Systems mapping: how to improve the genetic mapping of complex traits through design principles of biological systems

BackgroundEvery phenotypic trait can be viewed as a "system" in which a group of interconnected components function synergistically to yield a unified whole. Once a system's components and their interactions have been delineated according to biological principles, we can manipulate and engineer functionally relevant components to produce a desirable system phenotype.ResultsWe describe a conceptual framework for mapping quantitative trait loci (QTLs) that control complex traits by treating trait formation as a dynamic system. This framework, called systems mapping, incorporates a system of differential equations that quantifies how alterations of different components lead to the global change of trait development and function through genes, and provides a quantitative and testable platform for assessing the interplay between gene action and development. We applied systems mapping to analyze biomass growth data in a mapping population of soybeans and identified specific loci that are responsible for the dynamics of biomass partitioning to leaves, stem, and roots.ConclusionsWe show that systems mapping implemented by design principles of biological systems is quite versatile for deciphering the genetic machineries for size-shape, structural-functional, sink-source and pleiotropic relationships underlying plant physiology and development. Systems mapping should enable geneticists to shed light on the genetic complexity of any biological system in plants and other organisms and predict its physiological and pathological states.

[1]  J. Weiner Allocation, plasticity and allometry in plants , 2004 .

[2]  L. Hood,et al.  Reverse Engineering of Biological Complexity , 2007 .

[3]  James H. Brown,et al.  A general model for ontogenetic growth , 2001, Nature.

[4]  Jianhua Z. Huang Covariance selection and estimation via penalised normal likelihood , 2005 .

[5]  Zhongwen Huang,et al.  A Conceptual Framework for Mapping Quantitative Trait Loci Regulating Ontogenetic Allometry , 2007, PloS one.

[6]  H. Kitano Systems Biology: A Brief Overview , 2002, Science.

[7]  James F. Reynolds,et al.  A Coordination Model of Whole-plant Carbon Allocation in Relation to Water Stress☆ , 1997 .

[8]  G. Casella,et al.  Functional mapping of quantitative trait loci underlying the character process: a theoretical framework. , 2002, Genetics.

[9]  Detlef Weigel,et al.  QTL Mapping in New Arabidopsis thaliana Advanced Intercross-Recombinant Inbred Lines , 2009, PloS one.

[10]  Jean Dauzat,et al.  Carbon allocation in fruit trees: from theory to modelling , 2008, Trees.

[11]  T. Sang,et al.  Rice Domestication by Reducing Shattering , 2007 .

[12]  Sang Hong Lee,et al.  Predicting Unobserved Phenotypes for Complex Traits from Whole-Genome SNP Data , 2008, PLoS genetics.

[13]  James O. Ramsay,et al.  Principal differential analysis : Data reduction by differential operators , 1996 .

[14]  M. Daly,et al.  Genetic Mapping in Human Disease , 2008, Science.

[15]  Robert Tibshirani,et al.  An Introduction to the Bootstrap CHAPMAN & HALL/CRC , 1993 .

[16]  Shizhong Xu,et al.  A random model approach to interval mapping of quantitative trait loci. , 1995, Genetics.

[17]  Rongling Wu,et al.  Functional mapping of genotype-environment interactions for soybean growth by a semiparametric approach , 2010, Plant methods.

[18]  Jianhua Z. Huang,et al.  Covariance matrix selection and estimation via penalised normal likelihood , 2006 .

[19]  C. Daub,et al.  BMC Systems Biology , 2007 .

[20]  Hulin Wu,et al.  A Bayesian approach for estimating antiviral efficacy in HIV dynamic models , 2006 .

[21]  Rongling Wu,et al.  Comprar Statistical Genetics of Quantitative Traits · Linkage, Maps and QTL | Casella, George | 9780387203348 | Springer , 2007 .

[22]  Qian Qian,et al.  Natural variation at the DEP1 locus enhances grain yield in rice , 2009, Nature Genetics.

[23]  Roderick C. Dewar,et al.  Carbon Allocation in Trees: a Review of Concepts for Modelling , 1994 .

[24]  J. Gai,et al.  QTL mapping of ten agronomic traits on the soybean (Glycine max L. Merr.) genetic map and their association with EST markers , 2004, Theoretical and Applied Genetics.

[25]  Jiguo Cao,et al.  Parameter estimation for differential equations: a generalized smoothing approach , 2007 .

[26]  H Putter,et al.  A Bayesian approach to parameter estimation in HIV dynamical models , 2002, Statistics in medicine.

[27]  Christopher B. Field,et al.  Predicting responses of photosynthesis and root fraction to elevated [CO2]a: interactions among carbon, nitrogen, and growth* , 1994 .

[28]  Rongling Wu,et al.  Functional mapping of growth and development , 2010, Biological reviews of the Cambridge Philosophical Society.

[29]  R. Wu,et al.  Functional mapping — how to map and study the genetic architecture of dynamic complex traits , 2006, Nature Reviews Genetics.

[30]  J. Cheverud Genetics and analysis of quantitative traits , 1999 .

[31]  Eric S. Lander,et al.  Genetic Dissection of Complex Traits with Chromosome Substitution Strains of Mice , 2004, Science.

[32]  Lang Li,et al.  Estimation and Inference for a Spline‐Enhanced Population Pharmacokinetic Model , 2002, Biometrics.

[33]  James H. Brown,et al.  The fourth dimension of life: fractal geometry and allometric scaling of organisms. , 1999, Science.

[34]  Ritsert C. Jansen,et al.  Studying complex biological systems using multifactorial perturbation , 2003, Nature Reviews Genetics.

[35]  N. Yi,et al.  Bayesian LASSO for Quantitative Trait Loci Mapping , 2008, Genetics.

[36]  E. Lander,et al.  Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. , 1989, Genetics.

[37]  Brian J. Enquist,et al.  Consistency between an allometric approach and optimal partitioning theory in global patterns of plant biomass allocation , 2007 .

[38]  Annie E. Hill,et al.  Genetic architecture of complex traits: Large phenotypic effects and pervasive epistasis , 2008, Proceedings of the National Academy of Sciences.

[39]  T. C. Nesbitt,et al.  fw2.2: a quantitative trait locus key to the evolution of tomato fruit size. , 2000, Science.

[40]  Ep Heuvelink,et al.  Concepts of modelling carbon allocation among plant organs , 2007 .

[41]  G. Casella,et al.  Joint linkage and linkage disequilibrium mapping of quantitative trait loci in natural populations. , 2002, Genetics.

[42]  Paul C. Struik,et al.  Functional-Structural Plant Modelling in Crop Production , 2007 .

[43]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[44]  Christian Hermans,et al.  How do plants respond to nutrient shortage by biomass allocation? , 2006, Trends in plant science.

[45]  Qian Qian,et al.  Regulation of OsSPL14 by OsmiR156 defines ideal plant architecture in rice , 2010, Nature Genetics.

[46]  Z. Zeng Precision mapping of quantitative trait loci. , 1994, Genetics.

[47]  James H. Brown,et al.  A General Model for the Origin of Allometric Scaling Laws in Biology , 1997, Science.

[48]  Fei Zou,et al.  A Robust QTL Mapping Procedure. , 2009, Journal of statistical planning and inference.

[49]  W. Ewens Genetics and analysis of quantitative traits , 1999 .

[50]  E. Stone,et al.  The genetics of quantitative traits: challenges and prospects , 2009, Nature Reviews Genetics.

[51]  Hulin Wu,et al.  Parameter Estimation for Differential Equation Models Using a Framework of Measurement Error in Regression Models , 2008, Journal of the American Statistical Association.

[52]  James O. Ramsay,et al.  Functional Data Analysis , 2005 .

[53]  Leonid Kruglyak,et al.  Dissection of genetically complex traits with extremely large pools of yeast segregants , 2010, Nature.

[54]  Daniel R. Richards,et al.  Dissecting the architecture of a quantitative trait locus in yeast , 2002, Nature.

[55]  Rongling Wu,et al.  Statistical Genetics of Quantitative Traits: Linkage, Maps and QTL , 2007 .

[56]  Guifang Fu,et al.  Network Models for Dissecting Plant Development by Functional Mapping , 2009 .