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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: [email protected]
3
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.
4
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
5
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-
6
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
7
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’
8
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.
9
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
10
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).
11
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
12
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.
13
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
14
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
15
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
16
(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
17
(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
18
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.
19
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