Box 1: Why the genetics of adaptation matters for the evolutionary
ecology of fatty acids.
Quantitative genetic view of adaptation - Quantitative genetics
models generally assume that traits are continuous, normally
distributed, and controlled by many genes with small effects. In a model
with a single trait, selection moves the trait distribution according to
the breeder’s equation: Response to selection (R ) is a product of
heritability (h 2) and selection differential (S ) (Lynch
and Walsh 1998). Any heritable traits can evolve in response to
selection. In cases in which multiple traits are genetically correlated,
selection on one trait can bias the evolution of another genetically
correlated trait, and the evolutionary trajectories on adaptive
landscapes can be biased by a genetic variance-covariance matrix
(G-matrix; Lande and Arnold 1983; Schluter 1996; Schluter 2000).
Although quantitative genetics models are helpful to predict short-term
evolution of highly polygenic traits, genomic studies of fatty acid
composition have often identified loci with moderate to large effects
(Cesar et al. 2014; Xia et al. 2014; Lemos et al. 2016; Lin et al. 2018;
Horn et al. 2020), suggesting that an alternative view of the genetic
basis of adaptation may be more useful in some cases.
Genomic view of adaptation - Recent advances in genomic
technologies have improved our ability to elucidate the genetic details
of adaptation. For instance, studies have helped document how aspects of
genetic architecture, such as the number, effect sizes, pleiotropy,
linkage, and genomic location of adaptive loci, can influence the speed
and reversibility of adaptive evolution (Barton and Keightley 2002).
Such studies also aim to identify causative genes. For instance, animal
breeding studies have sought to identify genes and quantitative trait
loci that control the fatty acid composition of meat (e.g.,
Kelly et al. 2014; Zhang
et al. 2016). However, even when a locus with a major effect is
identified, this does not necessarily indicate the presence of a single
causative gene. Furthermore, even when a causative gene is identified,
it does not necessarily mean that a single mutation causes the
alteration of the gene function (Stern and Frankel 2013; Bickel et al.
2011). Therefore, an additional goal of genetic adaptation studies is to
identify specific causative mutations
(Lee et al., 2014;
Remington, 2015). For example, Fads2 duplication in freshwater
species derived from marine ancestors allows them to synthesize more n-3
LC-PUFA (Ishikawa et al. 2019). When adaptation occurs via standing
genetic variation, it may not be necessary to further dissect it into
the levels of individual mutations for predicting how adaptation
proceeds. This is because such adaptation occurs by replacement of
already-existing alleles with tightly linked adaptive mutations (Barrett
and Schluter 2008). However, to understand adaptation by de novomutations, it is essential to determine the number and nature of
responsible causative mutations (Stern and Frankel 2013). For example,
the bab locus explains over 60% of phenotypic variance of
pigmentation in Drosophila , but each single SNP explains only 1%
(Bickel et al., 2011).
Similarly, different Fads1 and Fads2 gene variants in
humans explain between 1-28.5% of the variation in the PUFA content of
blood phospholipids
(Schaeffer et al. 2006).
Once causative mutations that alter fatty acid synthesis are identified,
it is then possible to determine whether adaptive evolution has occurred
through a few large steps or multiple small steps (Orr 2005).
Box 1 Figure: Relationship between the enzymatic activity and fitness in
two different environments (left) and a trajectory of adaptive walk
(gray arrow) biased by G-matrix on a two-dimensional adaptive landscape
(right). Px, precursor of fatty acid X; Py, precursor of fatty acid Y;
Plus (+), the adaptive peak.