- Research article
- Open Access
Large-scale mapping of mutations affecting zebrafish development
- Robert Geisler1Email author,
- Gerd-Jörg Rauch1, 2,
- Silke Geiger-Rudolph1,
- Andrea Albrecht1, 3,
- Frauke van Bebber1, 4,
- Andrea Berger1,
- Elisabeth Busch-Nentwich1, 5,
- Ralf Dahm1, 6,
- Marcus PS Dekens1, 7,
- Christopher Dooley1,
- Alexandra F Elli1, 8,
- Ines Gehring1,
- Horst Geiger1,
- Maria Geisler1,
- Stefanie Glaser1,
- Scott Holley1, 9,
- Matthias Huber1, 10,
- Andy Kerr1,
- Anette Kirn1, 11,
- Martina Knirsch1, 12,
- Martina Konantz1,
- Axel M Küchler1, 13,
- Florian Maderspacher1, 14,
- Stephan C Neuhauss1, 15,
- Teresa Nicolson1, 16,
- Elke A Ober1, 17,
- Elke Praeg1, 18,
- Russell Ray1, 19,
- Brit Rentzsch1, 20,
- Jens M Rick1, 21,
- Eva Rief1,
- Heike E Schauerte1, 22,
- Carsten P Schepp1, 23,
- Ulrike Schönberger1,
- Helia B Schonthaler1, 24,
- Christoph Seiler1, 25,
- Samuel Sidi1, 26,
- Christian Söllner1, 27,
- Anja Wehner1, 28,
- Christian Weiler1 and
- Christiane Nüsslein-Volhard1
© Geisler et al; licensee BioMed Central Ltd. 2007
Received: 28 September 2006
Accepted: 09 January 2007
Published: 09 January 2007
Large-scale mutagenesis screens in the zebrafish employing the mutagen ENU have isolated several hundred mutant loci that represent putative developmental control genes. In order to realize the potential of such screens, systematic genetic mapping of the mutations is necessary. Here we report on a large-scale effort to map the mutations generated in mutagenesis screening at the Max Planck Institute for Developmental Biology by genome scanning with microsatellite markers.
We have selected a set of microsatellite markers and developed methods and scoring criteria suitable for efficient, high-throughput genome scanning. We have used these methods to successfully obtain a rough map position for 319 mutant loci from the Tübingen I mutagenesis screen and subsequent screening of the mutant collection. For 277 of these the corresponding gene is not yet identified. Mapping was successful for 80 % of the tested loci. By comparing 21 mutation and gene positions of cloned mutations we have validated the correctness of our linkage group assignments and estimated the standard error of our map positions to be approximately 6 cM.
By obtaining rough map positions for over 300 zebrafish loci with developmental phenotypes, we have generated a dataset that will be useful not only for cloning of the affected genes, but also to suggest allelism of mutations with similar phenotypes that will be identified in future screens. Furthermore this work validates the usefulness of our methodology for rapid, systematic and inexpensive microsatellite mapping of zebrafish mutations.
Large-scale mutagenesis screens in the zebrafish employing the mutagen ENU have isolated several hundred mutant loci that represent putative developmental control genes [1, 2]. In order to realize the potential of such screens, systematic genetic mapping of the mutations is necessary. Genome scanning by bulked segregant analysis with microsatellite markers is the method of choice for such purposes, as a rough map position can be quickly obtained [3, 4]. In the zebrafish it is easy to perform mapcrosses against a polymorphic reference line, followed by brother-sister matings among the F1 generation. Linkage to a microsatellite marker can then be found by comparing the band intensities of marker alleles in a pool of mutant F2 individuals with a pool of their wildtype siblings. Because full sibships are analyzed the genetic distance between the mutant locus and a microsatellite can be determined by a simple count of recombinations.
The established reference map for the zebrafish genome is the MGH map [5–7] which was generated by scoring 3,881 microsatellite markers (all of them CA repeats) on a panel of 48 diploid F2 fish of an India × AB reference cross. It covers 2,295 centimorgans (cM) at a resolution of 1.2 cM. Because the MGH markers do not necessarily show a usable polymorphism in reference crosses of Tü × WIK our first task was to identify markers that could be used in such a cross.
Results and discussion
Selection of markers for genome scanning
Sets of microsatellite markers used for scanning the zebrafish genome
Marker set G4
Marker set H2
Z4593, Z9394, Z5508, Z1705, Z1351, Z9704, Z11464, Z6802, Z1781
Z11913, Z6384, Z9977, Z6415, Z7287, Z9395, Z22319, Z10978, Z11618
Z7634, Z4662, Z3430, Z1406, Z6617, Z1703, Z20550
Z13620, Z7361, Z9361, Z13281, Z21490, Z7678, Z10302, Z22544
Z872, Z8208, Z15457, Z9964, Z11227, Z3725, Z20058, Z6019
Z8364, Z9843, Z7419, Z8681, Z9662
Z1525, Z9920, Z21636, Z7490, Z984
Z7629, Z10983, Z23058, Z9319, Z17278, Z6503, Z11566
Z15414, Z11496, Z6727, Z10456, Z1390, Z3804, Z14143, Z4299, Z1202
Z6916, Z9106, Z9109, Z9969, Z7313, Z7291, Z22523, Z21290, Z10484, Z7318, Z13304
Z740, Z13275, Z880, Z6624, Z10183, Z5294, Z13614, Z7666, Z4297, Z1680
Z15448, Z8447, Z22253, Z13328, Z8245, Z9254, Z6330, Z9652, Z17248, Z9230, Z21901
Z3273, Z10785, Z1206, Z4706, Z1182, Z1059, Z8156, Z1239, Z13880, Z13936, Z5563
Z7479, Z7555, Z7069, Z8975, Z8540, Z6273, Z10451, Z22628
Z1634, Z1068, Z4323, Z13412, Z21115, Z789, Z10929, Z3526
Z7962, Z11492, Z21483, Z10121, Z11946, Z23039, Z23009
Z1777, Z6268, Z4673, Z5080, Z1805, Z20031, Z10789, Z4577
Z22173, Z9923, Z6845, Z9439, Z6574, Z6336, Z9975
Z9199, Z6410, Z8146, Z13632, Z1145, Z9701, Z3260
Z6648, Z22661, Z8318, Z9574, Z8705, Z7558, Z15444
Z10919, Z3362, Z13411, Z1393, Z3527, Z1590
Z11865, Z7657, Z11067
Z1778, Z21911, Z1473, Z4188, Z1358
Z7409, Z8755, Z8460, Z10225, Z22666, Z6442, Z7834, Z20142, Z11903
Z1531, Z5643, Z6104, Z13611, Z5395, Z1627, Z7102, Z6657, Z1826, Z6007
Z10513, Z9049, Z11695, Z22022, Z20208, Z20379, Z9357, Z8617, Z11459
Z1523, Z5436, Z1536, Z5435, Z4203, Z22107, Z1226, Z3984, Z1801
Z6545, Z8471, Z11694, Z9720, Z4592, Z20663, Z20214, Z9789, Z11837
Z6312, Z6712, Z21982, Z4396, Z11320, Z13230, Z13822, Z7381, Z5223
Z20627, Z21452, Z22027, Z11323, Z7070, Z6024, Z13927
Z3741, Z21155, Z6365, Z10036, Z1215, Z4670
Z10217, Z10671, Z11452, Z20177, Z6329, Z6240, Z6293, Z7956, Z20704
Z4268, Z1490, Z22083, Z22674, Z9847, Z1408, Z4053
Z7625, Z9179, Z8862, Z9633, Z9830, Z13631, Z13643
Z1136, Z1144, Z13329, Z8488, Z10008, Z3558, Z9154, Z5321
Z14136, Z9484, Z8525, Z7142, Z13260, Z14011
Z4009, Z160, Z3782, Z3816, Z11403, Z6661, Z7926, Z1803
Z1544, Z22649, Z22818, Z13773, Z6079, Z13727, Z9050, Z7265, Z10273
Z9334, Z10056, Z11841, Z3964, Z7158, Z3954, Z22041, Z8554, Z4329
Z10177, Z17204, Z10901, Z10756, Z7568, Z6973
Z3476, Z1274, Z4492, Z10960, Z4425, Z1497, Z4074
Z6174, Z6243, Z8230, Z9728, Z9236, Z20446, Z6087
Z1148, Z10673, Z9402, Z230, Z10321, Z21243
Z6613, Z10028, Z10324, Z11262, Z20168, Z21507, Z21252, Z11679
Z8945, Z4003, Z15422, Z4421, Z3157, Z176, Z1773
Z8362, Z11495, Z20643, Z7550, Z11391, Z14008
Z5075, Z1584, Z5413, Z23011, Z3399, Z22375, Z5657, Z3901
Z7349, Z10961, Z10458, Z13695, Z10529, Z6438, Z21908, Z6296, Z7132, Z9673
GOF15, Z1378, Z3490, Z5669, Z1462
Z21929, Z21722, Z13232, Z15480, Z10010, Z10578, Z21181
The average distance between markers of the G4 set is 11.6 cM, and all distances are smaller than 36 cM, except for a 71.1 cM interval on LG21 (between Z4425 and Z1497). Within this particular interval few MGH markers are available, and no suitably polymorphic marker could be identified in our reference crosses. For the H2 set the average distance is 11.5 cM, and all distances are smaller than 53.8 cM, except for a 83.3 cM interval on LG21. The more uneven chromosomal distribution of markers in the H2 set reflects the fact that frequently the best markers available were already used in the G4 set.
Our mapping methodology as described below can theoretically detect significant linkage over a distance of approximately 36 cM (assuming the genotyping of 48 mutant individuals). However, since the LOD score is proportional to the number of individuals scored, this range can be easily increased by adding more mutant individuals if a linkage is questionable. Our marker sets therefore cover the genome adequately to detect significant linkage with the great majority of mutant loci. All the mutant loci mapped in this work have confirmed linkage to at least one G4 or H2 marker (not shown if the closest flanking markers were selected from outside the sets).
Mapping of mutant loci
We report here on the mapping of 319 mutant loci identified in the ENU-based Tübingen I mutagenesis screen [1, 2] and subsequent screening among the mutant collection (Additional file 1). For 42 of the loci the corresponding genes have already been identified by other researchers, as listed by the ZFIN database ; they are included as controls for our mapping procedure (see below). Not included are 70 successfully mapped loci for which the corresponding genes were already published by ourselves or such a publication is in preparation, or the carriers of which were lost after mapping.
For each mutation we crossed mutant carriers against the polymorphic reference line WIK which was established in our lab for this purpose . Brother-sister matings were performed in the F1 and the F2 progeny was sorted by phenotype. DNA was prepared on 96-well plates, and aliquots of 36 – 48 mutant F2 individuals and their wildtype siblings were pooled. Genome scanning was performed by PCR of the mutant and sibling pools with the markers of the G4 marker set, and the band intensities on agarose gels were quantified semi-automatically using NIH Image software as well as visually assessed to identify potential linkages. Mutant and sibling pools representing up to 24 different mutations were tested in parallel. Verification of the best potential linkages (up to six) for each mutation was then attempted by performing PCR of the respective marker with the individual mutants and siblings that had been used for pooling, and counting the recombinant genotypes (for the genotype data see Additional file 2). Siblings were always included on the same gel as a control to confirm that the marker is polymorphic and the two polymorphic bands appear at the proper frequency. If no potential linkage could be verified for a mutation and sufficient material was available, the procedure was repeated once with the G4 marker set, and another two times with the H2 marker set. If possible, DNA was prepared from a different F1 pair for each genome scan, since the Tübingen and WIK lines used are not isogenic and markers that show no usable polymorphism in progeny of one F2 pair are therefore sometimes usable in progeny of another one.
A potential linkage was considered confirmed if it had a two-point LOD score equal or greater than 3. The individuals were then genotyped for all polymorphic markers from the same marker set and chromosomal region in order to identify, if possible, a pair of markers flanking the mutation, and if that was not possible, the two closest markers on one side of the mutation. Occasionally additional markers not in the chosen marker set were also included in the genotyping. Decisions on whether or not a mutation was flanked by two markers were based on whether recombinations with the markers occurred independently. For details of the mapping procedure and the calculation of map positions see the Methods section and .
In total, mapping was attempted for 486 mutations from the Tübingen I screen and subsequent screens of the mutant collection and successful for 389, giving a success rate of 80 %. 12 of these could be mapped only with the H2 set. Unsuccessful mapping experiments were due to difficulties in obtaining sufficient F2 individuals and to PCR problems as well as to a lack of polymorphic markers in our marker set. Among the mutations to be mapped, a group of 63 was prioritized based on interest in their phenotypes. For each of these several additional mapcrosses were set up (data not shown). 56 mutations of this group, or 89 % were successfully mapped, providing a lower limit for the percentage of mutations that our marker sets and methodology is capable of mapping if sufficient F2 individuals are available. The biggest distance to markers on either side at which we could confirm linkage was 31.9 cM (for the mutation spt), approaching the theoretical cutoff of 36 cM.
Chromosomal distribution of mutant loci
Between 1,400 and 2,400 zebrafish genes have been estimated to have visible mutant phenotypes in embryonic and early larval development [1, 11]. Therefore the loci reported in this work represent at least one eighth and possibly as much as quarter of all the loci that can be mutated to give a visible phenotype.
Assessment of mapping quality
Comparison of mutant and gene positions
T51, HS, MOP
HS, T51, MOP, LN54
T51, LN54, MOP
MGH, LN54, T51
A comparison of the linkage group assignments shows that two of the 21 genes (frs/slc25a and ovl/ift88) are assigned to a different linkage group by ZMAP, in both cases based on results from the Heat Shock (HS) panel [14–16]. However, several published linkages to genetic markers support our linkage group assignment of frs/slc25a  while our assignment of ovl/ift88 is supported by the T51 panel (as shown on the ZFIN website) and by the latest version of the HS map . In conclusion, none of our linkage group assignments is conclusively contradicted by gene mapping.
Next we compared the map positions of the mutations with those of the genes on ZMAP (using the median of the ZMAP positions if a gene was placed on more than one mapping panel). If we assume the gene positions to be correct, we obtain a standard error of our mutant map positions of 6.1 cM. Further assuming a normal distribution of errors, we can predict that approximately 95 % of the genes should be within 12.2 cM (two standard errors) of the rough mapping position of the mutation. Indeed, 17 out of the 19 genes mapped on the same chromosome (90 %) are within two standard errors of the mutation, and 16 out of 19 (84 %) within one standard error. Actually both mutation and gene mapping contribute to the observed errors to an unknown degree, so that 6.1 cM merely represents an upper limit for the standard error of our mapping procedure.
We have obtained rough map positions for over 300 zebrafish mutants with an accuracy of approximately 6 cM and thereby validated the usefulness of our methodology for rapid, systematic and inexpensive microsatellite mapping of zebrafish mutations. The dataset that we have produced is a first step towards identification of the genes affected by the 277 mutations that are not yet cloned.
In candidate gene approaches, our data can substantially narrow down the number of candidate genes, since on the order of 99 % of the genome are outside the two-standard-errors confidence limit of our map positions. Positional cloning approaches in the absence of obvious candidate genes will still require fine mapping by genotyping of additional individuals and identification of more closely linked markers, using the flanking markers identified by us as starting points. Particularly thorough fine-mapping is required in centromeric regions because the genetic recombination rate is often several-fold reduced in such regions , an effect that can be easily observed in the zebrafish by comparing the genetic map and the radiation hybrid map . Nevertheless, we expect our map positions to be useful even without knowledge of the affected genes, as they can suggest allelism of mutations with a similar phenotype identified in future screens.
We have found that a relatively small number of microsatellite markers is sufficient to scan almost the entire genome and that the experimental procedures are robust and easy to perform. Other methods that have been proposed for the mapping of mutant loci in the zebrafish include half-tetrad analysis with microsatellite markers, genome scanning with SNPs and microarray based SNP mapping. While half-tetrad analysis requires only 25 markers to obtain a linkage group assignment [20–22], it has the disadvantage that gynogenetic diploid fish must be generated first, which makes this approach less convenient for high-throughput analysis. In the course of the ongoing zebrafish genome project, more than 50,000 SNPs have been identified  offering an enticing alternative to microsatellite markers, but SNP genotyping is far more costly than the agarose based method employed by us. Genotyping of SNPs in a bulked segregant panel is also possible by microarray hybridization . However, the SNPs identified to date are specific to the strains they were developed from and may not be informative in mapcrosses performed with different strains (such as ours). Furthermore such a microarray experiment replaces only two steps in our mapping procedure, namely the pooled PCR and its associated gel run, which represent only a minor part of the total mapping effort, as compared to fish breeding, sorting of F2 embryos and confirmation of the bulked segregant results by genotyping of F2 individuals. Future microarray based approaches may make it possible to dispense with the genotyping of individuals entirely, provided that a very large number of SNPs can be multiplexed in a single microarray hybridization such that it immediately provides a reliable map position. Meanwhile, genome scanning with microsatellite markers remains the method of choice as it is equally suitable for the mapping of individual mutations by laboratories with limited genomics resources, and for high throughput projects such as ours.
Mapcrosses were set up between mutant carriers and the laboratory reference line WIK  and brother-sister matings were performed between F1 individuals following standard laboratory procedures .
F2 embryos were sorted by phenotype and stored in Eppendorf tubes with 100 % MeOH at -70°C until use. Single embryos were arrayed on a 96-well microtiter plate with a glass Pasteur pipette. The MeOH was evaporated on a PCR block at 70°C and 25 μl of 1.7 mg/ml Proteinase K in 1 × TE was added to each well. The plate was covered with sealing film and heated to either 55°C or 70°C for 240 min and to 94°C for 10 min in a thermocycler. 10 μl of each of the sibling and mutant lysates was pooled and 45 μl sterile ddH2O was added to the remainder. Plates were stored at -20°C.
PCR was initially performed on mutant and sibling pools for genome scanning, and subsequently on the individuals that had been used for the pooling in order to confirm potential linkages to specific markers. 20 μl PCR reactions were set up from 14.28 μl of reaction mix (2 μl of 10 × PCR buffer, 0.04 μl each of 100 mM dATP, dCTP, dGTP and dTTP, 12.12 μl water), 0.16 μl each of 20 mM forward and reverse primer, 0.4 μl of 5U/μl Taq polymerase, and 5 μl of template DNA. 10 × PCR buffer contained 100 mM Tris-HCl (pH 8.3), 500 mM KCl, 15 mM MgCl2 and 0.1 % (w/v) gelatin. All pipetting was done with a Biomek 2000 robot. Cycling was carried out by initially denaturating at 94°C for 2 min, 35 cycles of denaturation at 94°C for 30 sec, annealing at 60°C for 30 sec and extension at 73°C for 1 min, and a final extension at 73°C for 5 min. 5 μl of 6 × loading buffer were added to each sample, and electrophoresis was carried out at 200 V for 45 min in 1 × TBE buffer, on 2 % agarose gels. Gels were imaged and scored semi-automatically with NIH Image and a set of custom-designed macros.
Calculation of map positions
Distances between mutations and markers were calculated by determining the recombination fraction in the mutant F2 individuals and applying the Kosambi mapping function. Linkages with a two-point LOD score equal or greater 3 were regarded as significant.
In order to place a mutation in the genetic interval between the closest marker and another linked marker we determined whether recombinations for both of them were correlated. For this purpose we considered only single recombinants for the closest marker, i.e. heterozygotes. If the majority of these were heterozygous for the second marker we regarded the recombinations as uncorrelated and placed the mutation in the interval between the markers. Otherwise we placed the mutation outside the interval in the direction opposite from the second marker.
Assuming complete meiotic interference, i.e. only a single recombination event per chromosome, all recombinants for the first marker should be either non-recombinant for the second marker if the markers flank the mutation, or heterozygous if both markers are on the same side of the mutation. In our data approximately half of the mutations gave results in between these extremes. This may be due to occasional contaminations of the PCR assays but also to less than complete meiotic interference, which would allow a second recombination in the same individual. We therefore did not eliminate any contradictory individuals from the calculation of genetic distances as they may represent a genuine second recombination.
If a mutation could be placed in an interval between two markers, a map position was calculated by scaling the observed distances between the mutation and the markers so as to fit into the published distance between the markers. In the remaining cases only the distance to the closest marker was used to calculate the map position. A FileMaker Pro 5 database was used to store the scoring data and perform the calculations . The latest version of the MGH map, available through ZFIN , was used as a reference for calculating map positions.
This paper is dedicated to the memory of two deceased colleagues: Pascal Haffter, who conceived the large-scale mapping approach and initially supervised the project; and Ulrike Martyn, who contributed to the experimental work. For contributions to the experimental work we are also indebted to Claudia Bernardo de Oliveira, Katy Hingst, Tüzer Kalkan and Jeremy Keenan. We would like to thank Hans-Georg Frohnhöfer (Tübingen zebrafish stockcenter) for providing mutant carriers for mapcrosses and for information on allele nomenclature and availability. This work was supported by the German Human Genome Project (DHGP Grant 01 KW 9627 and 01 KW 9919).
- Haffter P, Granato M, Brand M, Mullins MC, Hammerschmidt M, Kane DA, Odenthal J, van Eeden FJ, Jiang YJ, Heisenberg CP, Kelsh RN, Furutani-Seiki M, Vogelsang E, Beuchle D, Schach U, Fabian C, Nüsslein-Volhard C: The identification of genes with unique and essential functions in the development of the zebrafish, Danio rerio. Development. 1996, 123: 1-36.PubMedGoogle Scholar
- Haffter P, Odenthal J, Mullins MC, Lin S, Farrell MJ, Vogelsang E, Haas F, Brand M, van Eeden FJM, Furutani-Seiki M, Granato M, Hammerschmidt M, Heisenberg CP, Jiang YJ, Kane DA, Kelsh RN, Hopkins N, Nüsslein-Volhard C: Mutations affecting pigmentation and shape of the adult zebrafish. Dev Genes Evol. 1996, 206: 260-276. 10.1007/s004270050051.PubMedView ArticleGoogle Scholar
- Michelmore RW, Paran I, Kesseli RV: Identification of markers linked to disease-resistance genes by bulked segregant analysis: a rapid method to detect markers in specific genomic regions by using segregating populations. Proc Natl Acad Sci U S A. 1991, 88 (21): 9828–32-10.1073/pnas.88.21.9828.PubMedView ArticleGoogle Scholar
- Ruyter-Spira CP, Gu ZL, Van der Poel JJ, Groenen MA: Bulked segregant analysis using microsatellites: mapping of the dominant white locus in the chicken. Poult Sci. 1997, 76 (2): 386-391.PubMedView ArticleGoogle Scholar
- Knapik EW, Goodman A, Atkinson OS, Roberts CT, Shiozawa M, Sim CU, Weksler-Zangen S, Trolliet MR, Futrell C, Innes BA, Koike G, McLaughlin MG, Pierre L, Simon JS, Vilallonga E, Roy M, Chiang PW, Fishman MC, Driever W, Jacob HJ: A reference cross DNA panel for zebrafish (Danio rerio) anchored with simple sequence length polymorphisms. Development. 1996, 123: 451-460.PubMedGoogle Scholar
- Knapik EW, Goodman A, Ekker M, Chevrette M, Delgado J, Neuhauss S, Shimoda N, Driever W, Fishman MC, Jacob HJ: A microsatellite genetic linkage map for zebrafish (Danio rerio). Nat Genet. 1998, 18 (4): 338-343. 10.1038/ng0498-338.PubMedView ArticleGoogle Scholar
- Shimoda N, Knapik EW, Ziniti J, Sim C, Yamada E, Kaplan S, Jackson D, de Sauvage F, Jacob H, Fishman MC: Zebrafish genetic map with 2000 microsatellite markers. Genomics. 1999, 58 (3): 219-232. 10.1006/geno.1999.5824.PubMedView ArticleGoogle Scholar
- Rauch GJ, Granato M, Haffter P: A polymorphic zebrafish line for genetic mapping using SSLPs on high-percentage agarose gels. Technical Tips Online. 1997, T01208-Google Scholar
- Geisler R: Mapping and cloning. Zebrafish, A Practical Approach. Edited by: Nüsslein-Volhard C, Dahm R. 2002, Oxford , Oxford University Press, 175-212.Google Scholar
- The Zebrafish Information Network (ZFIN). [http://zfin.org]
- Amsterdam A, Nissen RM, Sun Z, Swindell EC, Farrington S, Hopkins N: Identification of 315 genes essential for early zebrafish development. Proc Natl Acad Sci U S A. 2004, 101 (35): 12792–7-10.1073/pnas.0403929101.PubMedView ArticleGoogle Scholar
- Golling G, Amsterdam A, Sun Z, Antonelli M, Maldonado E, Chen W, Burgess S, Haldi M, Artzt K, Farrington S, Lin SY, Nissen RM, Hopkins N: Insertional mutagenesis in zebrafish rapidly identifies genes essential for early vertebrate development. Nat Genet. 2002, 31 (2): 135-140. 10.1038/ng896.PubMedView ArticleGoogle Scholar
- Ensembl Zebrafish. [http://www.ensembl.org/Danio_rerio]
- Kelly PD, Chu F, Woods IG, Ngo-Hazelett P, Cardozo T, Huang H, Kimm F, Liao L, Yan YL, Zhou Y, Johnson SL, Abagyan R, Schier AF, Postlethwait JH, Talbot WS: Genetic linkage mapping of zebrafish genes and ESTs. Genome Res. 2000, 10 (4): 558-567. 10.1101/gr.10.4.558.PubMed CentralPubMedView ArticleGoogle Scholar
- Postlethwait JH, Woods IG, Ngo-Hazelett P, Yan YL, Kelly PD, Chu F, Huang H, Hill-Force A, Talbot WS: Zebrafish comparative genomics and the origins of vertebrate chromosomes. Genome Res. 2000, 10 (12): 1890-1902. 10.1101/gr.164800.PubMedView ArticleGoogle Scholar
- Woods IG, Kelly PD, Chu F, Ngo-Hazelett P, Yan YL, Huang H, Postlethwait JH, Talbot WS: A comparative map of the zebrafish genome. Genome Res. 2000, 10 (12): 1903-1914. 10.1101/gr.10.12.1903.PubMed CentralPubMedView ArticleGoogle Scholar
- Shaw GC, Cope JJ, Li L, Corson K, Hersey C, Ackermann GE, Gwynn B, Lambert AJ, Wingert RA, Traver D, Trede NS, Barut BA, Zhou Y, Minet E, Donovan A, Brownlie A, Balzan R, Weiss MJ, Peters LL, Kaplan J, Zon LI, Paw BH: Mitoferrin is essential for erythroid iron assimilation. Nature. 2006, 440 (7080): 96-100. 10.1038/nature04512.PubMedView ArticleGoogle Scholar
- Payseur BA, Nachman MW: Microsatellite variation and recombination rate in the human genome. Genetics. 2000, 156 (3): 1285-1298.PubMed CentralPubMedGoogle Scholar
- Guryev V, Berezikov E, Cuppen E: CASCAD: a database of annotated candidate single nucleotide polymorphisms associated with expressed sequences. BMC Genomics. 2005, 6: 10-10.1186/1471-2164-6-10.PubMed CentralPubMedView ArticleGoogle Scholar
- Stickney HL, Schmutz J, Woods IG, Holtzer CC, Dickson MC, Kelly PD, Myers RM, Talbot WS: Rapid mapping of zebrafish mutations with SNPs and oligonucleotide microarrays. Genome Res. 2002, 12 (12): 1929-1934. 10.1101/gr.777302.PubMed CentralPubMedView ArticleGoogle Scholar
- Brand M, Granato M, Nüsslein-Volhard C: Keeping and raising zebrafish. Zebrafish, A Practical Approach. Edited by: Nüsslein-Volhard C, Dahm R. 2002, Oxford , Oxford University Press, 7-37.Google Scholar
- Kwok C, Korn RM, Davis ME, Burt DW, Critcher R, McCarthy L, Paw BH, Zon LI, Goodfellow PN, Schmitt K: Characterization of whole genome radiation hybrid mapping resources for non-mammalian vertebrates. Nucleic Acids Research. 1998, 26 (15): 3562-3566. 10.1093/nar/26.15.3562.PubMed CentralPubMedView ArticleGoogle Scholar
- Kwok C, Critcher R, Schmitt K: Construction and characterization of zebrafish whole genome radiation hybrids. Methods Cell Biol. 1999, 60: 287-302.PubMedView ArticleGoogle Scholar
- Geisler R, Rauch GJ, Baier H, van Bebber F, Broß L, Dekens MP, Finger K, Fricke C, Gates MA, Geiger H, Geiger-Rudolph S, Gilmour D, Glaser S, Gnugge L, Habeck H, Hingst K, Holley S, Keenan J, Kirn A, Knaut H, Lashkari D, Maderspacher F, Martyn U, Neuhauss S, Haffter P, et al: A radiation hybrid map of the zebrafish genome. Nat Genet. 1999, 23 (1): 86-89. 10.1038/12692.PubMedView ArticleGoogle Scholar
- Hukriede NA, Joly L, Tsang M, Miles J, Tellis P, Epstein JA, Barbazuk WB, Li FN, Paw B, Postlethwait JH, Hudson TJ, Zon LI, McPherson JD, Chevrette M, Dawid IB, Johnson SL, Ekker M: Radiation hybrid mapping of the zebrafish genome. Proc Natl Acad Sci U S A. 1999, 96 (17): 9745-9750. 10.1073/pnas.96.17.9745.PubMed CentralPubMedView ArticleGoogle Scholar
- Chevrette M, Joly L, Tellis P, Knapik EW, Miles, J. Fishman M, Ekker M: Characterization of a zebrafish/mouse somatic cell hybrid panel. Genomics. 2000, 64 (1): 119-126. 10.1006/geno.1999.6124.PubMedView ArticleGoogle Scholar
- Postlethwait JH, Yan YL, Gates MA, Horne S, Amores A, Brownlie A, Donovan A, Egan ES, Force A, Gong Z, Goutel C, Fritz A, Kelsh R, Knapik E, Liao E, Paw B, Ransom D, Singer A, Thomson M, Abduljabbar TS, Yelick P, Beier D, Joly JS, Larhammar D, Talbot WS, et al: Vertebrate genome evolution and the zebrafish gene map. Nature Genetics. 1998, 18 (4): 345-349. 10.1038/ng0498-345.PubMedView ArticleGoogle Scholar
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