scholarly journals Natural Variation for Carbohydrate Content in Arabidopsis. Interaction with Complex Traits Dissected by Quantitative Genetics

2006 ◽  
Vol 141 (4) ◽  
pp. 1630-1643 ◽  
Author(s):  
Fanny Calenge ◽  
Véra Saliba-Colombani ◽  
Stéphanie Mahieu ◽  
Olivier Loudet ◽  
Françoise Daniel-Vedele ◽  
...  
Keyword(s):  
Quantitative Genetics ◽  
Complex Traits ◽  
Natural Variation ◽  
Carbohydrate Content

Evolution and Selection of Quantitative Traits

2018 ◽  
Author(s):  
Bruce Walsh ◽  
Michael Lynch
Keyword(s):  
Mathematical Models ◽  
Quantitative Genetics ◽  
Complex Traits ◽  
Evolutionary Biology ◽  
Quantitative Traits ◽  
Specific Gene ◽  
Real World Data ◽  
Holistic Treatment ◽  
Genetics And Genomics ◽  
Selection Of

Quantitative traits—be they morphological or physiological characters, aspects of behavior, or genome-level features such as the amount of RNA or protein expression for a specific gene—usually show considerable variation within and among populations. Quantitative genetics, also referred to as the genetics of complex traits, is the study of such characters and is based on mathematical models of evolution in which many genes influence the trait and in which non-genetic factors may also be important. Evolution and Selection of Quantitative Traits presents a holistic treatment of the subject, showing the interplay between theory and data with extensive discussions on statistical issues relating to the estimation of the biologically relevant parameters for these models. Quantitative genetics is viewed as the bridge between complex mathematical models of trait evolution and real-world data, and the authors have clearly framed their treatment as such. This is the second volume in a planned trilogy that summarizes the modern field of quantitative genetics, informed by empirical observations from wide-ranging fields (agriculture, evolution, ecology, and human biology) as well as population genetics, statistical theory, mathematical modeling, genetics, and genomics. Whilst volume 1 (1998) dealt with the genetics of such traits, the main focus of volume 2 is on their evolution, with a special emphasis on detecting selection (ranging from the use of genomic and historical data through to ecological field data) and examining its consequences. This extensive work of reference is suitable for graduate level students as well as professional researchers (both empiricists and theoreticians) in the fields of evolutionary biology, genetics, and genomics. It will also be of particular relevance and use to plant and animal breeders, human geneticists, and statisticians.

scholarly journals Metabolite profiling and quantitative genetics of natural variation for flavonoids in Arabidopsis

2012 ◽  
Vol 63 (10) ◽  
pp. 3749-3764 ◽  
Author(s):  
Jean-Marc Routaboul ◽  
Christian Dubos ◽  
Gilles Beck ◽  
Catherine Marquis ◽  
Przemyslaw Bidzinski ◽  
...  
Keyword(s):  
Quantitative Genetics ◽  
Metabolite Profiling ◽  
Natural Variation

Gene-to-phenotype models and complex trait genetics

2005 ◽  
Vol 56 (9) ◽  
pp. 895 ◽  
Author(s):  
Mark Cooper ◽  
Dean W. Podlich ◽  
Oscar S. Smith
Keyword(s):  
Quantitative Genetics ◽  
Complex Traits ◽  
Molecular Breeding ◽  
Complex Trait ◽  
Theoretical Framework ◽  
Breeding Strategies ◽  
Gene Effects ◽  
Gene Environment ◽  
Starting Point ◽  
Context Dependent

The premise that is explored in this paper is that in some cases, in order to make progress in the design of molecular breeding strategies for complex traits, we will need a theoretical framework for quantitative genetics that is grounded in the concept of gene-networks. We seek to develop a gene-to-phenotype (G→P) modelling framework for quantitative genetics that explicitly deals with the context-dependent gene effects that are attributed to genes functioning within networks, i.e. epistasis, gene × environment interactions, and pleiotropy. The E(NK) model is discussed as a starting point for building such a theoretical framework for complex trait genetics. Applying this framework to a combination of theoretical and empirical G→P models, we find that although many of the context-dependent effects of genetic variation on phenotypic variation can reduce the rate of genetic progress from breeding, it is possible to design molecular breeding strategies for complex traits that on average will outperform phenotypic selection. However, to realise these potential advantages, empirical G→P models of the traits will need to take into consideration the context-dependent effects that are a consequence of epistasis, gene × environment interactions, and pleiotropy. Some promising G→P modelling directions are discussed.

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