Using existing information on the genetics of important phenotypes from
other salmonid species, we mapped a database of 1,338 QTL markers to theSalvelinus sp. genome. This was based on a previously
published database of QTLs involved in traits related to morphology and
life history, derived from a range of salmonid species and previously
mapped to the Salmo salar genome . Additionally, a literature
search was conducted up to April 2021 to augment the existing database
with more recently published QTLs. This literature search was conducted
in Web of Science and Google Scholar using the search terms “QTL”,
“quantitative trait loci”, “salmonid”, and the common and scientific
names for rainbow trout, Atlantic salmon, Arctic charr, lake whitefish,
Chinook salmon, coho salmon, brook trout, and lake trout. QTL marker
sequences were gathered for 17 different phenotypes: body length, body
shape, body weight, Fulton’s condition factor, directional change,
disease resistance, embryonic development, gill rakers, growth rate,
hatching time, head shape, parasite resistance, salinity tolerance,
sexual maturation, smolting, spawning time, upper temperature tolerance
(Table S1).
Following Jacobs et al. (2017), the strategy of mapping the
QTL-linked markers to the Salvelinus sp. genome depended on the
QTL marker type: RAD loci were mapped using Bowtie2 v2.4.4 and
the very sensitive pre-set; microsatellite primer sequences,
which are shorter, were mapped using Bowtie v1.3.1 allowing for 3
mismatches. QTLs for which the flanking markers mapped to different
chromosomes were removed. Redundant QTLs, i.e., where two QTLs for the
same trait from the same species mapped to the same location, were
removed only keeping the QTL with the higher PVE or LOD score (following
. For QTLs where more than one marker was reported, we attempted to map
all markers. Position values for the QTLs markers were then compared to
positions of the phenotype associated SNPs using BEDtools , with a
cut-off of ±100kbp. This value was used so that we could consider SNPs
within the range to be proximal to a QTL peak while also accounting for
the large size of many of the QTLs in the database. In total, we
successfully mapped 669 QTL-linked sets of markers to theSalvelinus sp. genome after removing redundant QTLs (Table S2).
Genomic response to selection:
We investigated if the phenotype-associated SNPs identified in our
analyses showed signals of a response to selection and if those signals
were replicated across ecomorph pairs. To test this, for each ecomorph
pair we compared FST and DXY values for
phenotype-associated SNPs to a random background subset of SNPs. This
random subset was 100 SNPs randomly selected from the whole dataset and
the mean FST and DXY values for those
SNPs were calculated. This was repeated 10,000 times and the means for
FST and DXY were taken across all
permutations. These permuted values were then compared to the empirical
mean FST and DXY values for the
phenotype-associated SNPs using the t.test function in R.
Analyses of recombination rate
variation:
To test the effect of the recombination landscape on phenotype-genotype
association, we first estimated recombination rates using the published
Arctic charr linkage map (N=3,636) using MareyMap v1.3.6 . RAD
loci from the linkage map were aligned to the Salvelinus sp.
reference genome with Bowtie2 using the -very-sensitive setting.
Loci were kept if they uniquely mapped to one location, mapped to the
same chromosome as all other loci on their linkage group, and followed
the orientation of the linkage map (i.e., not reversed). The filtered
dataset was used to estimate the recombination rate across each
chromosome using a spline algorithm. Spar values were varied for each
chromosome from 0.5 to 0.9, depending on chromosome size, to best fit
the data . Subsequently, WindowScanR v0.1 (available at:
https://github.com/tavareshugo/WindowScanR) was used to summarize
recombination rate values in 1 MB windows along the genome. All SNPs
were assigned to these windows using BEDtools . A random subset of
100 SNPs was then selected and the mean recombination rate for those
SNPs was calculated based on their windows. This was repeated 10,000
times to generate a background mean recombination rate, which was then
compared to the mean recombination rate of the phenotype-associated SNPs
(based on their windows) using a t-test.