The Journal of Heredity 2001:92(1)
© 2001 The American Genetic Association 92:16-22
Composite Interval Mapping Reveals a Major Locus Influencing Embryonic Development Rate in Rainbow Trout (Oncorhynchus mykiss)
From the School of Biological Sciences, Washington State University, Pullman, Washington. B. D. Robison is currently at the Department of Biology, University of Oregon, Eugene, Oregon.
 |
Abstract |
|---|
 Top Abstract IntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Little is known about the genetics controlling the rate of embryonic development in salmonids, despite the fact that this trait plays an important role in the life history of wild and cultured stocks. We investigated the genetics of embryonic development rate by performing an analysis of quantitative trait loci (QTL) on two families of androgenetically derived doubled haploid rainbow trout produced from a hybrid of two clonal lines with divergent embryonic development rates. A total of 170 doubled haploid individuals were genotyped at 222 marker loci [219 amplified fragment length polymorphism (AFLP) markers, 2 microsatellites, and p53]. A genetic linkage analysis resulted in a map consisting of 27 linkage groups with 21 of the markers remaining unlinked at a minimum LOD of 3.0 and maximum
of 0.40. Eight of these linkage groups were matched to published rainbow trout linkage groups. Composite interval mapping (CIM) revealed evidence for two QTL influencing time to hatch, and suggestive evidence for a third. These QTL accounted for a total of 24.6% of the variation in time to hatch. One of these QTL had a large effect on development rate, especially in one family of doubled haploids, in which it explained 25.6% of the variance in time to hatch. QTL influencing embryonic length and weight at the commencement of exogenous feeding were also identified. The QTL with the strongest effect on embryonic length (lenR13) mapped to the same position as the QTL with the strongest effect on time to hatch (tthR13), suggesting a single QTL may have a pleiotropic effect on both these traits. These results suggest that the use of clonal lines with a doubled haploid crossing design is an effective way of analyzing the genetic basis of complex traits in salmonids. |
Introduction |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Embryonic development rate plays a significant role in the evolutionary biology and successful aquaculture of salmonid fishes. The shape of the embryonic development rate reaction norm is important in stabilizing timing of fry emergence among populations of salmonids (Brannon 1987), resulting in reduced predation rates (Godin 1982), optimal availability of prey upon commencement of exogenous feeding, and appropriate conditions for fry migration (Holtby et al. 1989). Significant genetic variation for development rate has been observed in both hatchery (Ferguson et al. 1985; McIntyre and Blanc 1973) and wild (Beacham 1988; Hebert et al. 1998; Tallman 1986) populations of salmonids. In addition, time from fertilization to hatch, an indicator of development rate, has been shown to be genetically correlated to length of fry in pink salmon (Oncorhynchus gorbuscha) (Beacham 1988), and is associated with larger size and earlier sexual maturity in some strains of rainbow trout (O. mykiss) (Allendorf et al. 1983).
With the exception of a rare regulatory mutation for the expression of Pgm-1 (Allendorf et al. 1983), the nature of the genes controlling the rate of embryonic development in salmonids remains largely unknown. Knowledge of the number, position, and average effects of genes influencing the rate of development would be valuable for further research into the economic and evolutionary implications of variation in this trait. Typically determination of the number, position, and effects of loci influencing quantitative traits such as development rate is accomplished by identifying quantitative trait loci (QTL) in conjunction with a genetic linkage map (Lander and Botstein 1989).
Rainbow trout provide an excellent model for the study of quantitative traits relevant to salmonid biology. They are an important species in biomedical applications as well as in the aquaculture and sportfishing industries. Homozygous clonal lines of rainbow trout are available which provide a defined genetic system in which the genetics of quantitative traits can be studied (Young et al. 1996). Recently a cross among clonal lines was used to produce a population of doubled haploid rainbow trout, which subsequently allowed the production of a genetic linkage map for the species (Young et al. 1998).
A separate clonal line of rainbow trout derived from a population in the Swanson River, Alaska, has been shown to have accelerated embryonic development relative to other clonal lines (Robison et al. 1999). The accelerated development rate of the Swanson River clone is reflective of an accelerated development rate in the source population (Robison and Thorgaard, unpublished data). This suggests that one or more QTL for development rate may be segregating among the clonal lines and that these QTL may be relevant in an evolutionary context.
The use of homozygous clonal lines for QTL analysis has two advantages. First, the ability to use a doubled haploid genetic design increases the power to detect QTL over a more traditional backcross (Carbonell et al. 1993). Second, the use of inbred clonal lines provides the ability to employ new analytical techniques such as composite interval mapping (CIM) (Zeng 1994), whereas other QTL studies in salmonids have relied upon more conventional single-marker analysis of variance (ANOVA) techniques (Jackson et al. 1998; Sakamoto et al. 1999) in a backcross design. CIM allows markers on the linkage map to be fitted as cofactors in the model, controlling for the effect of QTL outside the interval under examination. In this article we report the identification of QTL influencing time to hatch, embryonic length, and weight at swim-up in rainbow trout using CIM.
|
Materials and Methods |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Animals
Two homozygous clonal lines of rainbow trout have been previously shown to have divergent hatching times (Robison et al. 1999), suggesting the presence of segregating QTL. The clonal line derived from the Swanson River, Alaska, population of rainbow trout (SW) has an accelerated development rate relative to a clonal line derived from a domesticated rainbow trout from Oregon State University (OSU). The SW (all male YY) and OSU (all female XX) clones were crossed to produce an all male (XY) F1 generation. A sample of the largest F1 individuals was induced to spawn prior to 1 year of age by an intraperitoneal injection of 1 mg/kg body weight pituitary acetone extract (Sigma) three times a week. The sperm from these individuals was used to produce two families of androgenetic doubled haploid rainbow trout (Parsons and Thorgaard 1985). Family 1 was fertilized on December 16, 1998, and was incubated in a Heath-style incubator at a constant 10.4°C. An insufficient number of individuals (65) for a QTL analysis were generated in family 1, so a second family of doubled haploids was fertilized on March 18, 1999, which produced 224 individuals. Family 2 was incubated at a slightly higher temperature (11.6°C) due to a problem with the temperature control of the environmental chamber.
Traits
After eye pigmentation was visible in the embryos, they were transferred to 80 well incubation boxes and time to hatch was monitored as described in Robison et al. (1999). All embryos that hatched at a given time were removed and placed together in a different incubator. At the swim-up stage (when rainbow trout fry are ready to begin exogenous feeding), all of the surviving individuals of family 1 and 158 of the surviving individuals of family 2 were sacrificed and preserved in 95% ethanol. The remaining members of family 2 were placed in tanks and reserved for further experimentation. The total length of the preserved embryos (to the nearest millimeter) was measured, and wet weight was determined to the nearest tenth of a milligram with a Mettler H31AR balance.
Genetic Map
The cross used in this study (OSU x SW) differed from that used in the previously published linkage map (OSU x Arlee; Young et al. 1998), and thus a framework of evenly spaced molecular markers was not available. We therefore generated our own linkage map with the intention of aligning the results with the published map using markers segregating in both crosses.
One hundred and seventy doubled haploids (58 from family 1 and 112 from family 2) were genotyped at 222 marker loci. These loci consisted of 219 amplified fragment length polymorphism (AFLP) markers, the microsatellites Oneµ2 and Oneµ19 (Scribner et al. 1996), and an AluI polymorphism in the 3' untranslated region of the p53 gene. AFLP markers were produced following the guidelines in the Perkin Elmer Applied Biosystems AFLP Mapping Protocol for Genomes 500–6000 Mb. A total of 16 primer pairs were analyzed, producing an average of 13.7 polymorphisms per primer pair. AFLP fragments were visualized using a Cy5-labeled EcoRI primer on a Storm imaging system (Molecular Dynamics) as described by Roman et al. (1999). Previously mapped AFLP loci were named as in Young et al. (1998), with the selective nucleotides of the EcoRI primer followed by those of the MseI primer, and finally by the size in base pairs and the source of the band. All previously unmapped AFLP loci are named according to the selective nucleotides (EcoRI, MseI), followed by the order of appearance on the gel, bottom to top.
These marker loci were used to construct a genetic linkage map with MapMaker EXP version 3.1 (Lander et al. 1987) and MapMaker-II for the Macintosh (Dr. Scott Tingey, Dupont Experimental Station, Wilmington, DE). The F2 intercross genetic model used by MapMaker EXP gave very similar results to the doubled haploid genetic model employed in the Macintosh version of MapMaker. The doubled haploid genetic model in MapMaker II was used to produce the linkage map, and MapMaker EXP's error detection function was then used to identify candidate errors in the data. Markers segregating in both the OSU x Arlee cross used by Young et al. (1998) and the OSU x Swanson cross used in this study were employed to match the linkage groups produced by this study with those of the published map. Since no allozymes have been genotyped in the OSU x Swanson or OSU x Arlee mapping panels, direct comparison to the composite salmonid allozyme map (May and Johnson 1989) was not possible. Grouping of markers was initially performed at a minimum LOD of 3.0 and a maximum of 0.40. This analysis revealed two of the initial linkage groups (R7 and R17) which were larger than any seen in the published map. These linkage groups were subsequently checked at a minimum LOD of 4.0 and a maximum of 0.30, and split into subgroups which could be ordered more effectively.
QTL Analyses
Composite interval mapping (CIM; Zeng 1993, 1994) was used to scan the genome for evidence of QTL influencing development rate, embryonic length, and weight at swim-up. This was performed using model 6 in QTL cartographer (Basten et al. 1994, 1997) with a window size of 5.0 cM and five other markers used as cofactors in the model. Significance thresholds for each trait were determined by permutation tests at the 5% level (Churchill and Doerge 1994; Doerge and Churchill 1996) with 500 replicates each.
Since the two families of doubled haploids were incubated at slightly different temperatures, the significance of any QTL detected using the combined dataset was also tested in each family individually. Significance thresholds (5%) for time to hatch and length within each family were also determined using permutation tests with 500 replicates.
|
Results |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Linkage Map
The genetic linkage analysis resulted in 27 linkage groups, with 21 of the markers remaining unlinked at a LOD of 3.0 and a maximum
of 0.40 (Figure 1). The total size of the map was 1265.2 Kosambi cM, 48.2% of the estimated minimum genome size of rainbow trout (Young et al. 1998). The AFLP markers tended to cluster together, as has been observed previously in rainbow trout (Young et al. 1998) and some crops (Keim et al. 1997; Vuylsteke et al. 1997). The mean distance between adjacent markers was 7.21 cM, but when clustered markers (those separated by less than 1 cM) were not considered mean intermarker distance was 9.6 cM.
|
Of the 222 loci mapped, 9 (8 AFLP and 1 p53 locus) have been previously mapped in rainbow trout. Seven of these loci appear in the published rainbow trout map, while two (ACGAAG221a and p53) have been recently added to the published map (Young and Robison, unpublished results). These loci allow us to match our linkage groups with those published by Young et al. (1998). Eight of the 31 published linkage groups in rainbow trout can be accounted for in this manner (parenthetical roman numerals in Figure 1). Linkage group R2 contains the AFLP marker ACGAGA93a, which maps to linkage group XIV (Young et al. 1998), as well as the p53 locus, which maps to linkage group XX (Young WP and Robison BD, unpublished results). This suggests that linkage groups XIV and XX from Young et al. (1998) may be on the same chromosome.
QTL Analyses
Mean time to hatch of family 1 was 332.0 days, with a standard deviation of 20.8. Mean time to hatch of family 2 was 318.6 days with a standard deviation of 13.7. A group of OSU clones fertilized at the same time as family 2 had a mean time to hatch of 336.9 days and had a smaller standard deviation (6.1) than doubled haploids incubated at the same temperature. This is not unexpected, as the variance in time to hatch in the clonal population is due entirely to the environment, while the variance among the doubled haploids is due to the environment plus the effects of QTL segregating for time to hatch. The distributions of hatching times from the OSU clonal parent and both families of doubled haploids are shown in Figure 2.
|
Five hundred permutations of the data from both families resulted in 5% LOD significance thresholds of 2.72 for time to hatch, 2.66 for length, and 2.51 for weight. Any LOD score above 2.5 but below the 5% threshold was considered suggestive evidence for a QTL at that position. Significance thresholds for time to hatch in families 1 and 2 were 2.58 and 2.13, respectively, while thresholds for length in families 1 and 2 were 2.40 and 2.55. Significant QTL were observed on only four linkage groups—R13, R11, R9, and R6 (Figure 3). The proportion of variance explained by these QTL ranged from 4.6% to 14.7% (Table 1).
|
|
There is good evidence for two QTL influencing time to hatch in this cross, one on linkage group R13 (tthR13) and one on linkage group R6 (tthR6). In addition, there is suggestive evidence (LOD = 2.54) for a third QTL influencing time to hatch on linkage group R9 (tthR9). These three QTL accounted for a total of 24.6% of the total variance in time to hatch. The additive effects of these QTL were all negative, indicating that the allele for rapid development (early time to hatch) came from the fast developing Swanson River parent in all cases. The QTL tthR13 has a particularly strong influence on time to hatch, especially in family 2, where it explains 25.6% of the variance in time to hatch.
Two QTL influencing embryonic length were identified, one on linkage group R13 (lenR13) and one on linkage group R6 (lenR6). These two QTL accounted for a total of 22.6% of the variance in length among the doubled haploids. The additive effects of both these QTL were positive, indicating that in both cases the allele causing an increase in length came from the Swanson parent. The most likely positions for tthR13 and lenR13 are at the same location on linkage group R13, suggesting that a single QTL may be influencing both traits (Figure 3).
Two QTL influencing embryonic weight at swim-up were detected, one on linkage group R11 (wtR11) and one on linkage group R6 (wtR6). These QTL explained a total of 26.2% of the variance in weight. For wtR11, the Swanson River allele had an additive effect of -5.13, while the Swanson River allele for wtR6 had an additive effect of 6.70. On linkage group 6, both wtR6 and tthR6 were localized to the same position, again suggesting that a single QTL may be influencing both time to hatch and embryonic weight (Figure 3).
|
Discussion |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
Evidence for QTL influencing time to hatch was found on two of the 28 linkage groups, R13 and R6. The QTL with the largest effect in time to hatch, tthR13, can be placed at a previously unmapped AFLP locus (ACGAAG4) on linkage group R13, which is homologous to linkage group IX of the published rainbow trout map (Young et al. 1998). The evidence for the presence of a QTL at this location is very strong (LOD > 7.0) in the analysis of the pooled data and in the analysis of the data from family 2. In the larger of the families (family 2), tthR13 explains 25.6% of the total variance in time to hatch, suggesting that this QTL can have a major effect on embryonic development rate. Adaptation of embryonic development rate to specific temperature regimes has been indicated in several species of salmonids (Beacham 1988; Brannon 1987; Hebert 1998; Robison BD and Thorgaard GH, unpublished results; Tallman 1986), and identification of a QTL of major effect such as tthR13 will help in the study of the genetics involved in adaptation to new environments.
This study represents the first QTL analysis in fish using a doubled haploid design. Doubled haploids have been used extensively for QTL mapping in many crops, including barley (Romagosa et al. 1999; Spaner et al. 1999), wheat (Tixier et al. 1998), maize (Marhic et al. 1998), rape (Teutonico et al. 1995), and rice (He et al. 1998). The current rainbow trout linkage map (Young et al. 1998) was also produced using doubled haploids. A doubled haploid design is advantageous in that it has more power to detect QTL (Carbonell et al. 1993) than a backcross, and if required, doubled haploid lines can be maintained through successive rounds of androgenesis or gynogenesis.
The QTL with the lesser effect on time to hatch, tthR6, cannot as yet be assigned to any previously published linkage group. There is also suggestive evidence for a third QTL for time to hatch on linkage group 9, however, more individuals would have to be genotyped to verify that this QTL is real. The positions of both tth13 and tth6 are at the ends of their respective linkage groups and thus the exact positions of these QTL remain uncertain. More telomeric markers will have to be added to the map with the goal of flanking the QTL, which would allow a better estimate of position.
All of the QTL influencing time to hatch had additive effects less than zero, indicating that the alleles causing rapid embryonic development (and thus early time to hatch) came from the Swanson River clonal parent. This is consistent with the observation that both the Swanson River source population and the clone derived from this population have accelerated rates of development (Robison BD and Thorgaard GH, unpublished data; Robison et al. 1999). These data suggest that control of time to hatch is polygenic, although the large effect of tthR13 clearly shows that all loci influencing time to hatch do not have equal effects.
Two genes which may influence time to hatch in fish are phosphoglucomutase (PGM) and lactose dehydrogenase (LDH). A regulatory mutation causing liver expression of Pgm-1 has been shown to influence time to hatch in rainbow trout (Allendorf et al. 1983), presumably through a change in the rate of glycogen flux in the liver. However, neither the Swanson or the OSU clonal line show evidence of liver expression of Pgm-1 (Robison BD, unpublished data), indicating that this regulatory mutation is not segregating in the OSU x Swanson cross. LDH has been shown to influence rate of embryonic development in Fundulus (DiMichelle and Powers 1991), however, its effect on salmonid development is unknown.
Evidence for QTL influencing length at swim-up was found on the same two linkage groups carrying QTL for time to hatch, R13 and R6. The alleles causing increased length for both lenR13 and lenR6 came from the Swanson River clonal parent. The two QTL influencing length explain a total of 22.6% of the variance in length. The map positions of lenR13 and tthR13 are identical, suggesting the same QTL may have a pleiotropic effect on both traits. The current dataset is insufficient to determine if one gene underlies both these QTL or if the QTL represent two separate, but tightly linked genes.
Length of fry is genetically correlated to time to hatch in pink salmon (Beacham 1988), and the PGM regulatory mutation that influences time to hatch also caused increased growth rate in rainbow trout (Allendorf et al. 1983). The mechanism behind this relationship is unclear. Hatching time may be influenced directly by the size of the embryo, as embryos that reach a certain size earlier may have an easier time breaking through the chorion.
There was evidence for QTL influencing weight at swim-up on 2 of the 28 linkage groups, R11 and R6. The total variance explained by these QTL was 26.2%. Unlike the QTL influencing time to hatch and length at swim-up, wtR6 and wtR11 had additive effects that were opposite in sign. The Swanson River allele for wtR6 causes an average increase in weight of 6.70 mg, while the Swanson River allele for wtR11 causes an average decrease in weight of 5.13 mg. The QTL wtR6 maps to the same position as tthR6, suggesting that the same QTL may influence both traits. As in the case of tthR13 and lenR13, however, the fact that the QTL map to the end of the linkage group makes an exact position difficult to determine. No relationship between time to hatch and weight at swim-up has been previously identified in salmonids, and the mechanism determining the weight of the embryo before the commencement of exogenous feeding is not known. Differences in weight may be a result of the amount of yolk remaining, or may be a reflection of the efficiency with which yolk energy is transformed into somatic tissue during development.
The linkage map produced using the Swanson x OSU cross is 1265.2 Kosambi cM, roughly 48% of the estimated genome size of rainbow trout (Young et al. 1998). One likely reason for the reduced genome coverage of the OSU x Swanson map is the use of predominantly AFLP-type markers. AFLP markers tended to cluster together, as observed previously in rainbow trout (Young et al. 1998), corn (Vuylsteke et al. 1997), and soybeans (Keim et al. 1997). This is likely because of a centromeric bias in location of AFLP-based polymorphisms. The reduced genome coverage in this study suggests that additional QTL might be detected with the addition of more telomeric markers, such as VNTRs (Young et al. 1998) or PstI-based AFLP (Powell et al. 1997).
The evidence for QTL was evaluated using information from both families of doubled haploids simultaneously, but the fact that the families were incubated at slightly different temperatures resulted in different development rates. The significance of the QTL identified using the data from both families was therefore tested in each family independently. The QTL of largest effect on time to hatch, tthR13, had a significant effect on time to hatch in both families of doubled haploids (see Table 1). The evidence for the remaining QTL, however, did not exceed the LOD threshold in at least one of the families of doubled haploids, usually family 1. This is likely due to the reduced power to detect QTL when the number of individuals is low, as is the case in family 1. There are two other possible explanations for this observation, however. The first is that the environment may influence the effects of the QTL, resulting in a genotype x environment interaction. Given the small difference in incubation temperature, this explanation seems unlikely. Second, maternal effects such as variation in egg size may influence the effects of the QTL. More investigation into QTL x temperature and QTL x maternal environment interactions will be required to determine their role in producing the development rate phenotype.
The results presented here represent a foundation for future studies into the genetics of economically important traits in salmonids. If the effects of QTL influencing length and weight at swim-up translate into faster growth later in life (see Wu et al. 1999), then these QTL could be employed in a marker-assisted selection program. The QTL identified in this study can also be used to address important topics in the evolutionary biology of salmonids. Identification of potential genetic trade-offs has been successfully carried out using QTL data in Arabidopsis (Mitchell-Olds 1996), and there is some evidence of a trade-off between rate of embryonic development and spawning time in that earlier spawning populations of salmonids tend to develop slower than late spawning populations (Beacham 1988; Hebert 1998; Tallman 1987). The genetic architecture underlying the reaction norm of development rate can also be studied using the QTL identified here, as can the role of natural selection on time to hatch in natural populations.
The use of homozygous clones for the analysis of quantitative traits has tremendous potential. Clonal lines can be developed from source populations with adaptations important in either an economic or evolutionary context. This study demonstrates that these clonal lines can be used to map the loci underlying quantitative variation of important traits.
|
Acknowledgments |
|---|
This research was supported by the National Institutes of Health (NIEHS P01 ES04766 and BMMRP #1 RO1-RR06654). We wish to thank Krista Nichols for her help with data analysis, Paul Spruell and Kate Lindner for their assistance with the AFLP technique, and Bill Young for his assistance in constructing the linkage map. We are also indebted to Carolyn Decker for the use of the Storm imaging system. Two anonymous reviewers provided helpful comments on the manuscript.
|
Footnotes |
|---|
Address correspondence to Barrie D. Robison, Department of Biology, 1210 University of Oregon, Eugene, OR 97403, or e-mail: barrie{at}darkwing.uoregon.edu.
Corresponding Editor: Robert Angus
Received April 16, 2000
Accepted August 31, 2000
|
References |
|---|
|
TopAbstractIntroductionMaterials and MethodsResultsDiscussionReferences |
|---|
-
Allendorf FW, Knudsen KL, and Leary RF, 1983. Adaptive significance of differences in the tissue specific expression of a phosphoglucomutase gene in rainbow trout. Proc Natl Acad Sci USA 80:1397–1400.
Basten CJ, Weir BS, and Zeng ZB, 1994. Zmap—a QTL cartographer. In: Proceedings of the 5th World Congress on Genetics Applied to Livestock Production: Computing Strategies and Software, vol 22 (Smith C, Gavora JS, Benkel B, Chesnais J, Fairfull W, Gibson JP, Kennedy BW, and Burnside EB, eds). Guelph, Ontario, Canada: Organizing Committee, 5th World Congress on Genetics Applied to Livestock Production; 65–66.
Basten CJ, Weir BS, and Zeng ZB, 1997. QTL cartographer: a reference manual and tutorial for QTL mapping. Raleigh, NC: North Carolina State University.
Beacham TD, 1988. A genetic analysis of early development in pink and chum salmon at three different temperatures. Genome 30:89–96.[Medline]
Brannon EL, 1987. Mechanisms stabilizing salmonid fry emergence timing. In: Sockeye salmon (Oncorhynchus nerka) population biology and future management (Smith HD, Margolis L, and Wood CC, eds). Can Spec Publ Fish Aquat Sci 96:120–124.
Carbonell EA, Asins MJ, Baselga M, Balansard E, and Gerig TM, 1993. Power studies in the estimation of genetic parameters and the localization of quantitative trait loci for backcross and doubled haploid populations. Theor Appl Genet 86:411–416.
Churchill GA and Doerge RW, 1994. Empirical threshold values for quantitative trait mapping. Genetics 138:963–971.[Abstract]
DiMichelle L and Powers DA, 1991. Allozyme variation, developmental rate, and differential mortality in the teleost Fundulus heteroclitus. Physiol Zool 64:1426–1443.
Doerge RW and Churchill GA, 1996. Permutation tests for multiple loci affecting a quantitative character. Genetics 142:285–294.[Abstract]
Ferguson MM, Danzman RG, and Allendorf FW, 1985. Developmental divergence among hatchery strains of rainbow trout (Salmon gairdneri). I. Pure strains. Can J Genet Cytol 27:289–297.
Godin JG, 1982. Migrations of salmonid fishes during early life history phases: daily and annual timing. In: Proceedings of the Salmon and Trout Migratory Behavior Symposium (Brannon EL and Salo EO, eds), June 3–5, 1981. Seattle, WA: School of Fisheries, University of Washington; 22–50.
He P, Shen L, Lu C, Chen Y, and Zhu L, 1998. Analysis of quantitative trait loci which contribute to anther culturability in rice. Mol Breed 4:165–171.
Hebert KP, Goddard PL, Smoker WW, and Gharrett AJ, 1998. Quantitative genetic variation and genotype by environment interaction of embryo development rate in pink salmon (Oncorhynchus gorbuscha). Can J Fish Aquat Sci 55:2048–2057.
Holtby LB, McMahon TE, and Scrivener JC, 1989. Stream temperatures and inter-annual variability in the emigration timing of coho salmon smolts and fry and chum salmon fry from Carnation Creek, British Columbia. Can J Fish Aquat Sci 46:1396–1405.
Jackson TR, Ferguson MM, Danzmann RG, Fishback AG, Ihssen PE, O'Connel M, and Crease TJ, 1998. Identification of two QTL influencing upper temperature tolerance in three rainbow trout (Oncorhynchus mykiss) half-sib families. Heredity 80:143–151.[Web of Science]
Keim P, Schupp JM, Travis SE, Clayton K, and Webb DM, 1997. A high density soybean genetic map based upon AFLP markers. Crop Sci 37:537–543.
Lander ES and Botstein D, 1989. Mapping Mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics 121:185–199.
Lander ES, Green P, Abrahamson J, Barlow A, and Daly MJ, 1987. MAPMAKER: an interactive computer package for constructing primary genetic linkage maps of experimental and natural populations. Genomics 1:174–181.[Medline]
Marhic A, Antoine-Michard S, Bordes J, Pollacsek M, Murigineux A, and Beckert M, 1998. Genetic improvement of anther culture response in maize: relationships with molecular, Mendelian and agronomic traits. Theor Appl Genet 97:520–525.[Web of Science]
May B and Johnson KR, 1989. Composite linkage map of salmonid fishes (Salvelinus, Salmo, Oncorhynchus). In: Genetic maps (O'Brien, SJ, ed). Cold Spring Harbor, NY: Cold Spring Harbor; 4.151–4.159.
McIntyre JD and Blanc JM, 1973. A genetic analysis of hatching time in steelhead trout (Salmo gairdneri). J Fish Res Bd Can 30:137–139.
Mitchell-Olds T, 1996. Genetic constraints on life-history evolution: quantitative trait loci influencing growth and flowering in Arabidopsis thaliana. Evolution 50:140–145.[Web of Science]
Parsons JE and Thorgaard GH, 1985. Production of androgenetic diploid rainbow trout. J Hered 76:177–181.
Powell W, Baird W, Lawrence P, Booth A, Harrower B, McNicol JW, and Waugh R, 1997. Analysis of quantitative traits in barley by the use of amplified fragment length polymorphisms. Heredity 79:48–59.[Web of Science]
Robison BD, Wheeler PA, and Thorgaard GH, 1999. Variation in development rate among clonal lines of rainbow trout (Oncorhynchus mykiss). Aquaculture 173: 131–141.
Romagosa I, Han F, Ulrich SE, Hayes PM, and Wesenberg DM, 1999. Verification of yield QTL through realized molecular marker assisted selection responses in a barley cross. In: Molecular breeding: new strategies in plant improvement. Dordrecht: Kluwer Academic.
Roman BL, Pham VN, Bennett PE, and Weinstein BM, 1999. Non-radioisotopic AFLP method using PCR primers fluorescently labeled with Cy5. BioTechniques 26:236–238.[Web of Science][Medline]
Sakamoto T, Danzmann RG, Okamoto N, Ferguson MM, and Ihssen PE, 1999. Linkage analysis of quantitative trait loci associated with spawning time in rainbow trout (Oncorhynchus mykiss). Aquaculture 173:33–43.[Web of Science]
Scribner KT, Gust JR, and Fields RL, 1996. Isolation and characterization of novel salmon microsatellite loci: cross-species amplification and population genetic applications. Can J Fish Aquat Sci 53:833–841.
Spaner D, Rassnage BG, Legge WG, Scoles GJ, Eckstein PE, Penner GA, Tinker NA, Briggs KG, Falk DE, and Afele JC, 1999. Verification of a QTL affecting agronomic traits in two-row barley. Crop Sci 39:248–252.
Tallman RF, 1986. Genetic differentiation among seasonally distinct spawning populations of chum salmon, Oncorhynchus keta. Aquaculture 57:211–217.
Teutonico RA, Yandell B, Satagopan JM, Ferreira ME, Palta JP, and Osborn TC, 1995. Genetic analysis and mapping of genes controlling freezing tolerance in oilseed brassica. In: Molecular breeding: new strategies in plant improvement. Dordrecht: Kluwer Academic.
Tixier MH, Sourdille P, Charmet G, Gay G, Jaby C, Cadalen T, Bernard S, Nicolas P, and Bernard M, 1998. Detection of QTLs for crossability in wheat using a doubled haploid population. Theor Appl Genet 97:1076–1082.
Vuylsteke M, Antonise R, Bastiaans E, Senior ML, Stuber CW, and Kuiper M, 1997. A high density AFLP linkage map of Zea mays L. Poster abstract. The International Conference on the Status of Plant and Animal Genome Research, San Diego, CA, June 1997.
Wu WR, Li WM, Tang DZ, Lu HR, and Worland AJ, 1999. Time-related mapping of quantitative trait loci underlying tiller number in rice. Genetics 151:297–303.
Young WP, Wheeler PA, Coryell VH, Keim P, and Thorgaard GH, 1998. A detailed linkage map of rainbow trout produced using doubled haploids. Genetics 148: 839–850.
Young WP, Wheeler PA, Fields RD, and Thorgaard GH, 1996. DNA fingerprinting confirms isogenicity of androgenetically derived rainbow trout lines. J Hered 87:77– 81.
Zeng Z, 1993. Theoretical basis for separation of multiple linked gene effects in mapping quantitative trait loci. Proc Natl Acad Sci USA 90:10972–10976.
Zeng Z, 1994. Precision mapping of quantitative trait loci. Genetics 136:1457–1468.[Abstract]
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  U Grimholt, R Johansen, and A J Smith A review of the need and possible uses for genetically standardized Atlantic salmon (Salmo salar) in research Lab Anim, April 1, 2009; 43(2): 121 - 126. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Sekino and M. Hara Linkage Maps for the Pacific Abalone (Genus Haliotis) Based on Microsatellite DNA Markers Genetics, February 1, 2007; 175(2): 945 - 958. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
K. M. Nichols, K. W. Broman, K. Sundin, J. M. Young, P. A. Wheeler, and G. H. Thorgaard Quantitative Trait Loci x Maternal Cytoplasmic Environment Interaction for Development Rate in Oncorhynchus mykiss Genetics, January 1, 2007; 175(1): 335 - 347. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
R. B. Phillips, K. M. Nichols, J. J. DeKoning, M. R. Morasch, K. A. Keatley, C. Rexroad III, S. A. Gahr, R. G. Danzmann, R. E. Drew, and G. H. Thorgaard Assignment of Rainbow Trout Linkage Groups to Specific Chromosomes Genetics, November 1, 2006; 174(3): 1661 - 1670. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Z. Liu, A. Karsi, P. Li, D. Cao, and R. Dunham An AFLP-Based Genetic Linkage Map of Channel Catfish (Ictalurus punctatus) Constructed by Using an Interspecific Hybrid Resource Family Genetics, October 1, 2003; 165(2): 687 - 694. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||