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1 USDA-ARS, Bovine Functional Genomics Laboratory, Beltsville, MD 20705
2 Department of Animal Sciences, University of Illinois, Urbana 61801
3 Department of Animal Science, University of Minnesota, St. Paul 55108
4 USDA-ARS, Animal Improvement Programs Laboratory, Beltsville, MD 20705
5 Institute of Animal Sciences, ARO, The Volcani Center, Bet Dagan 50250 Israel
Corresponding author: Melissa S. Ashwell; e-mail: mashwell{at}anri.barc.usda.gov.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Key Words: quantitative trait locus • pregnancy rate • milk production traits
Abbreviation key: AIPL = Animal Improvement Programs Laboratory, BTA = Bos taurus autosome, DBDR = Dairy Bull DNA Repository, PL = productive life, DGAT1 = acylCoA:diacylglycerol acyltransferase gene, GHR = growth hormone receptor gene
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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In many species, especially livestock species, studies designed for identification of QTL are based on crosses of genetically distinct breeds or inbred lines. This approach has been less used in dairy cattle research because development of experimental populations is prohibitively expensive and the generation interval is long. In addition, the population structure that results from the use of elite AI bulls as sires is quite powerful and requires only modest amounts of genotyping for QTL identification. In 1991, the Dairy Bull DNA Repository (DBDR; Da et al., 1994) was initiated as a collection of semen from 35 dairy grandsire families that was available to collaborators for studies to identify QTL affecting economically important traits in Holstein cattle. Two genome-wide scans were conducted in DBDR families, focusing on identification of QTL affecting the milk production traits and SCS. Originally two groups conducted and reported independent research findings (Heyen et al. 1999; Ashwell et al. 2001). These datasets have now been merged for further analysis allowing a more thorough coverage of the bovine genome. Originally the independent genome scans were analyzed one marker at a time using analysis of variance methods, with interval regression analysis conducted only on specific chromosomes. Those studies lacked thorough grandsire-specific chromosomal coverage; the merged results presented here increased the coverage by combining genotypes from the different sets of DNA markers, such that interval regression analysis could be more thoroughly carried out on all 29 autosomes.
Since the two datasets were generated and merged, researchers with the USDA-ARS Animal Improvement Programs Laboratory (AIPL) have calculated genetic evaluations for female fertility (VanRaden et al., 2003). The female fertility evaluations are expressed as the pregnancy rate, which is defined as the percentage of nonpregnant cows that become pregnant during each 21-d period. Heritability for this trait has been estimated at 4% (VanRaden et al., 2003), making this an ideal candidate trait for a marker-assisted selection program.
The objective of this study was to identify QTL affecting milk production traits using the merged set of DBDR genotypes as well as provide the first report of putative QTL affecting the pregnancy rate trait.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Originally the two research groups (University of Illinois, Urbana/ARO, Israel and USDA-ARS, Beltsville) conducted independent genome scans. Each group selected eight families for study, six that were common across both studies (families 1 to 5, 8) and four families that were studied by only one of the two groups (families 6 and 7 by University of Illinois/ARO; families 9 and 12 by USDA-ARS).
Genotyping
DNA was extracted from semen as previously described (Ashwell et al., 1996; Heyen et al., 1999). The PCR was carried out using either fluorescently-labeled primers or by [
-32P] incorporation into PCR products as previously described (Ashwell et al., 1996; Heyen et al., 1999). For each individual genome scan, microsatellite markers were selected at approximately 20-cM intervals from published bovine maps (Bishop et al., 1994; Barendse et al., 1994; Ma et al., 1996; Barendse et al., 1997; Kappes et al., 1997). Heyen et al. (1999) selected 174 markers; Ashwell et al. (2001) selected 232 markers, 38 of which were genotyped by both groups. Duplicate markers were treated as separate loci because allele-calling methods were not consistent across both datasets. When the two datasets were merged, genome coverage was estimated to be 2713.5 cM (90%), assuming a 3000-cM genome. The average number of markers per chromosome was 12.5, with an average spacing of 7.4 cM.
Phenotypic Data
Data for milk yield and composition, SCS, and productive life (PL) collected through November 2001 were processed as part of the routine USDA/DHIA genetic evaluation procedure by the AIPL of USDA-ARS. The female fertility trait, pregnancy rate, is a new genetic evaluation being calculated by AIPL. Evaluations are expressed as the pregnancy rate, which is defined as the percentage of nonpregnant cows that become pregnant during each 21-d period (VanRaden and Tooker, 2003; VanRaden et al., 2003). The PTA values for pregnancy rate trait ranged from -4.9 to 3.3%, with an average of -0.4% and a standard deviation of 1.1 for the sires used in this study. Pregnancy status is determined from the date of last breeding and is verified using the next calving date or veterinary diagnosis when available. Cows that are sold for reproductive problems are assumed not pregnant. Pregnancy status is determined only within the first 250 d of lactation because many cows not pregnant by 250 d are not bred again but instead are culled. Mean pregnancy rate for Holsteins in 1995 was 23% and SD of sire evaluations was 1%. These evaluations for pregnancy rate are obtained from data on days open, and an increase of 4 d open equals a 1% decrease in pregnancy rate. Data since 1960 from the first 5 lactations are included, for a total of 40 million records. Data are adjusted for month of calving, age at first calf, and lactation number and are computed using the same animal model evaluation programs used for yield traits, productive life, and SCS (VanRaden and Tooker, 2003; VanRaden et al., 2003). The present study included 3 mo less data than the first official evaluations that were released in February 2003.
Statistical Analysis
Data from a total of eight traits were analyzed using a regression approach originally described by Haley and Knott (1992). A web-based version of this regression interval mapping method is now available (Seaton et al., 2001). The software, QTL Express (http://qtl.cap.ed.ac.uk), analyzes data from F2, half-sib, and sib-pair families to detect QTL. The software allows the user to fit one or two QTL in the model and includes tools for permutation and bootstrap analyses to calculate chromosome-wise significance thresholds and 95% confidence intervals, respectively. For this study, 1000 permutations were studied for each trait to determine the P < 0.05 and P < 0.01 chromosome-wise significance thresholds, and the regression interval analysis was conducted at 1-cM intervals along the chromosome. Both within- and across-family analyses were conducted, fitting one and two QTL in the model. Data included daughter deviations for milk, fat, and protein yield, fat and protein percentage, SCS, and PL, weighted by their respective reliabilities. At the time this analysis was conducted, no daughter deviations were available for pregnancy rate, therefore, PTA for pregnancy rate were used. Within family analyses correspond to a contrast of two sire marker alleles with one degree of freedom, while the across-family analysis tests for the evidence of different marker effects across families. Marker effects are nested within sire family so that no assumptions are made regarding the phase of QTL allelic effects in the across-family analysis.
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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). Only effects with a chromosome-wise P value of <0.01 are reported. Forty-five significant effects modeling one QTL were identified within families on 16 of the 29 autosomes. However, caution should be used when considering QTL identified in family 12 due to the limited number of informative sons from this family. Nine significant effects found on five chromosomes were identified in the across-family analysis (Table 2). No evidence was found favoring the two QTL model over the one QTL model in the within and across-family analyses. If the number of significant tests expected under the null hypothesis is compared to the number observed, evidence exists for QTL affecting milk production traits in this population. For these traits, a total of 1450 trait-chromosome-family combinations were tested, with 15 significant tests expected by chance at P < 0.01, and 45 were observed. Similarly, for across-family analyses, a total of 145 chromosome-trait combinations were tested. With this many tests, two significant tests are expected by chance and nine were observed. The most significant effects (F statistics > 15) were located on Bos taurus autosome (BTA) 3, BTA6, BTA14 and BTA20. Heyen et al. (1999) detected a significant effect on BTA3, Ashwell et al. (2001) detected effects on BTA6 and BTA20, and both studies detected an effect on BTA14. To date, the genes responsible for the effects observed on BTA3 and BTA6 have not been identified. However, many groups are currently working to fine map these QTL.
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). Recently, the same group (Blott et al., 2003) identified a mutation in the growth hormone receptor (GHR) gene that is associated with an effect on milk yield and milk composition. GHR mapped to BTA20 at 43 cM on their map and is associated with a major effect on milk protein percentage. Results from the DBDR merged dataset identified a QTL affecting milk protein percentage at 51 cM, based on the MARC map (Kappes et al., 1997). The QTL identified in our DBDR study most likely corresponds to the QTL identified by Blott et al. (2003).
Marker Effects on SCS and Productive Life
No evidence favoring the two QTL models over the one QTL model was found in the within and across-family analyses for SCS and productive life. Eight significant effects on SCS were identified within families on eight chromosomes when one QTL was modeled (Table 1). Four significant effects on SCS were identified in the across-family analysis (Table 2). For SCS, a total of 290 chromosome-family combinations were tested, with three significant tests expected by chance at P < 0.01 and eight were observed. In the across-family analysis, 29 tests were conducted, with less than one significant test expected by chance, and four were observed. All significant effects on this trait were detected in the independent DBDR studies, except for the within-family associations on BTA15 and BTA20. Boichard and Bishop (1997) also detected an association on BTA15 at 36 cM, close to the same location reported here. Although many groups have detected QTL on BTA20, none have been reported affecting SCS.
A putative QTL affecting SCS was detected on BTA26 at 0 cM in families 3 and 7. A SCS QTL on BTA26 was reported by Zhang et al. (1998) and Boichard and Bishop (1997); however, they report the location to be toward the telomere, between TGLA429 and BM804. DBDR families were not genotyped for marker TGLA429; however, families 3 and 7, as well as 4, 5, and 6, were heterozygous at BM804 and all their sons were genotyped. Therefore, one explanation for these findings is that two QTL affecting SCS are located on BTA26 at opposite ends of the chromosome and were detected in different families.
Only one significant effect on PL, modeling one QTL, was detected. The putative QTL was detected within family 1 on BTA7 (Table 1). Neither of the independent scans detected a significant association affecting PL, however the Heyen et al. (1999; see also http://cagst.animal.uiuc.edu/genemap/WEB/Table1.html) study detected a suggestive association (P < 0.1) in another family. Comparison of this finding with those from other groups is difficult because few groups have tried to identify PL QTL and the phenotype and genetic evaluation systems differ from country to country (van der Linde and de Jong, 2002). A total of 29 significance tests were conducted, with three significant tests expected by chance at P < 0.01; therefore the effect detected on BTA7 may be a false-positive association.
Marker Effects on Pregnancy Rate
The PTA for pregnancy rate (VanRaden et al., 2002) were recently computed, allowing, for the first time, identification of putative QTL affecting this trait. Seven significant effects were identified within families on six chromosomes (Table 3). A total of 290 significant tests were conducted, with three significant tests expected by chance at P < 0.01 and seven observed. The most significant within-family effect (F statistic = 14.1) was located on BTA18 in DBDR family 4 (Figure 1). The estimate of the difference of the allelic effect for this QTL was 0.57 (± 0.15) units (Table 3). The only significant associations previously detected on this chromosome affected rump angle; however, this was in a different DBDR family (Ashwell et al., 2001). No significant effects were detected across families. This is likely if the heterozygosity at the QTL is low due to selection (Heyen et al., 1999) and QTL will only be detected within families and not across the families. No evidence was found favoring the two QTL model over the one QTL model in the within- and across-family analyses for this trait.
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Schrooten et al. (2000) analyzed "nonreturn daughters" and "interval calving to first insemination" fertility traits in their QTL study. Suggestive linkage was found on BTA2 and BTA9 affecting the nonreturn daughters trait. These locations cannot be confirmed with the pregnancy rate findings. Larsson and Andersson-Eklund (2002) analyzed "interval calving to first insemination," fertility treatments, and a combined fertility index in their partial genome scan to identify QTL affecting reproduction traits in Swedish dairy cattle. Putative QTL affecting the interval from calving to first insemination were identified on BTA3, BTA11, and BTA25 and cannot be confirmed by the pregnancy rate findings. They also detected QTL on BTA6 affecting fertility treatments and the combined fertility index. Schrooten et al. (2000) also identified suggestive linkage on BTA6 and BTA17 affecting the interval from calving to first insemination. The putative QTL on BTA6 identified by Schrooten et al. (2000), located at 106 cM in their study, may be supported by the pregnancy rate QTL detected on this chromosome at 122 cM in our study. In addition, BTA6 is also known to harbor QTL effecting traits such as milk production (Kühn et al., 1999; Nadesalingam et al., 2001; Ron et al., 2001), clinical mastitis (Klungland et al., 2001) and calving ease (Schrooten et al., 2000; Larsson and Andersson-Eklund, 2002). Once the genes underlying these QTL are identified, researchers may be able to determine the pleiotropic effects on important traits in dairy breeding.
The putative pregnancy rate QTL on BTA14 may be explained by effects of the DGAT1 gene discussed above. DGAT1 maps to the centromeric end of BTA14 and is included in the region where a pregnancy rate QTL was detected. DGAT1 catalyzes the last step in triglyceride synthesis and Grisart et al. (2002) identified a mutation in this gene that is associated with a major effect on milk fat content. It has been shown that changes in the fat:protein ratio in milk during early lactation has a negative effect on fertility (de Vries and Veerkamp, 2000). Therefore mutations in the DGAT1 gene may have pleiotropic effects on the pregnancy rate.
Previously, no fertility QTL were reported on BTA27. However, a previous study (Ashwell et al., 2001) detected a QTL affecting the conformation trait, dairy form, on the telomeric end of BTA27 in the same region as the pregnancy rate QTL. Casas et al. (2002) also identified a putative QTL affecting marbling in beef cattle in the same region of chromosome 27. Therefore, results suggest at least one gene affecting fat metabolism is located on this chromosome that may also affect fertility. Rogers et al. (1999) studied the genetic correlations between type traits and three groups of diseases: reproductive, foot and leg, and metabolic and digestive diseases. Results from this study showed that the genetic correlation between dairy form and the three disease categories were negative and moderate in magnitude. Metabolic disorders (such as milk fever and ketosis) are indicators of negative energy balance (Rogers et al., 1999), which is known to affect reproductive performance (de Vries and Veerkamp, 2000). Therefore, selection for increased dairy form may lead to cows that are more prone to reproductive and metabolic diseases (Rogers et al., 1999).
It should be emphasized that the results associated with pregnancy rate are preliminary and require validation. However, based on these results, one can speculate that identification of the genes underlying the pregnancy rate QTL may actually be genes affecting body condition and the metabolic state of dairy animals. In several cases there are effects on milk production traits in regions where putative pregnancy rate QTL have been detected, so it may be difficult to improve female fertility without sacrificing some milk production. However, it may be more economically advantageous to sacrifice some gains in milk production in order to be able to breed cows in a shorter period of time.
These data are just the first step in identifying and locating QTL. Work is underway to validate these QTL. Once these results are validated, fine-mapping studies will begin, so the genes responsible for the observed effects can be identified and incorporated into marker-assisted selection programs.
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CONCLUSIONS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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ACKNOWLEDGEMENTS |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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Received for publication December 20, 2002. Accepted for publication June 1, 2003.
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMATERIALS AND METHODSRESULTS AND DISCUSSIONCONCLUSIONSACKNOWLEDGEMENTSREFERENCES |
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
), fat yield (
), protein yield (
), and SCS (x). Diamonds at top of plot indicate location of genotyped markers.