A computing platform to map ecological metabolism by integrating functional mapping and the metabolic theory of ecology

Qin Yan, Xuli Zhu, Libo Jiang, Meixia Ye, Lidan Sun, John S. Terblanche, Rongling Wu

Research output: Contribution to journalArticle

Abstract

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 metabolismof an organism. Our model provides a tool to detect pleiotropy and epistasis that cause the size- and temperature-dependent change of metabolic rate.

Original languageEnglish (US)
Pages (from-to)137-144
Number of pages8
JournalBriefings in Bioinformatics
Volume18
Issue number1
DOIs
StatePublished - Jan 1 2017

Fingerprint

Ecology
Metabolism
Temperature
Genes
Quantitative Trait Loci
Genome
Genome-Wide Association Study
Maximum likelihood
Genotype
Population

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Molecular Biology

Cite this

Yan, Qin ; Zhu, Xuli ; Jiang, Libo ; Ye, Meixia ; Sun, Lidan ; Terblanche, John S. ; Wu, Rongling. / A computing platform to map ecological metabolism by integrating functional mapping and the metabolic theory of ecology. In: Briefings in Bioinformatics. 2017 ; Vol. 18, No. 1. pp. 137-144.
@article{ab28874d69754312a78f88339abc4eb9,
title = "A computing platform to map ecological metabolism by integrating functional mapping and the metabolic theory of ecology",
abstract = "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 metabolismof an organism. Our model provides a tool to detect pleiotropy and epistasis that cause the size- and temperature-dependent change of metabolic rate.",
author = "Qin Yan and Xuli Zhu and Libo Jiang and Meixia Ye and Lidan Sun and Terblanche, {John S.} and Rongling Wu",
year = "2017",
month = "1",
day = "1",
doi = "10.1093/bib/bbv116",
language = "English (US)",
volume = "18",
pages = "137--144",
journal = "Briefings in Bioinformatics",
issn = "1467-5463",
publisher = "Oxford University Press",
number = "1",

}

A computing platform to map ecological metabolism by integrating functional mapping and the metabolic theory of ecology. / Yan, Qin; Zhu, Xuli; Jiang, Libo; Ye, Meixia; Sun, Lidan; Terblanche, John S.; Wu, Rongling.

In: Briefings in Bioinformatics, Vol. 18, No. 1, 01.01.2017, p. 137-144.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A computing platform to map ecological metabolism by integrating functional mapping and the metabolic theory of ecology

AU - Yan, Qin

AU - Zhu, Xuli

AU - Jiang, Libo

AU - Ye, Meixia

AU - Sun, Lidan

AU - Terblanche, John S.

AU - Wu, Rongling

PY - 2017/1/1

Y1 - 2017/1/1

N2 - 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 metabolismof an organism. Our model provides a tool to detect pleiotropy and epistasis that cause the size- and temperature-dependent change of metabolic rate.

AB - 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 metabolismof an organism. Our model provides a tool to detect pleiotropy and epistasis that cause the size- and temperature-dependent change of metabolic rate.

UR - http://www.scopus.com/inward/record.url?scp=85015907951&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85015907951&partnerID=8YFLogxK

U2 - 10.1093/bib/bbv116

DO - 10.1093/bib/bbv116

M3 - Article

C2 - 26801770

AN - SCOPUS:85015907951

VL - 18

SP - 137

EP - 144

JO - Briefings in Bioinformatics

JF - Briefings in Bioinformatics

SN - 1467-5463

IS - 1

ER -