Quantitative genetics: Small but not forgotten
N ew empirical evidence from a study of Arabidopsis thaliana biomass helps to answer a longstanding and fundamental question in biology: how genes influence complex phenotypes. How much of the genetic difference between individuals is due to the relatively large allelic effects of a handful of genes and how much is caused by the very small effects of a large number of genes? We know that typically there are several large to medium effect Quantitative Trait Loci (QTL) present in any given cross, but a new study by Kroymann and MitchellOlds (2005) provides empirical evidence that small, epistatic loci may be very wide spread. The last century of breeding experiments has revealed that most phenotypes of ecological and agricultural significance are quantitative: influenced by multiple genes and the environment. Heritable differences within or among populations are predominantly caused by the action of QTL: functionally diverged alleles that cause modest changes along a continuum of trait values (Mackay, 2001) Characterizing variation caused by QTL as well as cloning the underlying genes is important to a diverse range of biological disciplines, from pharmacogenomics to plant and animal breeding. Evolutionary biologists study QTL because they may help to answer the fundamental question of what maintains variation in fitness related traits within species. For these fields of study, knowing the distribution of QTL, their effects, and the prevalence of nonadditive interactions between loci (epistasis) is critical. The findings of Kroymann and Mitchell Olds are reminiscent of the infinitesimal model (Fisher, 1958) and thus not entirely unexpected. Theoretical work on the genetic basis of adaptation suggests that as organisms adapt to new environments, the allelic effects of loci that become fixed will be large at first and then become increasingly small as the fitness optimum is approached (Orr, 2005) Most QTL mapping studies find several large effect and then increasing numbers of smaller effect loci yielding an exponential or L shaped distribution (see review by Mackay (2004) for Drosophila) However, simulation studies show that allelic effects of large effect loci are often overestimated, and that mapping experiments can produce an L shaped distribution regardless of the true shape of the underlying curve (Beavis, 1994; Bost et al, 2001) Furthermore, most traditional QTL mapping studies have inherent limitations. In these studies, a segregating mapping population is phenotyped for traits of interest, genotyped throughout the genome, and then a genomewide scan is made to find statistical associations between marker allele states and trait values (Doerge, 2002) For practical reasons most studies are limited to several hundred individuals or lines (or fewer) as a result, the number of recombination events separating closely linked loci is small and overall power to detect small effect loci, especially when there are linked epistatic loci, is relatively low. Nevertheless, there are examples of very tightly linked epistatic loci. For example, dissection of a high temperature growth QTL in yeast revealed three closely linked genes with epistatic interactions (Steinmetz et al, 2002) The design used by Kroymann and Mitchell Olds overcomes both the power and resolution limitations of traditional studies, although it too has limitations. Instead of performing a genome wide scan, Kroymann and Mitchell Olds focused on 210 kb of a single chromosome and asked what effect, if any, segments within those 210 kb had on biomass accumulation.
Heredity