A comparison of variability and population structure for major histocompatibility complex and microsatellite loci in California coastal steelhead (
Oncorhynchus mykiss
Walbaum)
ANDRES AGUILAR and JOHN CARLOS GARZA
NOAA Southwest Fisheries Science Center, 110 Shaffer Road, Santa Cruz, CA 95060, USA, and Department of Ocean Sciences, University of California, 110 Shaffer Road, Santa Cruz, CA 95060, USA
Abstract
The major histocompatibility complex (MHC) contains genes integral to immune responsein vertebrates. MHC genes have been shown to be under selection in a number of vertebratetaxa, making them intriguing for population genetic studies. We have conducted a surveyof genetic variation in an MHC class II gene for steelhead trout from 24 sites in coastalCalifornia and compared this variation to that observed at 16 presumably neutral micros-atellite loci. A high amount of allelic variation was observed at the MHC when comparedto previously published studies on other Pacific salmonids. Elevated nonsynonymoussubstitutions, relative to synonymous substitutions, were detected at the MHC gene,indicating the signature of historical balancing selection. The MHC data were tested forcorrelations to and deviations from the patterns found with the microsatellite data. Estimatesof allelic richness for the MHC gene and for the microsatellites were positively correlated,as were estimates of population differentiation (
F
ST
). An analysis for
F
ST
outliers indicatesthat the MHC locus has an elevated
F
ST
relative to the neutral expectation, although asignificant result was found for only one particular geographical subgroup. Relativelyuniform allele frequency distributions were detected in four populations, although thisfinding may be partially due to recent population bottlenecks. These results indicate that,at the scale studied here, drift and migration play a major role in the observed geographicalvariability of MHC genes in steelhead, and that contemporary selection is relatively weakand difficult to detect.
Keywords
:
F
ST
outlier, major histocompatibility complex (MHC), microsatellites, selection, steelhead
Received 16 July 2005; revision accepted 14 November 2005
Introduction
The major histocompatibility complex (MHC) is a multigenefamily found in vertebrates that contains genes codingfor cell surface glycoproteins (Klein 1986). These MHCmolecules are responsible for the presentation of antigensto cells of the immune system, and are critical in any cell-mediated immune response. Class I and class II MHCmolecules differ in structure and in the origin of theantigen that is presented, with class I molecules presentingendogenous antigens and class II proteins presenting
exogenous antigens (Klein 1986). The peptide-bindingregion, the portion of the MHC molecule that is responsiblefor the presentation of antigens to the immune system,displays many features consistent with balancing selection.Functional genes that encode the peptide-binding regiondomains exhibit increased levels of nonsynonymous sub-stitutions, high allelic diversity, elevated heterozygosity, andretained ancestral polymorphisms (Hughes & Nei 1989;Hedrick 1994; Klein
et al
. 1998). The main selective forcehypothesized to be acting upon MHC genes is pathogen-mediated selection (Klein & O’hUigin 1994). There is alsoevidence that relatedness-based olfactory recognition ofMHC genotype is possible (Penn & Potts 1999). Subsequentmate choice, and other behaviours involving kin
Correspondence: Andres Aguilar, Fax: 1(831) 420 3977, E-mail:[email protected]
discrimination, may also be an important selective force(Rajakaruna
et al
. 2001; Olsen
et al
. 2002).MHC genes have received much attention from evolu-
tionary geneticists, as they are considered a prime exampleof the effects of balancing selection (Bernatchez & Landry2003; Garrigan & Hedrick 2003). Most population studiesshow high levels of MHC variability (Meyer & Thomson2001), even in species/populations that have undergonepast or recent population declines (e.g. Garza 1998; Aguilar
et al
. 2004). Testing for the effects of selection upon theMHC in natural populations has proven to be difficult.Within-population balancing selection can lead to relativeuniformity in allele frequency distributions (Watterson 1978),but large population sample sizes are needed to adequatelyestimate allele frequencies and test for such departures fromneutral expectations. Many studies have tested for allelefrequency uniformity, but there have generally been noconsistent results across and within studies. Geographicallyvarying directional selection, another form of balancingselection, can lead to increases in measures of populationdifferentiation relative to some neutral background (Luikart
et al
. 2003; Storz 2005). Likewise, selection for heterozy-gotes (overdominance) across many populations cangenerate a decrease in population differentiation for theaffected loci (Schierup
et al
. 2000). The difficulties in detect-ing selection at the MHC can be compounded by multipleselective forces that act simultaneously and at differentgeographical and evolutionary scales, as well as the pres-ence in many species of multiple copies of particular MHCgenes (Sato
et al
. 1998), some of which are pseudogenes(e.g. Hess
et al
. 2000).The detection of genes that have experienced recent
natural selection using measures of population differenti-ation has a long history in population genetics (Lewontin& Krakauer 1973).
F
ST
, or another measure of geneticdistance, is calculated for multiple genes and those genesidentified as outliers in the distribution are suspected ofbeing subject to selection. Of critical importance to theso-called
F
ST
outlier approach is a thorough estimation ofmeasures of population differentiation at ‘neutral’ genes inthe same populations. Since Lewontin & Krakauer (1973)first proposed a method to test for selective effects onspecific loci, there have been many advances in the
F
ST
outlier approach (Beaumont 2005; Storz 2005). Beaumont &Nichols (1996) have developed a simulation-based methodto identify
F
ST
outliers. This method uses estimated
F
ST
measures for a set of populations and a simulation-basedrejection algorithm to evaluate departures from neutralityfor loci with similar heterozygosity. This method has seldombeen used on MHC population genetic data and may beuseful in identifying geographically varying directionalselection acting upon the MHC.
Within-population tests for selection have receivedmore attention, with Watterson’s (1978) homozygosity test
perhaps the most well known. Deviations from neutralityare evaluated with an exact test (Slatkin 1994, 1996). Suchwithin-population tests for selection are usually appliedwith assumptions such as mutation–drift or migration–drift equilibrium.
Studies of MHC genes in fish populations have shownthat measures of population differentiation (e.g.
F
ST
) candiffer substantially from those estimated with neutralgenetic markers (Bernatchez & Landry 2003). In addition,geographical patterns of MHC variation in the Gila top-minnow have been used to identify units for conservation(Hedrick
et al
. 2001). Research on MHC genes in salmonhas shown that, like generally neutral microsatellites, theycan also be useful markers for estimating populationstructure parameters (Kim
et al
. 1999). A large study of MHCvariation in sockeye salmon (
O. nerka
) found evidence forgeographical heterogeneity in selection on the class IIB gene(Miller
et al
. 2001). Work on Atlantic salmon (
Salmo salar
)has shown that geographical scale can be an importantfactor in the detection of the potential signature of selectionin MHC genes (Landry & Bernatchez 2001).
Coastal steelhead trout (
Oncorhynchus mykiss
) are the mostwidely distributed Pacific salmonid, inhabiting freshwaterstreams from southern California to Russia, though theyhave been introduced around the world (Behnke 1992).Compared with other Pacific salmon, they possess morevariable life history traits. Steelhead are often iteroparous,and there is variation in age of reproduction, spawn timing,and the amount of time spent in fresh water prior to migra-tion and spawning (Shapovalov & Taft 1954; Withler 1966).In the southern portion of their North American range,steelhead have experienced recent population declines,which are at least partly due to habitat loss and/or altera-tions (Busby
et al
. 1996). This has prompted protection formost steelhead populations in California under the federalEndangered Species Act (Busby
et al
. 1996).In this study, we examine variation of an MHC class IIB
gene in coastal steelhead trout from California. Geographicalpatterns and within-population measures of variation atthis gene were compared with those from a set of micros-atellite loci, as were measures of population differentiationestimated from the two types of genetic loci. We thenformally assess deviations from neutrality at the MHClocus by testing for
F
ST
outliers across populations and forallele frequency distortions within populations.
Methods
Sampling
Fin clips were obtained nonlethally from juvenile steelheadduring the period June to October 2001, from 24 streamsin coastal California (Fig. 1). These populations are part offour evolutionarily significant units (ESUs), as defined by
. (1996). Genomic DNA was extracted withQIAGEN DNeasy 96 kits on a QIAGEN Bio Robot 3000.DNA was diluted 1:20 in ddH
2
O and used for subsequentpolymerase chain reaction (PCR) amplification.
Microsatellite genotyping
All individuals were then genotyped at 16 microsatelliteloci: OtsG3, OtsG43, OtsG85, OtsG103, OtsG243, OtsG249,OtsG253, OtsG401, OtsG409 (Williamson
et al
. 2002), One11,One13 (Scribner
et al
. 1996), Ssa85 (O’Reilly
et al
. 1996), Ssa289(McConnell
et al
. 1995), Oki23 (GenBank AF272822),Omy1011 (GenBank AY518334), and Omy77 (Morris
et al
.1996). Amplification and thermal cycling conditions areavailable from the authors upon request. PCR productswere electrophoresed on ABI 377 automated sequencers(Applied Biosystems Inc.) and genotypes were confirmedby two people independently.
MHC genotyping
A portion of exon 2 of the salmonid MHC class IIB genewas amplified with primers B1a-F and B1a-R (Miller
et al
.1997). Amplification and thermal cycling conditions areavailable from the authors upon request. Single strandconformational polymorphism (SSCP) was then used todiscriminate alleles in each population (Sunnucks
et al
.2000). PCR products were diluted 3:5 in buffer (95%formamide, 3.2 m
m
EDTA, 0.025% Bromophenol Blue,0.025% Xylene Cyanol), denatured at 100
°
C for 3 min,placed in ice water, and loaded on 6% acrylamide gels[0.5
×
TBE; 5% glycerol (v/v)] that were electrophoresedfor 7 h at 20 W. Gels were stained with 1
×
SYBR Gold(Molecular Probes) and visualized on an FX MolecularImager (Bio-Rad Inc.).
Due to the high amount of allelic variation, each uniqueelectromorph (allele) from every gel was sequenced and,
Fig. 1 Map of sites used in this study. Creekfrom major drainage is listed in parentheseswhen necessary.
when possible, more than one sample of each electromorphwas examined. Bands were isolated directly from each gel,suspended in ddH
2
O and subject to an additional PCRamplification. PCR products were then precipitated in thepresence of 20% polyethylene glycol and sequenced ineach direction with BigDye (version 3.1) sequencing mix(Applied Biosystems Inc.) on an ABI PRISM 377 automatedsequencer. For individuals in which SSCP electromorphscould not be directly sequenced, PCR products were clonedinto a TOPO-TA cloning vector (Invitrogen, Inc.), 6–8clones were directly PCR amplified with M13 primers, andPCR products were sequenced.
Population genetic summary statistics
Population genetic statistics were generated for each popu-lation, including observed heterozygosity, an unbiasedmeasure of expected heterozygosity (Nei 1987) and Weir &Cockerham’s estimator of
F
ST
(1984). These were estimatedwith the program
genetix
(Belkhir
et al
. 2004). Deviationsfrom Hardy–Weinberg equilibrium (HWE) were evaluatedusing the exact test in
genepop
(Raymond & Rousset 1995).Allelic richness was estimated using the rarefaction methodof Kalinowski (2005) by sampling 14 gene copies perpopulation. All statistics were estimated separately for themicrosatellite loci and the MHC gene.
Isolation by distance
An analysis of potential isolation by distance of the troutpopulations examined in this study was performed byestimating the correlation coefficient of
F
ST
/(1 –
F
ST
) andgeographical (stream distance + coastal contour) distancein kilometre between collecting sites. Significance of therelationship was evaluated with 100 000 permutations in aMantel test. To test for a correlation between populationdifferentiation estimated from the MHC gene and micro-satellite loci, a partial Mantel test was performed, to controlfor geographical distance, and significance was assessed with100 000 permutations. Mantel tests were performed withthe program
zt
(Bonnet & Van de Peer 2002). Neighbour-joining trees were constructed for the microsatellite loci andthe MHC locus separately using chord distance (Cavalli-Sforza & Edwards 1967) matrices and with the program
populations
(Langella 2002). Support for internal brancheswas evaluated with 1000 bootstrap replicates.
F
ST
outliers
To establish if the MHC locus possessed a higher or lowermeasure of population differentiation than the microsatelliteloci, the approach of Beaumont & Nichols (1996) wasemployed. Overall
F
ST
was estimated for microsatellite lociand the MHC locus using the program
datacal
. The mean
estimate of
F
ST
was then used as a starting point forcoalescent simulations, which were used to determine itsexpected variance. The simulation scenario consisted of100 demes, of which 24 were sampled. Thirty-two geneswere sampled from each of the sampled demes. Simulationswere run 50 000 times and for each simulation
F
ST
andexpected heterozygosity were recorded. The infinite allelesmodel (IAM) of mutation was used. The IAM is moreconservative than the stepwise mutation model (SMM), inthat
F
ST
decreases more rapidly with higher heterozygosityfor the SMM (Beaumont & Nichols 1996). Use of the IAMwill lower the probability of false positives (type I error) fordetecting the MHC gene as a high
F
ST
outlier and increasethe probability of type II errors for the microsatellite loci.Simulations were run with the program
fdist
2.
F
ST
was thenscaled by heterozygosity, and 95% quantiles were deter-mined. Significant deviations from this neutral expectationwere assessed by the proportion of simulations aboveor below each observed
F
ST
value. Software used toperform these analyses (
datacal
,
fdist
2) were obtainedfrom M. Beaumont’s website (www.rubic.rdg.ac.uk/
∼
mab/software.html).Previous work on geographical patterns of genetic
variation in steelhead has shown that distinct genetic breaks,or areas of reduced gene flow, occur along the Californiacoast (Bjorkstedt
et al
. 2005). These genetic breaks occur atSan Francisco Bay, between the Russian and Gualala rivers,and at Cape Mendocino (the Lost Coast). Populationswere divided into three groups based on these boundaries(Fig. 1). The two populations from the genetic group thatranges from San Francisco Bay to the Russian River wereexcluded due to an insufficient number of populationssampled. The aforementioned
F
ST
outlier method was thenperformed on each of these groups separately. Simulationparameters were similar for each of the three subgroups,100 populations were simulated with
n
populationssampled (
n
= actual number of populations sampled),
F
ST
was set to the observed
F
ST
for the subgroup, and thenumber of genes sampled was set to the median numberof genes sampled from each of the
n
populations. For thepopulations south of San Francisco Bay 32 genes weresampled from eight populations and the overall
F
ST
was setto 0.041. The parameters for the populations between theRussian River and Cape Mendocino consisted of sam-pling 40 genes from six populations with an
F
ST
of 0.057.For the populations from Cape Mendocino to the SmithRiver 36 genes were sampled from eight populations witha mean
F
ST
of 0.056. In all cases, 50 000 simulations wereperformed.
Within-population balancing selection
Watterson’s homozygosity test (1978) was used to evaluateif balancing selection was operating within each of the
sampled populations. This test examines deviations of theobserved allele frequency distribution from that expectedin a neutral population with the same number of allelesand with the same sample size. Observed and expected
F
values were estimated for each population. An exact test(Slatkin 1994, 1996) was then used to test for significantdeviations from the expected
F
with 10 000 permutationsin
pypop
(Lancaster
et al
. 2003). It should be noted that thistest does not evaluate deviations from HWE, but examinesthe uniformity of allele frequencies within a population thatmay be due to balancing selection. The normalized deviate of
F
(
F
nd
) that accounts for the variance in the expected homo-zygosity (Salamon
et al
. 1999) was also estimated. Negativevalues of
F
nd
indicate uniform frequency distributions.Recent population bottlenecks can also cause distortions
in allele frequency distributions that may lead to greateruniformity than expected under neutrality (Maruyama &Fuerst 1985). We attempted to account for this effect usinga test for recent population bottlenecks. We used the
M
-ratio method (Garza & Williamson 2001), which tests forreductions in the ratio of the observed number of alleles tothe total allele size range for microsatellite loci. The testwas performed on a larger data set described by Garza
et al
. (2004) that includes more sampled individuals fromeach of the locations studied here. The
M
-ratio methodevaluates significance by comparing the observed valuewith that from simulated equilibrium populations with thesame number of alleles. The simulations assumed a gener-alized SMM with 4
N
e
µ
= 10, the proportion of one stepmutations
P
= 0.9 and the mean change in number of repeatsfor larger mutations
∆
g
= 3.5. Two loci with patterns ofallelic variation inconsistent with the SMM were omittedin these analyses.
Historical balancing selection
To test for an historical signature of balancing selection onthe MHC alleles identified here, nonsynonymous (
d
N
) andsynonymous (
d
S
) substitutions were estimated with
mega
3(Kumar
et al
. 2004) and using the modified Nei andGojobori (with Jukes–Cantor correction) method (Nei &Kumar 2000). Substitutions were estimated for the entireexon 2 sequence, and also for putative antigen binding site(ABS), and non-ABS codons separately. The categorizationof these codons is based on the human HLA class IIB gene(Brown
et al. 1993) and takes into account an amino acidinsertion in the 5′ region of exon 2 for teleosts (Ono et al.1993). A Z-test was performed to evaluate the null hypothesisof equal substitutions (dN = dS) (Nei & Kumar 2000). Aneighbour-joining tree was constructed with the Oncor-hynchus mykiss MHC IIB alleles isolated here, and previouslydescribed alleles from Oncorhynchus keta (U34702–6),Oncorhynchus kisutch (U34692–6), Oncorhynchus nerka(AY038051–60), and Oncorhynchus tshawytscha (AF041009–
10; U80299–301). The Poisson-corrected amino acid distance(Nei & Kumar 2000) was used and 500 bootstrap replicateswere performed to assess stability of internal branches.
Results
Population summary statistics
Observed heterozygosity and allelic variation for the MHCgene were high in California steelhead (Table 1). Hetero-zygosity ranged from 0.632 in the Klamath-Blue Creekpopulation, to 1.0 in the Redwood Creek population. Thesevalues were comparable with, or slightly higher than,than the mean heterozygosity for the 16 microsatellite loci(Table 1), which ranged from 0.610 to 0.794. A largenumber of MHC alleles was found in the trout populationssampled here (88 total — GenBank Accession nos DQ272129–DQ272216). The number of alleles per population rangedfrom a low of 6 in Chorro Creek, the southernmost popu-lation, to a high of 19 in Wages Creek. Twelve of thesealleles showed high similarity to previously published(Shum et al. 2001) sequences from Oncorhynchus mykiss.Allelic richness for the MHC gene was positively correlated(rs = 0.84; P < 0.001; t = 6.352) with mean allelic richness forthe microsatellite loci (Fig. 2). There were several significantdeviations from HWE after Bonferroni correction. Theyinclude two loci (OtsG243 and OtsG85) in the Willow Creekpopulation and one each in the San Lorenzo (OtsG253) andKlamath-Hunter Creek (OtsG85) populations.
Isolation by distance
A positive correlation was found between geographicaldistance and FST/(1 – FST) (Fig. 3; r = 0.49) calculated fromthe microsatellite loci, and this relationship was highly
Fig. 2 Correlation between mean allelic richness for 16 microsatelliteloci and that from the exon 2 of the MHC class II B gene.
significant based on a Mantel test with 100 000 permutations(P < 0.0001). Mean overall FST for the microsatellite lociwas 0.075 (0.066–0.086: 95% CI), and for the MHC gene itwas 0.088. Pairwise FST for the MHC gene was positivelycorrelated with the mean pairwise FST for the microsatelliteloci (Fig. 4; r = 0.48) and this relationship was highlysignificant when controlling for distance (partial Manteltest; P = 0.0001). The neighbour-joining tree generated fromthe microsatellite loci reflects the geographical location ofthe populations and the signature of isolation by distance(Fig. 5A), whereas the tree based on the MHC gene showeda dissimilar pattern (Fig. 5B). Most clusters containedgeographically proximate localities only (i.e. Big Sur River,Carmel River and San Simeon Creek), while others con-tained populations from geographically distant popula-tions (i.e. Chorro Creek and Big River). Bootstrap supportabove 50% was not found for any internal branches ineither tree.
Table 1 Mean sample size (n), number of alleles (k), allelic richness (A), and observed (HO) and expected unbiased heterozygosities (HE).Measures for microsatellites are the mean over 16 loci. Populations are listed from southernmost to northernmost (see Fig. 1). Also reportedare observed and expected F values as well as the P value for uniformity in allele frequency distributions. Bold values indicate significantP values (P < 0.05) and values in italics are nearly significant (0.05 < P < 0.10). Statistically significant values for the M-ratio test for themicrosatellite data are given (NS, nonsignificant test result; *P < 0.05; **P < 0.01). Lines are indicated for population breaks (see Methods)
Fig. 3 Correlation between geographical distance and pairwiseFST/(1 – FST) for 24 steelhead populations. FST values werecalculated from the mean of 16 loci. Significance of the correlationwas established a Mantel test (100 000 permutations).
Over all 24 populations, FST for the MHC gene was notfound to be a significant outlier relative to the microsatelliteloci (Fig. 6A), as it fell on the boundary, but within the 95%quantile of the simulated data. However, the microsatellite
locus Ssa85 was a significantly high FST outlier and fourmicrosatellite loci, OtsG85, OtsG253, OtsG401 and One13,were low FST outliers. When the populations were split intothree known genetic groups along the California coast, theMHC gene and Ssa85 were found to be a high FST outlierssouth of San Francisco Bay (Fig. 6B), One11 was found tobe a high FST outlier for the group between the RussianRiver and Cape Mendocino (Fig. 6C) and no loci werefound to be FST outliers north of Cape Mendocino (Fig. 6D).
Within-population balancing selection
Twenty of the 24 populations had observed F values thatwere lower than the expected value (Table 1) and thesepopulations also had negative Fnd values, although onlytwo populations (Eel River and Redwood Creek) were sta-tistically significant after Bonferroni correction for multiplecomparisons. An additional four populations (San SimeonCreek, San Lorenzo River, Smith River, and Willow Creek)showed a trend towards significance (P < 0.10). All but theEel River, Klamath-Blue Creek, Noyo River, and SmithRiver populations had significant evidence for a recentpopulation bottleneck with the M-ratio test (Table 1).
Historical balancing selection
Elevated dN:dS was found for steelhead MHC class IIBalleles. This result was found for the entire 72 codonsequence, as well both the 23 putative ABS codons involvedin antigen presentation and the 49 non-ABS codonsconsidered separately (Table 2). The ABS codons had thehighest dN:dS of the three. The Z-test for inequality of dNand dS was statistically significant for the ABS codons(P = 0.003) and for all codons (P = 0.005). While the non-ABS codons possessed higher dN than dS, we could not rejectthe null hypothesis of equality between rates (P = 0.075).Trans-species evolution in the alleles isolated from O. mykisswas evident from the neighbour-joining tree of alleles(Fig. 7), where a group of Oncorhynchus keta/Oncorhynchusnerka MHC alleles are nested within the O. mykiss alleles.
Fig. 4 Correlation between pairwise population differentiation(FST) estimated from the mean of 16 microsatellite loci and that ofthe MHC IIB gene. Significance of the correlation was establisheda partial Mantel test (100 000 permutations) that controls for theeffects of geographical distance.
Fig. 5 Unrooted neighbour-joining dendrograms for the 16 micro-satellite loci (A) and the MHC locus (B) based on Cavalli-Sforza &Edwards’ (1967) chord distance.
Table 2 Nonsynonymous and synonymous substitutions for theexon 2 region of the Oncorhynchus mykiss class II B gene. Rateswere estimated for all codons, ABS and non-ABS codons (Brownet al. 1993). Standard error estimates are in parentheses. The teststatistic for the Z-test (HO: dN = dS) and corresponding P valuefollows
Elevated bootstrap support was found for branchesleading to the clusters that contained O. keta and O. nerkaalleles and Oncorhynchus kisutch/Oncorhynchus tshawytschaalleles (Fig. 7).
Discussion
Our results indicate that genetic drift and migration areimportant forces governing the magnitude and distributionof MHC variation in coastal California steelhead trout. Thisassertion comes from the observed positive correlations ofboth population allelic richness and pairwise estimates ofFST for the MHC gene and 16 microsatellite loci. FST for theMHC gene was elevated when compared to values for themicrosatellites, but it was not a significant outlier whenthe entire data set was considered. However, when examinedwithin smaller geographical regions, the boundaries ofwhich are defined by patterns of gene flow, the MHC genewas a significant FST outlier for the southernmost group ofpopulations. Within-population tests for allele frequencyuniformity revealed that MHC allele frequency distributionswere more uniform than expected for some populations.However, only two populations had statistically significantdeviations from neutral expectations and most of the studiedpopulations also had evidence of a recent populationbottleneck. These results contrast with the evidence forhistorical balancing selection that was found from thepattern of substitutions (dN:dS > 1) and trans-species evo-lution of alleles. This contrasting pattern highlights howthe effects of contemporary selection may not be evidentat a gene that is known to have been under historicalselection.
The observed positive correlation in allelic richness forthe MHC and microsatellite genes indicates that genetic
drift, or effective population size, is important in theobserved distribution of MHC diversity for steelhead.There was not evidence of equitable allelic variation acrosspopulations or increased variability in populations thatcontain low microsatellite diversity. This latter scenario,high MHC diversity relative to neutral variation, has beenattributed to the effects of selection coupled with smalleffective population sizes (Aguilar et al. 2004; Jarvi et al.2004). Allelic richness takes into account sample size differ-ences and precludes an effect of different sample sizes acrosspopulations as an explanation of the observed correlation.Microsatellite loci are an appropriate marker with whichto compare MHC genes, as they also possess both highheterozygosity and allelic variation, parameters whichhave important effects on FST estimates (Hedrick 1999).
Deviations from HWE were found in three populations.However, only one of the populations, Willow Creek, hadmore than one locus that deviated from HWE. It is possiblethat in this population sampling of closely related individ-uals could account for this observation. This phenomenonhas been reported in other salmonid studies (Allendorf &Phelps 1981), although one would expect a greater numberof loci to deviate significantly from HWE if many closelyrelated individuals were sampled.
This effect of migration appears to be important inmeasures of pairwise population differentiation for theMHC gene, as pairwise FST estimated from the MHC genewas significantly correlated with that from the microsatelliteloci. However, a partial Mantel test examining the correla-tion between FST for MHC and geographical distance(while controlling for microsatellite FST) is not significant(r = −0.069; P = 0.196). We also observed a negative correla-tion between allelic richness and mean population pairwiseFST for both microsatellite loci (rs = −0.516; P = 0.01; t = −2.81)
Fig. 6 FST outlier analysis. Solid lineindicates the median value and the hatchedlines the upper and lower 95% quantilesfrom 50 000 simulations. Solid circles areobserved values from the 16 microsatelliteloci and the open circle is the observedvalue from the MHC IIB gene. (A) All 24populations; (B) eight populations fromChorro Creek to Waddell Creek; (C) sixpopulations from the Gualala River toWages Creek; (D) eight populations fromBig Creek to Smith River (see Table 1).
Fig. 7 Neighbour-joining tree of theOncorhynchus mykiss MHC alleles and threerelated Oncorhynchus species based on thePoisson-corrected amino acid distance. Nodesupport was evaluated with 500 bootstrapreplicates and only support values above50% are shown. GenBank Accession nos:Oncorhynchus keta (U34702–6); Oncorhynchusnerka (AY038051–60); Oncorhynchus tshawyt-scha (AF041009–10; U80299–301).
and the MHC locus (rs = −0.573; P = 0.001; t = −3.634),indicating that within-population variability affects FSTestimates and may partially explain the correlation betweenmeasures from the two marker types (Hedrick 2005). Theeffects of distance-dependent migration were evident in theneighbour-joining tree constructed from the microsatellitedata, but less so in the one constructed from MHC distances.The difference in the topology of the trees could be duesimply to the variance associated with sampling a singlelocus. Therefore, the observed positive correlation betweenpairwise FST for MHC and microsatellite loci may not becompletely due to drift but rather partially an artefact ofthe hypervariable nature of the loci.
Results from the FST outlier analysis indicate that the FSTvalue for the MHC gene lies near the upper 95% quantileof the variance in estimates for all simulated populations,but it is not significant. When the analysis was performedfor the three genetic groups separately, the MHC gene wasa significant FST outlier for the southern populations. Themethod of Beaumont & Nichols (1996) has been shown tobe robust to a number of demographic scenarios, includingisolation by distance (Beaumont 2005). This is consistentwith the observation in our analysis of the majority of locifalling within the 95% quantiles of simulated FST values.The finding of a signature of selection in the southernmostpopulations, but not further to the north, indicates thatthere may be geographical heterogeneity in selection onthis MHC gene, as has been found in a study of sockeyesalmon in Canada (Miller et al. 2001). Strong balancingselection over all populations leads to the expectation of adecrease in FST for MHC loci when compared to neutralloci. This is contrary to the elevated pattern of FST that we,and others (Miller et al. 2001), have found when comparingMHC FST values to those generated from neutral loci.Our observation is consistent with geographically varyingdirectional selection operating at the MHC.
Two microsatellite loci, Ssa85 and One11, were also foundto be significantly high FST outliers. Ssa85 is an outlier forall populations together and in the southernmost groupof populations, whereas One11 is an outlier for the groupof populations that ranges from the Russian River to CapeMendocino. Ssa85 has been shown to be associated withthermal tolerance (Danzmann et al. 1999; Somorjai et al. 2003)and spawning time (Sakamoto et al. 1999) in QTL (quanti-tative trait loci) mapping studies. This result suggests thatthe genomic region that shows an association with thermaltolerance and spawn time in O. mykiss (and Salvelinus alpinus)may be under directional selection in southern populationsof California coastal steelhead. Higher, and more variable,stream temperatures in central California may be involvedin the observed pattern at Ssa85. However, further work isneeded to evaluate this hypothesis. No mapping studieshave found significant associations with One11 and a blastsearch of O. mykiss and Salmo salar EST databases (found at
www.tigr.org) with One11 flanking sequence did not findany homologous sequences. It is unknown what factorsmight account for the outlier status of locus One11.
Previous studies of MHC genetic variation in salmonidpopulations have also found that drift and migration areimportant evolutionary forces in shaping allele frequencydistributions. For chinook salmon (Oncorhynchus tshawytscha)in the Central Valley of California, allele frequency differ-ences for MHC IIB genes can be used to assign individualsto divergent temporal runs (Kim et al. 1999), as can multi-locus microsatellite genotypes (Banks et al. 2000). A studyon MHC variation in Atlantic salmon (S. salar) found thatat large geographical scales pairwise measures of popu-lation differentiation were correlated for MHC IIB andmicrosatellite loci (Landry & Bernatchez 2001). However,at smaller geographical scales measures of differentiationwere not correlated. A study on sockeye salmon in the FraserRiver system found higher levels of overall populationdifferentiation for the MHC gene than for microsatelliteloci, and also that selection varied geographically (Milleret al. 2001). Our results are similar to those of Landry &Bernatchez (2001), in that geographical scale is importantin the ability to detect deviations from the patterns ofneutral markers. Unlike the aforementioned studies, weutilized a simulation-based approach to identify FST outliers.Such an approach is more powerful in identifying outliers,as a better expectation for the variance in FST is employed.
We did not find strong evidence for within-populationbalancing selection, as only two populations had significantF values. Two other factors, aside from balancing selection,may contribute to the overall low F values: low samplesizes and the prevalence of population bottlenecks.Without greater sampling or analytical methodologiesthat can account for the effects of a population bottlenecks,we cannot definitively comment on within-populationselective processes for the sampled trout populations.
The high amount of allelic variation at the MHC geneuncovered here exceeds that found in other studies ofsalmonids. For Pacific salmon (genus Oncorhynchus), lowamounts of allelic variation have been found in population-level studies of the IIB gene in chinook (Miller et al. 1997;Kim et al. 1999) and sockeye salmon (Miller et al. 2001).Only four alleles were found in a study of chinook salmonfrom California’s Central Valley (Kim et al. 1999) and ninealleles were found in a survey of sockeye salmon from theFraser River system (Miller et al. 2001). In contrast a studyon allelic variation in lake trout (Salvelinus namaycush)found a high amount of variability, with 43 alleles found in74 sampled individuals (Dorschner et al. 2000). Our resultof high polymorphism is similar to that found in lake troutand an apparent early generalization of low MHC poly-morphism for Pacific salmonids (Miller & Withler 1996) isnot upheld when steelhead trout are included. Thereare a number of possible explanations for this apparent
discrepancy in allelic variation among salmonid species.These include historical population bottlenecks, low over-all effective population size or functional differences.
We assume that all of the alleles identified here are froma single-copy, functional MHC class IIB gene. Consistentwith this are that we never observed more than twosequences from a single individual, which strongly suggeststhat PCR amplification was targeting only a single genecopy. Additionally, the same primer pair (B1A-F & R) hasbeen shown to amplify a single gene copy in a wide arrayof other salmonid taxa (Miller et al. 1997; Dorschner et al.2000; Binz et al. 2001). There were several lines of evidenceindicating current or recent functionality of the gene fromwhich alleles were identified. First, we found a statisticallysignificant excess of nonsynonymous substitutions in boththe entire exonic sequence and in presumptive ABS codonsonly. While this observation is not evidence for contemporaryselection, it is evidence for historical balancing selectionacting upon the alleles at this locus. If this gene was anonfunctional pseudogene, we might also expect to finddisruptions of the reading frame or non-sense mutations(Hess et al. 2000), yet these were not observed. Another lineof evidence for the functionality of the MHC gene examinedhere is the exact or high (1-bp difference) similarity ofalleles described to those previously published, whichwere isolated from mRNA (Shum et al. 2001).
Our observation of trans-species evolution for the O. mykissMHC class IIB differs from what has been previouslyobserved in other Pacific salmonids (Miller & Withler 1996).Miller & Withler (1996) found that alleles of the class IIB genefrom Pacific salmon formed species-specific clusters. Wealso found species-specific allelic lineages for Oncorhynchusnerka and Oncorhynchus keta, and the alleles from Oncorhyn-chus kisutch and O. tshawytscha grouped together, suggestingthat the retention of ancestral polymorphism is primarilyfound in the O. mykiss lineage. Further sampling of MHCsequence variation in Pacific salmonids should shed lighton the demographic and selective processes that areimportant in the trans-specific evolution of MHC genes inthis group of fishes.
We have found a high degree of variation at the MHCclass IIB gene in California populations of coastal steelheadtrout, which is consistent with many other populationgenetic studies that utilize MHC genes as a molecular tool.This gene can thus provide population genetic informationsimilar to that from microsatellite loci, particularly withrespect to variability and population differentiation. Wedid not find a widespread signal of contemporary selectionacting on the MHC gene when the entire data set wasconsidered. However, when examined at local geograph-ical scales there were several signals of such selection:relatively uniform allele frequencies in some populationsand an increased measure of population differentiation inthe southern populations. This finding suggests that selection
is heterogeneous in nature at this geographical level, aresult consistent with other studies of salmonids (Landry& Bernatchez 2001; Miller et al. 2001). Our study thus high-lights the difficulty in assessing the role of natural selectionin shaping contemporary variability at MHC genes and inthe ability to detect signatures of such selection. However,it is clear that establishing the direct effects of selection onMHC genes, in the absence of measurement of the selectiveagent, can benefit from comparison with a neutral geneticbaseline and rigorous evaluation of FST outliers.
AcknowledgementsWe thank members of the SWFSC Santa Cruz Laboratory forcollecting samples used in this study and E. Mora for assistancewith Fig. 1. A.A. was supported by a UCOP fellowship. Thismanuscript was improved by comments from P. Hedrick andthree anonymous reviewers.
ReferencesAguilar A, Roemer G, Debenham S et al. (2004) High MHC
diversity maintained by balancing selection in an otherwisegenetically monomorphic mammal. Proceedings of the NationalAcademy of Sciences, USA, 101, 3490–3494.
Allendorf FW, Phelps SR (1981) Use of allelic frequencies todescribe population structure. Canadian Journal of Fisheries andAquatic Sciences, 38, 1507–1514.
Banks MA, Rashbrook VK, Calavetta MJ, Dean CA, Hedgecock D(2000) Analysis of microsatellite DNA resolves genetic structureand diversity of chinook salmon (Oncorhynchus tshawytscha) inCalifornia’s Central Valley. Canadian Journal of Fisheries andAquatic Sciences, 57, 915–927.
Beaumont MA (2005) Adaptation and speciation: what can FST tellus? Trends in Ecology & Evolution, 20, 435–440.
Beaumont MA, Nichols RA (1996) Evaluating loci for use in thegenetic analysis of population structure. Proceedings of the RoyalSociety of London. Series B, Biological Sciences, 263, 1619–1626.
Behnke RJ (1992) Native trout of western North America. AmericanFisheries Society Monograph, 6, 275.
Belkhir K, Borsa P, Chikhi L, Raufaste N, Bonhomme F (2004)GENETIX 4.05, logiciel sous WindowsTM pour la génétique des popu-lations. Laboratoire Génome, Populations, Interactions, CNRSUMR 5171, Université de Montpellier II, Montpellier, France.
Bernatchez L, Landry C (2003) MHC studies in nonmodel verte-brates: what have we learned about natural selection in 15years? Journal of Evolutionary Biology, 16, 363–377.
Binz T, Largiader C, Muller R, Wedekind C (2001) Sequencediversity of Mhc genes in lake whitefish. Journal of Fish Biology,58, 359–373.
Bjorkstedt EP, Spence BC, Garza JC et al. (2005) An Analysis ofHistorical Population Structure for Evolutionarily Significant Unitsof Chinook Salmon, Coho Salmon, and Steelhead in the North-CentralCalifornia Coast Recovery Domain. NOAA Technical MemorandumNMFS-SWFSC-382.
Bonnet E, Van de Peer Y (2002) zt: a software tool for simple andpartial Mantel tests. Journal of Statistical Software, 7, 1–10.
Brown JH, Jardetzky TS, Gorga JC et al. (1993) 3-Dimensionalstructure of the human class-II histocompatibility antigen HLA-DR1. Nature, 364, 33–39.
Danzmann RG, Jackson TR, Ferguson MM (1999) Epistasis inallelic expression at upper temperature tolerance QTL inrainbow trout. Aquaculture, 173, 45–58.
Dorschner MO, Duris T, Bronte CR, Burnham-Curtis MK,Phillips RB (2000) High levels of MHC class II allelic diversity inlake trout from Lake Superior. Journal of Heredity, 91, 359–363.
Garrigan D, Hedrick PW (2003) Perspective: detecting adaptivemolecular polymorphism: lessons from the MHC. Evolution, 57,1707–1722.
Garza JC (1998) Population genetics of the northern elephant seal. PhDdissertation, University of California Berkeley.
Garza JC, Williamson EG (2001) Detection of reduction in popula-tion size using data from microsatellite loci. Molecular Ecology,10, 305–318.
Garza JC, Gilbert-Hovarth L, Anderson J et al. (2004) Populationstructure and history of steelhead trout in California, NPAFCTechnical Report No. 5, 129–131.
Hedrick PW (1994) Evolutionary genetics of the major histocom-patibility complex. American Naturalist, 143, 945–964.
Hedrick PW (1999) Perspective: highly variable loci and theirinterpretation in evolution and conservation. Evolution, 53, 313–318.
Hedrick PW (2005) A standardized genetic differentiation measure.Evolution, 59, 1633–1638.
Hedrick PW, Parker KM, Lee RN (2001) Using microsatellite andMHC variation to identify species, ESUs, and MUs in the endan-gered Sonoran topminnow. Molecular Ecology, 10, 1399–1412.
Hess CM, Gasper J, Hoekstra HE, Hill CE, Edwards SV (2000)MHC class II pseudogene and genomic signature of a 32-kbcosmid in the house finch (Carpodacus mexicanus). GenomeResearch, 10, 613–623.
Hughes AL, Nei M (1989) Nucleotide substitution at major histo-compatibility complex class II loci: evidence for overdominantselection. Proceedings of the National Academy of Sciences, USA, 86,958–962.
Jarvi SI, Tarr CL, McIntosh CE, Atkinson CT, Fleischer RC (2004)Natural selection of the major histocompatibility complex(Mhc) in Hawaiian honeycreepers (Drepanidinae). MolecularEcology, 13, 2157–2168.
Kalinowski ST (2005) hp-rare 1.0: a computer program forperforming rarefaction on measures of allelic richness.Molecular Ecology Notes, 5, 187–189.
Kim TJ, Parker KM, Hedrick PW (1999) Major histocompatibilitycomplex differentiation in Sacramento River chinook salmon.Genetics, 151, 1115–1122.
Klein J (1986) Natural History of the Major Histocompatibility Complex.John Wiley, New York.
Klein J, O’hUigin C (1994) MHC polymorphism and parasites.Philosophical Transactions of the Royal Society of London. Series B,Biological Sciences, 346, 351–358.
Klein J, Sato A, Nagl S, O’hUigin C (1998) Molecular trans-speciespolymorphism. Annual Review of Ecology and Systematics, 29, 1–21.
Kumar S, Tamura K, Nei M (2004) mega3: integrated softwarefor Molecular Evolutionary Genetics Analysis and sequencealignment. Briefings in Bioinformatics, 5, 150–163.
Lancaster A, Nelson MP, Single RM, Meyer D, Thomson G (2003)pypop: a software framework for population genomics: analyzing
large-scale multi-locus genotype data. In: Pacific Symposiumon Biocomputing 8 (ed. Altman RB et al.), pp. 514–525. WorldScientific, Singapore.
Landry C, Bernatchez L (2001) Comparative analysis of popula-tion structure across environments and geographical scales atmajor histocompatibility complex and microsatellite loci inAtlantic salmon (Salmo salar) Molecular Ecology, 10, 2525–2539.
Langella O (2002) POPULATIONS (version 1.2.28). http://www.pge.cnrs-gif.fr/bioinfo/populations/.
Lewontin RC, Krakauer J (1973) Distribution of gene frequency asa test of the theory of the selective neutrality of polymorphisms.Genetics, 74, 175–195.
Luikart G, England PR, Tallmon D, Jordan S, Taberlet P (2003) Thepower and promise of population genomics: from genotypingto genome typing. Nature Reviews Genetics, 4, 981–994.
Maruyama T, Fuerst PA (1985) Population bottlenecks andnonequilibrium models in population genetics. 3. Genichomozygosity in populations which experience periodicbottlenecks. Genetics, 111, 691–703.
McConnell SK, O’Reilly P, Hamilton L et al. (1995) Polymorphicmicrosatellite loci from Atlantic salmon (Salmo salar): geneticdifferentiation of North American and European populations.Canadian Journal of Fisheries and Aquatic Sciences, 52, 1863–1872.
Meyer D, Thomson G (2001) How selection shapes variation of thehuman major histocompatibility complex: a review. Annals ofHuman Genetics, 65, 1–26.
Miller KM, Withler RE (1996) Sequence analysis of a polymorphicMhc class II gene in Pacific salmon. Immunogenetics, 43, 337–351.
Miller KM, Withler RE, Beacham TD (1997) Molecular evolution atMhc genes in two populations of chinook salmon Oncorhynchustshawytscha. Molecular Ecology, 6, 937–954.
Miller KM, Kaukinen KH, Beacham TD, Withler RE (2001)Geographic heterogeneity in natural selection on an MHC locusin sockeye salmon. Genetica, 111, 237–257.
Morris DB, Richard KR, Wright JM (1996) Microsatellites fromrainbow trout (Oncorhynchus mykiss) and their use for geneticstudy of salmonids. Canadian Journal of Fisheries and AquaticSciences, 53, 120–126.
Nei M (1987) Molecular Evolutionary Genetics. Columbia UniversityPress, New York.
Nei M, Kumar S (2000) Molecular Evolution and Phylogenetics.Oxford University Press, Oxford.
O’Reilly PT, Hamilton LC, McConnell SK, Wright JM (1996) Rapidanalysis of genetic variation in Atlantic salmon (Salmo salar)by PCR multiplexing of dinucleotide and tetranucleotide micro-satellites. Canadian Journal of Fisheries and Aquatic Sciences, 53,2292–2298.
Olsen KH, Grahn M, Lohm J (2002) Influence of MHC on siblingdiscrimination in Arctic char, Salvelinus alpinus (L.). Journal ofChemical Ecology, 28, 783–795.
Ono H, O’hUigin C, Vincek V, Klein J (1993) Exon-intron organ-ization of fish major histocompatibility complex class II betagenes. Immunogenetics, 38, 223–234.
Penn DJ, Potts WK (1999) The evolution of mating preferences andmajor histocompatibility complex genes. American Naturalist,153, 145–164.
Rajakaruna RS, Brown JA, Kaukinen K, Miller KM (2001) MHCgenes influence the kin recognition in juvenile Atlantic salmon.American Zoologist, 41, 1562–1562.
Raymond M, Rousset F (1995) genepop (version-1.2): populationgenetics software for exact tests and ecumenicism. Journal ofHeredity, 86, 248–249.
Sakamoto T, Danzmann RG, Okamoto N, Ferguson MM, Ihssen PE(1999) Linkage analysis of quantitative trait loci associatedwith spawning time in rainbow trout (Oncorhynchus mykiss).Aquaculture, 173, 33–43.
Salamon H, Klitz W, Easteal S et al. (1999) Evolution of HLA classII molecules: allelic and amino acid site variability acrosspopulations. Genetics, 152, 393–400.
Sato A, Figueroa F, O’hUigin C, Steck N, Klein J (1998) Cloningof major histocompatibility complex (MHC) genes fromthreespine stickleback, Gasterosteus aculeatus. Molecular MarineBiology and Biotechnology, 7, 221–231.
Schierup MH, Charlesworth D, Vekemans X (2000) The effect ofhitch-hiking on genes linked to a balanced polymorphism in asubdivided population. Genetical Research, 76, 63–73.
Scribner KT, Gust JR, Fields RL (1996) Isolation and characterizationof novel salmon microsatellite loci: cross-species amplificationand population genetic applications. Canadian Journal of Fisheriesand Aquatic Sciences, 53, 833–841.
Shapovalov L, Taft A (1954) The life histories of the steelheadrainbow trout (Salmo gairdneri gairdneri) and silver salmon(Oncorhynchus kisutch) with special reference to Waddell Creek,California, and recommendations regarding their management.California Department of Fish and Game, Fish Bulletin, 98, 375.
Shum BP, Guethlein L, Flodin LR et al. (2001) Modes of salmonidMHC class I and II evolution differ from the primate paradigm.Journal of Immunology, 166, 3297–3308.
Slatkin M (1994) An exact test for neutrality based on the Ewenssampling distribution. Genetical Research, 64, 71–74.
Slatkin M (1996) A correction to the exact test based on the Ewenssampling distribution. Genetical Research, 68, 259–260.
Somorjai IML, Danzmann RG, Ferguson MM (2003) Distributionof temperature tolerance quantitative trait loci in Arctic charr(Salvelinus alpinus) and inferred homologies in rainbow trout(Oncorhynchus mykiss). Genetics, 165, 1443–1456.
Storz JF (2005) Using genome scans of DNA polymorphism toinfer adaptive population divergence. Molecular Ecology, 14,671–688.
Sunnucks P, Wilson ACC, Beheregaray LB et al. (2000) SSCP is notso difficult: the application and utility of single-stranded confor-mation polymorphism in evolutionary biology and molecularecology. Molecular Ecology, 9, 1699–1710.
Watterson GA (1978) Homozygosity test of neutrality. Genetics, 88,405–417.
Weir BS, Cockerham CC (1984) Estimating F-statistics for theanalysis of population structure. Evolution, 38, 1358–1370.
Williamson KS, Cordes JF, May B (2002) Characterization ofmicrosatellite loci in chinook salmon (Oncorhynchus tshawyt-scha) and cross-species amplification in other salmonids.Molecular Ecology Notes, 2, 17–19.
Withler RE (1966) Variability in life history characteristics of steel-head trout (Salmo gairdneri) along the Pacific coast of North America.Journal of the Fisheries Research Board of Canada, 23, 365–393.
Andres Aguilar is a postdoctoral fellow in Carlos Garza’s researchgroup. The group’s main research focus is on the conservationgenetics of California salmonids. Other research interests of thegroups include the genetic analysis of marine mammal and fishpopulations.
