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Genetics and Molecular Biology

Print version ISSN 1415-4757

Genet. Mol. Biol. vol.33 no.3 São Paulo  2010

http://dx.doi.org/10.1590/S1415-47572010000300032 

EVOLUTIONARY GENETICS
SHORT COMMUNICATION

 

Analysis of the association between spawning time QTL markers and the biannual spawning behavior in rainbow trout (Oncorhynchus mykiss)

 

 

Nelson ColihuequeI; Rosy CárdenasI; Lorena RamírezI; Francisco EstayII; Cristian AranedaIII

IDepartamento de Ciencias Básicas, Universidad de Los Lagos, Osorno, Chile
IIPiscicola Huililco Ltda., Centro Ojos del Caburgua, Pucón, Chile
IIIDepartamento de Producción Animal, Facultad de Ciencias Agronómicas, Universidad de Chile, Santiago de Chile, Chile

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ABSTRACT

The rainbow trout is a salmonid fish that occasionally exhibits broodstocks with biannual spawning behavior, a phenomenon known as a double annual reproductive cycle (DARC). Spawning time quantitative trait loci (SPT-QTLs) affect the time of the year that female rainbow trout spawn and may influence expression of the DARC trait. In this study, microsatellite markers linked and unlinked to SPT-QTLs were genotyped to investigate the underlying genetics of this trait. SPT-QTLs influenced the DARC trait since in two case-control comparisons three linked markers (OmyFGT12TUF, One3ASC and One19ASC) had significant levels of allelic frequency differentiation and marker-character association. Furthermore, alleles of One3ASC and One19ASC had significantly higher frequencies in populations that carried the DARC trait.

Key words: association analysis, biannual spawning, microsatellite markers, rainbow trout.


 

 

Some rainbow trout (Oncorhynchus mykiss) broodstocks spawn twice a year, an unusual phenomenon known as the double annual reproductive cycle (DARC) or biannual spawning behavior (Hume, 1955; Aida et al., 1984; Gall and Crandell, 1992). The two spawnings occur at regular intervals of approximately six months: the first during a normal reproductive cycle and the second during an additional reproductive cycle. Only a fraction of the females that spawn during the normal cycle experience a second spawning (Aida et al., 1984). Broodstocks that carry the DARC trait have been the subject of various reproductive studies (Aida et al., 1984; Lou et al. 1984; Tazaki et al., 1993; Takano et al., 1995), although the underlying genetics of this trait remain largely unknown. Another reproductive trait possibly related to DARC in rainbow trout is known as spawning time (SPT) (Siitonen and Gall, 1989). This trait influences the time of year that females spawn and is controlled by numerous quantitative trait loci (QTLs) (Sakamoto et al., 1999; Fishback et al., 2000; O'Malley et al., 2003). Several markers closely linked to these chromosomal segments have been described. We propose that the underlying genetics of the DARC character in rainbow trout is similar to that of the SPT trait since both are related to the time of year when breeders spawn. To test this hypothesis, we undertook a marker-character association analysis for the DARC trait based on a panel of microsatellite markers closely linked to SPT-QTLs in rainbow trout.

Two broodstocks, Wytheville 02 (Wt-02, n = 52) and Wytheville 05 (Wt-05, n = 28) with a DARC trait frequency of 14%-35%, were used. The control stock, Steelhead (Sh, n = 35), had no DARC trait. These broodstocks were obtained from Piscicola Huililco Ltda., a commercial fish hatchery in southern Chile (39°28'04" S, 71°49'56" W). The DARC character was detected in this hatchery in 2001 in specimens that displayed this trait spontaneously. In these individuals, the DARC trait was characterized by a normal reproductive cycle (March-July; spring spawning) and an additional reproductive cycle (September-December; spring spawning). Blood samples were collected from a caudal vein and DNA was extracted by the phenol-chloroform method, as previously described (Taggart et al., 1992).

Five microsatellite markers linked to SPT-QTLs (OmyFGT12TUF, One3ASC, One19ASC, One112ADFG and Ssa103NVH) and four microsatellite markers not linked to these chromosomal regions (OmyFGT14TUF, OmyFGT15TUF, Omy27DU, Omy207UoG) were used (Table 1). The selected linked markers belonged to three different linkage groups in which a strong effect of QTLs on the SPT trait has been observed with significant association (p < 0.05) (Sakamoto et al., 1999; Fishback et al., 2000; O'Malley et al., 2003): One19ASC in linkage group OA-XXIV, One3ASC and Ssa103NVH in linkage group OA-XIX and One112ADFG in linkage group OA-VIII (Nichols et al., 2003) (Figure 1). The selected unlinked markers belonged to linkage groups that were different from those of the selected linked markers (OmyFGT14TUF in linkage group OA-X, Omy27DU in linkage group OA-II and Omy207UoG in linkage group OA-VIII) (Sakamoto et al., 1999, 2000; O'Malley et al., 2003) in which no association with SPT-QTL has been reported (Sakamoto et al., 1999; O'Malley et al., 2000). OmyFGT15TUF was considered to be unlinked since although it maps in the linkage group OA-III where a SPT-QTL exists (Sakamoto et al., 1999) there was no significant association with this QTL. The microsatellite markers were genotyped by electrophoresis in 6% polyacrylamide 7 M urea gels after amplification by PCR. The PCR mix consisted of 1 x Taq polymerase buffer, 0.13-0.28 μM of dNTPs, 1.3-2.5 mM MgCl2, 0.26-0.4 μM of each primer, 0.02 U of Taq polymerase/μL (Invitrogen) and 40 ng of template DNA/μL in a final volume of 15 μL. Amplicon size was determined by using a 25 bp DNA standard. The thermal profiles were standardized for each microsatellite based on the annealing temperature of the corresponding primer pair.

 

 

The extent of genetic association was assessed by determining the degree of: a) interpopulation genetic differentiation based on differences in the allele frequency using the Fisher exact test, with a Markov Chain Monte Carlo approach that provided an estimate of the exact probability (Raymond and Rousset, 1995), b) interpopulation genetic divergence, using the Wright (1965) FST and Nei (1972) Ds genetic distance indexes, and c) marker-trait associations using the LD statistic (Choulakian and Mahdi, 2000; Araneda et al., 2009). Further analysis assessed and corrected the population stratification (Pritchard and Rosenberg, 1999; Devlin and Roeder, 1999). The latter analysis served to identify possible spurious associations generated by stratification of the samples and was based on the use of unlinked markers to calculate the lambda factor (λ mean); this factor was subsequently used to correct the statistical significance of the linked marker through the χ2 value in a contingency test. All genetic analyses were done using GDA version 1.1 (Lewis and Zaykin, 2001) and TFPGA version 1.3 (Miller, 1997) software packages. Map positions for markers linked to SPT-QTLs were drawn using MapChart software version 2.1 (Voorrips, 2002).

Table 2 summarizes the results of the foregoing analyses. Comparison of Wt-02 with Sh (comparison 1) and Wt-05 with Sh (comparison 2) stocks revealed four linked microsatellites (OmyFGT12TUF, One3ASC, One19ASC and One112ADFG) with significant allelic differentiation (p < 0.05) in the Fisher exact test. In addition, two unlinked markers (OmyFGT15 and Omy207UoG) also showed significant allelic differentiation. The linked markers showed higher genetic divergence than those without allelic heterogeneity (comparison 1: Ds = 0.039-0.555 vs. 0.022-0.144, FST = 0.015-0.111 vs. 0.012-0.026; comparison 2: Ds = 0.054-0.847 vs. 0.070-0.077, FST = 0,024-0.149 vs. 0.025-0.039). Association analysis (LD) was only significant (p < 0.0002) for microsatellites linked to SPT-QTLs, two each in the first (OmyFGT12TUF and One3ASC) and second (OmyFGT12 and One19ASC) comparisons. These markers had alleles with a significantly higher representation in one of the two populations in each comparison, particularly the 175 bp allele of OmyFGT12 (Wt-02 = 17.1% vs. Sh = 66.7%; Wt-05 = 20% vs. Sh = 66.7%), the 203 bp allele of One3ASC (Wt-02 = 43.8% vs. Sh = 2.1%) and the 127 bp allele of One19ASC (Wt-05 = 63% vs. Sh = 18%) (Figure 2). Evaluation of comparisons 1 and 2 using the four unlinked markers showed that both comparisons had a significant level of stratification (comparison 1: χ2 = 55.346, DF = 25, p < 0.05; comparison 2: χ2 = 66.912, DF = 20, p < 0.05). The stratification correction obtained by applying the lambda factor (λ mean, calculated according to Devlin and Roeder (1999)) showed that two linked markers in comparison 1 (One3ASC and One112ADFG) and one linked marker in comparison 2 (One19ASC) were significantly associated with the DARC trait (p < 0.05) (Table 3). In this correction, an unlinked marker with high allelic frequency differentiation (Omy207UoG) was excluded to avoid compromising the corrective capacity of the method (Shmulewitz et al., 2004).

 

 

 

 

 

 

These results support the hypothesis that SPT-QTLs influence the DARC trait in rainbow trout. The QTLs would be those mapped in linkage groups OA-VIII, OA-XIX and OA-XIV of this species, based on information available for the markers linked to these chromosomal regions (Sakamoto et al., 1999; O'Malley et al., 2003). Further studies involving additional markers, as well as case-control groups without selection bias or stratification, are required to assess the association between microsatellites linked to SPT-QTLs and the DARC trait.

Other strategies that could help to clarify the underlying genetics of the DARC trait include a search for candidate genes (Lam, PhD thesis, Universidad de Chile, Santiago de Chile, 2009) and the mapping of QTLs responsible for expression of the trait using backcrosses in experimental populations. Both of these strategies are currently being used in our laboratory and should provide data that will improve our understanding of the genetics of DARC in rainbow trout.

 

Acknowledgments

The authors thank Susan Angus for translating the manuscript. This study was financed by FONDECYT Project no. 1060623.

 

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Internet Resources

GDA software, http://lewis.eeb.uconn.edu/lewishome/software.html (October 25, 2009).         [ Links ]

MapChart software, http://www.biometris.wur.nl/uk/Software/MapChart/ (December 4, 2010).         [ Links ]

TFPGA software, http://herb.bio.nau.edu/~miller (October 25, 2009).         [ Links ]

 

 

Send correspondence to:
Nelson Colihueque
Departamento de Ciencias Básicas
Universidad de Los Lagos, P.O.
Box 933, Osorno, Chile
E-mail: ncolih@ulagos.cl.

Received: December 21, 2009; Accepted: May 10, 2010.

 

 

Associate Editor: Fábio de Melo Sene
License information: This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.