Fruit fly genome ‘may become genetic Rosetta stone
London: Scientists have developed a new community resource that may help in unlocking the mystery behind the genetic basis of traits and diseases.
The new study describes the Drosophila Genetic Reference Panel (DGRP), which provides the highest-resolution view to date, of the genome structure and variation in a population of 192 fruit flies with diverse traits.
“One of the grand challenges of biology is to understand how genetic variants and environmental factors interact to produce variation in complex phenotypes such as height, behaviours, and disease susceptibility within populations,” Trudy Mackay, the study leader from North Carolina State University, said.
“This effort has been stymied by the lack of knowledge of all genetic variants in a population of a genetically tractable model organism. The DGRP sequences provide such a resource,” Mackay said.
Since long it has been known that genes often work in concert to produce different effects, or phenotypes. But determining the exact contribution of these genes and genetic changes within them to animal traits remains a key challenge in genetics.
That’s where model organisms like Drosophila melanogaster (the common fruit fly) shine. Using inbred strains of fruit flies in controlled environments, researchers can use whole genome data, which captures genetic changes at the nucleotide level, to better explain why strains exhibit variable traits.
The DGRP acts as a “living library” of this information, helping researchers understand both common traits and rare variants.
David Mittelman, with support from the NVIDIA Foundation’s Compute the Cure Award, aided the study by analysing genetic variation in the Drosophila population.
“To truly exploit whole genome sequencing as a means of determining the basis for traits and disease, it is critical to develop methods for detecting all forms of genetic variation. In this study, we developed a method for measuring tandem repeat variation, which has been shown to modulate gene function, traits, and disease,” Mittelman, associate professor at Virginia Bioinformatics Institute at Virginia Tech, said.
A companion paper describing this method has been submitted for publication to enable others to exploit these tools in their research.
The study has been recently published in Nature.