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A computing platform to map ecological metabolism by integrating functional mapping and the metabolic theory of ecology
Author(s) -
Qin Yan,
Xuli Zhu,
Libo Jiang,
Meixia Ye,
Lidan Sun,
John S. Terblanche,
Rongling Wu
Publication year - 2016
Publication title -
briefings in bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.204
H-Index - 113
eISSN - 1477-4054
pISSN - 1467-5463
DOI - 10.1093/bib/bbv116
Subject(s) - epistasis , pleiotropy , quantitative trait locus , biology , organism , association mapping , genome , population , computational biology , genetic association , model organism , range (aeronautics) , evolutionary biology , genetics , ecology , gene , genotype , phenotype , single nucleotide polymorphism , demography , sociology , materials science , composite material
Whole-organism metabolic rate co-varies allometrically with body mass, and is also affected by temperature through different biochemical mechanisms. Here we implement a computational platform to map specific quantitative trait loci (QTLs) that govern the dependence of metabolic rate on size and temperature. The model was formulated within settings of genetic mapping or genome-wide association studies through a mapping population genotyped by a set of molecular markers throughout the genome and phenotyped for metabolic parameters over a range of temperature. The model, estimated by a maximum-likelihood approach, allows a genome-wide search for the underlying metabolic QTLs and the estimation of genotype-specific parameters that specify the metabolism of an organism. Our model provides a tool to detect pleiotropy and epistasis that cause the size- and temperature-dependent change of metabolic rate.

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