96
M.S. Clark et al.
the growth hormone-releasing hormone (GHRH) locus explained a significant fraction (≈10%) of the phenotypic variation in early growth rate in the Arctic charr
(Salvelinus alpinus). They also reported marginally significant results for another
SNP marker located in the promoter of the GH gene. Contrasting nucleotide polymorphisms and species divergence in closely-related species at various gene loci
may also help in delineating the role of selection at a given locus. Recently, Faure
et al. (2007) showed that the second intron of the EF1α gene was under strong purifying selection in the vent mussel Bathymodiolus genus whereas it evolves neutrally
in other bivalve molluscs. Discriminating between selective sweeps, gene hitchhiking or population expansion was only made possible by the combined analysis
of polymorphisms in two distinct species.
The examples highlighted here mainly report polymorphisms in cis-regulatory
regions rather than coding sequences as the genetic basis of an observed phenotype. As more data is generated from marine species, the utility of cross-species
comparisons for identifying non-coding genomic regions potentially involved in
regulation of gene expression and phenotypes increases (cf. Lennard Richard et al.
2007). Whilst the case for sequence variation in non-coding regions as drivers for
phenotypic change is clear (e.g. Kashi et al. 1997, Britten et al. 2003), understanding if coding verses non-coding variation represents the major source supporting
phenotypic variation is a highly debated issue (see Hoekstra and Coyne 2007, Wray
2007). For aquatic organisms data relating ecological issues to polymorphisms in
the coding regions of candidate genes have already been reported. Examples have
been shown in studies as diverse as understanding of the genetic basis of reproductive isolation within- and among-species (Palumbi 1999, Moy et al. 2008) and
adaptation to toxicants (Cohen 2002).
3.4 Expression Studies and Environmental Genomics
Allied to population studies, which concentrate on the analysis of DNA, are the
studies of RNA or expressed sequences. Whilst DNA analyses define a population
(and indeed in some examples have indicated fitness traits), RNA studies specifically analyse the function and fitness of that population. For example how species
adapt to extreme environments and how they cope with change, a particularly critical
point to consider given the predicted changes to our climate over the coming years
(IPCC 2007). The layering of functional information onto populations is termed
“Environmental Genomics”. This “omics” off-shoot is a real mix of laboratory
based experimentation combined with environmental observations and sampling.
The two approaches have to be used in tandem to produce meaningful functional
data.
For example, the initial cloning and production of an assay for heat shock protein
(HSP70) genes in Antarctic molluscs was conducted at the environmentally unrealistic temperatures of 15ºC. Indeed this was the lowest temperature at which these
genes were expressed under laboratory conditions (Clark et al. 2008b). However,
M.S. Clark et al.
the growth hormone-releasing hormone (GHRH) locus explained a significant fraction (≈10%) of the phenotypic variation in early growth rate in the Arctic charr
(Salvelinus alpinus). They also reported marginally significant results for another
SNP marker located in the promoter of the GH gene. Contrasting nucleotide polymorphisms and species divergence in closely-related species at various gene loci
may also help in delineating the role of selection at a given locus. Recently, Faure
et al. (2007) showed that the second intron of the EF1α gene was under strong purifying selection in the vent mussel Bathymodiolus genus whereas it evolves neutrally
in other bivalve molluscs. Discriminating between selective sweeps, gene hitchhiking or population expansion was only made possible by the combined analysis
of polymorphisms in two distinct species.
The examples highlighted here mainly report polymorphisms in cis-regulatory
regions rather than coding sequences as the genetic basis of an observed phenotype. As more data is generated from marine species, the utility of cross-species
comparisons for identifying non-coding genomic regions potentially involved in
regulation of gene expression and phenotypes increases (cf. Lennard Richard et al.
2007). Whilst the case for sequence variation in non-coding regions as drivers for
phenotypic change is clear (e.g. Kashi et al. 1997, Britten et al. 2003), understanding if coding verses non-coding variation represents the major source supporting
phenotypic variation is a highly debated issue (see Hoekstra and Coyne 2007, Wray
2007). For aquatic organisms data relating ecological issues to polymorphisms in
the coding regions of candidate genes have already been reported. Examples have
been shown in studies as diverse as understanding of the genetic basis of reproductive isolation within- and among-species (Palumbi 1999, Moy et al. 2008) and
adaptation to toxicants (Cohen 2002).
3.4 Expression Studies and Environmental Genomics
Allied to population studies, which concentrate on the analysis of DNA, are the
studies of RNA or expressed sequences. Whilst DNA analyses define a population
(and indeed in some examples have indicated fitness traits), RNA studies specifically analyse the function and fitness of that population. For example how species
adapt to extreme environments and how they cope with change, a particularly critical
point to consider given the predicted changes to our climate over the coming years
(IPCC 2007). The layering of functional information onto populations is termed
“Environmental Genomics”. This “omics” off-shoot is a real mix of laboratory
based experimentation combined with environmental observations and sampling.
The two approaches have to be used in tandem to produce meaningful functional
data.
For example, the initial cloning and production of an assay for heat shock protein
(HSP70) genes in Antarctic molluscs was conducted at the environmentally unrealistic temperatures of 15ºC. Indeed this was the lowest temperature at which these
genes were expressed under laboratory conditions (Clark et al. 2008b). However,