A statistical model for the genetic origin of allometric scaling laws in biology

Rongling Wu, Chang Xing Ma, Ramon C. Littell, George Casella

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Many biological processes, from cellular metabolism to population dynamics, are characterized by particular allometric scaling (power-law) relationships between size and rate. Although such allometric relationships may be under genetic determination, their precise genetic mechanisms have not been clearly understood due to a lack of a statistical analytical method. In this paper, we present a basic statistical framework for mapping quantitative genes (or quantitative trait loci, QTL) responsible for universal quarter-power scaling laws of organic structure and function with the entire body size. Our model framework allows the testing of whether a single QTL affects the allometric relationship of two traits or whether more than one linked QTL is segregating. Like traditional multi-trait mapping, this new model can increase the power to detect the underlying QTL and the precision of its localization on the genome. Beyond the traditional method, this model is integrated with pervasive scaling laws to take advantage of the mechanistic relationships of biological structures and processes. Simulation studies indicate that the estimation precision of the QTL position and effect can be improved when the scaling relationship of the two traits is considered. The application of our model in a real example from forest trees leads to successful detection of a QTL governing the allometric relationship of third-year stem height with third-year stem biomass. The model proposed here has implications for genetic, evolutionary, biomedicinal and breeding research.

Original languageEnglish (US)
Pages (from-to)121-135
Number of pages15
JournalJournal of Theoretical Biology
Volume219
Issue number1
DOIs
StatePublished - Jan 1 2002

Fingerprint

Quantitative Trait Loci
Scaling laws
Statistical Models
Scaling Laws
statistical models
Statistical Model
Biology
quantitative trait loci
Biological Sciences
Biological Phenomena
Genes
Population dynamics
Scaling
stems
Model
Metabolism
Population Dynamics
Body Size
Biomass
forest trees

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Cite this

Wu, Rongling ; Ma, Chang Xing ; Littell, Ramon C. ; Casella, George. / A statistical model for the genetic origin of allometric scaling laws in biology. In: Journal of Theoretical Biology. 2002 ; Vol. 219, No. 1. pp. 121-135.
@article{f1e589b14669495c985a44f0e2be403e,
title = "A statistical model for the genetic origin of allometric scaling laws in biology",
abstract = "Many biological processes, from cellular metabolism to population dynamics, are characterized by particular allometric scaling (power-law) relationships between size and rate. Although such allometric relationships may be under genetic determination, their precise genetic mechanisms have not been clearly understood due to a lack of a statistical analytical method. In this paper, we present a basic statistical framework for mapping quantitative genes (or quantitative trait loci, QTL) responsible for universal quarter-power scaling laws of organic structure and function with the entire body size. Our model framework allows the testing of whether a single QTL affects the allometric relationship of two traits or whether more than one linked QTL is segregating. Like traditional multi-trait mapping, this new model can increase the power to detect the underlying QTL and the precision of its localization on the genome. Beyond the traditional method, this model is integrated with pervasive scaling laws to take advantage of the mechanistic relationships of biological structures and processes. Simulation studies indicate that the estimation precision of the QTL position and effect can be improved when the scaling relationship of the two traits is considered. The application of our model in a real example from forest trees leads to successful detection of a QTL governing the allometric relationship of third-year stem height with third-year stem biomass. The model proposed here has implications for genetic, evolutionary, biomedicinal and breeding research.",
author = "Rongling Wu and Ma, {Chang Xing} and Littell, {Ramon C.} and George Casella",
year = "2002",
month = "1",
day = "1",
doi = "10.1016/S0022-5193(02)93114-0",
language = "English (US)",
volume = "219",
pages = "121--135",
journal = "Journal of Theoretical Biology",
issn = "0022-5193",
publisher = "Academic Press Inc.",
number = "1",

}

A statistical model for the genetic origin of allometric scaling laws in biology. / Wu, Rongling; Ma, Chang Xing; Littell, Ramon C.; Casella, George.

In: Journal of Theoretical Biology, Vol. 219, No. 1, 01.01.2002, p. 121-135.

Research output: Contribution to journalArticle

TY - JOUR

T1 - A statistical model for the genetic origin of allometric scaling laws in biology

AU - Wu, Rongling

AU - Ma, Chang Xing

AU - Littell, Ramon C.

AU - Casella, George

PY - 2002/1/1

Y1 - 2002/1/1

N2 - Many biological processes, from cellular metabolism to population dynamics, are characterized by particular allometric scaling (power-law) relationships between size and rate. Although such allometric relationships may be under genetic determination, their precise genetic mechanisms have not been clearly understood due to a lack of a statistical analytical method. In this paper, we present a basic statistical framework for mapping quantitative genes (or quantitative trait loci, QTL) responsible for universal quarter-power scaling laws of organic structure and function with the entire body size. Our model framework allows the testing of whether a single QTL affects the allometric relationship of two traits or whether more than one linked QTL is segregating. Like traditional multi-trait mapping, this new model can increase the power to detect the underlying QTL and the precision of its localization on the genome. Beyond the traditional method, this model is integrated with pervasive scaling laws to take advantage of the mechanistic relationships of biological structures and processes. Simulation studies indicate that the estimation precision of the QTL position and effect can be improved when the scaling relationship of the two traits is considered. The application of our model in a real example from forest trees leads to successful detection of a QTL governing the allometric relationship of third-year stem height with third-year stem biomass. The model proposed here has implications for genetic, evolutionary, biomedicinal and breeding research.

AB - Many biological processes, from cellular metabolism to population dynamics, are characterized by particular allometric scaling (power-law) relationships between size and rate. Although such allometric relationships may be under genetic determination, their precise genetic mechanisms have not been clearly understood due to a lack of a statistical analytical method. In this paper, we present a basic statistical framework for mapping quantitative genes (or quantitative trait loci, QTL) responsible for universal quarter-power scaling laws of organic structure and function with the entire body size. Our model framework allows the testing of whether a single QTL affects the allometric relationship of two traits or whether more than one linked QTL is segregating. Like traditional multi-trait mapping, this new model can increase the power to detect the underlying QTL and the precision of its localization on the genome. Beyond the traditional method, this model is integrated with pervasive scaling laws to take advantage of the mechanistic relationships of biological structures and processes. Simulation studies indicate that the estimation precision of the QTL position and effect can be improved when the scaling relationship of the two traits is considered. The application of our model in a real example from forest trees leads to successful detection of a QTL governing the allometric relationship of third-year stem height with third-year stem biomass. The model proposed here has implications for genetic, evolutionary, biomedicinal and breeding research.

UR - http://www.scopus.com/inward/record.url?scp=0036426079&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0036426079&partnerID=8YFLogxK

U2 - 10.1016/S0022-5193(02)93114-0

DO - 10.1016/S0022-5193(02)93114-0

M3 - Article

C2 - 12392980

AN - SCOPUS:0036426079

VL - 219

SP - 121

EP - 135

JO - Journal of Theoretical Biology

JF - Journal of Theoretical Biology

SN - 0022-5193

IS - 1

ER -