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Research article
Characterisation of QTL-linked and genome-wide restriction site-associated DNA (RAD) markers in farmed Atlantic salmon
Ross D Houston*, John W Davey, Stephen C Bishop, Natalie R Lowe, Jose C Mota-Velasco, Alastair Hamilton, Derrick R Guy, Alan E Tinch, Marian L Thomson, Mark L Blaxter, Karim Gharbi, James E Bron and John B Taggart
Corresponding author:
Ross D Houston
The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Midlothian, EH25 9RG, UK
Institute of Evolutionary Biology, Ashworth Laboratories, King’s Buildings, University of Edinburgh, Edinburgh, EH9 3JT, UK
Landcatch Natural Selection Ltd., Alloa, Clackmannanshire, FK10 3LP, UK
The GenePool Genomics Facility, Ashworth Laboratories, King’s Buildings, University of Edinburgh, Edinburgh, EH9 3JT, UK
Institute of Aquaculture, University of Stirling, Stirling, FK9 4LA, UK
For all author emails, please .
BMC Genomics 2012, 13:244&
doi:10.64-13-244
The electronic version of this article is the complete one and can be found online at:
Received:31 January 2012
Accepted:15 May 2012
Published:15 June 2012
& 2012 Houston et al.; licensee BioMed Central L licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Restriction site-associated DNA sequencing (RAD-Seq) is a genome complexity reduction
technique that facilitates large-scale marker discovery and genotyping by sequencing.
Recent applications of RAD-Seq have included linkage and QTL mapping with a particular
focus on non-model species. In the current study, we have applied RAD-Seq to two Atlantic
salmon families from a commercial breeding program. The offspring from these families
were classified into resistant or susceptible based on survival/mortality in an Infectious
Pancreatic Necrosis (IPN) challenge experiment, and putative homozygous resistant
or susceptible genotype at a major IPN-resistance QTL. From each family, the genomic
DNA of the two heterozygous parents and seven offspring of each IPN phenotype and
genotype was digested with the SbfI enzyme and sequenced in multiplexed pools.
Sequence was obtained from approximately 70,000 RAD loci in both families and a filtered
set of 6,712 segregating SNPs were identified. Analyses of genome-wide RAD marker
segregation patterns in the two families suggested SNP discovery on all 29 Atlantic
salmon chromosome pairs, and highlighted the dearth of male recombination. The use
of pedigreed samples allowed us to distinguish segregating SNPs from putative paralogous
sequence variants resulting from the relatively recent genome duplication of salmonid
species. Of the segregating SNPs, 50 were linked to the QTL. A subset of these QTL-linked
SNPs were converted to a high-throughput assay and genotyped across large commercial
populations of IPNV-challenged salmon fry. Several SNPs showed highly significant
linkage and association with resistance to IPN, and population linkage-disequilibrium-based
SNP tests for resistance were identified.
Conclusions
We used RAD-Seq to successfully identify and characterise high-density genetic markers
in pedigreed aquaculture Atlantic salmon. These results underline the effectiveness
of RAD-Seq as a tool for rapid and efficient generation of QTL-targeted and genome-wide
marker data in a large complex genome, and its possible utility in farmed animal selection
Keywords: A RAD A Infectious R Single nuc Paralogous sequence variantBackground
Atlantic salmon (Salmo salar L.) is a species of economic importance to both wild fisheries and aquaculture production.
The worldwide production of Atlantic salmon through aquaculture has increased rapidly
over recent years, and is now approximately 1.5million tonnes per annum []. Large-scale salmon breeding programs were first established in the 1970s [], and the typical four-year generation interval means that selected lines of farmed
salmon remain just a few generations from ancestral wild fish []. Family-based selection has resulted in rapid improvement in economically important
traits, including growth, age at maturation and resistance to pathogens [] . Genetic markers have been critical for this process, and are often utilised for
family assignment and mapping loci of economic importance. Microsatellite and SNP
resources are available for Atlantic salmon [-], and the ongoing genome sequencing project is anticipated to result in further genetic
marker discovery []. However, the genomics resources available still lag behind terrestrial livestock
species, which hinders the application of genomic selection [,] and fine-scale analysis of the genomic regulation of scientifically and economically
important traits. Furthermore, due to the diverse ancestral origins of different breeding
programmes, marker assays developed from a single reference genome or a limited set
of genomes may not be fully informative in a population or family of interest.
Advances in high-throughput short-read sequencing technology have facilitated rapid
and cost-efficient generation of gigabases of data by individual laboratories. This
has led to new approaches for simultaneous discovery and genotyping of dense genetic
markers on a scale that represents a step change from the state of the art prior to
this technology []. One such approach is Restriction-site Associated DNA (RAD) sequencing (RAD-Seq),
which is a genome complexity reduction technique that sequences at depth flanking
regions of restriction enzyme cleavage sites []. This enables reliable base calling and SNP identification. RAD-Seq is typically
applied to multiplexed samples, where DNA fragments are ligated to a sample-specific
barcode sequence for pooled sequencing, with subsequent in silico assignment of reads to samples enabling individual and population-level genotyping
[,]. RAD-Seq data can be readily analyzed without a reference genome, which makes the
technique particularly applicable to non-model organisms, including Atlantic salmon.
It can be applied in lieu of or in addition to other SNP assay technologies, because RAD-Seq can generate a
large-scale population-specific marker set with individual genotypes.
One of the main foci of QTL mapping studies in Atlantic salmon to date has been the
genetic regulation of host resistance to disease [-]. The most striking example is the discovery of a locus explaining almost all of the
genetic variation in resistance to Infectious Pancreatic Necrosis [,]. IPN is a viral disease, the causal agent of which is a highly contagious birnavirus
that can cause high levels of mortality at both the early freshwater and sea water
stages of the salmon lifecycle []. An individual's possession of one or two favourable copies of the resistance allele
markedly reduces the likelihood of mortality, the magnitude of which is dependent
on the severity of the overall epidemic. However, studies by Scottish and Norwegian
research groups have independently verified the large difference in mortality between
alternate QTL homozygotes in both controlled challenge and ‘field’ seawater exposures,
with estimates of mortality proportion ranging from 0.36 to 1 [,,,]. The resistance allele has been selected for in breeding programs using marker-assisted
selection [,], but the causal gene and mutation remain elusive. The position of the QTL has been
narrowed to a 3 cM confidence interval on linkage group 21 []. However, locating and investigating the underlying gene(s) requires a higher density
of genetic markers within the QTL region than is currently available.
The salmon genome, in common with other salmonid species, has undergone a duplication
event 25–100 MYA, and it demonstrates residual tetrasomic inheritance. This is evidenced
by extensive gene duplication and homeology across much of the genome and quadrivalent
formation at meiosis in males [,]. A related complication in salmonid genomics is the high number of paralogous sequence
variants (PSVs) which can appear as putative SNPs in sequence alignment analysis,
and need to be distinguished from true segregating SNPs. For example, Hohenlohe et al. used excess heterozygosity to exclude PSVs from RAD-derived SNP panels generated
for both rainbow trout (Oncorhynchus mykiss) and cutthroat trout (O. clarkii) [], while Sanchez et al. utilised double-haploid rainbow trout to filter out PSVs discovered using reduced
representation sequencing []. Additionally, there is extreme heterochiasmy in salmon, with very little recombination
over large sections of the genome in males, while females demonstrate more typical
patterns of crossover and recombination. This is evident in the linkage maps that
have been created for Atlantic salmon to date, where ratios of female:male recombination
rates have been estimated at between 1.4:1 and 16.9:1 [-,]. The most recent Atlantic salmon linkage map highlights a large difference in the
genomic distribution of male and female recombination, with male recombination more
common near putative telomeres []. A recent study using RAD-Seq also demonstrated a general lack of male recombination
in rainbow trout by the clustering of the majority of markers proximal to putative
centromeric positions in the linkage map [].
In the current study, our aim was to discover, verify and genotype a large number
of polymorphic markers in the Atlantic salmon genome, with a particular focus on markers
linked to the IPN resistance QTL. By applying RAD-seq to parent and offspring samples,
we aimed to verify marker segregation and help identify and distinguish between SNPs
and PSVs. We also utilised the lack of salmon male recombination to analyse genome-wide
patterns of RAD marker segregation from sires to offspring to identify putative linkage
groups. To identify trait and QTL-linked markers, we used heterozygous parents and
their offspring of known IPN resistance phenotype and QTL genotype (putative homozygous
resistant or homozygous susceptible) to identify candidate resistance and susceptibility
alleles though linkage. A subset of trait and QTL-linked SNP markers were then verified
using a high-throughput assay applied to larger populations of IPNV-challenged Atlantic
RAD-seq data processing
Two families were identified from an earlier QTL mapping experiment as having both
sire and dam heterozygous for the IPN resistance QTL []. Using microsatellite markers from Atlantic salmon linkage group 21 (corresponding
to chromosome 26 []), seven offspring from each family were classified as homozygous for the resistance
allele and seven of their siblings were classified as homozygous for the susceptibility
allele (details on genotyping and classification of animals into QTL genotypes given
in []). DNA from each of these fish was used to construct multiplexed RAD-Seq libraries
and sequenced at high depth using a 100 bp paired-end strategy on an Illumina platform.
Detailed explanations of the RAD-Seq technique have been given elsewhere [,]. However, for an explanation of some of the terminology used in the current study,
please refer to the ‘RAD-Seq Terminology’ section of the Methods.
The experiment was designed to generate very high Illumina sequence coverage for the
parents to obtain a clearly defined reference set of RAD alleles to which the lower-coverage
offspring sequences could be compared (Table ). Following read demultiplexing using the sample-specific penta-nucleotide barcodes,
there were between 9.2 and 14.0 million reads per parent, and between 1.6 and 6.4
million reads per offspring (Table ). In total, 123,739 RAD alleles were detected, with 110,303 and 113,639 alleles defined
in families 1 and 2 respectively, comprising 100,203 common alleles and 23,536 family-specific
alleles. These RAD alleles were clustered into 71,404 and 70,938 RAD loci in families
1 and 2 respectively, of which 69,286 were common (shared at least one allele) (Figure
, Additional files
and ). As the SbfI restriction site is palindromic, reads are obtained in both directions
from the site and, therefore, the number of RAD loci is expected to be twice the number
of restriction sites in the genome.
Additional file 1. Details of Family 1 RAD loci, number of RAD alleles and fragment counts for all individuals.
Format: TXT
Size: 17.6MB
Additional file 2. Details of Family 2 RAD loci, number of RAD alleles and fragment counts for all individuals.
Format: TXT
Size: 18MB
Experimental design and details of total read counts per individual (after quality
control filtering of reads)
Frequency of single allele and bi-allele and multiple allele RAD loci in the two families.
Genome-wide RAD loci
The number of RAD alleles per RAD locus ranged from one (81% of all loci) to 5,764
(Figure , Additional files
and ). RAD loci containing many (e.g. hundreds or thousands) RAD alleles are likely to
be due to an SbfI site within frequently occurring repeat elements, which are common
in the salmon genome [,]. A single allele at a RAD locus does not necessarily indicate monomorphism because
a polymorphism in the SbfI restriction site itself would still result in an observable
presence/absence segregation pattern in the offspring. The second most common type
of RAD loci (15%) were those with exactly two RAD alleles (10,238 loci in family 1
and 10,930 loci in family 2), which are likely to contain both SNPs and PSVs. For
the current study, we focus on these bi-allelic RAD loci to detect genome-wide SNPs/PSVs,
as well as QTL-linked SNPs. A minority of RAD loci (4%) had three or more RAD alleles.
These less tractable polymorphisms, possibly the result of polymorphic duplicated
regions of the genome (multisite variants), repeat elements containing SbfI sites,
multi-allelic SNPs or multiple SNPs, were not considered further in this study. Overall,
the frequency pattern of clustering of RAD alleles into RAD loci was similar for the
two families (Figure ).
Bi-allelic loci containing SNPs and PSVs
To identify candidate bi-allelic segregating SNPs, and to distinguish these from non-segregating
bi-allelic PSVs, filtering of RAD loci containing exactly two RAD alleles was performed
as described in the Methods. RAD loci where both RAD alleles were present in both
parents and all offspring (allowing missing alleles in up to two individuals to account
for the possibility of a RAD allele not being sampled in particular individuals due
to variation in read coverage) were classified as putative PSVs. We identified 3,768
bi-allelic RAD loci with this excessive heterozygosity in family 1 and 3,491 in family
2, of which 2,674 were common between the two families (Table ). The overlap between the probable PSV-locus-derived RAD alleles in the two families
is expected, since these are likely to reflect fixed differences between homeologous
genomic regions. We filtered the remaining RAD loci (4,362 in family 1, 3,646 in family
2, 3,197 in common) to remove those where there was & 3 base divergence between the
alleles, as this level of divergence exceeds that expected in simple allelic loci.
The final set of high-confidence bi-allelic SNPs (with ≤ 3 base divergence between
the pairs of RAD alleles) included 4,725 SNPs identified in 4,111 RAD loci in family
1, and 3,927 SNPs identified in 3,405 RAD loci in family 2. In this final set of bi-allelic
RAD markers across the two families there were 6,712 segregating SNPs, of which 1,940
were segregating in both families (Table
and Additional file ). There were an approximately equal number of transitions (cytosine-thymine and adenine-guanine
SNPs) and transversions (all other SNPs) in both families (Table ).
Additional file 3. List of RAD allele sequences containing SNPs marked using IUPAC ambiguity code.
Format: TXT
Size: 422KB
Details of the bi-allelic RAD loci and filtered putative SNPs and PSVs in Family 1
and Family 2
Frequency of the six possible nucleotide substitutions at the putative SNPs in Family
1 and Family 2
Genome-wide segregation patterns
Sire-based linkage clusters of RAD markers were developed by applying a similar approach
to Baxter et al.[], and utilising the paucity of male recombination in Atlantic salmon. Within the filtered
set of bi-allelic RAD markers, the frequency of segregation patterns of all RAD alleles
that were observed in the sire and showed a presence/absence segregation pattern in
the offspring (indicating sire heterozygosity) was compared. The most frequently observed
segregation patterns are expected to correspond to a set of fully linked RAD markers
in these families (i.e. 58 patterns corresponding to the 2n number of chromosomes
in Atlantic salmon). For a full explanation of the expected segregation patterns using
RADtools refer to ‘RAD Allele Segregation’ in the Methods.
In family 1, 1,631 sire-segregating RAD markers were identified, of which 1,337 (82%)
clustered into the most frequent 58 (29 pairs of) different segregation patterns (Figure
). The remaining 294 RAD markers displayed 240 different segregation patterns. These
data indicate that much of the surveyed genome is inherited without male recombination
in this family, although further insight into this phenomenon will require a more
detailed SNP genotyping and linkage analysis in larger families. In comparison, the
same analysis of dam-based segregation revealed 1,292 segregating RAD markers, of
which 602 (47.0%) clustered into the top 29 most frequently occurring pairs of segregation
patterns (Figure ). In family 2, 626 (72%) of the 868 identified sire-segregating markers clustered
into the most frequent 27 pairs of segregation patterns (Figure ), with the remaining 242 markers displaying 202 different segregation patterns. However,
due to the overall lower number of segregation patterns in this family, the distinction
between patterns likely to correspond to linkage groups and other patterns was not
as clear-cut as for family 1 (see Discussion.) Twenty seven pairs of segregation patterns
was the most obvious empirical cut-off.
RAD marker segregation patterns in family 1. The top 75 most frequently observed RAD marker sire segregation patterns from the
filtered bi-allelic loci in family 1. Note the cluster of 29 sire-segregation patterns
likely to correspond to RAD markers from regions of distinct linkage groups inherited
without recombination in this family. (i) Sire-segregating RAD alleles. (ii) Dam-segregating
RAD alleles.
RAD marker segregation patterns in family 2. The top 75 most frequently observed RAD marker sire segregation patterns from the
filtered bi-allelic loci in family 2. (i) Sire-segregating RAD alleles. (ii) Dam-segregating RAD alleles.
When the RAD marker allele sequences showing the most frequent segregation patterns
(linkage clusters) were compared across the two families, matches could be made between
specific linkage clusters. Due to the need for sire heterozygosity and dam homozygosity
for the segregation pattern at a given polymorphism to be observed (as described under
‘RAD Allele Segregation’ in the Methods), only a proportion of the RAD alleles from
any of the linkage clusters would be expected to match between families. For example,
even given a minor allele frequency of 0.5, the chance of observing a SNP matching
this pattern in any given family is 0.25 [i.e. prob (sire heterozygous) x prob (dam
homozygous), or 0.52]. It was possible to match 25 out of the 29 putative RAD linkage clusters in family
1 through this allelic homology to putative linkage clusters in family 2 (Table ). With only one exception, RAD marker alleles from one linkage cluster in family
1 matched exclusively to one cluster from family 2, and vice versa. This suggests
that the linkage clusters are consistent between the two families.
Details of the sire-based linkage clusters based on the most frequently observed bi-allelic
RAD marker allele segregation patterns in the two families
QTL-linked RAD alleles
To identify RAD alleles linked to the IPN resistance locus in the two families, all
RAD alleles were screened for segregation patterns that matched the QTL genotype (defined
by the microsatellite markers spanning the QTL region) and the IPN mortality phenotype
(Table ). In family 1, there were 90 resistance-linked RAD alleles and 78 susceptibility-linked
RAD alleles (Table ). In family 2, there were 22 resistance and 21 susceptibility-linked RAD alleles.
Both resistance and susceptibility-linked RAD alleles were identified in 43 RAD markers
in family 1, compared to six RAD markers in family 2, with two of these loci being
common across families. For calling QTL-linked SNPs, RAD markers that contain resistance
or susceptibility RAD alleles were also screened for RAD alleles that nearly match
the QTL genotype pattern (e.g. with two or fewer individuals showing putative recombination.)
In total, there were 50 QTL-linked SNPs identified using these criteria, of which
four were segregating in both families, and three of these common SNPs were perfectly
linked to the QTL. A subset of SNPs with sufficient flanking sequence data for a high-throughput
genotyping assay design were identified, and details of all suitable QTL-segregating
SNPs in both families and a subset of those only segregating in family 1 (total n = 17)
were provided to Kbioscience Ltd (Hoddesdon, Herts) for assay design (Additional file
). Of these, 13 assays were successful and four failed. From the successful assays,
two putative SNPs returned monomorphic genotype data in a test plate of QTL-segregating
samples, leaving 11 true segregating RAD SNPs suitable for larger-scale genotyping
and linkage analysis.
Additional file 4. List of high-throughput assay SNPs used for QTL mapping and population-wide association
Format: TXT
RAD alleles and putative SNPs linked to the IPN resistance QTL in families 1 and 2
QTL linkage mapping
The 11 IPN QTL-linked SNPs were genotyped across the linkage mapping population (ten
full-sib families, total n = 1,341 []). Three previously published SNPs [] and five novel SNPs, closely linked to the QTL (within BAC contig fps378 – see Methods),
were also genotyped across the same samples (total = 19 SNPs). 3Sequence details of
the SNPs used in the QTL mapping analysis are given in Additional file . Finally, an additional two microsatellite markers also from contig fps378 (SSA374
and SSA680) were genotyped across the same samples. These new genotypes were combined
with existing data from three microsatellite markers [] in the mapping analysis. All markers (SNPs and microsatellites) showed highly significant
linkage to each other and to the QTL in our mapping population. A dam-based linkage
map was then built for the QTL region (Table ), with markers omitted if the Crimap software could not position the markers in the
map. The map covered 37.6 cM which indicates that the QTL-linked markers detected
through RAD sequencing were dispersed over a large section of the chromosome.
Details of the updated dam-based linkage map for the IPN Resistance QTL region on
A dam-based regression interval mapping analysis of the map, genotype and trait (IPN
mortality) data was performed using GridQTL []. The QTL was mapped to a position of 21 cM on the new map, and the bootstrap analysis
(10,000 permutations) gave the average QTL location as 21.3 cM and defined the confidence
interval as 2 cM between 20 and 22 cM (Table
and Figure ). The flanking markers for the confidence interval were SSA0139ECIG and RAD_HT01/SSA0019ECIG.
These results provide a reduced confidence interval (10 cM down to 2 cM) for the QTL
in our populations compared to previous work [], and a minor reduction compared to study of Moen et al. (C.I. 3 cM, []).
QTL likelihood profile. The IPN Resistance QTL likelihood profile on LG 21 with the addition of the new markers.
The solid red line is the QTL F Ratio statistic from the linear regression and the
blue bars are number of bootstrap samples highlighting the QTL confidence interval.
SNP-trait association
To assess the population-wide association between the most tightly QTL-linked SNPs
(SSA0019ECIG, RAD_HT01, SSA0139ECIG) and IPN mortality, the SNPs were genotyped across
a population of 9,000 IPNV-challenged salmon fry from 400 full-sib families deriving
from two year groups of the Landcatch Natural Selection broodstock. The markers RAD_HT01
and SSA0139ECIG showed consistent and highly significant association with IPN mortality
(and therefore by inference IPN resistance/susceptibility) across the two yearclasses
of fish (Table ), implying linkage disequilibrium between the SNP alleles and the QTL alleles in
these populations. SSA0019ECIG showed inconsistent effects in the two year groups
(Additional file : Table S1) which is likely to be due to its segregation in only a few families (MAF = 0.13.)
The contrast in mortality level between the homozygous resistant SNP genotype and
the homozygous susceptible SNP genotype was 53% and 46% for RAD_HT01 and SSA0139ECIG
respectively.
Additional file 5. Table S1. Details of the primers and SNPs for the BAC contig fps378.
Format: DOC
Size: 33KB This file can be viewed with:
Population-wide association between two closely-linked SNPs in the IPN QTL region
and mortality in a freshwater IPNV challenge (all associations significant at P & 0.05)a
Discussion
In this study, RAD-Seq has been used to discover, verify and genotype novel genetic
markers in pedigreed Atlantic salmon. By targeting individuals of known disease resistance
phenotype and genotype at a major QTL, we discovered and scored novel QTL-linked SNPs
with flanking sequence. The use of pedigreed (parent and offspring) samples allowed
us to examine segregation of RAD markers, linkage patterns, and to distinguish RAD
loci containing putative SNPs from those containing putative paralogous sequence variants
(PSVs). The outcomes of the study include a new SNP resource for Atlantic salmon,
high-coverage sequence data at sites dispersed throughout the genome, improved knowledge
of a genome region harbouring a QTL of major importance to salmon aquaculture and
improved population LD-based genetic tests for resistance to IPN.
The RAD library sequence data were analysed with the RADtools pipeline []. In this method, unique RAD reads are filtered based on quality score and clustered
into RAD loci based on sequence similarity within and across individuals. Further
analyses of the data defined putative SNPs and PSVs within RAD loci and examined the
segregation patterns of alleles within these loci by looking at presence or absence
of alleles in individual animals using methods similar to those used by Baxter et al.[]. These analyses were suitabl the thresholds we chose for defining
RAD loci and for distinguishing genuine segregation patterns from fluctuations in
read counts were empirically derived and conservative. Genotypes in our dataset were
defined as ‘presence’ or ‘absence’ of a RAD allele, and as such the RAD markers were
effectively acting as dominant markers. Although we did not attempt it in our study,
it may be possible to use the fragment count data to differentiate homozygous and
heterozygous genotypes, or to identify putative multisite variants based on an excess
of one particular allele. Indeed, the recently published software pipeline ‘Stacks’
also detects and genotypes SNPs in short-read sequence data, and uses a maximum likelihood
algorithm to call heterozygous and homozygous genotypes based on read counts []. This software has recently been used to create linkage maps in the spotted gar []. As RAD-Seq continues to develop as a means of genotyping by sequencing, the analysis
pipeline is likely to become increasingly robust, standardised and automated, which
will broaden its utility and improve consistency.
A notable outcome from our analyses of the most frequent patterns of segregation
was the degree of clustering of sire-based segregation patterns (Figure ). In one family, 82% of sire segregating RAD markers clustered into the 58 most frequent
presence/absence patterns (i.e. two pairs of 29 mirror patterns) which correspond
to the number of chromosomes in European Atlantic salmon, without similar clustering
in a dam-based analysis. The remaining patterns of segregation may represent male
recombination, but are also likely to include patterns that are artefacts due to sequencing
errors or false negative allele nulls due to read coverage fluctuation for example.
It is well-established that recombination rate is low in regions of the male salmon
genome [-], and the current data are consistent with an absence of recombination over much of
the genome sampled with SbfI in these families. A similar analysis of dam-based linkage
patterns in the diamondback moth assigned approximately 65% of RAD markers to 31 pairs
of binary patterns, a species with 31 chromosomes and no recombination in females
[]. The most recent salmon linkage map suggests that the differences in recombination
between males and females are mainly due to the location of crossovers, which are
thought to generally cluster towards the telomeres in males []. Therefore, it is likely that the RAD linkage clusters in the current study correspond
to non-telomeric regions where male recombination is very low. It is noteworthy that
in the recent study of Miller et al.[], the vast majority of identified SNPs in their hybrid rainbow trout populations also
clustered towards the putative centromeres. However, the physical distances encompassed
by these linkage clusters are unknown, and may include the majority of the chromosome.
Our segregation data are based on analyses of two families containing 14 offspring
each, and further insight into recombination patterns between the RAD markers will
require construction of a linkage map in larger families using the SbfI RAD markers.
In both the QTL analysis and the bi-allelic segregation pattern analysis, there were
notably fewer RAD markers in family 2 compared to family 1. There were some differences
in the sequencing technology used for these libraries, and we examined the quality
scores and their drop-off by position in the read for both families. Family 1 offspring
had marginally better average sequence quality readings than family 2, but the number
of RAD alleles defined, the number of RAD alleles per locus, and the number of SNPs
were all reasonably consistent between the two families (Figure
and Table ). In the overall unfiltered RAD dataset, there were 6,594 and 5,985 RAD alleles in
family 1 that show sire and dam segregation patterns respectively, versus 5,491 and
7,038 in family 2. Therefore, given that sire heterozygosity and dam homozygosity
are required to observe a sire-segregation pattern, the differences could reflect
greater homozygosity in the family 2 sire and/or greater heterozygosity in the dam.
However, there was zero inbreeding for the four parents of these families, making
substantial differences in homozygosity unlikely.
Some dissimilarity in RAD marker clustering was also observed between the two families.
For example, the top ranked linkage cluster (ranked by number of observed markers)
in family 2 was only the 18th largest cluster in family 1, and RAD alleles in linkage cluster 21 in family 1 were
split over two linkage clusters in family 2 (Table ). While these observations may be due to technical bias, it is also possible that
they indicate real differences in the rate and/or position of chiasma formation between
the two male parents. The extent and pattern of tetravalent pairing in male salmonids
and resultant residual tetrasomic manifestations are considered to be influenced by
the degree of similarity among the chromosome complement of individuals. Aberrant
segregations are thought to be more common in genomes from crosses between genetically
divergent individuals []. Furthermore, Robertsonian polymorphisms have been observed between and within Atlantic
salmon populations with 2n chromosome number thought to vary between 56 and 58 []. Therefore, it is possible that genetic heterogeneity, including possible karyotypic
differences within the farm strain could explain some of the differences between the
A subset of QTL-linked SNP markers were genotyped at a population level and assessed
for linkage and association with IPN mortality. Previous studies by our group [] and Moen et al.[] have mapped the IPN-resistance QTL to a region of linkage group 21 with a confidence
interval of 10 cM and 3 cM respectively. In the current study, the genotyped QTL-linked
RAD SNPs were spread across a large region of our linkage map (37.6 cM) and the QTL
confidence interval was narrowed marginally to 2 cM. In the study of Moen et al.[], microsatellite marker haplotypes showing population-level association with IPN mortality
were identified by establishing the phase-relationship between the QTL allele and
the marker haplotype in QTL-heterozygous parent []. However, several different marker haplotypes were associated with a particular QTL
allele which hinders the practical application of population LD- based selection.
Here we demonstrate that a RAD-derived SNP (RAD_HT_01) and a previously published
SNP (SSA0139ECIG []) show highly significant population-level association with IPN mortality, implying
strong LD between these SNPs and the QTL in the Landcatch Natural Selection broodstock
population. We do not know how physically close these SNPs are to the QTL causal mutation,
and the level of LD is likely to vary from population to population. The short timescale
and cost-efficiency of our RAD-Seq approach highlights its utility for QTL-linked
marker generation and fine-mapping. Additional QTL-linked RAD markers can be generated
by using a different restriction enzyme, and the RAD approach we applied herein can
be applied to map loci affecting other economically important traits.
Conclusions
We have used RAD-Seq in pedigreed Atlantic salmon to discovery, verify and genotype
novel markers dispersed throughout the genome, including SNPs linked to a major QTL.
These markers are likely to be important for future salmonid genomics research, and
will have applications in aquaculture for selective breeding. Integration with existing
salmon genome maps is a prerequisite for this, and is underway. We have discovered
50 segregating SNP markers linked in at least one family to a major QTL affecting
resistance to the viral disease IPN, and have used high-throughput genotyping assay
for a subset of markers to identify those with population-wide utility as tests for
resistance to the disease. RAD-Seq of pooled animals of disparate phenotypes or QTL
genotypes is likely to have broad utility for mapping the genomic regulation of important
quantitative traits in a cost and time efficient manner.
Animals and disease challenge experiment
The fish used in the RAD sequencing experiment were a subset of the population described
elsewhere []. Briefly, twenty families from the breeding nucleus of Landcatch Natural Selection
Ltd at Ormsary, Scotland were transported to the Centre of Environment, Fisheries
and Aquaculture Science (Cefas) at Weymouth, England as fertilised eggs. Two replicate
tanks of ~100 fry per family were bath-challenged with virulent IPNV, alongside a
mock-challenged tank of ~100 fry per family. Mortalities due to IPN were recovered,
and the experiments were terminated once mortalities were negligible and survivors
sampled. The ten families with the highest IPN mortality level were then used for
QTL mapping using microsatellite markers. In the current study, families 1 and 2 correspond
to families C and B in [], and were established as having both parents heterozygous for the QTL. All four parents,
plus seven QTL homozygous resistant, and seven QTL homozygous susceptible offspring
were chosen from each family for the RAD sequencing experiment. The sex of the offspring
was unknown. The experiments were performed with a UK Home Office license and under
approval of the Cefas ethical review committee.
RAD-seq terminology
The purpose of this section is to define some of the terms used in the current study.
A read is an individual raw sequence of a fragment of DNA; reads can be single or, in our
study, paired-end (reads determined from both ends of the fragment). Reads were processed
using the RADtools software pipeline []. Reads with identical sequences at both ends are collapsed into fragments to remove PCR duplicates, corresponding to unique DNA fragments in the initial sheared
genomic DNA sample. A RAD locus is a collection of one or more fragments, theoretically corresponding to all fragments
from the genomic region either upstream or downstream of a particular SbfI restriction
site. Each RAD locus contains one or more RAD alleles, depending on whether or not the locus contains polymorphic/paralogous variation
within and/or across individuals. Each RAD allele has a read count and a fragment
count in each individual. A RAD marker is a polymorphic RAD locus segregating in the analysed population(s). A segregation pattern is a binary pattern of presence/absence of a particular RAD allele at a RAD marker
across all sampled individuals. A linkage cluster is a group of RAD markers with a common segregation pattern.
RAD library preparation and sequencing
DNA was extracted from caudal fin tissue using the REALPure genomic DNA extraction
kit (Durviz S.L.) and treated with RNAse to remove residual RNA. Each sample was quantified
by spectrophotometry (Nanodrop) and its quality assessed by agarose gel electrophoresis,
and was then diluted to a concentration of 50 ng/μL in 5 mM Tris, pH 8.5. The RAD
library preparation protocol followed closely the methodology originally described
in [] and latterly comprehensively detailed []. The RAD specific P1 & P2 paired–end adapters and library amplification PCR primer
sequences used in this study are detailed in []. Briefly, each sample (1.5 μg parental DNA/0.5 μg offspring DNA) was digested at
37°C for 30 min with SbfI high fidelity restriction enzyme (New England Biolabs -NEB)
using 6U SbfI per μg genomic DNA in 1× Reaction Buffer 4 (NEB) at a final concentration
of 1 μg DNA per 50 μL reaction volume. The reactions (75 μL/25 μL final volumes for
parental/offspring samples respectively) were then heat inactivated at 65°C for 20
minutes. Individual specific P1 adapters, each with a unique 5 base barcode (Table
), were ligated to the SbfI digested DNA at 22°C for 30 minutes by adding 3.75/1.25
μL 100 nM P1 adapter, 0.9/0.3 μL 100 mM rATP (Promega), 1.5/0.5 μL 10× Reaction Buffer
2 (NEB), 0.75/0.25 μL T4 ligase (NEB, 2 M U/mL) and reaction volumes made up to 90/30
μL with nuclease free water for each parental/offspring sample. Following heat inactivation
at 65°C for 20 minutes, the ligation reactions were then combined in appropriate multiplex
pools (either two parental samples or seven offspring samp Table
). For each library pool 100 μL (c. 2 μg digested DNA) was sheared to c. 150–700 bp
size range (Covaris sonicator). The sheared DNA was column purified (PCR MinElute
Kit, Qiagen), being eluted in 35 μL EB buffer (Qiagen). Each of the six library samples
were then size selected (c. 250–500 bp) by gel electrophoresis (0.5× TAE; 1.1% gel).
Gels were run (2 V/cm for 10 8 V/cm for 50 min) in ice-cold buffer – to minimise
small fragment diffusion. Marker lanes (100 bp ladder) were cut out of the gel by
scalpel, quickly stained with EtBr, viewed under UV, and the appropriate size range
flagged by ‘nicking’ the marker lane. The gel was then reassembled and the identified
size selected band was excised using a clean scalpel blade. In this way, the size-selected
DNA was not exposed to EtBr or UV radiation.
The remainder of the library construction (i.e. end repair, dA overhang
addition, P2 paired-end adapter ligation and library amplification) followed the original
protocol [,] exactly. A total of 150 μL of each amplified library (16–18 PCR cycles) was prepared,
column purified, eluted in 35 μL EB buffer and size selected (c. 300–550 bp) by gel
electrophoresis, as described above. Following a final gel elution step into 20 μL
EB buffer (MinElute Gel Purification Kit, Qiagen), the libraries were QCed by electrophoresis
(Bioanalyser, Agilent) and accurately quantified by fluorimetry. One parent library
and two offspring libraries were produced from each family. Each library was sequenced
(100 base pair paired-end reads) on the Illumina GAIIx or HiSeq 2000 platform at the
GenePool Genomics Facility, University of Edinburgh ( ).
Generating candidate RAD loci and RAD alleles
Raw Illumina reads were processed into candidate RAD loci using the open source RADtools
pipeline ( ) and following the protocol described in Baxter et al.[]. Briefly, loci were inferred for each family separately as follows: reads were separated
by barcode using the programme ‘RADpools’, candidate RAD loci were inferred for each individual in a family using ‘RADtags’ with a cluster distance of nine (allowing for up to nine base mismatch between reads).
Loci were then merged across all individuals within each family with ‘RADmarkers’, merging loci with shared RAD alleles, allowing up to three mismatches between alleles.
PCR duplicates were removed by collapsing reads with identical sequences at both ends
into a single unique sequence (a fragment). We ran analyses with a minimum threshold
of fragment coverage per allele (discarding RAD alleles with fewer than five fragments),
but the results presented herein are from analyses of all RAD alleles for which at
least one fragment was observed.
SNP discovery
The full set of RAD loci was filtered to include only those with exactly two RAD alleles
in each family. These bi-allelic loci were excluded from further analysis if they
were considered likely to contain errors as indicated by (i) either allele was absent
in both parents, (ii) fewer than two (of 14) offspring inherited an allele at the
locus, (iii) the average fragment count for an allele within the locus was lower than
ten in the parents [average parental fragment count was 38.7 (family 1) and 31.2 (family
2)], (iv) the average fragment count for an allele within the locus was lower than
five in the offspring [average offspring fragment count was 13.3 (family 1) and 11.2
(family 2)], (v) any animal contained neither allele. Bi-allelic RAD loci from this
subset were identified as containing putative PSVs if both RAD alleles were present
in all 16 individuals). RAD loci where both alleles were present in all 16 individuals,
except for one or two absences, were included in this list as it is very unlikely
that these are segregating loci, and very likely that particular individuals were
not sequenced for an allele by chance due to fluctuating read counts across individuals
and loci. After removal of putative PSVs, the two alleles within each remaining RAD
locus were aligned and the number of differences between them counted. Bi-allelic
RAD loci with between one and three mismatches (putative SNPs) were then converted
to single sequences with IUPAC ambiguity codes as the final set of filtered non-repeat
region SNPs.
RAD allele segregation
Within the filtered bi-allelic dataset, each RAD allele was scored as present (1)
or absent (0) in all sequenced animals of a full-sibling family, thus producing a
16-digit binary string per allele (corresponding to the two parents and two groups
of seven offspring in each family) which is the segregation pattern. At each bi-allelic
sire-heterozygous RAD marker, the grandpaternal sire allele will be transmitted to
one group of offspring, and the grandmaternal sire allele will be transmitted to the
remaining offspring, resulting in two ‘mirror’ segregation patterns per marker (see
[]). However, only a subset of linked RAD markers will result in observable segregation
patterns and, of this subset, only one of the two mirror patterns will be seen. Observing
a sire-segregation pattern is dependent on the dam being fixed for one of the alleles
at the RAD marker because there is no means to differentiate between homozygous and
heterozygous offspring using this method. In the remaining linked RAD markers, to
explain why only one of the two ‘mirror’ segregation patterns is observed, consider
RAD markers containing a single bi-allelic SNP where the sire is heterozygous AB,
and ‘A’ is the grandpaternal sire allele and ‘B’ is the grandmaternal sire allele.
If the dam is homozygous BB, only the A (grandpaternal) sire allele will show the
visible segregation pattern. For another sire-heterozygous RAD marker containing a
single SNP on the same chromosome, the dam may be homozygous AA, in which case only
the B (grandmaternal) sire allele will show the visible segregation pattern. Therefore,
a subset of fully-linked sire-heterozygous and dam-homozygous RAD markers will fall
into one of two ‘mirror’ sire segregation patterns depending on whether the visible
allele is on the grandpaternal or grandmaternal sire chromosome, which in turn depends
on which allele is fixed in the dam. For comparison, the analysis was repeated for
alleles present in the dam but not the sire. The text and sequence file analyses were
performed using custom Perl scripts (Additional files
and ) and the online genomics resource ‘Galaxy’ [].
Additional file 6. Perl script to identify the most frequent segregation patterns in the RAD loci (Additional
files and).
Format: TXT
Additional file 7. Perl script to merge SNP-containing bi-allelic RAD loci into a single list of sequences
with IUPAC ambiguity code (Additional file ).
Format: TXT
BAC-end sequencing
The BAC contig fps378 was identified as being closest to the IPN QTL region based
on the aligned linkage and physical maps given in ( []). Using BAC-end sequence data, PCR primer sets were designed using Primer3 [] to amplify short regions (predicted to be from 405 to 686 bp) dispersed along the
BAC contig (Additional file ). These regions were PCR amplified in the four parental samples from family 1 and
2, and sequenced on an ABI 3730xl instrument at the ARKGenomics laboratory at the
Roslin Institute (Edinburgh, UK;
.) SNPs were identified by aligning the sequences using ClustalW [].
Additional file 8. Table S2. Population-wide association between genotype at SSA0019ECIG and mortality in a freshwater
IPNV challenge (all associations significant at P&0.05).
Format: DOC
Size: 35KB This file can be viewed with:
Linkage and QTL analysis
QTL-linked SNPs were defined from a list of RAD loci containing allele segregation
patterns matching the QTL genotype (allowing up to two discordances). Details of a
subset of 17 SNPs with sufficient flanking sequence were sent to Kbiosciences (Hoddesdon,
Herts) for high-throughput SNP assay design, and the QTL mapping population [] were genotyped for all SNPs. Paired-end contigs (see []) were developed for the RAD loci containing these SNPs to provide additional flanking
sequence information. Microsatellite markers SSA374 and SSA680 (also from BAC contig
fps378 and therefore close to the QTL) were genotyped in a multiplex PCR across the
population after optimising fragment amplification on a TProfessional Gradient thermocycler
(Biometra, Gottingen, Germany) and using an ABI-377 mediated fluorescent detection
to create allelic profiles. A linkage map of the QTL region was constructed using
Crimap Version 2.4 []. A ‘twopoint’ analysis was initially used to calculate the pairwise linkage between
markers. Due to a lack of male recombination, only informative dam meioses were used
for determining marker order and position using a ‘build’ analysis. A ‘flipsn’ analysis
was then used to verify that the obtained marker order was the most likely order.
The GridQTL software was then used to calculate the most likely QTL position. The
significance threshold was calculated empirically by permutation analysis (10,000
permutations), and the confidence interval for the QTL was defined using a bootstrapping
approach (10,000 bootstrap samples.)
SNP-trait association and population-wide verification
Two separate IPNV challenge trials of families from the breeding nucleus of Landcatch
Natural Selection Ltd (LNS) were performed at Cefas, Weymouth, UK. The two groups
of families were from the yeargroups of the LNS broodstock stripped in 2006 and 2007,
disease challenged as fry (approximately two months post-hatching) in 2007 and 2008
respectively. Details on the challenge protocol are given in [,]. Briefly, for each yeargroup, there were two mixed-family challenge tanks. The tanks
comprising fry from all families were given an immersion IPNV challenge with V0512-1
serotype Sp A2. The challenge experiment was run until IPN-related mortalities had
ceased which was 42 days post-challenge. Overall mortality rates due to IPN were 41%
in the 2006-strip yeargroup (total n = 4,846) and 29% in the 2007-strip yeargroup
(total n = 5,247). Samples were obtained from mortalities and survivors and fry were
assigned to family using an in-house microsatellite multiplex genotyping panel. The
most tightly linked SNPs to the QTL on LG 21 were successfully genotyped in 9000 animals.
The association between SNP genotype and the binary trait of IPN mortality was assessed
using a REML model with tank and family fitted as fixed effects in Genstat [].
Competing interests
The authors declare they have no competing interests.
Authors’ contributions
RDH, JWD, SCB, KG, MLB, JEB and JBT conceptualised and designed the RAD sequencing
experiment. JWD and RDH analysed the data. RDH wrote the manuscript. NRL sequenced
and discovered BAC end SNPs. MLT contributed to the RAD library preparation and sequencing.
KG planned and coordinated the sequencing of RAD libraries. JBT prepared the RAD libraries.
JCM and AH performed the microsatellite genotyping. JCM, AH, DRG and AET designed
and managed the population-wide association and verification experiment and its associated
genotyping. All authors read and approved the final manuscript.
Accession numbers
Raw RAD sequence data from this article have been deposited in the European Nucleotide
Archive under accession number ERP001162.
Acknowledgements
The authors gratefully acknowledge Andy Law for bioinformatics assistance, staff at
the Genepool Genomics facility for assistance with the RAD protocol and Illumina sequencing,
staff at the ARKGenomics facility for assistance with ABI sequencing, David Verner-Jeffreys,
Richard Paley, Georgina Rimmer and Ian Tew for planning and performing the disease
challenge studies. The authors acknowledge funding from the British Biotechnology
and Biological Sciences Research Council (BBSRC), grant numbers BB/H and BB/F,
and the Natural Environment Research Council (NERC), grant number NE/H.
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