Human Molecular Genetics, 2002, Vol. 11, No. 3 207-215
© 2002 Oxford University Press
Direct measurement of the male recombination fraction in the human ß-globin hot spot
Medical Research Council Molecular Haematology Unit, Institute of Molecular Medicine, University of Oxford, John Radcliffe Hospital, Headington, Oxford OX3 9DS, UK and 1Institute of Biological Anthropology, University of Oxford, 58 Banbury Road, Oxford OX2 6QS, UK
Received August 10, 2001; Revised and Accepted November 28, 2001.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Recombination was measured across nine intervals in the human ß-globin gene cluster by single-sperm analysis. A recombination fraction of
0.9% was calculated across an 11 kb region using a new method to estimate recombination fractions from single-sperm typing data. No recombination was detected in an adjacent 90 kb region that extends upstream of the ß-globin cluster. These data are consistent with previous estimates based on population genetic analysis, and suggest a recombination rate of nearly two orders of magnitude greater than the genome average of 1 cM/Mb. Because recombination hot spots will destroy linkage disequilibrium across small physical regions, knowledge about the location and strength of such hot spots could be extremely valuable for genetic association studies. |
INTRODUCTION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Most molecular and population genetic studies assume that recombination frequencies are proportional to physical distance. Comparisons of genetic and physical maps indicate that the average rate of recombination in humans is
1 cM/Mb; however, there is growing evidence for non-uniform recombination in eukaryotic genomes. For example, on the genome scale, recombination ‘deserts’ and ‘jungles’ have been identified from comparisons of genetic and physical maps (1). Detailed single-gene studies have identified recombination hot spots in organisms such as yeast (2), mice (3) and humans (4–9). In addition, studies of eukaryotic genomes have also revealed regions of low recombination (10).
The ß-globin gene cluster contains one of the first hot spots identified in humans. It was initially proposed following analysis of RFLP haplotype data for the entire ß-globin gene cluster (11). A subsequent study estimated a three to 30 times greater rate of recombination than expected from the genome average in a 9.1 kb area upstream of the ß-globin gene (4). Detailed investigation of DNA sequence haplotypes in an 3 kb region including the ß-globin gene also implicated the action of a hot spot (12). Finally, six recombination breakpoints have been localized to the ß-globin hot spot region in families of diverse geographical origin (13).
Although the results from the studies mentioned above are consistent with the presence of a recombination hot spot, none of these analyses quantified its recombination rate directly. For example, the population genetic approach employed by Chakravarti et al. (4) cannot differentiate the roles of molecular and population processes in creating current patterns of haplotype diversity. This method relies upon an analysis of linkage disequilibrium, a measure that can be influenced by factors other than recombination (9). For example, population admixture (14), mutation history and genetic drift can affect patterns of linkage disequilibrium. In addition, because of ascertainment bias, the six recombination breakpoints found in families with a history of ß-thalassaemia cannot be used to estimate the hot spot recombination fraction in normal human populations.
To measure the recombination fraction directly, single-sperm typing (15–19) was applied to several intervals in the ß-globin cluster. Sperm analysis was selected for two reasons. First, it provided a large number of informative meioses. Secondly, it eliminated the confounding factor that males and females often exhibit different rates of recombination across the same physical region of the genome (20).
The estimation of recombination rates across relatively small physical intervals in the ß-globin cluster by single-sperm typing allowed comparison with estimates derived from new population genetic methods. Because non-uniform recombination is likely to affect patterns of linkage disequilibrium (21,22), the localization and characterization of recombination hot spots could provide information about the number and distribution of SNP markers required for genome association studies.
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RESULTS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Polymorphisms and intervals
Six RFLP markers were used for single-sperm typing analysis. The HincII, TaqI, RsaI, AvaII and HinfI polymorphisms have been previously described by Chakravarti et al. (4). The MspI polymorphism was identified for the purposes of this study (Materials and Methods).
The RFLPs described above were used to study nine intervals across the ß-globin cluster (Fig. 1). A previous study defined the ß-globin hot spot as a region between a TaqI site upstream of the 
-globin gene, and a HgiAI site in exon 1 of the ß-globin gene (4). This TaqI site was used for single-sperm typing. An AvaII site located 453 bp downstream of the HgiAI polymorphism was also analyzed.
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The intervals considered can be divided into four categories: small intervals that include the hot spot (TaqI–AvaII, TaqI–HinfI); large intervals that include the hot spot (MspI–AvaII, HincII–HinfI); intervals that do not include the hot spot (MspI–TaqI and HincII–TaqI), and intervals in or near the ß-globin gene (RsaI–HinfI, RsaI–AvaII, AvaII–HinfI). Most intervals were studied to compare recombination fractions between hot spot and non-hot spot regions. The three ß-globin gene intervals were considered in an attempt to refine the hot spot boundaries, because two recombination breakpoints downstream of the AvaII site had been discovered in families (13).
Each single sperm was analyzed by PCR and restriction enzyme digestion at two loci to screen for recombination events. The results from one 96-well microtiter plate of flow-sorted single sperm analyzed at both loci comprised a ‘batch’. Figure 2 shows data from restriction enzyme digestions for one TaqI–AvaII batch. As shown in Figure 2, one single-sperm batch has six 20-sperm positive controls and 16 no-sperm negative controls.
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x174 HaeIII digest (New England Biolabs). In the first photograph (TaqI), the larger band is 113 bp (uncut) and the pair of smaller bands is 74 and 39 bp (cut). In the second photograph (AvaII), the larger band is 122 bp (uncut) and the pair of smaller bands is 72 and 50 bp (cut).No individual was informative for all of the intervals considered; therefore, sperm were analyzed from six different donors. Five donors were European, and one was of Chinese origin. Two intervals were studied in more than one donor (TaqI–AvaII and HincII–TaqI); however, the total number of sperm scored was too small and the recombination fractions were too low to evaluate rate heterogeneity among individuals.
A total of 7225 single-sperm wells from 85 batches were scored. Of the 7225 single-sperm wells considered, 4858 yielded a parental or recombinant genotype. The remaining wells showed one of the 12 genotypes that occur because of experimental error (Table 1 and Materials and Methods).
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Table 1 displays all genotypes observed from the analysis of 85 single-sperm batches; however, the data from 55 batches with no evidence for unwanted PCR product transfer to negative control wells are shown in parentheses (Materials and Methods). The discussion of the results focuses on the complete data set of 85 batches, because as shown in Table 2, the complete and reduced data sets gave essentially the same result.
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As shown in Table 2, the results of the Fisher’s exact test for the unadjusted data clearly indicate that the small hot spot intervals showed a significantly greater number of recombination events than the cold regions. Furthermore, because recombination fractions were not significantly different between the large hot spot and small hot spot intervals (unadjusted complete data set 0.96% versus 1.2%, Fisher’s exact test, 2P = 0.69, adjusted complete data set 0.64% versus 0.88%, Fisher’s exact test, 2P = 0.64), it is likely that most recombination events in the hot spot are concentrated in the TaqI–HinfI interval.
The unadjusted results in Table 2 confirm that the hot spot exists in the previously defined physical region of the ß-globin cluster (4). However, in order to estimate the absolute hot spot recombination fraction, the single-sperm typing results were adjusted with a new method that considers the rate of experimental errors (Materials and Methods). Adjustment of the data did not alter the patterns observed in the unadjusted results (Table 2). Although the estimates of the absolute average recombination fraction provided by the adjustment method are subject to considerable uncertainty, the intervals that include the hot spot exhibited consistently larger average recombination fractions than the cold regions (Fig. 1). Furthermore, analysis of the data with two additional, previously published approaches to estimate average recombination fractions and 95% confidence intervals (CIs) (23,24) gave similar results (data not shown).
The adjusted, absolute recombination fraction for the small hot spot intervals (0.88%, 95% CI 0.1–1.8%), suggests that the absolute rate of recombination is approximately 80 times greater than expected for an 11 kb region recombining at the genome-average recombination rate. Therefore, the single-sperm typing results strongly support the presence of a ß-globin recombination hot spot, and suggest that this hot spot has a recombination fraction comparable to that proposed by Chakravarti et al. (4) on the basis of linkage disequilibrium and population genetic analysis.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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This study provides the first direct measurement of the ß-globin hot spot recombination fraction. The results presented here confirm previous, qualitative characterizations of recombination in this region (11–13), and an indirect estimate of the ß-globin hot spot recombination fraction (4). Furthermore, the absence of recombination events observed in the MspI–TaqI interval by single-sperm typing is consistent with the small number of ß-globin 5' sub-haplotypes recorded in a study of 14 geographical populations (25). The scarcity of common haplotypes observed in these populations is consistent with a historically low level of recombination in the MspI–TaqI region.
The single-sperm typing results presented in this study provide an estimate of the ß-globin hot spot recombination fraction; however, the use of other methods to study human recombination, such as small-pool PCR (9,26,27), might be able to refine the physical boundaries of this hot spot. Reduction of the hot spot’s physical size would accentuate the elevated recombination fraction in this region. For example, if the ß-globin hot spot were refined from 11 to 1 kb, it would exhibit an 800 (rather than an 80) -fold greater recombination rate relative to an average genomic region. In addition to refining the boundaries of the ß-globin hot spot, further study of this region could also focus on determining the frequency of complex exchange events caused by gene conversion or double crossovers.
Because studies of human recombination can only investigate the products of meiosis, little is known of mechanisms that drive human recombination hot spot activity. However, it is interesting that the ß-globin hot spot is located near a replication origin (28), since replication and recombination co-occur in viruses (29), bacteriophage (30) and yeast (31). The coincidence of replication and recombination in specific regions of DNA has been explained as a shared need for open chromatin (32), which could render specific genomic regions particularly susceptible to binding of proteins that mediate replication and recombination. Further study of recombination hot spot mechanisms may reveal additional clues why recombination events often cluster in defined physical regions.
The importance of recombination hot spots in genetic association studies
The results presented here are derived from analysis of a single gene cluster; however, there is increasing evidence for large-scale recombination rate heterogeneity in the human genome (19). Several examples of small-scale variation in recombination rates have been identified in humans (8,33–37), and recent work suggests that recombination hot spots could have an important effect on the underlying haplotype structure of the human genome (38). Improved methods for detecting ß-globin-like recombination hot spots throughout the genome will offer insight regarding the complicated patterns of linkage disequilibrium that have been observed in humans (39), although other factors such as population history will also be important (22).
The direct measurement of the ß-globin hot spot recombination fraction highlights that substantial variation in the frequency of recombination events can occur over small physical distances. This finding is relevant to developing strategies to detect associations between SNP markers and phenotypes, a topic that has been the subject of recent debate (40,41). Many association studies assume substantial linkage disequilibrium between genetic markers located within several kilobases of a causative SNP (42). Because recombination is a key molecular process that destroys linkage disequilibrium, identifying hot spot regions would be beneficial to genetic association studies.
A recent study on the genome-scale provides empirical proof that regions with increased recombination show lower levels of linkage disequilibrium (1). However, small-scale recombination rate heterogeneity, such as that observed in the ß-globin cluster, will also influence the ability to pinpoint a causative mutation. For example, it would be useful to locate a ß-globin-like hot spot in a candidate region for an association study so that an appropriate density of markers could be screened on either side of the hot spot.
Despite the relevance of recombination hot spots to linkage disequilibrium mapping, it is difficult to detect ß-globin-like hot spots without using technically challenging sperm typing methods. Current comparisons of genetic and DNA sequence maps screen only a few markers per megabase in 100–200 meioses (1,43), and have only been able to identify regions of elevated recombination on the megabase scale, but not at the single gene level. In addition, these genome-wide surveys have not detected strong correlations between simple DNA sequence motifs and areas of elevated recombination (1). The inability of genetic and physical map comparisons to identify small-scale hot spots, coupled with the considerable technical challenges involved in sperm typing methods, suggests that new approaches are required to examine small-scale recombination rate heterogeneity throughout the human genome.
Comparison of results to population genetic estimators of recombination
Several population genetic methods have been developed to estimate recombination rates from DNA sequence data (44–46). In addition, it is likely that new methods will emerge as more is learned about the molecular processes that affect DNA sequence diversity. For example, a recent study of the ß-globin replication origin/recombination hot spot observed a significantly elevated level of genetic diversity relative to the surrounding DNA (47). If elevated nucleotide diversity is a general feature of these regions, this information could be used to help identify new hot spots in the human genome.
Another approach that builds upon previous work by Griffiths and Marjoram (45) estimated the population recombination rate from DNA sequence data from an 3 kb ß-globin gene sequence in a UK population (48). Assuming an effective population size of 10 000, a 510 bp sequence in the hot spot region (located 850 bp upstream of the AvaII site in Fig. 1), yielded 0.036% recombination per kilobase (P.Fearnhead and P.Donnelly, unpublished data). This estimate was calculated from C = 4Nc, where N is the effective population size, and c is the recombination rate per generation. The results from Fearnhead and Donnelly’s estimator were as follows: C = 14.2 per kilobase for the 510 bp sequence located in the hot spot region, and C = 0.3 for the 3'-most 1723 bp of the 3 kb sequence investigated by Harding et al. (48). This estimate is comparable to the single-sperm typing recombination fraction for the small hot spot intervals (0.08% per kilobase). The concurrence of these results is promising; however, further molecular analyses of recombination in other regions by sperm typing will be required to confirm the reliability of population genetic approaches.
Although recombination hot spots in the human genome may complicate attempts at linkage disequilibrium mapping, the presence of a hot spot in one gene region implies that other areas of the genome experience low recombination. These areas of low recombination may exhibit elevated levels of linkage disequilibrium, and could require relatively low marker densities for successful association studies assuming that levels of gene conversion are not too high (49). In any case, detailed information about local recombination rates beyond what is currently available from human genetic maps will improve the success of genetic association studies.
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MATERIALS AND METHODS |
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TOPABSTRACTINTRODUCTIONRESULTSDISCUSSIONMATERIALS AND METHODSREFERENCES |
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Polymorphisms
As mentioned above, all of the polymorphisms considered in this study except for the MspI site were previously described by Chakravarti et al. (4). In order to find the MspI polymorphism, primers were designed to amplify the region containing this polymorphism from DNA sequence near the breakpoints of the
ß-thalassaemia-1 and ß-thalassaemia-2 deletions (50). These deletions are separated by 6 kb of DNA and are located 100 kb upstream of the ß-globin gene. After the MspI polymorphism was identified by restriction enzyme mapping, the intervening 6 kb was sequenced to design primers suitable for single-sperm typing (51).
Allele discrimination in single cells
Sperm from six donors was flow-sorted using a fluorescence activated cell sorter (FACS) into 96-well microtiter plates (Costar), lysed and neutralized as previously described by Leeflang et al. (52). Each 96-well microtiter plate of single sperm contained three positive controls (wells flow-sorted to contain 20 sperm) and eight negative controls (wells with no sperm). Positive controls verified amplification efficiency and negative controls were used to monitor contamination (see below).
Two rounds of PCR were performed for each single sperm in a laminar flow hood using sterile technique and dedicated pipettes. In the first round, two loci were amplified simultaneously in a total volume of 50 µl by adding 40 µl of a reaction mix containing potassium-free amplification buffer [final concentrations: 10 mM Tris–Cl pH 8.35, 2.5 mM MgCl2 and 0.01% (w/v) gelatin], 200 µM (final concentration) dNTPs (Pharmacia Biotech), 2.5 pmol of each primer, and 1 U Taq DNA polymerase (Boehringer).
Each reaction was overlaid with 42 µl of light white mineral oil (Sigma). Thermal cycling was performed in a DNA Engine Tetrad Thermal Cycler (MJ Research) at 96°C for 5 min, 10 cycles of 94°C for 1 min, 60°C for 4 min, 15 cycles of 94°C for 1 min, 60°C for 3 min, and one cycle of 72°C for 5 min.
For the second-round PCR, 5 µl of each first-round reaction was transferred into two new 96-well microtiter plates to amplify each locus separately. The total volume of the second-round PCR reactions was 50 µl, including 45 µl of a reaction mix with amplification buffer [final concentrations: 50 mM KCl, 10 mM Tris–Cl pH 8.35, 1.5 or 2.5 mM MgCl2 depending upon the locus amplified, 0.005% (w/v) gelatin], 200 µM (final concentration) dNTPs, 10 pmol of each primer, and 2 U Taq DNA polymerase.
Each reaction was overlaid with 42 µl of light white mineral oil. Thermal cycling was performed in a DNA Engine Tetrad Thermal Cycler at 96°C for 5 min, 23 cycles of 94°C for 1 min, 60°C for 1 min, and one cycle of 72°C for 5 min. All primer sequences for PCR amplification are available at http://hmg.oupjournals.org.
Three microliters of loading dye (15% Ficoll and 0.05% bromophenol blue in 1x TAE) was added to 8 µl of PCR product. This mixture was electrophoresed in 3.2% agarose gels for 1 h in 1x TAE (0.05 M Tris–acetate, 0.001 M EDTA) to verify amplification. These gels contained 250 ng of ethidium bromide and were photographed during illumination with short-wave ultraviolet light.
Next, 15 µl of PCR product was digested with the appropriate restriction enzyme (New England Biolabs) under the conditions and temperature specified by the manufacturer in a total volume of 20 µl. Six microliters of loading dye (15% Ficoll and 0.05% bromophenol blue in 0.8x TBE) was added to each 20 µl digestion. This mixture was electrophoresed on 4.8% gels [ratio of agarose to NuSeive 3:1 agarose (FMC BioProducts) = 1.4:1] for 2 h in 0.8x TBE (1x TBE = 0.09 Tris–borate, 0.002 M EDTA). The gels contained 250 ng of ethidium bromide and were photographed during illumination with short-wave ultraviolet light.
The results were read manually from each photograph and entered into the computer program SPERM3 (written by A.J.Boyce). SPERM3 tabulates the frequencies of the 16 single-sperm genotypes observed in studies of two loci (Table 1).
Sources of error
Four sources of error can affect single-sperm typing results. First, the FACS may fail to flow-sort a sperm into a well, or secondly, it may flow-sort more than one sperm into a well. Thirdly, amplification could fail at one or both loci investigated. Finally, PCR amplicons could be transferred among wells during first- or second-round amplification.
In addition to the possibility that PCR amplicons can be transferred among wells that contain a flow-sorted single sperm, unwanted PCR product transfer can also affect negative control wells. Of the 86 batches of single sperm analyzed, 21 showed a single-allele PCR product in one negative control well. In seven batches, PCR product was detected in two negative control wells, and in three batches, PCR product was detected in three negative control wells. Therefore, a total of 44 negative control wells showed evidence for unwanted PCR product transfer. The 55 remaining batches showed no evidence for PCR product in the negative control wells. The incidence of unwanted PCR product transfer was related to the technical challenge of amplifying single sperm. A higher rate of unwanted PCR product transfer was observed in the negative control wells in experiments that were not conducted under the laminar flow hood (data not shown). Because the occurrence of unwanted PCR product transfer is related to the physical execution of PCR amplification, it is likely to have occurred randomly with respect to batch, locus and allele.
Estimation of apparent recombination fractions
Before estimating recombination fractions in the ß-globin cluster, the single-sperm typing data were analyzed to determine whether any batches showed evidence for excessive transfer of PCR product across wells. Among the 31 batches with evidence for PCR product transfer to negative control wells, the observed number of first- and second-round transfer events was compared to the expected number from the Poisson distribution. Only one batch showed evidence for excessive PCR transfer events. This batch had PCR product in three negative control wells, and was omitted from the analysis.
The four fundamental errors outlined above can occur in different combinations during a single-sperm typing experiment. Certain combinations of these errors will generate genotypes that are neither parental nor recombinant (Table 1). However, these errors will have no direct impact upon estimates of the recombination fraction. False parental genotypes may also be created by genotyping error; however, these false parental genotypes will be overwhelmed by the large number of true parental sperm. In contrast, combinations of errors that create false recombinants can affect the results substantially, because the recombination fractions of the intervals considered in the experiment are low. Therefore, the frequency of errors that create false recombinants was estimated in order to use the results of a single-sperm typing experiment to calculate recombination fractions.
Table 3 indicates the six most likely outcomes in single-sperm typing experiments. Outcome A represents a successful single-sperm typing event, whereas outcomes B–F are combinations of errors that can create false recombinants. If more than two sperm are flow-sorted into a well, additional combinations of errors may occur; however, no more than two sperm were detected in a flow-sorting control experiment (Table 4), therefore, more complex combinations of errors are unlikely to have affected the results (Table 3).
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The estimates for the number of false recombinants produced by outcomes B–F in Table 3 were obtained by multiplying the total number of wells analyzed in the experiment (7225) by the frequencies of all fundamental errors that contribute to each outcome. In Table 3, the first column indicates the number of flow-sorted sperm in a well. These frequencies were estimated from a control experiment in which hundreds of single-sperm were flow-sorted onto microscope slides and examined (Table 4). The second column indicates the number of PCR failures that contribute to an outcome. The frequency of PCR failure was estimated from the number of one-allele genotypes in the experiment (Table 1, 1137/7225 = 16%). Blank wells were not included in this calculation because the number of NN wells in Table 1 agrees with the frequency of 0 sperm wells in Table 4 (948/7225 = 13% versus 66/461 = 14%). In the third column of Table 3, the number of contaminants (caused by unwanted PCR product transfer, see above) that enter each well was estimated by the number of negative control wells with evidence for cross-contamination [41/(85 x 8) = 6%]. The fourth column indicates the fraction of wells in an outcome that will create a false recombinant, since the number of false recombinants (rather than the total number of genotyping errors) is the key quantity required to adjust the observed recombination fraction.
It is interesting that the results of a direct control experiment performed to estimate the number of false recombinants arising from the situation in which two sperm are flow-sorted into a well and two PCR failures occur agree with the estimate in Table 3. Nine batches intentionally flow-sorted to contain two sperm per well (three TaqI–AvaII batches, two HincII–HinfI batches, one MspI–TaqI batch, one HincII–TaqI batch and two RsaI–HinfI batches), revealed that false recombinants occur by double PCR failure at a rate of 1.7% (Table 5). Using the results from Table 4, a direct estimate of the number of false recombinants produced in the experiment by two sperm wells and double PCR failure (0.009 x 0.017 x 7225 wells analyzed) is only slightly more than the estimate in Table 3.
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The reasoning illustrated in Table 3 can be used to estimate the number of false recombinants in a single-sperm typing experiment; however, we used a previously published method to estimate the number of false recombinants more directly from the data. This approach assumes that the number of false recombinants in an experiment equals the number of uninformative two-allele genotypes (BN or NB genotypes; see Table 1). The reasoning behind this approach is outlined in Tables 6 and 7. Navidi and Arnheim (24) first suggested that the number of uninformative two-allele genotypes should estimate the number of false recombinants in a single-sperm typing experiment. Furthermore, using the number of uninformative two-allele genotypes as an estimate for false recombinants provides a slightly more conservative estimate of the absolute recombination fraction. Eighteen uninformative two-allele genotypes were observed in the experiment, whereas only 16 false recombinants were estimated in Table 3.
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In order to estimate the average values and 95% CIs for the nine ß-globin cluster intervals presented in Figure 1, recombination fractions (R) were calculated by subtracting the average frequency of uninformative two-allele genotypes across all experiments (F) from the apparent recombination fraction (R + F) in each interval. The average incidence of false recombinants was used because the number of uninformative two-allele genotypes was equivalent among the nine intervals studied (2 x 9 Fisher’s exact test 2P = 0.828). CIs for the adjusted, absolute recombination fractions were obtained using a profile likelihood (53) on the joint Poisson likelihoods for R + F and F with a custom-written program in the STATA statistical package.
In addition to estimating recombination frequencies by the new method outlined above, the data were also analyzed using an alternative method (23), which incorporates the frequencies of all 16 single-sperm genotypes observed experimentally (Table 1), and considers the effect of locus- and allele-specific differences in contamination and amplification rates. The results obtained using the approach of Lazzeroni et al. (23) were similar to the results of the new method described above. In addition, including locus- and allele-specific amplification and contamination in the analysis had little effect upon the recombination fraction estimates (data not shown).
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ACKNOWLEDGEMENTS |
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We thank Esther Leeflang and Norman Arnheim, Molecular Biology Program, University of Southern California, Los Angeles, for expert advice about single-sperm typing; Leon Maciocia and Bill Ledger, Nuffield Department of Obstetrics and Gynaecology, John Radcliffe Hospital, Oxford, for coordinating sample collection; Sarah Walker, MRC Clinical Trials Unit, London, for statistical advice and writing the customized STATA program; and Rosalind Harding, Institute of Molecular Medicine, John Radcliffe Hospital, Oxford as well as three expert reviewers for comments on the manuscript. J.A.S. was supported by a Wellcome Trust studentship.
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FOOTNOTES |
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+ To whom correspondence should be addressed at present address: Genaissance Pharmaceuticals, Five Science Park, New Haven, CT 06511, USA. Tel: +1 203 786 3516; Fax: +1 203 562 9377; Email: j.schneider@genaissance.com
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