262
M.L. Cancela et al.
signature of selection at 24 loci, of which eight loci showed a signature of selection
in the mapping families (Rogers and Bernatchez 2005, 2007). From complementary
transcriptome (expression) profiling (eQTL), six candidate genes related to white
muscle modulating swimming activity in the dwarf ecotype of white fish and the
sympatric cisco turned out to be upregulated (Derome et al. 2006, see above). Four
genes were upregulated in cisco, pointing to its greater physiological potential to
exploit the limnetic trophic niche. A follow-up study on the sympatric pairs of dwarf
and normal whitefish in two natural lakes through transcriptome analysis of the
liver showed that 6.45% of significantly transcribed genes showed regulation either
in parallel or in different directions. Dwarf whitefish showed consistently significant overexpression of genes potentially associated with survival through enhanced
activity. The normal ecotype showed more overexpressed genes associated with
growth. These original results show a first mechanistic genomic basis for major life
history trade-offs in both ecotypes. In short, enhanced survival through active swimming increases energetic costs translating into slower growth and reduced fecundity
in the dwarf morph (St-Cyr et al. 2008).
7.7.3 A Vision of the Future
Although the concepts of population genetics have been with us for a long time,
molecular tools for testing evolutionary hypotheses under challenging oceanic
environmental conditions were largely missing. Technological development has progressed to such an extent that the genome, transcriptome and proteome have become
accessible for hypothesis testing in species and populations of key ecological relevance under field and laboratory conditions alike. Therefore it is interesting to have
a concise look at future developments.
Fundamental knowledge on fish and fisheries genomics is likely to benefit from
the advances in genomics. The technical capacity of genomics keeps on progressing towards higher throughput at a lower cost per base pair (Gupta 2008).
Hence many hypotheses of population genetics might finally be testable. If genome
based monitoring is linked to the latest developments in seabed and water column based monitoring and tracking (Delaney 2007), much is to be expected from
real-time genotyping. This will of course vastly increase the amount of data generated. Bioinformatics is foreseen to play an increasingly important role for data
management and analysis.
Genomics holds great promise for future applications to identify management
units in marine fish. Gene flow is generally high and accordingly, spatial and
temporal patterns of genetic differentiation are subtle. The information signal on
evolutionary separated units originating from neutral genomic variation is too subtle
to understand the full picture of dispersal and connectivity among local populations. The recent focus on adaptive traits holds great promise for understanding
the dynamics of population structure in space and time (e.g. Pogson and Fevolden
2003, Larsen et al. 2007). Full genomes of populations adapt to their environment,
M.L. Cancela et al.
signature of selection at 24 loci, of which eight loci showed a signature of selection
in the mapping families (Rogers and Bernatchez 2005, 2007). From complementary
transcriptome (expression) profiling (eQTL), six candidate genes related to white
muscle modulating swimming activity in the dwarf ecotype of white fish and the
sympatric cisco turned out to be upregulated (Derome et al. 2006, see above). Four
genes were upregulated in cisco, pointing to its greater physiological potential to
exploit the limnetic trophic niche. A follow-up study on the sympatric pairs of dwarf
and normal whitefish in two natural lakes through transcriptome analysis of the
liver showed that 6.45% of significantly transcribed genes showed regulation either
in parallel or in different directions. Dwarf whitefish showed consistently significant overexpression of genes potentially associated with survival through enhanced
activity. The normal ecotype showed more overexpressed genes associated with
growth. These original results show a first mechanistic genomic basis for major life
history trade-offs in both ecotypes. In short, enhanced survival through active swimming increases energetic costs translating into slower growth and reduced fecundity
in the dwarf morph (St-Cyr et al. 2008).
7.7.3 A Vision of the Future
Although the concepts of population genetics have been with us for a long time,
molecular tools for testing evolutionary hypotheses under challenging oceanic
environmental conditions were largely missing. Technological development has progressed to such an extent that the genome, transcriptome and proteome have become
accessible for hypothesis testing in species and populations of key ecological relevance under field and laboratory conditions alike. Therefore it is interesting to have
a concise look at future developments.
Fundamental knowledge on fish and fisheries genomics is likely to benefit from
the advances in genomics. The technical capacity of genomics keeps on progressing towards higher throughput at a lower cost per base pair (Gupta 2008).
Hence many hypotheses of population genetics might finally be testable. If genome
based monitoring is linked to the latest developments in seabed and water column based monitoring and tracking (Delaney 2007), much is to be expected from
real-time genotyping. This will of course vastly increase the amount of data generated. Bioinformatics is foreseen to play an increasingly important role for data
management and analysis.
Genomics holds great promise for future applications to identify management
units in marine fish. Gene flow is generally high and accordingly, spatial and
temporal patterns of genetic differentiation are subtle. The information signal on
evolutionary separated units originating from neutral genomic variation is too subtle
to understand the full picture of dispersal and connectivity among local populations. The recent focus on adaptive traits holds great promise for understanding
the dynamics of population structure in space and time (e.g. Pogson and Fevolden
2003, Larsen et al. 2007). Full genomes of populations adapt to their environment,