Hierarchical partitioning of outlier Loci – Methods, results and interpretation
Loci were partitioned on the basis of the divergence between the northern and southern
lineages (represented in grey and red respectively on figure 1), and divergence between
populations in the southern lineages. The first outlier analysis conducted in BAYESCAN (Foll
& Gaggiotti 2008) comprised all 14 populations. The second included 12 populations
derived from the southern refugium. Outlier loci from both analyses in were grouped in
three categories in order to identify the loci that could be attributed to the two hierarchical
levels (Figure 1b). The first comprised outliers detected in the 14 populations only (A), the
second comprised the outlier loci that were in common between the two analyses (B) and
the third reflected outlier loci unique to the 12 population comparison (C).
Figure 1 – Description of the analysis conducted with BAYESCAN. a) description of the origin of the populations
used in this analysis: the populations derived from the southern refugium are represented in red, the
populations derived from the northern refugium are represented in grey. b) Number of outliers identified in
the analysis comprising all populations (blue circle), in the analysis comprising only the populations derived
from the southern refugium (red circle). A, B and C correspond to the number of outlier loci unique to the first
analysis, in common between the two analysis and unique to the second analysis respectively. The number of
mapped loci in each category is represented in parenthesis.
A total of 83 outlier loci under positive selection were unique to the 14 population analysis
(group A, Figure 1b), 147 markers were outliers in both analyses (group B) and 32 were
unique to the 12 population analysis (group C).
To determine whether each group of outliers could be attributed to a specific divergence
level (deeper or shallower), we conducted a locus-specific AMOVA for each category (A, B,
C) of outlier loci using HIERFSTAT (Goudet 2005). For each locus, we estimated FST and FCT
(differentiation among the groups, here the lineages) associated to the deeper divergence
(FCT-deeper) and to the shallower divergence (FCT-shallower). FCT was standardized over loci by
dividing the locus specific FCT by the locus specific FST value, since FST values differed across
loci. Outlier loci attributed to the deeper adaptive divergence were expected to have a high
FCT-deeper /FST-14 ratio, whereas those attributed to the shallow divergence were expected to
have a high FCT-shallower /FST-12 ratio, where FST-14 and FST-12 are explained by the two different
datasets.
Figure 2 - FCT/FST values are
given for each group of outlier
loci, where FCT-deeper/FST-14 is
represented in black and FCTshallower/FST-12 is represented in
grey. A: outlier loci only in
analysis based on all 14
populations; B: outlier loci in
common between the two
analyses; C: outlier loci only in
the analyses of 12 populations in
the Puget SoundSP_SUM_F, Interior
ColumbiaSUM_F and Lower
ColumbiaSP__F.
Outlier in group A mostly exhibit high FCT-deeper /FST-14 ratios (Figure 2), indicating that most
outlier loci in that group were explained by the deeper divergence. A different pattern was
observed for the outlier loci in group B, for which most of the FCT-deeper /FST-14 ratios were
low, whereas high ratios were observed for FCT-shallower /FST-12 (Figure 2), suggesting that the
outlier loci falling in group B were explained mostly by the divergence between the three
more recent lineages. These loci were therefore attributed to the recent hierarchical level.
Finally, outlier loci in the group C had high FCT-shallower /FST-12 values and were attributed to
the shallower divergence.
Using the mapped markers only, the outlier loci appeared to have a non-uniform
distribution across chromosomes arms after correcting for the number of markers on each
chromosome arm for each category of outlier loci (chi-square test for uniform distribution
of the outlier loci associated with the deeper divergence: χ2 = 48.22, df = 33, p = 0.042 ; chisquare test for uniform distribution of the outlier loci associated with the shallower
divergence: χ2 = 53.95, df = 33, p = 0.012).
Interpretation of the results
In the main paper, we discuss some of the limitations of the hierarchical outlier analyses.
We add here that it is important to note that the methodology that was used might have
been incorrectly attributed a subset of loci to the shallower divergence, or these loci might
even be shared between the two hierarchical levels. Categorization relied on two separate
outlier analyses, and subsequent hierarchical partitioning of outlier loci between the
groups. Loci that were shared between the two analyses (group B in our study) were
assigned to the recent divergence, based on the FCT/FST analyses. In this case, however, the
number of loci misassigned would be relatively small. It is also possible that the power of
the outlier analyses was affected by the number of populations involved in the two
analyses. We therefore conducted an additional test for selection with BAYESCAN using the
two populations from the Interior ColumbiaSP_SUM and ten populations, randomly selected,
from the other lineages to have a total of twelve populations. The results were not affected
by the lower number of populations (data not shown). The identification of chromosomal
regions consistent with adaptive divergence in this study was conservative. We relied on
consensus between marker placement on the genome map, sliding window analyses and
outlier loci detected with a large number of populations.
Genomic distribution of outlier loci
One of the interesting findings of the genome wide analysis of outlier regions is that their
distribution among and along the chromosomes is not uniform. While not an unusual
finding in teleost fishes (Hohenlohe et al. 2010; Bourret et al. 2013; Bradbury et al. 2013;
Hemmer-Hansen et al. 2013), this result is interesting when interpreted in light of the
evolution of the salmonid genome following the whole genome duplication event.
Duplicated pairs of chromosome arms (homeologs) have largely diverged (Wright et al.
1983; Allendorf & Danzmann 1997), but re-diploidization has occurred at different rates
between and along chromosome arms (Lien et al. 2011; Berthelot et al. 2014; Brieuc et al.
2014; Kodama et al. 2014). Divergence between homeologs tends to be lower at the distal
ends of the chromosome. It has been argued that duplicated coding regions may provide
the raw material for evolutionary innovation, because they are free to take on additional
functions (reviewed in Mayfield-Jones et al. 2013). It is possible that newer mutations in
duplicated genes that have recently or are still undergoing divergence provide novel
opportunities for adaptive evolution in salmon species (Kodama et al. 2014). This outcome
might explain the finding that most of the “outlier” regions in this study are located distally
on the chromosome arms, especially since the test for such regions is biased towards
detecting recent selective sweeps. It is very important to point out, though, that duplicated
regions have not been genotyped in this study. Eight pairs of chromosome arms with a high
proportion of duplicated markers (Kodama et al. 2014) are particularly under represented.
Additionally, Roesti et al. (2012) suggested that heterogeneous recombination, especially
recombination around the centromere, should lead to a bias towards identifying outlier loci
close to the centromere. However, most regions of higher divergence were located in distal
regions from the centromere. Therefore, chromosome center-biased divergence, does not
seem to be an issue in this study.
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