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Identifying a Major QTL Associated with Salinity Tolerance in Nile Tilapia Using QTL-Seq

Overview of attention for article published in Marine Biotechnology, January 2018
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Title
Identifying a Major QTL Associated with Salinity Tolerance in Nile Tilapia Using QTL-Seq
Published in
Marine Biotechnology, January 2018
DOI 10.1007/s10126-017-9790-4
Pubmed ID
Authors

Xiao Hui Gu, Dan Li Jiang, Yan Huang, Bi Jun Li, Chao Hao Chen, Hao Ran Lin, Jun Hong Xia

Abstract

Selection of new lines with high salinity tolerance allows for economically feasible production of tilapias in brackish water areas. Mapping QTLs and identifying the markers linked to salinity-tolerant traits are the first steps in the improvement of the tolerance in tilapia through marker-assisted selection techniques. By using QTL-seq strategy and linkage-based analysis, two significant QTL intervals (chrLG4 and chrLG18) on salinity-tolerant traits were firstly identified in the Nile tilapia. Fine mapping with microsatellite and SNP markers suggested a major QTL region that located at 23.0 Mb of chrLG18 and explained 79% of phenotypic variation with a LOD value of 95. Expression analysis indicated that at least 10 genes (e.g., LACTB2, KINH, NCOA2, DIP2C, LARP4B, PEX5R, and KCNJ9) near or within the QTL interval were significantly differentially expressed in intestines, brains, or gills under 10, 15, or 20 ppt challenges. Our findings suggest that QTL-seq can be effectively utilized in QTL mapping of salinity-tolerant traits in fish. The identified major QTL is a promising locus to improve our knowledge on the genetic mechanism of salinity tolerance in tilapia.

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Geographical breakdown

Country Count As %
Unknown 51 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 22%
Student > Ph. D. Student 11 22%
Student > Master 6 12%
Other 3 6%
Student > Postgraduate 2 4%
Other 5 10%
Unknown 13 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 30 59%
Biochemistry, Genetics and Molecular Biology 4 8%
Chemistry 1 2%
Unknown 16 31%