TY - JOUR T1 - Quantitative Seq-LGS – Genome-Wide Identification of Genetic Drivers of Multiple Phenotypes in Malaria Parasites JF - bioRxiv DO - 10.1101/078451 SP - 078451 AU - Hussein M. Abkallo AU - Axel Martinelli AU - Megumi Inoue AU - Abhinay Ramaprasad AU - Phonepadith Xangsayarath AU - Jesse Gitaka AU - Jianxia Tang AU - Kazuhide Yahata AU - Augustin Zoungrana AU - Hayato Mitaka AU - Paul Hunt AU - Richard Carter AU - Osamu Kaneko AU - Ville Mustonen AU - Christopher J. R. Illingworth AU - Arnab Pain AU - Richard Culleton Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/01/30/078451.abstract N2 - 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.Significance Statement This paper describes a powerful and rapid approach to the discovery of genes underlying medically important phenotypes in malaria parasites. This is crucial for the design of new drug and vaccine interventions. The approach bypasses the most time-consuming steps required by traditional genetic linkage studies and combines Mendelian genetics, quantitative deep sequencing technologies, genome analysis and mathematical modeling. We demonstrate that the approach can simultaneously identify multigenic drivers of multiple phenotypes, thus allowing complex genotyping studies to be conducted concomitantly. This methodology will be particularly useful for discovering the genetic basis of medically important phenotypes such as drug resistance and virulence in malaria and other apicomplexan parasites, as well as potentially in any organism undergoing sexual recombination. ER -