Scientists have developed a new community resource that may act as a Rosetta stone for revealing the genetic basis of traits and disease.
A paper in the Feb. 9 issue of Nature 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. The study was led by Trudy Mackay, professor of genetics at North Carolina State University, in collaboration with the Human Genome Sequencing Center at Baylor College of Medicine and David Mittelman, associate professor at Virginia Bioinformatics Institute at Virginia Tech.
"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, behaviors, and disease susceptibility within populations. 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 noted.
It's been known for a long time 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.
Mittelman, with support from the NVIDIA Foundation's Compute the Cure Award, aided the study by analyzing genetic variation in the Drosophila population. Said Mittelman, "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." A companion paper describing this method has been submitted for publication to enable others to exploit these tools in their research.
The study has far reaching effects that span animal breeding, pesticide development, and personalized medicine.
About Virginia Bioinformatics Institute
The Virginia Bioinformatics Institute at Virginia Tech is a premier bioinformatics, computational biology, and systems biology research facility that uses transdisciplinary approaches to science, combining information technology, biology, and medicine. These approaches are used to interpret and apply vast amounts of biological data generated from basic research to some of today's key challenges in the biomedical, environmental, and agricultural sciences.
With more than 320 highly trained multidisciplinary, international personnel, research at the institute involves collaboration in diverse disciplines such as mathematics, computer science, biology, plant pathology, biochemistry, systems biology, statistics, economics, synthetic biology, and medicine. The large amounts of data generated by this approach are analyzed and interpreted to create new knowledge that is disseminated to the world's scientific, governmental, and wider communities.
The full listing of authors and their affiliations for this paper is as follows:
Trudy F. C. Mackay1* (http://cals.ncsu.edu/genetics/index.php/people/trudy-mackay/), Stephen Richards2*, Eric A. Stone1*, Antonio Barbadilla3*, Julien F. Ayroles1†, Dianhui Zhu2, Ṣnia Casillas3†, Yi Han2, Michael M. Magwire1, Julie M. Cridland4, Mark F. Richardson5, Robert R. H. Anholt6, Maite Barrón3, Crystal Bess2, Kerstin Petra Blankenburg2, Mary Anna Carbone1, David Castellano3, Lesley Chaboub2, Laura Duncan1, Zeke Harris1, Mehwish Javaid2, Joy Christina Jayaseelan2, Shalini N. Jhangiani2, Katherine W. Jordan1, Fremiet Lara2, Faye Lawrence1, Sandra L. Lee2, Pablo Librado7, Raquel S. Linheiro5, Richard F. Lyman1, Aaron J. Mackey8, Mala Munidasa2, Donna Marie Muzny2, Lynne Nazareth2, Irene Newsham2, Lora Perales2, Ling-Ling Pu2, Carson Qu2, Miquel Ràmia3, Jeffrey G. Reid2, Stephanie M. Rollmann1†, Julio Rozas7, Nehad Saada2, Lavanya Turlapati1, Kim C.Worley2, Yuan-Qing Wu2, Akihiko Yamamoto1, Yiming Zhu2, Casey M. Bergman5, Kevin R. Thornton4, David Mittelman9 (http://www.vbi.vt.edu/faculty/personal/David_Mittelman, and Richard A. Gibbs2
1Department of Genetics, North Carolina State University, Raleigh, North Carolina 27695, USA
2Human Genome Sequencing Center, Baylor College of Medicine, One Baylor Plaza, Houston, Texas 77030 USA
3Genomics, Bioinformatics and Evolution Group, Institut de Biotecnologia i de Biomedicina - IBB/Department of Genetics and Microbiology, Campus Universitat Auṭnoma de Barcelona, 08193 Bellaterra, Spain
4Department of Ecology and Evolutionary Biology, University of California - Irvine, Irvine, California 92697, USA
5Faculty of Life Sciences, University of Manchester, Manchester M13 9PT, UK
6Department of Biology, North Carolina State University, Raleigh, North Carolina 27695, USA
7Molecular Evolutionary Genetics Group, Department of Genetics, Faculty of Biology, Universitat de Barcelona, Diagonal 645, 08028 Barcelona, Spain
8Center for Public Health Genomics, University of Virginia, PO Box 800717, Charlottesville, Virginia 22908, USA
9Virginia Bioinformatics Institute and Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia 24061, USA
†Present addresses: FAS Society of Fellows, Harvard University, 78 Mt Auburn Street, Cambridge, Massachusetts 02138, USA (J.F.A.)
Functional Comparative Genomics Group, Institut de Biotecnologia i de Biomedicina - IBB, Campus Universitat Auṭnoma de Barcelona, 08193 Bellaterra, Spain
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