The proverb, ‘a rising tide lifts all boats’, is often used in a political context, but is also useful in considering mechanisms of heterosis. Debates about the underlying basis of heterosis usually focus on mechanisms defined by standard quantitative genetic models (Falconer & Mackay, 1996), but often include discussions of whether there are other novel mechanisms that are independent of specific genes in biochemical and developmental pathways that more broadly affect many traits by increasing overall vigor. In this issue of New Phytologist, Stephen Goff proposes in his Tansley review (pp. 923–937) that hybrid vigor results from a reduced metabolic cost of protein recycling in hybrids owing to the opportunity for the cell to select alleles that produce stable proteins and thereby reduce the metabolic expense of processing nonfunctional proteins.
‘…research on heterosis is still limited by the number of genotypes that can be measured for performance in relevant contexts …’
The terminology utilized by Goff distinguishes single-trait heterosis from multigenic heterosis. As most traits for which we might measure heterosis are likely to be controlled by multiple genes, I believe that it is more accurate to distinguish single-trait heterosis (the single ‘boat’) as that clearly attributable to defined underlying pathways within the context of standard models of quantitative inheritance vs pleiotropic heterosis (the ‘rising tide’), which is pathway independent and results, in some way, from general vigor stimulated by heterozygosity.
One argument for mechanisms resulting in pleiotropic heterosis is that a large number of genetic and genomic studies have not defined specific biochemical or developmental pathways underlying heterosis. Traits such as grain yield, for which heterosis has most often been measured, are highly complex traits that result from the combination of a suite of developmental, biochemical and plant-protection component traits. A recent study in tomato corroborates that even a very simple single-gene example of heterosis for yield is manifested through the multiplicative interaction of component traits (Krieger et al., 2010). Results from quantitative trait locus mapping studies (Buckler et al., 2009) that have the scale to detect and resolve small effects are consistent with the hypothesis that genetic variation for most quantitative traits is controlled by many genes with small effects. In addition, recent genomic studies (Gore et al., 2009) provide experimental support for previous observations (Moll et al., 1963) that recombination (or lack thereof) influences our interpretations of quantitative genetic parameters, such as the average degree of dominance, and provides a basis for heterotic patterns in natural and breeding populations. Given a model of many genes with small effects in the context of current evidence that there are a number of regions in the genome with high linkage-disequilibrium, it is reasonable to make the counter-argument that our inability to identify specific pathways is more an issue with the genetic complexity of the target trait and the scale of our experiments and less that there is a need to identify novel mechanisms of pleiotropic heterosis.
Various models have been proposed that generally fall into the category of mechanisms resulting in pleiotropic heterosis. These include genome-wide changes in DNA methylation (Tsaftaris & Polidoros, 2000), organellar complementation (Srivastava, 1981), small RNA expression (Ha et al., 2009) and the current proposal of Goff. One attractive feature of the Goff proposal is that it provides a cell-based mechanism to account for differences in metabolic efficiency that have been observed in hybrids vs inbreds. Manifestation of very small differences in efficiency among cells and across development can have a substantial cumulative effect over the lifetime of an organism. Current advances in sequencing, proteomics, nanotechnology, imaging and metabolomics provide an unprecedented depth of information with which to study biological phenomena such as heterosis. This wealth of information, coupled with novel computational algorithms and informatics approaches, will provide new insights. However, despite the exponential advances in technology, increasing outputs and decreasing costs, research on heterosis is still limited by the number of genotypes that can be measured for performance in relevant contexts (e.g. maize yield in the field). This limitation reduces the power to detect small genetic effects and complex interactions. Furthermore, limited amounts of genetic recombination confound linkage with the estimation of various genetic parameters.