RT Journal Article
SR Electronic
T1 Quantitative Seq-LGS – Genome-Wide Identification of Genetic Drivers of Multiple Phenotypes in Malaria Parasites
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 078451
DO 10.1101/078451
A1 Hussein M. Abkallo
A1 Axel Martinelli
A1 Megumi Inoue
A1 Abhinay Ramaprasad
A1 Phonepadith Xangsayarath
A1 Jesse Gitaka
A1 Jianxia Tang
A1 Kazuhide Yahata
A1 Augustin Zoungrana
A1 Hayato Mitaka
A1 Paul Hunt
A1 Richard Carter
A1 Osamu Kaneko
A1 Ville Mustonen
A1 Christopher J. R. Illingworth
A1 Arnab Pain
A1 Richard Culleton
YR 2016
UL http://biorxiv.org/content/early/2016/09/30/078451.abstract
AB 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.