J. Dairy Sci. 2008. 91:802-813. doi:10.3168/jds.2007-0367
© 2008 American Dairy Science Association ®
Detection and Analysis of Quantitative Trait Loci Affecting Production and Secondary Traits on Chromosome 7 in Israeli Holsteins
J. I. Weller1,
M. Golik,
S. Reikhav,
R. Domochovsky,
E. Seroussi and
M. Ron
Institute of Animal Sciences, Agricultural Research Organization, The Volcani Center, Bet Dagan 50250, Israel
1 Corresponding author: weller{at}agri.huji.ac.il
 |
ABSTRACT
|
|---|
A total of 5,459 Israeli Holstein cows, daughters of 11 sires, were genotyped for 29 microsatellites spanning chromosome 7 and analyzed by the daughter design for 9 economic traits: milk, fat, and protein yield, fat and protein percentage, somatic cell score, female fertility, herd life, and milk persistency. Quantitative trait loci at chromosome-wise 0.05 significance were obtained for fat and protein yield, fat percentage, somatic cell score, and female fertility. Peak F-values were obtained at 29 cM for fat and protein yield and fat percentage, at 60 cM for somatic cell score, at 74 cM for herd life, and at 11 cM for female fertility. The 0.95 confidence intervals for quantitative trait loci locations were 20 cM for kilograms of fat, 27 cM for fertility, and 51 cM for somatic cell score. Two loci affecting fertility at opposite ends of the chromosome are apparently segregating in the population. A quantitative trait locus for fertility near the centromere was confirmed by application of the modified granddaughter design to a single family. Estimated frequency of the economically favorable allele in the Israeli Holstein cattle was less than 0.5. Significant genetic gain for fertility seems possible by marker-assisted selection.
Key Words: quantitative trait loci • genetic marker • female fertility • dairy cattle
|
INTRODUCTION
|
|---|
Although traditional selection has been very efficient for milk production traits, which have moderate heritability, this is not the case for secondary traits, which generally have lower heritability and are more difficult to measure. Genetic trend for fertility was negligible throughout the last 20 yr in the Israeli Holstein population (Weller and Ezra, 2004), and fertility has declined in the US Holstein population (Washburn et al., 2002). Many studies have shown that genetic progress can be enhanced by selection on markers linked to QTL (Weller, 2001). Although QTL for traits such as fertility have great potential for use in genetic improvement, the lower the heritability, the more difficult it should be to identify QTL affecting these traits (Weller et al., 1990).
Ron et al. (2004) completed a genome scan for QTL affecting economic traits in the Israeli dairy cattle population, based on a daughter design analysis of 11 sire families. Eight traits were analyzed, including female fertility, but no significant effects were found for this trait. Although only 3 markers on Bos taurus autosome (BTA) 7 were included in the genome scan, significant effects were detected in 2 families for the 5 milk production traits, SCS, and herd life. Thus, we decided to fine-map the QTL on this chromosome for all 11 families.
Significant associations of genetic markers with ovulation rate (Kappes et al., 2000; Kirkpatrick et al., 2000) and twinning rate (Lien et al., 2000) have been identified in dairy cattle. Arias and Kirkpatrick (2004) and Cruickshank et al. (2004) described a QTL affecting multiple ovulations and twinning in cattle in the centromeric region of BTA7. In both cases the QTL was identified in families with Holstein-Friesian ancestry. Arias and Kirkpatrick (2004) analyzed specific families from a genetically diverse USDA research population selected for ovulation and twinning rate. The ancestor of the family in which the BTA7 QTL was identified was a Swedish Friesian sire, son of a Canadian Holstein bull.
In this study, we report on significant effects for female fertility on BTA7 in the same regions in which Arias and Kirkpatrick (2004), Cruickshank et al. (2004), and Lien et al. (2000) found QTL affecting multiple ovulations and twinning rate. To estimate QTL allelic frequencies, we applied the modified granddaughter design to a single family (Weller et al., 2002). In addition, QTL in different regions on this chromosome affecting milk production traits, SCS, and herd life are also reported.
|
MATERIALS AND METHODS
|
|---|
Population
Semen samples of the 11 Israeli-Holstein sires and blood samples of 6,040 of their putative daughters were analyzed. All cows were genotyped for at least 5 microsatellites to confirm paternity as described by Weller et al. (2004). Cows that did not have either of the alleles of their putative sire for at least 2 loci were assumed to have incorrect paternity identification and were deleted from further analysis, leaving 5,639 cows with confirmed paternity. There were 5,459 cows with at least 1 marker genotyped on BTA7 and genetic evaluations for production traits. The number of daughters per sire is given in the last row of Table 1
.
Genotyping Methods
The DNA from frozen blood or semen was extracted by the salting-out procedure (Ma et al., 1996). The DNA was diluted to 7 ng/µL, and 5 µL was aliquoted to 96-well and 384-well plates using the Hydra robotic system (Robbins Scientific, Sunnyvale, CA). The DNA in plates was dried and stored at –20°C. The PCR protocols for DNA isolated from semen and blood cells were as described by Ron et al. (1995a) using a DNA engine thermocycler (MJ Research Inc., Waltham, MA). Annealing temperatures of PCR ranged from 55 to 64 degrees, with 30 cycles of amplification. The PCR reactions were run on the ABI 377 DNA sequencer (Applied Biosystems, Foster City, CA). Automated fragment analysis, size calling, and binning were then used by GeneScan (Version 3.1) and Genotyper (Version 2.0) genetic software (Applied Biosystems) to identify the alleles of each of the microsatellite loci.
Sires were genotyped for a total of 29 microsatellites spanning nearly the length of BTA7. In a daughter design, markers are genotyped only for those microsatellites for which their sires were heterozygous. The number of cows genotyped per marker and the number of informative daughters per marker are given in Table 2. For a daughter design analysis, markers are informative only if the daughter genotype is different from the sire (Ron et al., 1995b). The numbers of daughters genotyped per sire per marker are given in Table 1
. Each family was genotyped for at least 6 markers. The total number of valid genotypes was 47,083. Of these, 33,007 (0.70) were informative, which is similar to previous results for this population (Ron et al., 2004). Only recombination events of the sires are of interest for this analysis. The male genetic map was computed by the fixed option of CRI-MAP (http://linkage.rocke-feller.edu/soft/crimap/), using the marker order given by the US Meat Animal Research Center (MARC) linkage map (http://www.marc.usda.gov/genome/cattle/cattle.html). Marker map locations by CRI-MAP and the MARC map are also given in Table 2. The map derived in our analysis was 24% longer than the corresponding MARC map. This is somewhat surprising considering that the male recombination map is generally shorter than the female recombination map, and the MARC map is sex-averaged. However, our map is based on the meioses of only 11 bulls, and the number of bulls contributing to the map at the extreme ends is even less. Major discrepancies between the 2 maps were obtained at the beginning of the chromosome and in the region of marker IL4. Deleting these markers from the analysis did not appreciately affect the map length. Furthermore, an alternative map was constructed using the build option of CRI-MAP, but this map was 8 cM longer than that derived from the MARC order. The accuracy of MARC marker order was also tested by the flip option of CRI-MAP. This option compares the likelihood obtained by the primary map, in this case the MARC order, to the likelihood obtained by reversing the order of each pair of markers sequentially. Two flips, between BM9289 and IDVGA90 and between BMS713 and DIK4378, resulted in increased likelihood. The flip option was again applied on the revised order, but no significant new flips were found. Length of the revised map was then determined by the fixed CRI-MAP option. Although the base-10 logarithm likelihood was increased by 15 relative to the MARC order, the map length was only reduced by 1 cM. Therefore, the MARC order was used for all further analyses.
Marker information content was computed over all families and is plotted in Figure 1. Information content was highest near position 30 cM, and there was a secondary peak near position 90. Information content was greater than 0.5 from the beginning of the chromosome to position 100 cM.
View larger version (7K):
[in this window]
[in a new window]
|
Figure 1. Marker information content for all 5,459 cows from the 11 families genotyped. Positions of markers are indicated by arrows.
|
|
Genetic Evaluations
The official Israeli Holstein genetic evaluations are computed twice yearly at the Agricultural Research Organization. All analyses were based on the June 2006 evaluations, and the genetic base was the mean EBV of cows born in 2000. The EBV for 305-d milk, fat, and protein production, SCS, female fertility, and milk persistency were computed by multitrait animal models, preadjusted for calving age and month (Weller and Ezra, 2004). Milk persistency was defined as the predicted milk production 180 d after peak divided by peak production in percent (Weller et al., 2006). Female fertility was computed as the inverse of the number of inseminations to conception (Weller and Ezra, 1997). Evaluations for herd life were computed by a single trait animal model as described by Settar and Weller (1999). The EBV for fat percentage were derived as follows:
where BVFP, BVF, and BVM = the EBV of the cow for fat percentage, fat yield, and milk, and MF, MM, and MFP = mean adjusted first-parity fat yield, milk, and fat percentage of cows born in 2000. The EBV for protein percentage were computed similarly with protein yield and percentage, instead of fat yield and percentage. Numbers of records and the means and standard deviations of the EBV of the cows genotyped for the 5 production traits, SCS, herd life, female fertility, and milk persistency are given in Table 3, and the correlations among the evaluations are given in Table 4.
QTL Linkage Analysis
Preliminary QTL analysis for all markers was by the following model:
where BVijkl = the EBV for trait i of cow l, daughter of sire j, that received paternal allele k; Sij = the effect of sire j on trait i; Mijk = the effect of paternal allele k of sire j on trait i; and eijkl = the random residual associated with each record. A significant paternal allele effect is indicative of a segregating QTL linked to the genetic marker.
For all 9 traits, interval mapping was performed by the algorithm of Spelman et al. (1996) considering all 11 families jointly. Chromosome-wise significance values were determined by a permutation analysis (Churchill and Doerge, 1994) separately for each trait. Two thousand permutation samples were generated, and the empirical 0.005, 0.01, and 0.05 significance levels were compared with the nominal, or comparison-wise, significance levels of the highest F-value for each sample.
Traits with significant within-family effects (absolute value of the contrast > twice the standard error) for at least 3 families were reanalyzed. The 0.95 and 0.90 confidence intervals (CI) for the QTL location were determined by the bootstrap method (Visscher et al., 1996; Ron et al., 2001) including only families with significant contrasts at the overall F-value peak. Chromosome-wise significance values for each number of families with significant contrasts were also determined by the permutation analysis of a single trait, SCS. In each permutation sample, interval mapping was performed on the families with significant contrasts. Critical F-values for the 0.01 and 0.05 significance levels were determined separately for each number of families with significant contrasts. Interval mapping was not performed for permutations in which there were no families with significant contrasts.
In addition, all 11 families were analyzed separately for the traits with significant contrasts in the joint analyses. The program of Spelman et al. (1996) was modified so that haplotype effects at each centimorgan were printed. If in the individual family QTL analysis 2 test statistic peaks were obtained for a single trait, and the sign of the effect changes in the valley between the 2 peaks, then the 2 effects are in repulsion for this sire. Otherwise, the 2 QTL are in coupling phase.
Modified Granddaughter Design
Significant effects were verified by application of the modified granddaughter design to a single sire family, 3070 (Weller et al., 2002). This family was chosen, because it was segregating for QTL effects on most of the traits analyzed, and a large number of maternal granddaughters were available for analysis. Six markers heterozygous in this grandsire spanning positions 14.7 to 30.5 cM of BTA7 were genotyped for 605 maternal granddaughters and 15 of their sires. These markers are denoted by an asterisk in Table 2. As described by Weller et al. (2002), granddaughter records were deleted if there were less than 2 microsatellites with ascertainable genotypes or if there was a conflict between genotypes of granddaughters and of their respective putative sires. After these edits, there were 540 valid granddaughter records. For each trait, putative position of the QTL was varied across the chromosomal segment to obtain the location with the greatest contrast between the grandpaternal alleles. Allelic frequencies were estimated for significant QTL effects as described by Weller et al. (2002).
|
RESULTS
|
|---|
The F-values, comparison-wise, and corresponding chromosome-wise probabilities from the permutation analysis of all 11 families are presented in Table 5 for the 0.005, 0.01, and 0.05 chromosome-wise probabilities. Results are not presented for percentage of protein, milk persistency, and herd life, because F-values for the actual data did not approach nominal significance for these traits (Table 6). The F-values at each significance level were similar for all 6 traits, but minor differences in the F-values result in major differences in the nominal probabilities at extreme values. The average F-value and comparison-wise probability for all 6 traits are given in the last row. Using these values, the chromosome-wise probabilities are approximately 7-fold the comparison-wise probabilities at all 3 significance levels. The results of the analyses of the families with significant contrasts from the permutation analysis of SCS are presented in Table 7. There were 391 permutations with no significant within-family contrasts but only 19 permutations with 4 significant within-family contrasts. For permutations with 3 significant family contrasts, the 0.05 and 0.01 critical F-values were 8.4 and 9.2.
View this table:
[in this window]
[in a new window]
|
Table 5. The F-values, comparison-wise, and corresponding chromosome-wise probabilities for interval mapping of BTA7 as derived by permutation analysis of SCS
|
|
View this table:
[in this window]
[in a new window]
|
Table 6. Maximum F-values, their chromosomal positions, and families with significant contrasts with all 11 families analyzed jointly
|
|
View this table:
[in this window]
[in a new window]
|
Table 7. Frequencies of families with t-values >2 in the permutation analysis of SCS and F-values for 0.05 and 0.01 chromosome-wise significance
|
|
The maximum F-values, the comparison-wise F-probabilities, and the denominator degrees of freedom for the interval mapping analyses of all 11 families are presented in Table 6. The F-values for fat yield and percentage were significant at the 0.005 chromosome-wise level, whereas protein yield, SCS, and fertility were significant at the 0.05 chromosome-wise level. The families with significant contrasts (P < 0.05) are also listed. There were 6 families with significant contrasts for female fertility, 5 families for fat yield and female fertility, and 3 families for each of the other traits with chromosome-wise significance. Of the 6 families with significant contrasts for fertility, 5 were significant at the position with the overall peak F-value. There were also 3 families with significant contrasts for herd life, even though the F-value was below the critical value, but only 1 was significant at the position of the peak F-value.
The peak F-values for the 3 milk production traits were all at 29 cM. Sire 2357 had significant contrasts for all 3 traits. Furthermore, the directions of the effects were the same for all 3 traits; the haplotype associated with increased fat was also associated with increased protein and fat percentage. This was also the case for sires 2283, 3241, and 3274. For sire 3212, the significant effects on fat and protein were in the same direction, but the nonsignificant effect on fat percentage was not. Thus, it is likely that the same QTL is responsible for the effects observed on these 3 traits. The QTL affecting female fertility is clearly different from the other effects detected.
The interval mapping results for the significant families are plotted in Figures 2 to 7 for fat yield, fat percentage, protein yield, SCS, female fertility, and herd life. The overall F-value for all families with significant effects and the individual family F-values are plotted. In addition to the 5 sires with significant contrasts for fertility near the centromere, 2 sires had significant contrasts for fertility near the acrocentric end of the chromosome, 3258 and 3274. Sire 3258 had significant contrasts at both locations. The effects of the 2 QTL are in repulsion, because the signs of 2 effects were different. Thus, the haplotype associated with increased fertility near the beginning of the chromosome is associated with reduced fertility near the distal end. In this case, the positions of both peaks should be biased outward (Ron et al., 2001).
View larger version (10K):
[in this window]
[in a new window]
|
Figure 2. Interval mapping for fat yield. ––, all 5 bulls;• • •, sire 2283; – –, sire 2357; –– ––, sire 3212; –– - ––, sire 3241; –– - - ––, sire 3274. Positions of markers are indicated by arrows.
|
|
View larger version (9K):
[in this window]
[in a new window]
|
Figure 3. Interval mapping for fat percentage. ––, all 3 bulls; • • •, sire 2283; – –, sire 2357; –– - ––, sire 3274. Positions of markers are indicated by arrows.
|
|
View larger version (11K):
[in this window]
[in a new window]
|
Figure 4. Interval mapping for protein yield. ––, all 4 bulls; • • •, sire 2357; – –, sire 3070; –– ––, sire 3212, –– - ––, sire 3241. Positions of markers are indicated by arrows.
|
|
View larger version (11K):
[in this window]
[in a new window]
|
Figure 5. Interval mapping for SCS. ––, all 3 bulls; • • •, sire 2357; – –, sire 3070; –– - ––, sire 3258. Positions of markers are indicated by arrows.
|
|
View larger version (11K):
[in this window]
[in a new window]
|
Figure 6. Interval mapping for female fertility. ––, all 6 bulls; • • •, sire 2278; – –, sire 3070; –– ––, sire 3089; –– - ––, sire 3208; –– - - ––, sire 3258; – - –, sire 3274. Positions of markers are indicated by arrows.
|
|
View larger version (10K):
[in this window]
[in a new window]
|
Figure 7. Interval mapping for herd life. ––, all 3 bulls; • • •, sire 2357; – –, sire 3070; –– - ––, sire 3274. Positions of markers are indicated by arrows.
|
|
The maximum F-values, their positions, and the CI for QTL location for families with significant contrasts are given in Table 8 for traits with 0.05 chromosome-wise significance and herd life. Although the probability values listed are for the comparison-wise F-values, SCS was nearly significant at the 0.05 chromosome-wise level, and fat percentage was significant at the 0.01 level, as determined by the permutation analysis results in Table 7. Both fat yield and fertility can be considered significant at the 0.005 level, because there were only 4 permutation samples out of 2,000 (0.002) with 5 within-family significant contrasts.
View this table:
[in this window]
[in a new window]
|
Table 8. Maximum F-values, their positions, and 0.95 confidence intervals for QTL location for families with significant contrasts
|
|
Considering only the families with significant contrasts, the peak F-value for herd life shifted from position 20 to 74 cM. Two of the 3 sires with significant contrasts, 2357 and 3070, had peak F-values near this position. It is possible that the same QTL affecting SCS is also responsible for the marginal effect observed on herd life near the middle of the chromosome. The locations of the peak F-values are similar, and both families with significant contrasts near the middle of the chromosome for herd life also had significant contrasts for SCS. For all 4 sires with significant effects for either SCS or herd life, the haplotype associated with decreased SCS was associated with increased herd life. This result corresponds with the –0.3 correlation between the genetic evaluations for these traits (Table 4).
The estimated substitution effects for the individual families are also given (Table 8). The substitution effects ranged in magnitude from 0.1 to 0.5 units relative to the phenotypic standard deviations in Table 3. Because EBV of cows were analyzed, these values underestimate the actual substitution effects (Israel and Weller, 1998). On the other hand, the effects deemed significant are a selected sample and therefore overestimate the actual substitution effects, unless the estimated effect is much greater than the critical value for selection (Weller et al., 2005). The substitution effects were estimated from the peak with the highest F-value regardless of chromosomal location. Sire 2357 had the greatest contrast for fat yield and percentage but not protein yield.
In general, effects for bulls were similar for each trait. The exceptions were the effects of sire 3274 on fertility and herd life. For herd life, the effect associated with this bull was more than double the effects associated with the other bulls. For both traits, the contrasts estimated were in 2 different chromosomal locations, as shown in Figures 6 and 7. For female fertility, the peak F-values for bulls 3258 and 3274 were near positions 120 cM, whereas for the other bulls, the peak values were near the centromere. Sire 3258 was apparently heterozygous for both QTL, but the peak near position 120 cM was higher. For herd life, the maximum F-value for sire 3274 was near the centromere, whereas the peak values for the other 2 bulls were near the middle of the chromosome. Sire 3274 was not heterozygous for any of the markers from positions 0 to 24 cM. The fact that the F-value for this bull was relatively high from positions 0 through 120 cM indicates that probably more than a single QTL affecting herd life is segregating for this sire.
A CI was not computed for herd life, because this trait was not significant at the 0.05 chromosome-wise level, and the curve for sire 3274 was clearly different from the other 2 bulls. The 0.95 CI are presented for all other traits. As expected, the CI generally decreased with increase in significance. The 0.95 CI for fat yield spanned only 20 cM. The CI for fertility and SCS did not overlap, clearly indicating that different QTL are responsible for these effects. Because 2 QTL are apparently segregating for fertility in sire 3258, the CI for female fertility was estimated without this sire. There was minimal overlap between the CI for the QTL affecting female fertility and the QTL affecting fat yield.
Results of the modified granddaughter design analysis for sire 3070 are presented in Table 9. Significant contrasts were found for female fertility and herd life, confirming the daughter design results for these traits but not for SCS. However, the maximum F-value for SCS for sire 3070 was at position 57, well outside the chromosomal segment analyzed by the modified granddaughter design. For both traits, the maximum contrast was obtained for the interval between DIK2211 and BL5, at 27 cM, which was 1 cM from the location of the peak value for fertility for sire 3070. The substitution effects obtained by the modified granddaughter design were very similar to the effects obtained by the daughter design analysis. For both traits, the estimated frequencies for the positive QTL alleles were <0.5, but the standard errors of these estimates were so large that the entire range of 0 to 1 is within the CI.
|
DISCUSSION
|
|---|
Even though a highly significant effect on milk persistency was found in the individual marker analyses for DIK2211, the F-value from the interval mapping analysis was not significant for this trait. Previous studies in other populations did not find significant effects for milk persistency on BTA7 (Boichard et al., 2003; Harder et al., 2006). The large effect on fat yield found in this study was not found in other populations, although an effect on protein yield was found in French Holsteins in the same chromosomal region (Boichard et al., 2003) and also near position 115 cM (Heyen et al., 1999). Effects on SCS near the middle of the chromosome were found in US and German Holsteins (Ashwell et al., 2001, 2004; Kuhn et al., 2003) and near the distal end of BTA7 (Heyen et al., 1999). The effect on herd life was not significant at the 0.05 chromosome-wise level but was significant in the modified granddaughter design analysis of a single family. Effects on female fertility were detected in the same chromosomal regions that Arias and Kirkpatrick (2004), Cruickshank et al. (2004), and Lien et al. (2000) found effects on twinning and ovulation rate. Of course, the number of QTL claims in dairy cattle is now so extensive, especially for production traits, that QTL for all traits analyzed have been detected on nearly all chromosomes. Therefore, the question of verification is of paramount importance.
The verification of female fertility by the modified granddaughter design is the second case in which a QTL detected by a daughter or granddaughter design was verified by this method (Weller et al., 2002) and the first case for a nonproduction trait. Unlike the QTL for protein percentage on BTA6, it was not possible in the current analysis to obtain accurate estimates for the QTL allelic frequencies for either fertility or herd life, because the QTL effects in the current study were much smaller relative to their standard errors. The relationship between the standard error for allelic frequency and the QTL effect was demonstrated by Weller et al. (2002) for simulated data. Nevertheless, assuming the estimated frequency of 0.38 for the allele that increases fertility, then the expected frequency of heterozygous sires, assuming Hardy-Weinberg equilibrium, is 0.47. In fact, their frequency for the QTL near the centromere was 5/11 = 0.45, which is nearly perfect correspondence. Likewise, assuming the estimated frequency of 0.15 for the favorable allele for herd life, then the Hardy-Weinberg frequency of heterozygous sires is 0.27, is in agreement with the realized number of heterozygote sires (3/11) = 0.25. The modified granddaughter design analysis indicated that the frequencies of the favorable alleles in the Israeli Holstein population are <0.5 for both fertility and herd life, implying that there is significant scope to apply marker-assisted selection. This was not the case for the ABCG2 polymorphism on BTA6, in which the favorable allele for milk protein percentage is already at high frequencies in nearly all dairy cattle populations (Cohen-Zinder et al., 2005; Ron et al., 2006). Application of the modified granddaughter design to identify QTL with favorable alleles at low frequencies may be used to determine priority for quantitative trait nucleotide identification.
Sartori et al. (2006) found that multiple ovulating cows have a significantly higher circulating progester-one concentration on d 7 and a tendency for a greater conception rate on d 25 to 32 than single ovulators. Such hormonal change could have positive effects on fertilization and early embryonic development (Kerbler et al., 1997). Thus, it is possible that the QTL affecting ovulation rate and twinning detected in the centromeric region of BTA7 (Lien et al., 2000; Arias and Kirkpatrick, 2004; Cruickshank et al., 2004) is the same QTL that was found to affect conception rate in the current study.
Recently, Ron and Weller (2007) reviewed methods to determine the polymorphism responsible for QTL detected in animal populations. The methods described, including determination of likely candidates and linkage disequilibrium mapping, will be used to ascertain the causative polymorphisms for the QTL described in this study.
|
ACKNOWLEDGEMENTS
|
|---|
This research was supported by grants from the Israel milk marketing board and the European Sixth Research and Technological Development Framework Programme, Proposal No. 016250-2 SABRE. We thank E. Ezra (Israeli Cattle Breeders’ Association) for providing phenotypic data for analysis, R. Spelman (Livestock Improvement Corp. Ltd.) for use of his interval mapping program, and N. Silinakov (ARO, The Volcani Center) for useful discussions.
Received for publication May 15, 2007.
Accepted for publication October 10, 2007.
|
REFERENCES
|
|---|
Arias, J., and B. W. Kirkpatrick. 2004. Mapping of bovine ovulation rate QTL: An analytical approach for three generation pedigrees. Anim. Genet. 35:7–13.[CrossRef][Medline]
Ashwell, M. S., D. W. Heyen, T. S. Sonstegard, C. P. Van Tassell, Y. Da, P. M. VanRaden, M. Ron, J. I. Weller, and H. A. Lewin. 2004. Detection of quantitative trait loci affecting milk production, health, and reproductive traits in Holstein cattle. J. Dairy Sci. 87:468–475.[Abstract/Free Full Text]
Ashwell, M. S., C. P. Van Tassell, and T. S. Sonstegard. 2001. A genome scan to identify quantitative trait loci affecting economically important traits in a US Holstein population. J. Dairy Sci. 84:2535–2542.[Abstract]
Boichard, D., C. Grohs, F. Bourgeois, F. Cerqueira, R. Faugeras, A. Neau, R. Rupp, Y. Amigues, M. Y. Boscher, and H. Leveziel. 2003. Detection of genes influencing economic traits in three French dairy cattle breeds. Genet. Sel. Evol. 35:77–101.[CrossRef][Medline]
Churchill, G. A., and R. W. Doerge. 1994. Empirical threshold values for quantitative trait mapping. Genetics 138:963–971.[Abstract]
Cohen-Zinder, M., E. Seroussi, D. M. Larkin, J. J. Loor, A. Evertsvan der Wind, J. H. Lee, J. K. Drackley, M. R. Band, A. G. Hernandez, M. Shani, H. A. Lewin, J. I. Weller, and M. Ron. 2005. Identification of a missense mutation in the bovine ABCG2 gene with a major effect on the QTL on chromosome 6 affecting milk yield and composition in Holstein cattle. Genome Res. 15:936–944.[Abstract/Free Full Text]
Cruickshank, J., M. R. Dentine, P. J. Berger, and B. W. Kirkpatrick. 2004. Evidence for quantitative trait loci affecting twinning rate in North American Holstein cattle. Anim. Genet. 35:206–212.[CrossRef][Medline]
Harder, B., J. Bennewitz, N. Reinsch, G. Thaller, H. Thomsen, C. Kuhn, M. Schwerin, G. Erhardt, M. Forster, F. Reinhardt, and E. Kalm. 2006. Mapping of quantitative trait loci for lactation persistency traits in German Holstein dairy cattle. J. Anim. Breed. Genet. 123:89–96.[CrossRef][Medline]
Heyen, D. W., J. I. Weller, M. Ron, M. Band, J. E. Beever, Y. Da, G. R. Wiggans, P. M. VanRaden, and H. A. Lewin. 1999. A genome scan for QTL influencing milk production and health traits in dairy cattle. Physiol. Genomics 1:165–175.[Abstract/Free Full Text]
Israel, C., and J. I. Weller. 1998. Estimation of candidate gene effects in dairy cattle populations. J. Dairy Sci. 81:1653–1662.[Abstract]
Kappes, S. M., G. L. Bennett, J. W. Keele, S. E. Echternkamp, K. E. Gregory, and R. M. Thallman. 2000. Initial results of genomic scans for ovulation rate in a cattle population selected for increased twinning rate. J. Anim. Sci. 78:3053–3059.[Abstract/Free Full Text]
Kerbler, T. L., M. M. Buhr, L. T. Jordan, K. E. Leslie, and J. S. Walton. 1997. Relationship between maternal plasma progester-one concentration and interferon-
synthesis by the conceptus in cattle. Theriogenology 47:703–714.[CrossRef][Medline]
Kirkpatrick, B. W., B. M. Byla, and K. E. Gregory. 2000. Mapping quantitative trait loci for bovine ovulation rate. Mamm. Genome 11:136–139.[CrossRef][Medline]
Kuhn, C., J. Bennewitz, N. Reinsch, H. Thomsen, C. Looft, G. A. Brockmann, M. Schwerin, C. Weimann, S. Hiendieder, G. Erhardt, I. Medjugorac, M. Forster, B. Brenig, F. Reinhardt, R. Reents, I. Russ, G. Averdunk, J. Blumel, and E. Kalm. 2003. Quantitative trait loci mapping of functional traits in the German Holstein cattle population. J. Dairy Sci. 86:360–368.[Abstract/Free Full Text]
Lien, S., A. Karlsen, G. Klemetsdal, D. I. Vage, I. Olsaker, H. Klung-land, M. Aasland, B. Heringstad, J. Ruane, and L. Gomez-Raya. 2000. A primary screen of the bovine genome for quantitative trait loci affecting twinning rate. Mamm. Genome 11:877–882.[CrossRef][Medline]
Ma, R. Z., J. E. Beever, Y. Da, C. A. Green, I. Russ, C. Park, D. W. Heyen, R. E. Everts, S. R. Fisher, K. M. Overton, A. J. Teale, S. J. Kemp, H. C. Hines, G. Guerin, and H. A. Lewin. 1996. A male linkage map of the cattle (Bos taurus) genome. J. Hered. 87:261–271.[Abstract/Free Full Text]
Ron, M., Y. Blank, and M. Band. 1995a. Determination of the optimal tissue source and number of microsatellites for detection of zygotic origin of cattle twins. Anim. Biotechnol. 6:27–37.
Ron, M., M. Cohen-Zinder, C. Peter, J. I. Weller, and G. Erhardt. 2006. ABCG2 polymorphism in Bos indicus and Bos taurus cattle breeds. J. Dairy Sci. 89:4921–4923.[Abstract/Free Full Text]
Ron, M., E. Feldmesser, M. Golik, I. Tager-Cohen, D. Kliger, V. Reiss, R. Domochovsky, O. Alus, E. Seroussi, E. Ezra, and J. I. Weller. 2004. A complete genome scan of the Israeli Holstein population for quantitative trait loci by a daughter design. J. Dairy Sci. 87:476–490.[Abstract/Free Full Text]
Ron, M., D. Kliger, E. Feldmesser, E. Seroussi, E. Ezra, and J. I. Weller. 2001. Multiple QTL analysis of bovine chromosome 6 in the Israeli Holstein population by a daughter design. Genetics 159:727–735.[Abstract/Free Full Text]
Ron, M., H. Lewin, Y. Da, M. Band, A. Yanai, Y. Blank, E. Feldmesser, and J. I. Weller. 1995b. Prediction of informativeness for microsatellite markers among progeny of sires used for detection of economic trait loci in dairy cattle. Anim. Genet. 26:439–441.[Medline]
Ron, M., and J. I. Weller. 2007. From QTL to QTN identification in livestock – Winning by points rather than knock-out: A review. Anim. Genet. 38:429–439.[CrossRef][Medline]
Sartori, R., A. Gumen, J. N. Guenther, A. H. Souza, D. Z. Caraviello, and M. C. Wiltbank. 2006. Comparison of artificial insemination versus embryo transfer in lactating dairy cows. Theriogenology 65:1311–1321.[CrossRef][Medline]
Settar, P., and J. I. Weller. 1999. Genetic analysis of cow survival in the Israeli dairy cattle population. J. Dairy Sci. 82:2170–2177.[Abstract]
Spelman, R. J., W. Coppieters, L. Karim, J. A. M. van Arendonk, and H. Bovenhuis. 1996. Quantitative trait loci analysis for five milk production traits on chromosome six in the Dutch Holstein-Friesian population. Genetics 144:1799–1808.[Abstract]
Visscher, P. M., R. Thompson, and C. S. Haley. 1996. Confidence intervals in QTL mapping by bootstrapping. Genetics 143:1013–1020.[Abstract]
Washburn, S. P., W. J. Silvia, C. H. Brown, B. T. McDaniel, and A. J. McAllister. 2002. Trends in reproductive performance in southeastern Holstein and Jersey DHI herds. J. Dairy Sci. 85:244–251.[Abstract]
Weller, J. I. 2001. Quantitative Trait Loci Analysis in Animals. CABI Publ., London, UK.
Weller, J. I., and E. Ezra. 1997. Genetic analysis of somatic cell concentration and female fertility of Israeli Holsteins by the individual animal model. J. Dairy Sci. 80:586–594.[Abstract]
Weller, J. I., and E. Ezra. 2004. Genetic analysis of the Israeli Holstein dairy cattle population for production and nonproduction traits with a multitrait animal model. J. Dairy Sci. 87:1519–1527.[Abstract/Free Full Text]
Weller, J. I., E. Ezra, and G. Leitner. 2006. Genetic analysis of persistency in the Israeli Holstein population by the multitrait animal model. J. Dairy Sci. 89:2738–2746.[Abstract/Free Full Text]
Weller, J. I., E. Feldmesser, M. Golik, I. Tager-Cohen, R. Domochovsky, O. Alus, E. Ezra, and M. Ron. 2004. Factors affecting incorrect paternity assignment in the Israeli Holstein population. J. Dairy Sci. 87:2627–2640.[Abstract/Free Full Text]
Weller, J. I., Y. Kashi, and M. Soller. 1990. Power of daughter and granddaughter designs for determining linkage between marker loci and quantitative trait loci in dairy cattle. J. Dairy Sci. 73:2525–2537.[Abstract]
Weller, J. I., M. Shlezinger, and M. Ron. 2005. Correcting for bias in estimation of quantitative trait loci effects. Genet. Sel. Evol. 37:501–522.[CrossRef][Medline]
Weller, J. I., H. Weller, D. Kliger, and M. Ron. 2002. Estimation of quantitative trait locus allele frequency via a modified granddaughter design. Genetics 162:841–849.[Abstract/Free Full Text]
This article has been cited by other articles:
|
|
|
|
 
J. I. Weller, M. Golik, E. Seroussi, M. Ron, and E. Ezra
Detection of Quantitative Trait Loci Affecting Twinning Rate in Israeli Holsteins by the Daughter Design
J Dairy Sci,
June 1, 2008;
91(6):
2469 - 2474.
[Abstract]
[Full Text]
[PDF]
|
|
|
TOP
ABSTRACT
INTRODUCTION
