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Major QTL Affect Resistance to Infectious Pancreatic Necrosis in Atlantic

Salmon (Salmo salar)

Ross D. Houston*, Chris S. Haley*, Alastair Hamilton†, Derrick R. Guy†, Alan E.

Tinch†, John B. Taggart‡, Brendan J. McAndrew‡ and Stephen C. Bishop*

* Division of Genetics and Genomics, Roslin Institute and Royal (Dick) School of

Veterinary Studies, Roslin BioCentre, Midlothian EH25 9PS, UK.

† Landcatch Natural Selection Ltd., Alloa, Clackmannanshire FK10 3LP, UK.

‡ Institute of Aquaculture, University of Stirling, Stirling FK9 4LA, UK.

Genetics: Published Articles Ahead of Print, published on February 1, 2008 as 10.1534/genetics.107.082974

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Running Head: IPN resistance QTL in Atlantic salmon

Key words: Atlantic salmon, Disease Resistance, Recombination, QTL, Marker-

assisted selection

Corresponding Author:

Ross Houston, Division of Genetics and Genomics, Roslin Institute and Royal (Dick)

School of Veterinary Studies, Roslin BioCentre, Midlothian EH25 9PS, UK.

Tel: +441315274460

Fax: +441314400434

E-mail: ross.houston@bbsrc.ac.uk

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SUMMARY

Infectious Pancreatic Necrosis (IPN) is a viral disease currently presenting a major

problem to the production of Atlantic salmon (Salmon salar). IPN can cause

significant mortality to salmon fry within freshwater hatcheries, and to smolts

following transfer to seawater, although challenged populations show clear genetic

variation in resistance. To determine whether this genetic variation includes loci of

major effect, a genome-wide QTL scan was performed within ten full-sib families that

had received a natural seawater IPN challenge. To utilize the large difference

between Atlantic salmon male and female recombination rate, a two-stage mapping

strategy was employed. Initially, a sire-based QTL analysis was used to detect

linkage groups with significant effects on IPN resistance, using two to three

microsatellite markers per linkage group. A dam-based analysis with additional

markers was then used to confirm and position any detected QTL. Two genome-wide

significant QTL and one suggestive QTL were detected in the genome scan. The

most significant QTL was mapped to linkage group 21, and was significant at the

genome-wide level in both the sire and dam-based analyses. The identified QTL can

be applied in marker-assisted selection programs to improve the resistance of salmon

to IPN and reduce disease-related mortality.

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INTRODUCTION

Infectious Pancreatic Necrosis (IPN) is a viral disease that currently presents a major

problem to the production of Atlantic salmon, and other salmonid species, in many

countries. This highly contagious disease has the unusual characteristic of impacting

on farmed salmon during two specific windows of the life cycle (ROBERTS and

PEARSON 2005). In the freshwater phase of the salmon life cycle, IPN outbreaks in

fry have been observed for several decades, with up to 70% mortality. In the marine

environments, the emergence of problematic IPN outbreaks (up to 40% mortality) is

more recent, coinciding with the dramatic expansion of salmon aquaculture (ROBERTS

and PEARSON 2005). The causative agent for IPN is a double-stranded, non-

membraned RNA birnavirus, of which several subtypes have been characterised

(ROBERTS and PEARSON 2005). Levels of mortality during an IPN outbreak are

determined by numerous factors, although it is increasingly clear that a strong genetic

component to IPN resistance exists in salmon (GUY et al. 2006).

The elucidation of the molecular genetic basis of economically important traits in all

salmonid species is complicated by the structure and properties of their genome. The

salmonid genome is thought to have undergone a relatively recent duplication event

(25-100 million years ago), and is currently evolving toward a fully diploid state

(ALLENDORF and THORGAARD 1984, ALLENDORF and DANZMANN 1997). Salmonid

fish exhibit several remnants of their tetraploid ancestry, including some exchange of

chromatid segments between ancestrally homeologous chromosome arms following

the formation of multivalent structures at meiosis (WRIGHT et al. 1983). These

structures are also thought to constrain recombination, particularly toward

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centromeric regions of the chromosome (ALLENDORF and DANZMANN 1997;

SAKAMOTO et al. 2000). Interestingly, these phenomena are unique to male meiosis,

and as a result there are striking sex-specific differences in linkage maps within

salmonid species, with estimations of female:male map distance ratios of 3.25 in

rainbow trout (Oncorhynchus mykiss) (SAKAMOTO et al. 2000), 3.92 (GILBEY et al.

2004) or 8.26 (MOEN et al. 2004a) in Atlantic salmon, and 2.64 for brown trout

(Salmo trutta) (GHARBI et al. 2006). The implications of these recombination

differences for mapping quantitative trait loci (QTL) include the increased power of

QTL detection, combined with the reduced ability to position the QTL in males

(HAYES et al. 2006).

Several Atlantic salmon genetic linkage maps currently exist (e.g. MOEN et al. 2004a;

GILBEY et al. 2004). The most detailed map currently available is the composite map

of Høyheim and Danzmann (http://grasp.mbb.sfu.ca/GRASPlinkage.html), which

combines molecular marker and mapping data from the Genomic Research on

Atlantic Salmon Project (GRASP; http://grasp.mbb.sfu.ca/), the Salmon Genome

Project (SGP; http://www.salmongenome.no/cgi-bin/sgp.cgi and the EU collaborative

SALMAP project (http://www.salmongenome.no/cgi-bin/sgp.cgi). These maps have

facilitated the detection of QTL in salmon affecting body weight and condition factor

(REID et al. 2005), while molecular markers have also been linked to disease

resistance against Infectious Salmon Anemia (ISA) (GRIMHOLT et al. 2003; MOEN et

al. 2004b, 2007), furunculosis (GRIMHOLT et al. 2003) and the ectoparasite

Gyrodactylus salaris (GILBEY et al. 2006). Furthermore, in the closely related

rainbow trout, two QTL have been identified with a highly significant effect on

freshwater IPN resistance in a backcross family (OZAKI et al. 2001). These IPN-

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resistance QTL in trout explained between 27 and 34 % of the phenotypic variance,

demonstrating that major loci affecting resistance to IPN can be segregating in

salmonid species.

In common with the majority of aquaculture species, the selective breeding of

Atlantic salmon is relatively new in comparison to terrestrial livestock. Farmed

Atlantic salmon typically have a four-year generation interval and, therefore, are

currently just a few generations from their wild ancestors (MUIR 2005). The genetic

progress made in readily measurable traits (such as growth or sexual maturation) has

been rapid and substantial. Where the traits are more difficult or impossible to

measure on the selection candidate (such as disease resistance or fillet quality), full-

sib phenotype information has been effectively utilized in selection programs

(SONESSON 2005). However, for these difficult or expensive to measure traits,

marker-assisted selection (MAS) directly on the selection candidate could

substantially increase the accuracy of selection in salmon breeding programs

(SONESSON 2005). The detection of QTL is an effective starting point for the

application of marker-assisted, or gene-assisted, selection. Furthermore, identification

of the genes underlying the QTL may lead to fundamental knowledge of genetic

regulation of viral disease resistance, and host-virus interactions in fish.

Both experimental tank challenges and large-scale field challenges have resulted in

consistent evidence for familial differences in resistance to IPN (STORSET et al. 2003;

GUY et al. 2006). Selective breeding for IPN resistance, utilizing challenge

information from siblings of commercial broodstock fish, is currently practiced in the

breeding programs of major salmon breeding companies. The narrow-sense

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heritability for resistance to the marine phase of the disease has been estimated from

field challenges at approximately 0.4, which is higher than is typically associated with

disease resistance traits (GUY et al. 2006). The purpose of the current study is to

utilize the extensive data and samples available from IPN field challenge trials,

combined with a salmonid-specific strategy for a genome-wide molecular marker

scan, to detect QTL affecting resistance to the marine phase of IPN in a commercial

Atlantic salmon population.

MATERIALS AND METHODS

Animals: The fish chosen for genotyping and analysis were from a cohort of

approximately 200 families that were siblings to the broodstock fish of Landcatch

Natural Selection Ltd. These fish were spawned in 1999 in Ormsary, UK and

subsequently incubated and hatched into individual family tanks in March 2000,

before being moved to larger mixed tanks in October 2000. The fish all received a

routine vaccination against the bacterial disease furunculosis in November 2000. In

April 2001, approximately 55,000 smolts were transferred from the freshwater tanks

to a single seawater site in Shetland, UK where the IPN virus was known to be

endemic. IPN is known to cause mortalities typically between 2 and 3 months post-

transfer to seawater (ROBERTS and PEARSON 2005). Therefore, dead fish were

collected from the water between 5-12 weeks following transfer and veterinary

inspection confirmed that deaths during this period were due to IPN. Approximately

16,000 mortalities were attributed to IPN, representing an overall mortality rate of

approximately 30%. Of these, approximately 5000 dead fish and 5000 surviving fish,

chosen at random, were sampled and genotyped to assign to family (see ‘genotyping’

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below). Ten full-sib families were chosen from this cohort based on having a large

number of fish with DNA available, with the average full-sib family size being 58

(ranging from 51 to 70), giving a total sample size of 584 offspring with

approximately equal numbers of mortalities and survivors. Large families were

chosen to increase the statistical power for within-family linkage analysis, while the

strategy of having close to equal numbers of mortalities and survivors was used to

increase the chance that any IPN resistance QTL would be segregating within the

chosen families.

Genotyping: DNA was extracted from all fin-clips using a Biosprint DNA kit

(Qiagen, Crawley, UK) following the manufacturers protocol and fish were assigned

to family by DNA profiling, using the 10 marker assignment multiplex system

described in GUY et al. (2006). The genome scan was accomplished using

microsatellite markers informed by the composite linkage map available from the

GRASP website (http://grasp.mbb.sfu.ca/GRASPlinkage.html; hereafter referred to as

the ‘composite linkage map’). Each new marker was optimised for ABI 377-

mediated fluorescent detection by screening at annealing temperatures from 47°C-

67°C using a Mastercycler gradient themal cycler (Eppendorf, Hamburg, Germany) to

determine the optimal annealing temperature for each marker. Markers with

compatible PCR parameters and size-ranges were combined into PCR multiplexes.

Primer details and Genbank accession numbers, where available, for the microsatellite

loci are given in Supplementary Table 1.

The genotyping strategy for the genome scan was designed to optimize the resources

available, accounting for the low recombination rate in males compared to females.

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Therefore, a two-stage genotyping and analysis strategy was employed, whereby

initially 2 to 3 markers per linkage group (29 linkage groups in total; Atlantic salmon

2n = 58) were genotyped on all parents and progeny, and QTL effects were assessed

using a sire-family-based regression analysis (see ‘QTL Mapping’ below). Additional

markers were then genotyped on the linkage groups that showed statistically

significant evidence for a QTL (Supplementary Table 3), and the position of the QTL

on the linkage group was estimated using a dam-family-based regression analysis.

The genotype data were used to assign offspring to families and were checked for

potential Mendelian inheritance errors using the Family Assignment Program (FAP,

TAGGART 2007). In the overall dataset, including the chosen QTL families, the

successful family assignment rate was >99% for survivors and >94% for mortality

samples (lower due to some instances of degraded template).

Linkage Map: Markers were chosen for the genome scan based on their position in

the composite linkage map (http://grasp.mbb.sfu.ca/GRASPlinkage.html) to achieve

wide coverage within and across linkage groups. The linkage between all markers

was initially evaluated using the ‘twopoint’ option in Crimap version 2.4 (GREEN et

al. 1990). A LOD score of >3 was considered as significant linkage between markers.

Markers showing significant linkage were then grouped and, for linkage groups with

more than 2 markers, the most likely marker order was evaluated using the ‘build’ and

‘flipsn’ options. The genetic distance between the markers was then calculated using

the ‘fixed’ option, with the large difference in recombination patterns between the

sexes requiring the calculation of sex-specific map distances. The second stage of

genotyping involved adding additional markers to the linkage groups and families

showing evidence for a QTL from stage one, with the marker order and positions

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being re-evaluated using the methods described above. In this stage, the ‘chrompic’

option was also used, to highlight putative phase errors, which were then removed

from the analysis.

It is important to note that the numbers assigned to Atlantic salmon linkage groups are

not fully consistent between the existing linkage maps. In the current study, the

linkage groups are numbered consecutively from 1 to 29. However, in the composite

linkage map (http://grasp.mbb.sfu.ca/GRASPlinkage.html) the linkage groups are

numbered from 1 to 32, but with the omission of numbers 26, 27, and 29.

QTL Mapping: The phenotype data were binary (1=died, 0=survived) and, under

the assumption that underlying IPN resistance is a continuous variable, the phenotypic

expression of which is dependent on a critical threshold, it is appropriate to apply the

same QTL mapping methods as used for quantitative traits (VISSCHER et al. 1996a;

ZHANG et al. 2004). For the genome scan, a two-stage linear-regression approach

(KNOTT et al. 1996) was used for QTL detection, using the web-based software

package ‘QTL Express’ (SEATON et al. 2002). Briefly, the conditional probability of

inheriting a particular haplotype from the sire or dam was inferred from the marker

genotypes in all offspring, at 1 cM intervals. Subsequently, the phenotypic value (i.e.,

died or survived) was regressed on the probability that a particular haplotype allele

was inherited from the sire or dam. The large full-sib families available facilitated the

choice of either sire or dam as the mapping parent within families. However, the

detection of QTL in the initial genome scan was based on the sire analysis, due to the

low recombination giving greater power to detect QTL using few markers per linkage

group (HAYES et al. 2006).

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Where significant evidence for a QTL was detected in the sire-based scan, additional

markers were genotyped in the QTL-segregating families, and the QTL analysis

described above was repeated using a dam-family-based analysis. An estimation of

the percentage of within-family variance explained (PVE) by the QTL was calculated

according to formula of KNOTT et al. (1996). In the sire-based analysis, the formula

of h2QTL = 4[1-(MSEfull/MSEreduced)] was used, where MSEfull is the mean squared

error of the model including the QTL, and MSEreduced is the mean squared error of the

model fitting only a family mean. Additionally, an estimate of PVE was obtained

from both the sire and dam analysis in stage 1, such that h2QTL = 2{[1-

(MSEfull/MSEreduced)Sire]+[1-(MSEfull/MSEreduced)

Dam]}.

Significance Thresholds and Confidence Intervals: The appropriate significance

thresholds for the study were determined empirically by a permutation analysis

(CHURCHILL and DOERGE 1994). The chromosome-wide thresholds were calculated

for each linkage group, using 10,000 permutations. With 29 linkage groups, we

expect approximately 1.45 false positives per genome scan. The genome-wide

thresholds (the level at which one false positive is expected in 20 genome scans;

LANDER and KRUGLYAK 1995) were calculated by applying a Bonferroni correction

to account for the analysis of 29 independent linkage groups, as described in KNOTT

et al. (1998). Significance thresholds were initially calculated for the genome scan,

and then recalculated with the additional markers used to position the QTL on the

significant linkage groups. In the analysis of QTL position, confidence intervals were

calculated using a bootstrapping approach (VISSCHER et al. 1996b), whereby the top

and bottom 2.5% of resampled position estimates define the 95% confidence interval

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for the QTL. For significant QTL, parents were judged to be segregating (i.e.

heterozygous for alternative QTL alleles) based on the t-test of the estimated allelic

effect of that parent. For this test, where the overall QTL effect had already been

declared as significant, the nominal 5% significance threshold was used.

RESULTS

Linkage Map: The mapping of QTL affecting resistance to IPN was achieved by

analysing 10 full-sib families with intermediate mortality levels from a natural IPN

field challenge. Initially, by tracking the inheritance of microsatellite marker alleles

from parents to offspring, the linkage groups and genetic distance between markers

were defined (Supplementary Table 1). The linkage groups calculated were

consistent with the composite linkage map

(http://grasp.mbb.sfu.ca/GRASPlinkage.html). The expected pattern of reduced male

recombination was evident in the linkage maps, with the total map distance for males

being 253 cM compared to 1209 cM for females. Therefore, the female:male ratio

was 4.77:1. This ratio is comparable with other Atlantic salmon linkage maps

(GILBEY et al. 2004; MOEN et al. 2004a,

http://grasp.mbb.sfu.ca/GRASPlinkage.html).

Sire-based QTL analysis: The relevant significance thresholds for the first stage

sire-based QTL genome scan were calculated using a permutation analysis. The

genome-wide significance threshold was F = 3.4, with the chromosome-wide

thresholds ranging from F = 1.8 to 2.0.

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The sire-based genome scan of the 29 Atlantic salmon linkage groups revealed two

genome-wide significant QTL on linkage groups 21 (LG 21) and 26 (LG 26), and a

suggestive QTL on LG 19 (Table 1). For the most significant QTL, on LG 21, four

sires showed evidence for segregation , and the mean additive effect on mortality in

those sires was 0.41 (SE 0.13). Furthermore, the evidence for this QTL also reached

genome-wide significance in a dam-based analysis, with three dams showing

statistically significant evidence for QTL segregation (Table 2). The percentage of

within-family variation (across all ten families) explained by the LG 21 QTL is 24.6

%, when estimated using the sire-based analysis only, and 20.9 % when estimated

using both sire and dam analysis. Within the four segregating families, the estimated

percentage of within-family variation explained is 79%. There was a single family (n

= 57) where both sire and dam were segregating for the LG 21 QTL, and there was a

difference in mortality rate of 75% between the alternative QTL homozygotes (as

predicted from flanking marker genotypes; Table 3).

The LG 26 QTL also surpassed the genome-wide threshold for significance in the

sire-based analysis. The mean effect of the QTL on mortality in the four segregating

sires was 0.46 (SE 0.17), and the estimated PVE was 18.2% (Table 1, Table 2). The

LG 19 QTL reached the chromosome-wide level of significance, with two segregating

sires with an average effect on mortality of 0.40 (SE 0.17).

Dam-based QTL analysis: In the second stage of genotyping and analysis, to

position the QTL on the linkage group, additional markers from LG 21 were

genotyped in the families where the data suggested that the dam was segregating for

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the QTL. Five additional microsatellite markers were added to LG 21

(Supplementary Table 2), and the linkage mapping and QTL analysis were rerun to

reveal the best-estimated position of the QTL at 69 cM (Figure 1). The F Ratio

associated with the QTL is substantially higher than in the sire-family-based analysis,

although this can be partially explained by the reduced number of families included in

this analysis. The genome-wide threshold for significance was F = 6.2, while the

chromosome-wide threshold was F = 3.6. The 95% confidence interval associated

with this QTL, estimated using bootstrapping, was approximately 10cM (from 63 cM

to 73 cM), although the possibility that the QTL may be distal to the end of the

linkage map cannot be excluded.

An additional three markers were added to position the suggestive QTL on LG 26,

and two markers were added to position the suggestive QTL on LG 19 (although one

of these was poorly informative and removed from the analysis). With the additional

markers on LG 26, it is clear that there is a large gap between BHMS437 and the rest

of the linkage group (Supplementary Table 3), and the QTL was positioned within the

interval (55 cM). The additional markers on LG 19 (Supplementary Table 4) assisted

with positioning the QTL at 52 cM, although the majority of evidence for this QTL

stems from a single segregating dam (Table 2).

DISCUSSION

This study is the first to report the detection and positioning of major loci affecting

resistance to IPN in Atlantic salmon. Significant evidence for QTL segregation

within the ten chosen families was found in the sire-based analysis on LG 21, LG 26

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and LG 19. In the dam-based analysis, the LG 21 QTL was positioned with a

reasonable degree of accuracy (CI ~ 10 cM) given the limited number of molecular

markers available in Atlantic salmon compared to many model organism and

agricultural species. The size of effect of this QTL, and its potential for utility in

marker-assisted selection, was highlighted in a single family where both parents were

segregating, with the difference in mortality between the predicted alternative QTL

homozygotes being approximately 75%.

In the first stage sire analysis, the QTL on LG 21 was estimated to explain

approximately 25 % of the observed within-family variance in the overall dataset,

while the QTL on LG 26 and LG 19 were estimated to explain approximately 18 %

and 9 % of the variance, respectively. The corresponding figures, when calculated

using data from both the sire and dam analyses in stage 1, were approximately 21%

for LG 21, 14% for LG 26 and 6% for LG 19. These figures may be overestimates

due to the intermediate mortality in the families chosen, the binary nature of the data,

and the tendency for QTL studies to overestimate the size of effect of significant QTL

Nonetheless, the evidence does suggest that the identified QTL are significant

contributors to the within-family variation in IPN mortality in the studied salmon

population.

The dam-based second stage analysis with additional markers provided highly

significant evidence to confirm the large QTL on LG 21. However, when additional

markers were added to LG 26 and LG 19 in the dam-based analysis, the evidence for

a QTL only reached the suggestive level. It is interesting to note that LG 26 and LG

19 share several duplicated marker loci in the composite linkage map

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(http://grasp.mbb.sfu.ca/GRASPlinkage.html). This strongly suggests that parts of

these two linkage groups are ancestrally homeologous, and raises the possibility that

the two QTL detected may represent two functional paralogs of the same gene(s). For

example, the growth hormone gene has two functional paralogs (GH1 and GH2) in

salmonids that are both inherited in a diploid fashion (MCKAY et al. 2004), although

the locations of these genes on the salmon linkage map are unknown.

The Atlantic salmon genome shows a great deal of homology to other salmonids,

including rainbow trout, Arctic charr and brown trout (DANZMANN et al. 2005;

GHARBI et al. 2006). The two previously identified QTL affecting IPN resistance in

rainbow trout (RT) map to RT LG 3 (IPN R/S 1) and RT LG 22 (IPN R/S 2; OZAKI et

al. 2001, 2005), for which there has not been any clearly identified homology to the

Atlantic salmon QTL linkage groups identified here (DANZMANN et al. 2005).

However, the locus OmyRGT44TUF maps to the opposite end of RT LG 22 to the

IPN R/S 2 QTL (OZAKI et al. 2001, 2005), and OmyRGT44TUF also maps to the

opposite end of AS LG 21 to the QTL identified in the current study. This raises the

prospect that these QTL may be due to the effect of the same gene in the two species,

although further evidence of homology between RT LG 22 and AS LG 21 would be

required to assess this possibility. The markers closest to the RT IPN-resistance QTL

were tested in our populations, but they proved uninformative.

The salmonid QTL screening strategy of utilizing the low male recombination to

detect QTL in an initial scan has clear advantages in terms of minimizing the required

genotyping resources, and increasing the experimental power due to the use of fewer

independent tests. In addition, the two-stage analysis provides a QTL detection stage

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(sire-based), and a confirmation and positioning stage (dam-based). However, one

potential drawback of the approach is that the lower male recombination is not

universal across the genome, and toward the telomeres of the chromosomes the sex-

specific recombination ratios may actually reverse (SAKAMOTO et al. 2000; GHARBI et

al. 2006). As a result, the initial QTL screening using sire-based segregation of just

two or three markers may fail to detect QTL residing toward the telomeres of the

chromosomes. However, data from the current Atlantic salmon linkage maps suggest

that the majority of available markers are inherited with very tight linkage, and often

almost as a single unit (MOEN et al. 2004a; GILBEY et al. 2004;

http://grasp.mbb.sfu.ca/GRASPlinkage.html). Therefore, either the increased male

recombination toward the telomeres is less pronounced in Atlantic salmon than other

salmonid species, or simply there are few markers available for Atlantic salmon

telomere regions. In either case, the two-stage QTL mapping strategy utilized in the

current study would appear to be an effective use of the available markers.

Resistance to IPN is a key target trait for breeders of Atlantic salmon, and a trait

where marker-assisted selection (MAS) may have crucial advantages over traditional

selection based on sib-challenge trials (SONESSON 2005). The IPN resistance QTL

can be applied immediately in within-family MAS in commercial breeding programs,

providing IPN resistance data have been collected from sibs of the selection

candidates thus enabling the phase relationship between the marker alleles and the

QTL alleles to be established. However, to improve the utility of the QTL in MAS,

and to move toward the identification of positional candidate genes, fine-mapping of

the QTL to a smaller region of the chromosome is necessary. Unfortunately, the

number of available markers currently limits the possibility for fine-mapping QTL in

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Atlantic salmon, therefore the development of additional markers in the QTL regions

is an important future target. Comparative mapping and exploitation of the extensive

physical genetic map available (NG et al. 2005) may facilitate the identification of

further markers or candidate genes in the QTL regions for Atlantic salmon. These

additional markers, in combination with the analysis of additional IPN-challenged

populations, will be critical to fine-mapping the QTL.

The identified QTL may represent the physiological effect of variation in genes

critical to the prevention of, or response to, IPN infection in the marine environment.

Whether these genes play a similar role in the resistance of salmon fry to freshwater

IPN is unknown, although there is a strong genetic correlation between fresh and

marine water IPN resistance (STORSET et al. 2003). Experimental infection of

Atlantic salmon with the IPN virus has revealed that the interferon pathways are

paramount in the host response, and this is particularly evident through the up

regulation of interferon-induced Mx gene expression (e.g. LOCKHART et al. 2007).

Investigation of the physiological/immune response to infection by fish with

alternative QTL genotypes may give valuable insight into the genes and pathways

responsible for the QTL effects. The integration of such functional studies with the

aforementioned fine-mapping approach may be an effective route toward the

identification of the genes underlying the major IPN resistance QTL in Atlantic

salmon.

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ACKNOWLEDGEMENTS

The authors gratefully acknowledge Professor William Davidson (Simon Fraser

University, Canada), Dr. Bjorn Høyheim (Norwegian College of Veterinary

Medicine, Norway) and Dr. Karim Gharbi (University of Glasgow, UK) for assisting

with the provision of microsatellite markers for the project. We acknowledge funding

from the British Biotechnology and Biological Sciences Research Council (BBSRC)

and the European Animal Disease Network of Excellence for Animal Health and

Food Safety (EADGENE).

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Table 1: Details of the significant and suggestive QTL from the genome scan (Stage

1) showing the significance levels and estimated percentage of within-family variation

explained, and the QTL positioning using additional markers for segregating dams

(Stage 2) showing the significance level, position and confidence intervals.

Stage 1: Genome Scan

Linkage Group Sire

F Ratio

Significance level Dam

F Ratio

Estimated PVE

(%)

21 4.77 Genome-Wide 3.57 24.6

26 3.74 Genome-Wide 2.54 18.2

19 2.32 Chromosome-Wide 1.45 8.9

Stage 2: QTL Positioning

Linkage Group Dam

F Ratio

Significance level QTL Position

(cM)

95% Confidence

Interval (cM)

21 14.40 Genome-Wide 69 63-73

26 4.96 Chromosome-Wide 55 3-72

19 4.27 Chromosome-Wide 52 27-52

25

Table 2: The QTL effect on mortality and associated absolute T values in segregating

individual parents for the significant and suggestive QTL.

Stage 1: Genome Scan

LG Sire QTL effect estimate

(standard error)

Absolute T Value

21 L3M3076 0.57 (0.13) 4.5

L2M0167 0.50 (0.12) 4.1

L2M0243 0.26 (0.12) 2.1

L3M3080 0.26 (0.13) 2.0

26 L2M0167 0.52 (0.14) 3.6

L3M3051 0.40 (0.15) 2.7

L3M3069 0.60 (0.25) 2.4

L2M0149 0.33 (0.14) 2.4

19 L3M3076 0.39 (0.15) 2.6

L2M0082 0.40 (0.17) 2.3

Stage 2: QTL Positioning

LG Dam QTL effect estimate

(standard error)

Absolute T Value

21 L2F0056 0.73 (0.14) 5.3

L2F0067 0.35 (0.12) 2.8

L2F0133 0.32 (0.13) 2.6

26 L2F0034 0.68 (0.20) 3.4

L2F0622 0.47 (0.22) 2.1

19 L2F0622 0.55 (0.16) 3.4

26

Table 3: The differences in IPN mortality levels between alternative LG 21 QTL

genotypes, within a single full-sib family where both parents were segregating for the

QTL.

* The haplotypes were determined by the segregation of alleles at the markers

BHMS217 and Rsa476 to offspring.

Sire QTL Haplotype*

Resistant Susceptible

Resistant 9% mortality

(n=11)

62% mortality

(n=13) Dam QTL

Haplotype* Susceptible

22% mortality

(n=9)

84% mortality

(n=19)

27

0

1

2

3

4

5

6

0 1 2 3 4 5 6 7 8 9 10 11

Location (cM)

F r

atio

Omy44TUF

Rsa476

BHMS217

0

2

4

6

8

10

12

14

16

0 10 20 30 40 50 60 70

Location (cM)

F ra

tio

0

500

1000

1500

2000

2500

3000

3500

Omy44TUF

CL68944

Rsa354

CL18304

CL13225

Rsa476

BHMS217

Alu333

Bo

otst

rap

Figure 1: The likelihood profile for linkage group 21 following the sire-based

genome scan (A) and the dam-based QTL positioning with additional markers (B).

A

B

Genome-wide Significance

Genome-wide Significance

28

Supplementary Table 1: Details of the markers used in the study, and the linkage map calculated for the genome scan

Position (cM)

LG Marker Name Male Female

Forward Primer Reverse Primer

Genbank

Accession

1 BHMS216 0 0 AGAGCGAATACAACAGCC GGACACAACAAACATTACC AF256728

1 Alu032 1 17.6 TTTCATCGTCTGGACTGG CCATCCTCAATGTTCTTC AF271435

1 Ssa202 1.2 86.8 CTTGGAATATCTAGAATATGGC TTCATGTGTTAATGTTGCGTG -

2 BHMS292 0 0 GGTAAGTCAAGGTTTCACC TTACTCCCCAACTCTGAG AF256729

2 BHMS201 4.2 16.3 CCCCATGATGTGTTCTTC CACAATGAGGCTTGACAC AF256678

2 BHMS360 8.7 16.3 ATCCCTTCCTTCATCACG AAGCCATGAGGAGACTATC AF256701

3 BHMS429 0 0 CCCCTGTCAAACGTCTTC AGCACACTGGATTCAAGG AF256719

3 BHMS367 1.6 11.4 TGTTCTCCCAGGAAGCAC AGCCTAGCAGCTCATTGG AF256704

29

4 Alu054 0 0 CCAACTCACTTTGCTATGAC TTCTCGCTCTTTCTCGCC AF271446

4 Ssa171 0.6 10 TTATTATCCAAAGGGGTCAAAA GAGGTCGCTGGGGTTTACTAT -

4 Omy27/1INRA 17.6 73.4 CCAATCACCATCTGCTGGG GCCCATCGTTTAGCCAGG -

5 SSsp2201 0 0 TTTAGATGGTGGGATACTGGGAGGC CGGGAGCCCCATAACCCTACTAATAAC AY081807

5 BHMS328 6.7 13.3 GATGGGTGCTATTGACTC CCACACAATCACCGTTGC AF256731

6 Ssa64/2 0 0 ATCACCAGGAAGTTCTGC AGCTTTGTCCTCAAGGAG AF019183

6 Omy21/1INRA 0.3 14.2 GCATTGGCGTAATGAGAAGG CTGACGGACATATCAGCCC -

6 SSsp2210 0.5 18.8 AAGTATTCATGCACACACATTCACTGC CAAGACCCTTTTTCCAATGGGATTC AY081808

7 BHMS117B 0 0 TTCCCCTCTGATCCCAAG TGTTCTCTACACAGTTGCC AF257048

7 BHMS269 1.2 100 AACACACATGACGCAGCC CAATTCAGCCTCTCCCAC AF256689

7 BHMS337 16.1 136 TCCCACTGCCAACTACAG GTTTAATCAAAGCATTCGCC AF256750

30

8 BHMS313A 0 0 TTCATGTGTGCGAGAGCG AGAATGCAGTATTAGACTGG AF257052

8 Ssa401UOS 1.8 14.9 ACTGGTTGTTGCAGAGTTTGATGC AAACATACCTGATTCCCGAACCAG AJ402718

8 Ssa197 3.2 41.8 GGGTTGAGTAGGGAGGCTTG TGGCAGGGATTTGACATAAC -

9 SSspG7 0 0 CTTGGTCCCGTTCTTACGACAACC TGCACGCTGCTTGGTCCTTG AY081813

9 BHMS221A 31.8 14.5 ACTTCCATTCAGATGACAC CCTGTATCTCCTCCATTAC AF257053

10 Ssa48 0 0 ACAGGTCTGCCATTGGAG CGAAATCCTAATAAACTCCC AF019176

10 BHMS330 3.8 0 CTAGATCACTCACCCAGG GTGCTTTTGGCTTATGTTAG AF256748

10 SSOSL85 4.6 27.9 AGACTAGGGTTTGACCAAG ATTCAGTACCTTCACCACC Z48596

11 Ssa417UOS 0 0 AGACAGGTCCAGACAAGCACTCA ATCAAATCCACTGGGGTTATACTG AJ402734

11 BHMS184B 16.6 46.9 GCAGCATAGAGAGTAATGG TGGAAAAGTCCCTCACCC AF257049

31

12 Alu059 0 0 TGTGTTTGTCGTCTGTAAC ATGTCCAACTGGTCTCAC AF271448

12 BHMS522 0.5 100 TCTGTCACTGTCACCCTG CACACGTCTCTATCCGTG AF256697

13 Ssa407UOS 0 0 TGTGTAGGCAGGTGTGGAC CACTGCTGTTACTTTGGTGATTC AJ402724

13 BHMS233 0 14.8 GCACTGGGGTTTAATGTC TGTATAGGGGCAATCAGC AF256845

13 BHMS288 2.4 20.4 TATCACCCAGTGAACGTG CAAATGAGCCATCAACAG AF256696

14 BHMS111 0 0 TCCTCTATCATACGGCTG ATCAGATGACCCAGTGGC AF256661

14 BHMS321 3.1 12.8 CTGTCATTCCCTTGGCAC GATGCTGCTAGGAGAGAG AF256743

15 BHMS386 0 0 CGTTAAAACCCCGTGGAG GACTAAAAAGCGTCTGGC AF256712

15 BHMS379 2.8 42.1 GAACAACTTCAGAACTTGAC CGCCTCATAGCTGATATTTAAC AF256708

16 BHMS277 0 0 ACTAAGAGTCCACATTTGAG TTAGGATGGAGAATGGTAG AF256692

16 BHMS155 1.1 10.4 TGAAACTAGGATGCCTGG TCTGACCCACACACAAGC AF256667

32

16 BHMS176 1.5 36.1 GACTTGGAGACTCTTTGG GAGAGGGAGATAGCATCG AF256672

17 Alu1 0 0 AACAGCCACATCATAAGAC TTCCATACATCTCATCTGG AF271465

17 BHMS7-028 2.9 100 TTGTGGGTGGGTGTAAGC CTCTGTCATGGCAGGATG AF256656

17 Ssa410UOS 4.4 100.3 GGAAAATAATCAATGCTGCTGGTT CTACAATCTGGACTATCTTCTTCA AJ402727

18 Ssa85 0 0 AGGTGGGTCCTCCAAGCTAC ACCCGCTCCTCACTTAATC -

18 SSsp1605 15.5 35.5 CGCAATGGAAGTCAGTGGACTGG CTGATTTAGCTTTTTAGTGCCCAATGC AY081812

19 BHMS235 0 0 AGCGAGCTTTCTTTCCAG AGCTGTCTATTCACGACTC AF256846

19 Rsa277A 18.6 14.1 GCTAATAGATACTGTGGCTC TGAGTCATACACCATTTGTC -

20 Ssa20 0 0 TCATGGAGAAGAGGAGGC ACAGACAGCTTACACAACTC AF019162

20 BHMS241 10 75.9 TGGTAGATGTCTTCTCCC GATCTGGTGACCTGTTCC AF256683

33

21 OmyRGT44TUF 0 0 GAGGGTTGGAGTACACAGAAGG ATGTGGGGACATATTAACTGGC -

21 Rsa476 11.4 29.9 ATGGTGCGGACCTCATTC CTTCATCGTTGTGTCGTC -

21 BHMS217 11.6 34.2 GCTGTTCATTTCTGAGCAG GACACACCGAATCAGTGC AF256786

22 Ssa404UOS 0 0 ATGCAGTGTAAGAGGGGTAAAAAC CTCTGCTCTCCTCTGACTCTC AJ402721

22 BHMS332 9.5 68.8 ACCTTTTGGCTGAATGAC TAACCGAATGACTGTGAG AF256749

23 Ssa20.19NUIG 0 0 TCAACCTGGTCTGCTTCGAC CTAGTTTCCCCAGCACAGCC AJ290344

23 BHMS283 36.8 71.6 CCATTTATCACTCGACCC GCATATACAGTGCCGTCG AF256695

24 Omy14INRA 0 0 GTCAGCGATAATCCACATGG CCGTTATGGAGATGTGTAGGG -

24 Ssa418UOS 3.7 11.4 CACACCTCAACCTGGACACT GACATCAACAACCTCAAGACTG AJ402735

25 Ssa421UOS 0 0 CAGGGTCTGTGGTGGACTGTTC CGTTTGCACATTGTGAGGTGTC AJ402738

25 Ssa79NUIG 1.3 26.9 TGGGACCAAATAGAACAG ATGGAGTCTCTTGTCACT AJ290377

34

25 SSLEER15 5.9 42.3 ACAACAGCGTCACCTGTC ACTGACTTGAAGGACATTAC U86708

26 BHMS373 0 0 AATAAGAGGGCAGTGGAG TGCACCAGAGAGAGTAGC AF256705

26 Ssa405UOS 0 1.8 CTGAGTGGGAATGGACCAGACA ACTCGGGAGGCCCAGACTTGAT AJ402722

26 BHMS437 29.6 79.9 AGAGAAGTATAAACCCTGC AATATGGTAGGAAGACACAG AF256816

27 Rsa274 0 0 TAGCCCCAAAAATGGATG GTGACCCCCAATCTTTCC -

27 Ogo4 1.6 7.4 GTCGTCACTGGCATCAGCTA GAGTGGAGATGCAGCCAAAG AF009796

28 BHMS144 0 0 TTGTGCCGATTTAGGACG GCCTTTAACGTAAGTGGTAG AF256665

28 Ssa224 19.1 47.8 ACAGACAGAACTGTGCATC TGACTGCATTTATCAGAGAG AF019168

29 Ssa65 0 0 TGTTGTGGCTCGTGACAG GAACACAGGGTAGAGTGG AF019184

29 BHMS392 0 2.6 CGTTCAATTCTCCCATATC GACAGATTTACCAGGAGC AF256810

29 Ssa64/1 0 21.8 ATCACCAGGAAGTTCTGC AGCTTTGTCCTCAAGGAG AF019183

35

Supplementary Table 2: The linkage map calculated after the addition of markers to linkage group 21

LG 21:

Position (cM) Marker

Name F M

Forward Primer Reverse Primer Genbank

Accession

Omy44TUF 0 0 GAGGGTTGGAGTACACAGAAGG ATGTGGGGACATATTAACTGGC -

CL68944 31.0 0 CAGCACCACCACCCAACA TCCATCGGCCTCCCTCTA -

Rsa354 35.7 11.5 TGTTACTTAGGGTTGAACG AGGTTGGAACTGTTGCTG AY543989

CL18304 49.6 14.3 GACTTCAAACGGTGTCGG CTTGCCTAGTTAAATAAAGGTG -

CL13225 49.6 21.8 GGTTGAGGTCAGGGGGTA TGTTCAGTGGCACATTTTGA -

Rsa476 58.2 27.0 ATGGTGCGGACCTCATTC CTTCATCGTTGTGTCGTC AY544054

BHMS217 64.3 27.0 GCTGTTCATTTCTGAGCAG GACACACCGAATCAGTGC AF256786

Alu333 74.7 27.0 TTCATAGTCCAAGAACAGTG GCTGAGTTTACATTACACCTG AY543859

36

Supplementary Table 3: The linkage map calculated after the addition of markers to linkage group 26

LG 26:

Position (cM) Marker

Name F M

Forward Primer Reverse Primer Genbank

Accession

Alu005A 0.0 0.0 TATGTGATTAGGGCTTGC CTTGGCGTAGTTTAGTGC AF271427

BX873441/i 7.7 19.8 GAAGAGTTCCGGTCCATCGG CGTGCATGTAATTCAGCCTGC BX873441

SSA405UOS 14.9 21.0 CTGAGTGGGAATGGACCAGACA ACTCGGGAGGCCCAGACTTGAT AJ402722

BHMS373 16.4 21.0 AATAAGAGGGCAGTGGAG TGCACCAGAGAGAGTAGC AF256705

BHMS437 72.0 61.3 AGAGAAGTATAAACCCTGC AATATGGTAGGAAGACACAG AF256816

37

Supplementary Table 4: The linkage map calculated after the addition of markers to linkage group 19

LG 19:

Position (cM) Marker

Name F M

Forward Primer Reverse Primer Genbank

Accession

Rsa424/1 0 0 TGTGAGAGACGGAGACAG GAGGGGTTGATACAGACC AY544020

BHMS235 27.5 3.2 AGCGAGCTTTCTTTCCAG AGCTGTCTATTCACGACTC AF256846

Rsa277A 52.7 17.4 GCTAATAGATACTGTGGCTC TGAGTCATACACCATTTGTC -

38