Identifying Recent Adaptations in Large-Scale Genomic Data
Author(s)
Andersen, Kristian G.; Tabrizi, Shervin; Winnicki, Sarah; Yen, Angela; Park, Daniel J.; Griesemer, Dustin; Karlsson, Elinor K.; Wong, Sunny H.; Cabili, Moran N.; Adegbola, Richard A.; Bamezai, Rameshwar N.K.; Hill, Adrian V. S.; Vannberg, Fredrik O.; Rinn, John L.; Schaffner, Stephen F.; Sabeti, Pardis C.; Grossman, Sharon Rachel; Shlyakhter, Ilya, 1975-; Lander, Eric Steven; ... Show more Show less
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Show full item recordAbstract
Although several hundred regions of the human genome harbor signals of positive natural selection, few of the relevant adaptive traits and variants have been elucidated. Using full-genome sequence variation from the 1000 Genomes (1000G) Project and the composite of multiple signals (CMS) test, we investigated 412 candidate signals and leveraged functional annotation, protein structure modeling, epigenetics, and association studies to identify and extensively annotate candidate causal variants. The resulting catalog provides a tractable list for experimental follow-up; it includes 35 high-scoring nonsynonymous variants, 59 variants associated with expression levels of a nearby coding gene or lincRNA, and numerous variants associated with susceptibility to infectious disease and other phenotypes. We experimentally characterized one candidate nonsynonymous variant in Toll-like receptor 5 (TLR5) and show that it leads to altered NF-κB signaling in response to bacterial flagellin.
Date issued
2013-02Department
Whitaker College of Health Sciences and Technology; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory; Massachusetts Institute of Technology. Department of Biology; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer ScienceJournal
Cell
Publisher
Elsevier
Citation
Grossman, Sharon R., Kristian G. Andersen, Ilya Shlyakhter, Shervin Tabrizi, Sarah Winnicki, Angela Yen, Daniel J. Park, et al. “Identifying Recent Adaptations in Large-Scale Genomic Data.” Cell 152, no. 4 (February 2013): 703–713. Copyright © 2013 Elsevier Inc.
Version: Final published version
ISSN
00928674
1097-4172