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Confirmation of quantitative trait loci using a low-density single nucleotide polymorphism map for twinning and ovulation rate on bovine chromosome 5^1,2

USDA, ARS, US Meat Animal Research Center, Clay Center, NE 68933

	Abstract

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Traditional genetic selection in cattle for traits with low heritability, such as reproduction, has had very little success. With the addition of DNA technologies to the genetic selection toolbox for livestock, the opportunity may exist to improve reproductive efficiency more rapidly in cattle. The US Meat Animal Research Center Production Efficiency Population has 9,186 twinning and 29,571 ovulation rate records for multiple generations of animals, but a significant number of these animals do not have tissue samples available for DNA genotyping. The objectives of this study were to confirm QTL for twinning and ovulation rate previously found on BTA5 and to evaluate the ability of GenoProb to predict genotypic information in a pedigree containing 16,035 animals when using genotypes for 24 SNP from 3 data sets containing 48, 724, or 2,900 animals. Marker data for 21 microsatellites on BTA5 with 297 to 3,395 animals per marker were used in conjunction with each data set of genotyped animals. Genotypic probabilities for females were used to calculate independent variables for regressions of additive, dominance, and imprinting effects. Genotypic regressions were fitted as fixed effects in a 2-trait mixed model analysis by using multiple-trait derivative-free REML. Each SNP was analyzed individually, followed by backward selection fitting all individually significant SNP simultaneously and then removing the least significant SNP until only significant SNP were left. Five significant SNP associations were detected for twinning rate and 3 were detected for ovulation rate. Two of these SNP, 1 for each trait, were significant for imprinting. Additional modeling of paternal and maternal allelic effects confirmed the initial results of imprinting done by contrasting heterozygotes. These results are supported by comparative mapping of mouse and human imprinted genes to this region of bovine chromosome 5.

Key Words: GenoProb • multiple-trait derivative-free restricted maximum likelihood • single nucleotide polymorphism • twinning

	INTRODUCTION

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Fine mapping and marker development for marker-assisted selection (MAS) in all species has had limited success; however, with technological advances in genomics and the ongoing development of the bovine whole-genome sequence, the rate of success in the discovery of DNA markers for MAS should accelerate. To date, successes in MAS have resulted from phenotypic traits with moderate to high heritability, and commercialization of markers for MAS in beef cattle has been limited to traits related to meat tenderness and fat deposition (Page et al., 2004; Schenkel et al., 2005; Casas et al., 2006).

Reproductive efficiency is a primary component of profitability for the cow-calf producer (Dickerson, 1970; Doyle et al., 2000; Martinez et al., 2005). The theoretical relative economic value of reproductive traits was shown to be 3.24 times greater than for final product consumable traits (Melton, 1995). However, direct improvement for reproductive efficiency using traditional selection methods in the bovine has resulted in limited success because of the long generation interval and low heritability. Discovery of DNA markers for reproductive traits will give the producer a tool to identify superior or inferior animals and improve selection response, further enhancing economic gains for the cow-calf producer.

The objective of this study was to confirm the region segregating a QTL for ovulation rate on BTA5 by using a low-density SNP map in a multigeneration pedigree. An additional objective was to evaluate the extent to which a previous investment in microsatellite genotyping for linkage analysis could be leveraged to reduce the current cost of SNP genotyping for linkage disequilibrium analysis. To do this, we evaluated the use of GenoProb (Thallman, 2002) with 3 sets of genotyped animals, containing 48, 724, or 2,900 animals, in a large multigenerational pedigree. Comparisons of significance levels between these analyses were evaluated to help understand the predictive power of allelic peeling in GenoProb.

	MATERIALS AND METHODS

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The US Meat Animal Research Center Animal Care and Use Committee approved these experimental procedures.

Population

A selection experiment was initiated in 1981 at the USDA-ARS US Meat Animal Research Center (US-MARC) to increase the frequency of dizygotic twin births in cattle (Gregory et al., 1997; Allan et al., 2007). The USMARC Production Efficiency population (formerly USMARC Twinning population) structure is a large multigeneration pedigree representing 12 different breeds of cattle with a broad array of phenotypes for reproduction, growth, and carcass traits (Gregory et al., 1996; Echternkamp and Gregory, 1999a,b). The population was derived from 345 founding gametes. Presently, the population is maintained with a fall- and spring-calving season and continues to undergo selection for twinning rate. Current rates are 48% for multiple ovulations in 12- to 18-mo-old heifers (Cushman et al., 2005) and greater than 60% for twin or multiple births in cows (Echternkamp, 2000; Echternkamp et al., 2007). Additionally, a large repository of DNA samples has been created for mapping production traits that includes most of the founding sires and a portion of the males and females that have undergone selection since 1981. The repository includes essentially all individuals born since 1996 that have produced progeny in the population or that have generated phenotypes for ovulation rate.

Genotyping

Discovery of SNP was done in a selected group of Production Efficiency animals representing sires whose 4 founding alleles constitute 30% of the genetic composition of the population at the peak of the QTL on BTA5. Animals in this group were homozygous for the favorable founding QTL allele or homozygous for the allele not contributing to the effect of the QTL. Additionally, 1 founder sire (Pinzgauer bull 19784403) known to be heterozygous for the QTL (Kappes et al., 2000) was sequenced, bringing the discovery set of animals to 24.

Primers were designed from the bovine genome sequence (3.1) by using Primer 3 (Rozen and Skaletsky, 2000; code available at http://primer3.sourceforge.net; last accessed May 2007), or selected from the USMARC database from earlier bovine sequencing projects. For PCR amplification, 25 ng of genomic bovine DNA extracted from ear notch or semen samples was used in a total 12-µL volume containing 1.5 mM MgCl₂, 0.25 mM each dNTP, 0.5 U of AmpliTaq Gold with 1x MgCl₂-free supplied buffer (Perkin-Elmer, Branchburg, NJ), and 0.2 µM each primer. Thermal cycling conditions included an initial denaturing step at 95°C for 5 min, followed by 40 cycles of a denaturing step at 95°C for 30 s, annealing at 58°C for 45 s, and extension at 72°C for 1 min, with a final extension period at 72°C for 5 min. A portion of each sample was separated on a 2% agarose gel to verify quality.

A portion of the PCR reaction (3 µL) was incubated with 0.1 U of Exonuclease I (USB, Cleveland, OH) at 37°C for 1 h, followed by heat inactivation at 65°C for 20 min, and the product was purified by using ethanol precipitation. Purified PCR products were sequenced by using ABI BigDye Terminator chemistry (Perkin Elmer, Foster City, CA) and analyzed on an ABI 3730 sequencer.

Sequence information was analyzed from chromatograms stored in the USMARC database; bases were called with Phred and assembled in contigs with Phrap (Ewing and Green 1998; Ewing et al., 1998). The results were visually assessed by using Consed (Gordon et al., 1998). Sequence information was subjected to the BLAST (Basic Local Alignment Search Tool) function of the National Center for Biotechnology Information bovine genome project release 3.1 (http://www.ncbi.nlm.nih.gov/genome/guide/cow/index.html; last accessed December 2007). Scaffolds with significant hits (E-value 0.0) were then queried against the bacterial artificial chromosome (BAC) end clone sequence data by using BLAST. The BAC end sequence results were merged with the integrated bovine BAC map (Snelling et al., 2007) to identify chromosome and approximate map position (Table 1). Bovine genome sequence (3.1) order within contigs was used to refine order when the integrated composite map was unresolved.

Microsatellite genotyping included data for BTA5 generated previously (Kappes et al., 2000) and data generated by using standard PCR and infrared dyes (IRD700, IRD800), followed by electrophoresis and analysis on a LI-COR 4200 IR² system (LI-COR, Lincoln, NE). Recently genotyped microsatellite data included an additional 128 progeny-tested sires and all males and females born since 1999. All microsatellite data, including the number of genotyped animals, were used in combination with SNP data in the integrated map described previously (Table 1).

Analysis

Genotype probabilities for all animals in the extended pedigree (n = 16,035) were calculated by using an extended version of the GenoProb software (Thallman, 2002), and the integrated map for BTA5 including 21 microsatellites and 24 SNP (Table 1). GenoProb is an allelic peeling algorithm program that can be used to predict ordered genotypic distributions on pedigreed animals with incomplete genotyping data (Thallman et al., 2001a,b). All GenoProb analyses used the micro-satellite data and the whole pedigree. However, varying amounts of SNP genotypes were used. This procedure was repeated on a data set that included either 48 animals genotyped for SNP (sequence population), 724 animals genotyped for SNP (influential animal data set), or 2,900 animals (all animals with DNA sample). When comparing the results of these 3 analyses, the 2,900-animal SNP data set was used as a standard. Only SNP probabilities were used in subsequent association analyses; probabilities were generated and used for all 16,035 animals.

Twinning and ovulation rate were fit together in a 2-trait, repeated records analysis in multiple-trait derivative-free REML (Boldman et al., 1995), using the model described by Van Vleck and Gregory (1996) with additional details listed in Allan et al. (2007). Briefly, the model for twinning rate included the fixed effects of year of parturition, season of parturition, age at parturition, and genotypic regressions of the SNP marker(s). The model for ovulation rate included the fixed effects of birth-year season, age at ovulation, month of ovulation, and genotypic regressions of the SNP marker(s). Both traits included random effects for breeding value, with full relationships accounted for and environmental variance common to the repeated records of the animal in addition to the residual. The number of phenotypic records used in the analyses included 29,571 for ovulation rate and 9,186 for twinning rate records. Independent regression variables representing additive, dominance, and imprinting genetic effects were calculated by using the genotypic probabilities computed by GenoProb (Allan et al., 2007). For each SNP, genotypic regressions for additive, dominance, and imprinting (ADI) were fitted as fixed effects in the model and run in a single-marker analysis. When imprinting was not significant, these analyses were repeated for models containing only the regressions of additive and dominance (AD). If the additive and dominance effects were confounded because of low minor allele frequencies, models containing only additive and imprinting (A+I) regressions or additive (A)-only models were fitted. All markers with F-tests that were at least approaching significance were fit simultaneously in one model. A step-down procedure was performed to establish the most significant markers for the region. After each analysis, if nonsignificant SNP associations existed, the SNP with the least significant F was eliminated from the model and the analysis was rerun. This procedure was repeated until only significant SNP remained in the model.

Imprinting in the initial ADI or A+I model was defined as the difference between the effects of the heterozygous genotypes (Allan et al., 2007). To better define imprinting, we developed additional analyses that allowed us to examine whether the pattern of inheritance was maternal (M), paternal (P), or both (similar to Thomsen et al., 2004) rather than additive or dominant. The values for the independent variables for animal j were obtained from the vector x_j:

where p_aa is the probability that j is homozygous for allele a, p_AA is the probability that j is homozygous for allele A, p_aA is the probability that j inherited allele a from its dam and allele A from its sire, and p_Aa is the probability of the heterozygote with opposite parental origins. The regressors are x_M and x_P for M and P imprinting components of gene action, respectively. Imprinting was replaced by M, P, or both effects in the final 2,900 genotyped animal group model only.

	RESULTS

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Construction of the map order for BTA5 used a combination of the Shirakawa Institute of Animal Genetics-USMARC linkage map (Snelling et al., 2005), the bovine BAC map, and the 3.1 genome sequence build. The BAC map order agreed with the linkage map order, except for the marker BM6026, which mapped out of order in the BAC map relative to the linkage map and bovine sequence. The linkage map order and BAC map order otherwise were in agreement for the remainder of this region of BTA5. Some discrepancies did exist when comparing the sequence assembly to the BAC and linkage maps. When these disagreements arose, the BAC and linkage map order was used instead of the genome sequence order.

Genotypic frequencies for each marker, based on animals with phenotypic records for twinning rate for each GenoProb run, are listed in Table 2. Genotypic frequencies for each marker were similar between the ovulation and twinning rate phenotypes (data not shown). Slight differences can be attributed to the distribution of animals contributing phenotypic observations for each trait. Because of the small number of SNP genotypes used in the 48 animal analyses, GenoProb computed substantial probabilities of null alleles for some of the markers, perhaps because several animals in the sample of 48 were homozygous for these markers. For example, in the 48-animal data set, GenoProb generated genotypes with probabilities greater than 0.95, with null alleles for markers 11929_712 (n = 298), 52331_182 (n = 161), and 55053_598 (n = 33). As sample size increased, the frequencies of the null alleles were almost completely eliminated for the 724- and 2,900-animal data sets (see Table 2 for the previous 3 markers).

	DISCUSSION

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Previously, a QTL scan was done to identify regions of the genome affecting ovulation rate in the USMARC Production Efficiency population (Kappes et al., 2000). A significant QTL was detected on bovine chromosome 5. Microsatellite markers for this region are presently being used for MAS in conjunction with 2 regions on BTA7. In addition to the MAS information, predicted breeding values for ovulation rate and twinning rate, and EPD for birth, weaning, and yearling BW are being used to make genetic selection decisions. A previous study found a QTL for twinning rate in a Norwegian cattle population just distal to the QTL peak in this study (Lien et al., 2000). Their peak appeared to be located at 70 cM, with a slight peak nonsignificant, centered at BMC1009 (approximately 42 cM), where the USMARC QTL resides. Additional analyses using combined linkage and linkage disequilibrium confirmed the peak at approximately 65 cM in the Norwegian population (Meuwissen et al., 2002). Additionally, Cruickshank et al. (2004) found evidence for QTL at approximately 70 cM, confirming the QTL found by Meuwissen et al. (2002), but not at the location identified by Kappes et al. (2000).

The practical problem in developing commercial tests based on SNP markers is to identify the minimal set of SNP that account for most of the variation occurring within a region of a chromosome. Using the step-down approach with marker association analyses eliminates markers that may be significant in a single-marker analysis, which may explain a portion of the variation shared with another marker. The linkage of these markers makes it more probable that the markers explain similar sources of variation for the traits of interest. Systematic removal of SNP in each round of the step-down approach resulted in a more concise and meaningful model and perhaps concentrates efforts for the development of additional SNP. As advancements in software and theory in this field enable using large numbers of newly identified SNP, fitting haplotypes and their interactions may become the models of choice (Kuehn et al., 2007).

Allele frequencies can have implications for association results of SNP analysis. Minor alleles that are rare often may result from a single founder or may be breed specific. This classification can result in spurious associations, often referred to as a "stratification" effect, of the single founder or breed. By fitting a polygenic animal model, the chances of finding a spurious association should be greatly reduced. However, significant SNP with low minor allele frequency need to be validated in additional populations to confirm the association (Allan et al., 2007).

Because of the larger number of genotyped animals, the 2,900-animal data set was expected to produce the most accurate set of markers. When only 48 animals were genotyped for SNP, markers were removed from the final model that remained significant in the final analyses of the 724 and 2,900 genotyped animals. Additionally, SNP in the final 48 genotyped animal model failed to remain in the final models for the 724 and 2,900 genotyped animal analyses. Numbers of animals with null alleles were significantly greater in the 48-animal genotyped data set, decreasing the ability to detect associations for markers that were significant in the final model. Additionally, this data set appears to be generating some false positives for associations. The use of 724 genotyped animals did relatively well in predicting marker effect sizes relative to the 2,900 genotyped animal analysis. It should be noted that special emphasis was placed on selection of the 384 females used in the 724 data set. These females had a minimum of 2 parturitions each and represented a broad sampling of the founding genetics over the last 14 yr.

The region examined on BTA5 in the present study is gene dense and provides a strong argument for the limitations of using a candidate gene approach to identify useful genetic markers (quantitative trait nucleotides) within a chromosomal region. In this region of BTA5, there are several strong candidate genes with potential involvement in follicular development, embryonic development, or both that could increase ovulation or twinning rate. There are genes involved in cell proliferation (CDCA3, IGF-I, IGFBP6, KL, MYF5, MYF6, SSR3), apoptosis (Apaf-1, TEGT, SOCS2), and signal transduction (ALK-1, FRS2, PTPRR, PTK9, SKR-3) throughout this region, and by chance, several of the SNP that were used fall within the introns of several these genes (FRS2, PTPPR, TAIP-12). Although it may be tempting to focus in depth on any one of these candidates, the use of a large number of SNP provides the ability to use haplotype analysis and linkage disequilibrium to fine map a region of interest, and could provide informative markers more rapidly and cost effectively than focusing on any single gene or SNP.

Although the genetic correlation between ovulation rate and twinning rate is high, there are SNP within this region on BTA5 that associate with one or the other phenotype, but not both. The association of SNP with one trait, but not the other, may result from the number of measurements for each trait. It should be noted that in single-marker analysis, some of the SNP were significant for both traits, yet none of these remained for both traits in the final models. However, some of these differences may be artifacts of the model selection process. Furthermore, there is some potential biology that would support the possibility of more than one gene under the peak of the QTL with specificity for each trait. The ability to ovulate multiple follicles is a key component of multiple births in cows; however, the twinning rate phenotype represents the ability of a cow to conceive successfully and carry twins to term. Therefore, the components of the 2 phenotypes are different. Ovulation rate requires successfully growing multiple follicles to a stage and functional status at which they can undergo further growth in response to the FSH surge (i.e., be selected). They must synthesize LH receptors and be responsive to the LH surge (i.e., ovulate and form a functional corpus luteum). Therefore, the genes involved in ovulation rate would be those involved with establishment of the ovarian reserve, activation and growth of follicles to the early antral stage, selection of ovulatory follicles, and successful ovulation and luteinization. These would all be components of twinning rate, because multiple ovulations are required as the first step in dizygotic twinning. However, the twinning rate phenotype would also include genes involved in fertilization, early embryonic development, successful maintenance of pregnancy, and uterine capacity.

The value of the twinning rate phenotype to developing genetic markers for fertility in monovulatory cattle seems clear. As stated above, these would represent genetic regions associated with fertilization, early embryonic development, and maintenance of pregnancy, all physiological processes that would need to occur in monovulatory cows as part of successful reproduction. Evaluating these components in the presence of multiple ovulations provides more opportunities for failure, and therefore greater phenotypic variation. The ovulation rate phenotype is less clear. If all the genes associated with ovulation rate are markers of multiple ovulations, then they may have less value to the reproductive efficiency of monovulatory cows. However, successful initiation of ovulation in the postpartum period is one of the greatest reproductive obstacles a beef cow must overcome. It is possible that genes associated with ovulation rate, and therefore associated with successful follicle growth, selection, and ovulation, could be genetic markers for successful initiation of fertile estrous cycles in the postpartum period of monovulatory cows, if they could be used without resulting in multiple ovulations. Additionally, these markers may be useful in decreasing ovulation rate in populations such as dairy cattle, for which twin births are detrimental to the reproductive fitness of the female.

Understanding M and P allele genetic effects, referred to as imprinting, should be included in association analyses. Markers 55053_598 and 7189_241 for ovulation and twinning, respectively, were significant in the final models for both the 724- and 2,900-animal genotyped data sets. Because of these results, we further analyzed a model for P (and M) genetic effects. The P-values of both markers decreased relative to the earlier analysis. For the 2,900-animal genotyped data set, we observed paternally influenced inheritance effects for both ovulation and twinning. Comparative mapping of the mouse and human places this region of BTA5 on mouse chromosomes 10 and 15 and human chromosome 12. A maternally expressed gene, Decorin (Dcn), in mice has been identified on mouse chromosome 10 (Mizuno et al., 2002). This gene maps to 19 Megabases (Mb) on BTA5, placing it 10 Mb proximal to the QTL and marker 55053_598. Additionally, Slc38a4, paternally expressed in mice (Smith et al., 2003), maps to 33 Mb, approximately 4 Mb distal from marker 55053_598. These imprinted mouse genes give direct evidence that the paternally inherited effects we observed in the model fit potential biological imprinting evidence for this region of the bovine genome. Khatib (2005) explored the possibility of Dcn being imprinted in cattle by using monoallelic analysis of SNP from the coding region of Dcn for various tissues. Results suggested that bovine Dcn transcripts were not imprinted in bovine tissues. In a similar experiment, Zaitoun and Khatib (2006) also concluded that SLC38A4 transcripts were not imprinted in bovine tissues. Results suggesting that SLC38A4 is not imprinted may decrease the excitement about this gene as a candidate for the QTL on BTA5, but it should be noted that these conclusions were made from 3 cows and fetuses collected from a slaughter facility, which were heterozygous for the polymorphism in SLC38A4. This limited sampling, with no consideration taken of the genetic background of the sampled animals, keeps these conclusions from being definitive. More recently, C12 was found to be methylated in various tissues in humans on human chromosome 12 (Shen et al., 2007). This gene maps to BTA5 at 26 Mb, 3 Mb from marker 55053_598. This additional evidence in humans supports the possibility of a clustering of imprint-regulated genes in this region. This evidence strongly supports the genetic observation found with the maternal-paternal allele analysis and warrants additional investigation of the underlying mechanisms for the reproductive rate QTL in this region.

In conclusion, GenoProb is an extremely valuable tool in generating marker information when DNA may not be available. However, caution should be taken in the number of animals genotyped. In this study, the 48-animal data set worked well for SNP discovery. However, extrapolation of the SNP genotypes from the 48 animals to the rest of the population was not adequate to make conclusions about genetic associations, even with the extremely high number of microsatellite markers. The DNA markers from this study with significant associations with ovulation rate (55069_549, 55053_598, and 52309_281) and twinning rate (2019_224, 34336_607, 55057_461, and 7189_241) may be useful to increase or decrease the incidence of twinning in industry populations for the QTL segregating in this region of BTA5. It should be noted that these markers need to be validated in additional populations before they are used for MAS in an industry setting.

	Footnotes

 
¹ Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the USDA.

² The authors thank L. Flathman for technical assistance and D. Griess (USDA, ARS, US Meat Animal Research Center, Clay Center, NE) for secretarial support.

³ Corresponding author: Larry.Kuehn{at}ars.usda.gov

Received for publication February 15, 2008. Accepted for publication September 2, 2008.

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Top Abstract INTRODUCTION MATERIALS AND METHODS RESULTS DISCUSSION LITERATURE CITED

 


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