Assessment of genetic variability among strains of rainbow and cutthroat trout using multilocus DNA fingerprints

Aquaculture Aquaculture 149 (1997) 47-56

Assessment of genetic variability among strains of rainbow and cutthroat trout using multilocus DNA fingerprints Yniv Palti a,*, James E. Parsons b, Gary H. Thorgaard a,c ADepurtment of Genetics and Cell Biology. Wmhington Star Unil~ersity Pullman. WA 99164-4234, USA h Blue Lakes Trout Furm Inc., P.O. Box 1237 TkbainFulls, ID 83303. USA ’ Department qfZoolop. Washington State Utzirrrsity Pulbtum. WA 99164-4236, USA

Accepted IO August 1996

Abstract DNA fingerprint banding patterns of mixed DNA samples from 17 to 28 individuals from five strains of rainbow trout (Oncorh_~chus m~$&s) and two strains of cutthroat trout (Oncorh~nchus clarki) were analyzed to quantify genetic differences among the seven populations. Levels of within-population genetic variability were estimated by comparing DNA fingerprint banding patterns of individuals. Three multilocus oligonucleotide probes were used to detect the DNA fingerprints of individuals and mixtures. Scanning image analysis and customized software programs were used to assign band identity and to determine the degree of band-sharing between and within populations. The distinct genetic differences we identified between the Yellowstone cutthroat (0. c. houcien’) strain and the westslope cutthroat (0. c. lewisi) strain, and between the two cutthroat subspecies and the five rainbow trout strains, are consistent with previous studies. Lower genetic variabilities were observed within the cutthroat trout strains, which is consistent with their previously reported lower heterozygosities at protein loci. Varying levels of genetic variability were identified within the rainbow trout strains. and the low level of variability detected among individuals from one of the strains is probably associated with previously observed symptoms of inbreeding depression in that strain. Our results suggest that multilocus DNA markers can be best utilized in studies of genetic variability among closely related populations and in breeding programs. Krvvords:

Multilocus DNA fingerprints:

~ Corresponding

Genetics;

author. Tel.: (509) 335-1526;

0044.8486/97/$17.00 Copyright PII SOO44-8486(96)01428-7

RFLP: Trout; Oncorhynchus

Fax: (509) 335-1907;

nt>!&s; Oncor!-q,~chm c/or/G

E-mail: Ypaltiewsunix.wsu.edu

0 1997 Elsevier Science B.V. All rights reserved.

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1. Introduction

Genetic variation is the basic resource for any successful animal breeding program. The loss of genetic variation resulting from inbreeding is generally associated with the loss of general vigor and fertility, and the effects of inbreeding depression in hatchery stocks are well documented (e.g. Allendorf and Phelps, 1980; Gall, 1987). Since its development in the mid- 1960s protein electrophoresis has been the method of choice to estimate levels of genetic variability between and within populations of salmonid fishes (Utter et al., 1987), but it does not detect as much variability as do some DNA markers (Utter et al., 1987; Guyomard, 1993). Highly variable, multi-band RFLP patterns, “fingerprints”, detected by DNA probes were first reported in humans (Jeffreys et al., 1985). Since then DNA fingerprints have been reported for a variety of organisms, including salmonid fishes (Fields et al., 1989; Lloyd et al., 1989; Taggart and Ferguson, 1990; Bentzen et al., 1991; Prodohl et al., 1992; Heath et al., 1994, 1995a,b; Spruell et al., 1994; Taylor, 1995). Multilocus DNA fingerprints of salmonids are complex and their objective analysis is difficult (Bentzen et al., 1991; Spruell et al., 1994; Heath et al., 1995a). Comparing DNA fingerprint patterns of individuals located a distance from each other on the same gel or on different gels increases that difficulty (Hillel, 1992). Such obstacles may be overcome by computer analysis of the banding patterns (Spruell et al., 1994) and by the use of DNA mixes to facilitate the comparison of different populations (Dunnington et al., 1990; Spruell et al., 1994). Single locus markers typically reveal banding patterns which are much easier to analyze (Taggart and Ferguson, 1990; Bentzen et al., 1991; Heath et al., 1994, 1995b; Taylor, 1995) but considerable effort must be invested to obtain and analyze sufficient numbers of such markers to provide useful data. Initial observations suggested that cutthroat trout fry from Henry’s Lake, Idaho, and hybrids of rainbow and Henry’s Lake cutthroat trout, are significantly less susceptible than a typical rainbow trout strain to IHN virus infection (J. Parsons, unpublished data, 1995). DNA mixing and subsequent fingerprinting might be applied to identify DNA markers linked to IHNV resistance in a gene introgression breeding approach. This would involve screening for markers associated with resistance in the progeny of hybrids backcrossed to rainbow trout and could greatly increase the efficiency of selection in the backcrosses. A second benefit of fingerprinting in the backcross generations could be selection against the unwanted (cutthroat) genome among resistant individuals because the proportion of each of the two genomes will vary around the expected values (Hillel, 1992). The primary goals of this study were to: (1) quantify DNA fingerprint banding pattern differences between rainbow trout and cutthroat trout; (2) estimate levels of genetic variability within various strains of rainbow and cutthroat trout by comparing DNA fingerprint banding patterns of individuals; and (3) assess the feasibility of using multilocus DNA fingerprints for genetic variability estimates and population analyses by comparing our results to those obtained by protein electrophoresis in similar studies (e.g. Allendorf and Leary, 1988).

Y. Palti et al./Aquaculture

2. Materials

149 (19971 47-56

49

and methods

2. I. Fish strains, sample collection

and preparation

Tissue samples (fin clips) were transported to Washington State University in 95% ethanol with the exception of the Oregon State University semen samples which were delivered on ice. Rainbow trout fin clips were collected from: (1) University of Washington School of Fisheries in Seattle, Washington; (2) Mt. Shasta hatchery of the California Department of Fish and Game; (3) Oregon State University Food Toxicology and Nutrition Laboratory hatchery in Corvallis, Oregon; (4) Blue Lakes Trout Farm Inc. located near Twin Falls, Idaho; and (5) Clear Springs Foods located near Buhl, Idaho. Cutthroat trout samples were collected from: (1) Henry’s Lake hatchery of the Idaho Department of Fish and Game; and (2) Colville hatchery of the Washington Department of Fish and Wildlife. The Colville strain originated from fish from Priest Lake, Idaho (Bob Johns, Washington State Department of Fish and Wildlife, personal communication, 199.5). The Oregon State University strain originated from the Mt. Shasta strain (Hendricks, 1982). All samples were collected between May and August 1994. Genomic DNA was extracted following the proteinase K-SDS digestion protocol described by Sambrook et al. (1989). DNA samples from individuals were quantified by fluorometry (Hoefer Scientific Instruments, model TKO 100) and digested to completion with HaeIII restriction enzyme (Gibco BRL). DNA mixes were prepared by mixing equal weights of requantified digested DNA samples for each population; 17-28 individuals from each population were included in the mixes (Table 1).

2.2, DNA fingerprinting

techniques

For each lane 2.5 pg of digested DNA was electrophoresed in 1 X TAE at 50V for 24h in 0.8% agarose to analyze DNA fingerprint patterns of individuals from each

Table I Number of individual fingerprint analysis

rainbow

and cutthroat

trout pooled

in each of the mixed DNA samples

Population

Number of individuals

Colville (cutthroat) Henry’s Lake (cutthroat) UW 1 a (rainbow) UW2 A (rainbow Mt. Shasta (rainbow) OSU (rainbow) Blue Lakes (rainbow) Clear Springs (rainbow)

21 20 14 14 28 20 20 17

’ In each of the two UW mixes there is a representative

of each of the 14 families sampled.

for DNA

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population, or for 22 h in 1% agarose for mixed DNA fingerprint analysis. Electrophoresis was conducted at 14°C and the buffer was recirculated (Super-Sub, Hoefer Scientific Instruments). DNA was transferred to a nylon filter (Magnagraph, MSI) by capillary transfer in 10 X SSC. Oligonucleotides based on a sequence related to the Drosophila Per gene (Shin et al., 1985) and a GATA tetranucleotide repeat were prepared as described by Spruell et al. (1994). Additional pre-labelled probes based on the trinucleotides ATC and CAC were purchased from FMC BioProducts. FMC probes are 25-35 bases long (n repeats, Gastier et al., 1995). The probes were hybridized to the nylon filters as described by Spruell et al. (1994) with the following modifications: (1)

Kb

12 -->

.._, --> .__>

-->

.__>

2 -->

Co1 Ct

Wl

W2

MS OS RT CS

lib

<-- 12

<-<-<--

<--

<-- 3

<-- 2

Fig. 1. DNA fingerprints of eight mixed DNA samples from two cutthroat trout and five rainbow trout populations as detected by the ATC probe. From right to left: Colville cutthroat trout (Cal); Henry’s Lake cutthroat trout (Ct); University of Washington rainbow trout mix 1 (Wl); University of Washington rainbow trout mix 2 (W2); Mt. Shasta rainbow trout (MS); Oregon State University rainbow trout (OS); Trout Lodge rainbow trout (RT) and Clear Springs rainbow trout (CS). Molecular weights arc given in kilobase pairs.

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51

the temperature of hybridization was 50°C for Per, GATA and ATC, and 56°C for CAC; (2) the concentration of the FMC probes was 2 p16ml--’ of hybridization solution. DNA fingerprints were visualized as described by Spruell et al. (1994). Nylon filters were stripped and reprobed as described by Spruell et al. (1994). 2.3. Computer analysis qf,fingerprint patterns The DNA fingerprint patterns were analyzed using the NCSA GelReader program (Redman and Jacobs, 1991) and a customized software program as previously described index (BS) to (Spruell et al., 1994; Thorgaard et al., 1995). We used a band-sharing quantify levels of genetic relatedness between and within strains. The index was calculated as BS = 2N,,/(N, + Nb) (Wetton et al., 1987) where Nab is the number of shared bands, N, is the number of bands in one lane, and N,, is the number of bands in the other lane. The bands included in the band-sharing index calculations were between 2 and 12 kb for the mixes and between 3 and 12 kb for the individuals. Bands which

Oregon State University (RI)-ATC

12.216-> x. 11.198-> I

<-12,216 ' c-11,198

10.180-> *

1-10.180

9.162s> 'L

<-9.162 c-8.144 c-7.126 <-6,108

q-4.072

Fig. 2. DNA fingerprints of 17 rainbow trout individuals from the Oregon State University Food Toxicology and Nutrition Laboratory hatchery as detected by the ATC probe. The two flanking lanes and the tenth lane from left are molecular weight standards. Molecular weights are given in base pairs.

52

Y. Palti et al./Aquaculture

149 (1997) 47-56 Henry’s

Lake

Colville UWl ~

uw2 Mt Shasta osu Blue Lakes Clear Springs

I’

1

0.5

0.4

’

I 0.3

I

’

0.2

’

I 0.1

1

’

0.0

DFP Difference

Fig. 3. Cluster diagram using UPGMA based on average percentage DNA fingerprint (DFP) difference estimates for mixed DNA samples from each of the rainbow trout and cutthroat trout populations. The diagram is based on the data obtained by the three probes used for visualizing the DNA fingerprint patterns (Per, GATA and ATC). Colville and Henry’s Lake are westslope and Yellowstone cutthroat trout populations, respectively, and the other six are rainbow trout populations. UWl and UW2 are the two University of Washington mixes, and OSU represents the Oregon State University stock.

were outside the range of the molecular weight markers of the 1 kb standard (Gibco BRL) were excluded because we have found GelReader to be less accurate in assigning molecular weights to those bands. Estimates of DNA fingerprint difference between samples of mixed DNA representing the trout populations were calculated by subtracting the band sharing value between

Table 2 DNA fingerprint difference estimates a based on DNA fingerprint of the seven populations b Population

Colville Henry’s Lake UWl uw2 Mt. Shasta osu Blue Lakes

patterns of mixed DNA samples from each

Population Henry’s Lake

UWl

UW2

Mt. Shasta

osu

Blue Lakes

Clear Springs

0.73

0.70 0.66

0.66 0.67 0.20

0.61 0.69 0.65 0.65

0.67 0.63 0.58 0.55 0.50

0.70 0.67 0.65 0.56 0.61 0.61

0.65 0.73 0.67 0.63 0.54 0.67 0.41

a Estimate of difference is calculated by subtracting the index of band sharing derived from comparison of DNA fingerprint pattern from 1.00 (see text). Colville and Henry’s Lake are westslope and Yellowstone cutthroat trout populations, respectively, and the other five are domesticated rainbow trout populations. ’ Estimates of DNA fingerprint difference were calculated from a combined presence-absence matrix of the three probes, Per, GATA and ATC (see methods for description).

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149 (1997147-56

every pair of populations from 1.00 (Thorgaard et al., 199.5). Estimates of genetic variability within populations were based on the average band-sharing value calculated for each pair of individuals on the same gel. The quantity of DNA extracted from the Clear Springs samples was insufficient for analysis of band-sharing between individuals from this strain. A presence-absence matrix composed of the three presence-absence matrices produced from hybridizing the nylon filter of the mixed DNA samples with ATC, GATA and Per was prepared (Thorgaard et al., 1995) and a binary, unrooted tree of relationship among the fish strains was prepared based on the DNA fingerprint difference values (Table 2) calculated from this presence-absence matrix. UPGMA cluster analysis was conducted as previously described (Thorgaard et al., 1995). 3. Results The ATC, Per and GATA probes produced readable banding patterns for the mixed DNA samples (e.g. Fig. l), and the ATC, CAC and GATA produced patterns with good resolution for the DNA fingerprints of individuals (e.g. Fig. 2). The average number of bands per mixed DNA sample was 30 with ATC and 20 with the Per and GATA probes. For individuals, the averages detected with the ATC, CAC and GATA probes were 32, 28 and 16, respectively.

Table 3 Level of genetic variability within strains estimated by average band-sharing DNA fingerprint patterns of each pair of individuals in the same gel Species

Stock

indices derived by comparison

Probe

Cutthroat (westslope)

Colville

Cutthroat (yellowstone)

Henry’s Lake

Rainbow

Mt. Shasta 1 ’

Rainbow

Mt. Shasta 2 ’

Rainbow

osu

Rainbow

Blue Lakes

Rainbow

UW 1’

Rainbow

UW2’

*

of

Nb

ATC A

CAC a

GATA a

0.592 (* 0.097) 0.602 (rtO.08) 0.493 (+0.106) 0.561 (*0.082) 0.602 (+O.OSS) 0.573 (*0.10) 0.561 ( f 0.075) 0.455 ( + 0.094)

0.564 (+0.15) 0.62 1 (+ 0.07) 0.506 (+0.132) 0.504 (*0.122) 0.603 (+0.087) 0.558 ( f 0.087) 0.522 ( f 0.09) 0.522 (&O.ll)

0.503 ( f 0.097) 0.477 (+0.15) 0.397 (+0.105) 0.459 ( & 0.098) 0.569 t&0.095) 0.46 1 (&0.107) 0.389 (*0.104) 0.345 (+0.116)

17 17 14 I4 17 17 14 14

d Figures represent mean BS ( + SD). b N = number of individuals sampled from each population. ’ We generated two gels for both Mt. Shasta and University of Washington (UW) stocks. DNA from individuals was analyzed in each gel. ’ All of the Oregon State University (OSU) DNA samples were obtained from males.

14

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As expected, the two University of Washington mixes (UWl and UW2) clustered tightly (Fig. 3) as they are a profile of the same 14 families. The Yellowstone cutthroat (Henry’s Lake) and the westslope cutthroat (Colville) appeared as distinct from each other as they are from the cluster of rainbow trout strains. Among the rainbow trout strains, Blue Lakes clustered with Clear Springs and Mt. Shasta clustered with the Oregon State University (OSU) strain. The two cutthroat trout strains and the OSU rainbow trout strain had higher levels of within-population band-sharing than the other rainbow trout strains (Table 3). The Mt. Shasta and University of Washington strains had lower levels of within-population band-sharing and the Blue Lakes strain displayed an intermediate level of within-population band-sharing.

4. Discussion Notable differences were detected among DNA fingerprint patterns of the mixed DNA samples from five domesticated rainbow trout strains and two cutthroat trout strains. The distinct difference between the Yellowstone cutthroat strain (Henry’s Lake) and the westslope cutthroat strain (Colville), and between the two cutthroat subspecies and the five rainbow trout strains, is consistent with previous studies of genetic relationships among those groups (Loudenslager and Thorgaard, 1979; Leary et al., 1987; Allendorf and Leary, 1988). The clustering of the OSU and Mt. Shasta stocks is consistent with the derivation of the OSU stock from the Mt. Shasta stock (Hendricks, 1982). The clustering of the Clear Springs and the Blue Lakes strains suggests that the two aquaculture strains from southern Idaho were established by gametes from similar sources. The detection of large numbers of RFLPs in both cutthroat and rainbow trout and the differences in DNA fingerprint banding patterns between cutthroat trout from Henry’s Lake and the rainbow trout strains examined in this study suggest that DNA marker(s) associated with the improved resistance of cutthroat trout to IHNV may be identified. This would involve screening for markers associated with resistance in the progeny of hybrids backcrossed to rainbow trout and could greatly increase the efficiency of selection in the backcrosses. The numerous multilocus RFLP probes available for salmonids (Fields et al., 1989; Lloyd et al., 1989; Taggart and Ferguson, 1990; Bentzen et al., 1991; Prodohl et al., 1992; Spruell et al., 1994; Heath et al., 1995a; Thorgaard et al., 1995) make this large scale screening feasible. The band-sharing levels detected using multilocus RFLPs in this study provide useful insights into within-population levels of genetic variability. The lower level of genetic variability we observed within the two cutthroat strains is consistent with the previously observed low level of heterozygosity of protein loci within Yellowstone and westslope cutthroat trout populations (Allendorf and Leary, 1988). Similarly, the low level of variability detected among individuals from the OSU stock is consistent with previously observed signs of inbreeding depression (J.D. Hendricks, Oregon State University, personal communication, 1996). In contrast with the low level of genetic variability previously detected among individuals from the University of Washington “Donaldson (Allendorf and Utter, 19791, we observed a strain” using protein electrophoresis

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5.5

relatively low level of DNA fingerprint band-sharing within this strain. This discrepancy may be partially attributed to outcrossing of the original Donaldson strain with strains chosen for specific traits since the previous protein study, although less than 25% of the current strain’s genome is derived from these other strains. (W.K. Hershberger, University of Washington School of Fisheries, personal communication, 1996). DNA fingerprints of mixed DNA samples can be useful for assessing relationships between closely related populations because of the high level of genetic differentiation detected by this method. However, the analysis of mixed samples collected randomly from the populations of interest is complicated by the increase in the background “noise” and band diffusion in the mixed samples. The mixing approach can be particularly useful in breeding programs where inbred lines or full-sib families are studied because of improved resolution of banding patterns (Dunnington et al., 1990; Plotsky et al., 1993). Multilocus RFLP probes should also have applications in selection programs because of the high ratio of markers per unit of effort that they provide. Many PCR-generated single locus markers have become available recently for salmonids (e.g. McConnell et al., 199Sa,b; Morris et al., 1996); such markers should also be very useful in assessment of genetic variability and in breeding programs.

Acknowledgements This project was primarily supported by the USDA/ SBIR grant number 94-3361001 13 and partially supported by the NIEHS grant number POl-ES04766. We thank Derek Weaver for tracing visible DNA fingerprint bands onto a transparent overlay.

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