Quantitative Seq-LGS: Genome-Wide Identification of Genetic Drivers of Multiple Phenotypes in Malaria Parasites

Abstract
Identifying the genetic determinants of phenotypes that impact on disease severity is of fundamental importance for the design of new interventions against malaria. Traditionally, such discovery has relied on labor-intensive approaches that require significant investments of time and resources. By combining Linkage Group Selection (LGS), quantitative whole genome population sequencing and a novel mathematical modeling approach (qSeq-LGS), we simultaneously identified multiple genes underlying two distinct phenotypes, identifying novel alleles for growth rate and strain specific immunity (SSI), while removing the need for traditionally required steps such as cloning, individual progeny phenotyping and marker generation. The detection of novel variants, verified by experimental phenotyping methods, demonstrates the remarkable potential of this approach for the identification of genes controlling selectable phenotypes in malaria and other apicomplexan parasites for which experimental genetic crosses are amenable.

Citation
Abkallo HM, Martinelli A, Inoue M, Ramaprasad A, Xangsayarath P, et al. (2016) Quantitative Seq-LGS: Genome-Wide Identification of Genetic Drivers of Multiple Phenotypes in Malaria Parasites. Available: http://dx.doi.org/10.1101/078451.

Acknowledgements
This work was supported by grants from the Naito Foundation (to R.Cu); the JSPS (project numbers Nos. JP25870525, JP24255009 and JP16K21233) (to R.Cu), A Royal Society Bilateral Grant for Co-operative Research (to R.Ca and R.Cu) and a Sasakawa Foundation Butterfield Award (to R.Cu), faculty baseline funding from the King Abdullah University of Science and Technology (KAUST) to AP, and Grants-in-Aids for Scientific Research on Innovative Areas JR23117008, MEXT, Japan (to OK). CJRI was supported by a Sir Henry Dale Fellowship, jointly funded by the Wellcome Trust and the Royal Society (Grant Number 101239/Z/13/Z). This work was conducted in part at the Joint Usage / Research Center on Tropical Disease, Institute of Tropical Medicine, Nagasaki University. We thank Ho Y. Shwen for initial contributions to the project and Andrej Fisher for discussions and for the provision of code used in the jump-diffusion analysis. AM is supported by GI-CoRE funded to the Research Center for Zoonosis Control in Hokkaido University.

Publisher
Cold Spring Harbor Laboratory

DOI
10.1101/078451

Additional Links
http://www.biorxiv.org/content/early/2017/05/16/078451

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