome segment homozygosity as the probability that random chromosome segments sampled from a population are IBD. Meuwissen and Goddard (2007) used chromosome segment homozygosity to calculate IBD probabilities at putative QTL positions based on the marker information at neighboring positions. These IBD probabilities were calculated at the midpoint of each marker bracket, which was regarded as the putative position for a QTL. Only the bracket midpoints were considered, because for a dense marker map, individual positions within the bracket would have similar probabilities. The IBD probability depends on the effective population size, which was assumed to equal 100. The matrix of IBD probabilities between haplotypes at position i is denoted Gi. The last step was to compare the correlations in the Gi matrix to those in the data by using a REML analysis. The statistical model used for this analysis was


Formula 1[1]

where y is an n x 1 vector of records (i.e., daughter yield deviations for the trait in question); µ is the overall mean; 1 is a vector of 1’s; h is a vector of random haplotype effects of dimension q x 1, where q is the number of different haplotypes; Z is an n x q incidence matrix relating observations and haplotype effects; u is a vector of random polygenic effects; and e is a vector of residuals. The variances of h, u, and e are GiFormula 1, AFormula 1, and RFormula 1, respectively, where Gi is the matrix of IBD probabilities among haplotypes, A is the additive genetic relationship matrix, and R is a diagonal matrix with nj–1 on the diagonals (nj is the number of daughters of bull j).

For each marker bracket, the log-likelihood of a model containing the QTL [LogL(Gi)] was calculated as well as a model fitting only background genes [LogL(0)] by using the ASREML package (Gilmour et al., 2002). A likelihood ratio test-statistic (LRT) was calculated as LRT = LogL(Gi) – LogL(0). The marker bracket with the greatest LRT was expected to contain the QTL, if the LRT of that bracket was considered significant. The linkage analysis was used to test the detected QTL for its chromosome-wise significance. Approximate nominal significance levels were found by using the LRT, where 2 x LRT is approximately a chi-square distributed with 1 degree of freedom. To achieve a nominal significance level of 0.001, 2 x LRT must exceed 10.8; that is, LRT greater than 5.4 were regarded as significant. For technical reasons, the brackets were numbered from 2 to 154; that is, bracket number 2 referred to the interval between the first and second marker.

Multiple-QTL Analyses Using Linkage and Linkage Disequilibrium.
A complete multiple-QTL analysis (for instance, as described by Meuwissen and Goddard, 2001) could not be performed because of convergence problems caused by the highly correlated Gi matrices arising from the small bracket sizes. Instead, we used the same analysis as for the single-QTL analysis, but included a random effect of a QTL in another specified marker bracket. That is, each bracket that showed a high LRT in the single-QTL analysis was included as a random effect in the QTL model in turn, and the analysis was repeated. These analyses search for additional QTL, given that the QTL in the bracket is accounted for and is similar to the fitting of cofactors (Jansen, 1993).

The above-mentioned analyses were also performed with the effect of a specific marker fitted in model 1 (instead of the bracket effect) to search for markers in high LD with the QTL. The QTL search was also repeated with the effects of both brackets 66 and 74 included in the model to investigate whether there was any evidence of more QTL segregating in other brackets.

Haplotype Analysis
Linkage phases between markers for all animals were estimated by multi-locus iterative peeling (Meuwissen, 2006). The resulting haplotypes were imported into the Haploview program (http://www.broad.mit.edu/mpg/haploview/; Barrett et al., 2005) for calculation of LD (r2) between markers and construction of haplotype blocks.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 CONCLUSIONS
 REFERENCES
 
Results for single-QTL analysis of CDdir are shown in Figure 1Go. The figure reveals the presence of several peaks. The largest peak was found in bracket number 74 (i.e., the interval between markers LAP3_281 and BTA-114677), with an LRT of 13.9. A peak with similar LRT (13.4) was found in bracket 66, which is the interval between markers BTA-75776 and BTA-75780. The intervals between these 2 brackets also showed rather large test statistics because the LRT ranged from 8 to 10 for most of these brackets. A third peak was found in bracket 15 (BTA-75976 to BTA-75979), with an LRT of 9.4. All these peaks were highly significant, with nominal P-values of <0.0001. Several other peaks also exceeded the nominal significance level of LRT = 5.4 (P = 0.001). No significant results for CDmat were found.


Figure 1
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  		Figure 1. Single-QTL analysis for a direct effect of calving difficulty. The abscissa denotes the marker bracket number and the ordinate denotes the likelihood ratio test-statistic (LRT). Points illustrate bracket midpoints.

 
The multiple peaks for CDdir could be due to either the presence of more than one QTL or the presence of one QTL with carryover effects to other regions; thus, a multiple-QTL analysis was performed. First, a QTL was fitted in bracket 74 (i.e., the interval between markers LAP3_281 and BTA-114677), and the other brackets were scanned for additional QTL. As shown in Figure 2Go, a sharp peak was seen in bracket 66, but the LRT was just above 4, and hence below the significance threshold. All other variation in the region was explained by the fitted QTL effect.


Figure 2
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  		Figure 2. Multiple-QTL analysis with bracket 74 included in the model. Only the first 90 brackets are shown to improve the readability of the figure. LRT = likelihood ratio test.

 
The result of including the effect of a QTL in bracket 66 (BTA-75776 to BTA-75780) is shown in Figure 3Go. The peak in bracket 74 remained, but its LRT was reduced to approximately 5. There were also a few smaller but not significant peaks, of which bracket 15 had the greatest LRT (approximately 4.5).


Figure 3
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  		Figure 3. Multiple-QTL analysis with bracket 66 included in the model. Only the first 90 brackets are shown to improve the readability of the figure. LRT = likelihood ratio test.

 
Figure 4Go illustrates the result of fitting a QTL in bracket 15 (BTA-75976 to BTA-75979). All signals in the proximal half of the region were removed, but the peaks in brackets 66 and 74 remained. However, the LRT of these brackets were largely reduced as compared with the single QTL analysis, with the LRT of brackets 66 and 74 now being approximately 8.5 and 6, respectively.


Figure 4
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  		Figure 4. Multiple-QTL analysis with bracket 15 included in the model. Only the first 90 brackets are shown to improve the readability of the figure. LRT = likelihood ratio test.

 
When QTL effects in the remaining brackets were fitted, similar results as for the single-QTL analysis were found. Thus, our data do not show any evidence of further QTL in any other brackets.

The analyses yielded strong support for one or more QTL in brackets 74, 66, or both and seemed to exclude the possibility of further QTL. To verify this result, we also extended model [1] to include the effects of both of these brackets simultaneously. The resulting curve was completely flat (not shown), and again no evidence of further QTL in other brackets, including bracket 15, was found in our data.

Next, we aimed to identify markers in LD with the QTL by including marker effects in the QTL model. Figure 5Go shows the results of including BTA-75979 (marker 15, i.e., the right boundary of bracket 15). By using this model, the bracket 15 peak was removed, whereas the LRT of brackets 66 and 74 were reduced to approximately 9 and 8, respectively. Surprisingly, such results were not found for any of the other markers; thus, only one of the 154 genotyped markers was in considerable LD with the QTL.


Figure 5
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  		Figure 5. Multiple-QTL analysis with marker number 15 included in the model. Only the first 90 brackets are shown to improve the readability of the figure. LRT = likelihood ratio test.

 
Finally, linkage phases of all animals were imported into the Haploview program (Barrett et al., 2005) for calculation of LD (r2) between markers and investigation of haplotype block structure in the QTL regions. In general, levels of LD were low for the entire genotyped region, and few haplotype blocks could be constructed based on the degree of LD. None of the 6 markers surrounding brackets 15, 66, and 74 was included in a haplotype block. In addition, the LD between these markers was surprisingly low. The greatest LD was found between markers 14 and 15 (BTA-75976 and BTA-75979), with an r2 of 0.19. Marker 14 was also in some degree of LD with BTA-75780 (marker 66), with an r2 of 0.15. For the other marker pairs, r2 varied between 0.003 and 0.059. Figure 6Go illustrates r2 for the markers pairs between markers 65 and 74.


Figure 6
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  		Figure 6. Linkage disequilibrium expressed as r2 x 100 between markers in the BTA-75776 (marker no. 65) to BTA-114677 (marker no. 74) region.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
CONCLUSIONS
 REFERENCES
 
Preliminary analyses in Norwegian Red had indicated the presence of a QTL affecting the direct effect of calving difficulty in the middle part of BTA6 (unpublished results). The aim of this study was to refine the position of this QTL further to search for candidate genes.

The results of this study strongly confirm the presence of one or more QTL with an effect on CDdir on BTA6. The single-QTL analysis yielded 3 peaks that were all significant at the nominal 0.0001 level. These peaks were situated in brackets 15, 66, and 74. However, the subsequent analyses showed that not all peaks represented true QTL. The peak in bracket 15 was reduced to below the significance threshold, both in the analysis in which bracket 74 was included and in the cases in which bracket 66 and both brackets 66 and 74 were included. Such a result could be explained by LD between the markers surrounding bracket 15 and a more distal QTL. However, the result from Haploview did not reveal high levels of LD between pairs of markers surrounding bracket 15 and markers surrounding bracket 66 or 74. Still, the fact that the peaks in brackets 66 and 74 were markedly reduced when the effect of bracket 15 was included in the model strongly indicates the existence of LD between the bracket 15 markers and combinations of alleles of several markers (i.e., haplotypes) surrounding the QTL. The same result was found when the effect of marker number 15 was fitted. Thus, we can conclude that the bracket 15 peak was merely an artifact caused by LD between the markers surrounding bracket 15 and a true QTL further downstream.

The situation for brackets 66 and 74 was somewhat less clear. The peak in bracket 74 had the greatest LRT in the single-QTL analysis, and no other brackets showed significant results when a QTL was fitted in this bracket. A reasonable explanation is then that only one QTL was segregating in our data and that this QTL was situated in bracket 74. However, the LRT of bracket 66 was reduced to only slightly below the significance threshold and was not completely removed. The fact that the 66 peak was not completely explained by a QTL in the bracket 74 QTL could indicate that bracket 66 did contain other polymorphisms with an effect on calving difficulty but that this effect was not statistically significant. Therefore, the possibility of a second QTL here cannot be completely excluded. A third possibility is that the 2 peaks could be caused by one QTL positioned somewhere between the 2 brackets. This hypothesis was supported by the high LRT in these brackets obtained by the single-QTL analysis. On the other hand, the fact that fitting a QTL in any of these brackets did not remove the QTL signals at brackets 66 and 74 contradicts this hypothesis.

Our conclusion is that the most likely explanation for the presented QTL signals is the presence of only one QTL, which was situated in bracket 74. This bracket is bordered by LAP3_281 and BTA-114677, which are separated by a physical distance of less than 150 kb. However, we cannot completely rule out the possibility of a second QTL segregating in bracket 66, or alternatively, the presence of only one QTL situated somewhere between these brackets. This explanation expands the most likely location of the QTL to a region of approximately 500 kb bounded by the SNP BTA-75776 and BTA-114677.

The reason for the difficulties in determining the correct QTL position(s) can be found from the analyses in which the effect of each marker was included in the QTL model. According to these results, marker 15 was the only one of the 154 markers whose alleles segregated in some concordance with the QTL alleles. Because the calving difficulty QTL was found in a region where much effort had been undertaken to identify a QTL for milk production (Olsen et al., 2007), the SNP density in that region was very high. Despite the high map density, the genotyped markers were not found to be causal mutations or in high LD with the real mutation. The true causal mutation could be identified by performing a systematic SNP search in the region and redoing the analyses with this new set of markers. Given the relative narrow mapping of the QTL, even resequencing the entire 500-kb region in animals carrying different QTL alleles appeared to be an affordable endeavor when using the new sequencing technology (Albert et al., 2007).

The region around brackets 66 and 74 contains several genes that can be regarded as interesting functional or positional candidates, or both. This region of BTA6 contains at least 6 known genes: osteopontin (OPN), extracellular matrix phosphoglycoprotein (MEPE), integrin-binding sialoprotein (IBSP), leucine amino-peptidase 3 (LAP3), mediator of RNA polymerase II transcription, subunit 28 homolog (Saccharomyces cerevisiae; EG1, also denoted as MED28), and NCAPG non-SMC condensin I complex, subunit G (HCAP-G). One SNP in OPN, OPN_607, was genotyped in our study and constitutes the boundary of brackets 61 and 62. The MEPE SNP BTA-02519 constitutes the boundary between brackets 67 and 68. The IBSP was not genotyped in the present study, but is mapped to the interval between MEPE and LAP3 (Cohen-Zinder et al., 2005). Of these, OPN, IBSP, and MEPE are included in a cluster of bone-tooth mineral extracellular matrix phosphoglycoproteins (Rowe et al., 2000). Although the cluster is thought to be involved in several biological processes, such as branching during tubulogenesis of the uretic bud in the kidney (Stuart et al., 1995) and branching of the mammary epithelial ductal system (Talhouk et al., 1992), it is primarily associated with bone and cartilage morphogenesis. As an example, MEPE is thought to play an inhibitory role in bone formation, and a disruption of one of its alleles was shown to cause significantly increased bone mass in the mouse (Gowen et al., 2003). The size of the calf as compared with its mother is one of the main factors contributing to calving difficulty (e.g., Johanson and Berger, 2003); thus, this extracellular matrix cluster represents very good functional candidate genes. Several SNP of LAP3 and HCAP-G are genotyped in our study. The most likely QTL position is in bracket 74, which is the interval between the last SNP of LAP3 and the marker BTA-114677. Bracket 74 also contains the gene EG1. Very little information about the function of these genes can be found, but all are close enough to the QTL to be regarded as positional candidates.

Several studies have detected QTL for traits related to calving performance on BTA6. Holmberg and Andersson-Eklund (2006) reported a QTL for CDdir close to marker BM143 and for CDmat at BM1329 in Swedish dairy cattle. Based on our unpublished linkage analysis map, the distance between the bracket 74 QTL and BM143 is approximately 4 cM. Kühn et al. (2003) reported a QTL for calving difficulty and stillbirth in the proximal end of BTA6 in German Holsteins at approximately the same position where Schrooten et al. (2000) found the QTL affecting calving difficulty, size, and dairy character in Dutch Holsteins. Casas et al. (2000) reported a QTL for birth weight, which is a major cause of calving difficulty, close to BMS2508 in beef cattle. This marker is situated approximately 4 cM proximal of our QTL; thus, the results of several of these papers could reflect the presence of the same QTL segregating in different breeds.


   CONCLUSIONS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
REFERENCES
 
Our results clearly demonstrate that at least one QTL for a direct effect of calving difficulty is segregating on BTA6 in Norwegian Red. The most likely position is in a 150-kb interval between markers LAP3_281 and BTA-114677. Some evidence was found for a second QTL between markers BTA-75776 and BTA-75780. The distance between the 2 putative QTL is less than 500 kb. Several interesting candidate genes can be found in this region, including a gene cluster affecting bone and cartilage morphogenesis.

Received for publication January 7, 2008. Accepted for publication June 25, 2008.


   REFERENCES
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 CONCLUSIONS
 REFERENCES
 


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