Quantitative traits – like height, weight, blood pressure, and disease susceptibility – are important for animal and plant breeding, adaptive evolution, and human biology, including susceptibility to common diseases. Quantitative traits have continuous phenotypic variation between individuals in natural populations because many genes with individually small effects on the phenotype affect each trait, and because the traits are sensitive to the environment to which the individuals are exposed. Recent advances in genomic technology have revealed a large amount of molecular variation in the DNA sequences between individuals, enabling the mapping of variants associated with quantitative traits. However, in most species, including humans, precise mapping is difficult because variants in close proximity tend to co-segregate in populations.
Drosophila melanogaster is a model genetic organism with many advantages for genetic analyses of quantitative traits, including a genome 10 times smaller than humans or mice; a short generation interval; and the ability to both inbreed and crossbreed in the laboratory. The D. melanogaster Genetic Reference Panel (DGRP) is a community resource of inbred, sequenced lines that have been used by many laboratories to study a wide range of quantitative trait phenotypes using genome wide association (GWA) analyses. The small size of the current DGRP is mitigated somewhat by the ability to evaluate many individuals of each line for the quantitative traits of interest in a controlled laboratory setting. Further, the precision of mapping can be high since variants in close proximity are not correlated. An advanced review recently published in WIREs Developmental Biology summarizes insights about the genetic basis of variation of quantitative traits from the many GWA analyses of morphological, behavioral, life history and other traits that have been published to date using the DGRP and populations derived from it.
The DGRP exhibits a remarkable range of phenotypic variation for all traits reported to date, even though all lines are by definition ‘wild type’. Not surprisingly, many variants are associated with each trait, and they typically have subtle effects. Variants that are not common in the population tend to have larger effects and affect more traits than more common variants. However, the effects of variants are context-dependent, and vary between males and females, different environments, and different genetic backgrounds. Most of the variants associated with quantitative traits are presumably regulatory and located in intronic or intergenic regions. GWA analyses identify novel associations between computationally predicted genes and quantitative traits, and novel associations between genes whose functions have been annotated and quantitative traits. These associations have a high rate of functional validation using mutations and RNA interference to knock down gene expression. Further, the genes associated with quantitative traits often participate in known genetic and protein interaction networks. Thus, GWA analyses using the DGRP complement classical mutational analyses of quantitative traits. These analyses show that networks of interacting variants, rather than single variants or genes, are the relevant functional units affecting variation in quantitative traits.
Kindly contributed by Trudy Mackay.