Genetic Mapping of Developmental Instability: Design, Model and Algorithm

Abstract

Developmental instability or noise, defined as the phenotypic imprecision of an organism in the face of internal or external stochastic disturbances, has been thought to play an important role in shaping evolutionary processes and patterns. The genetic studies of developmental instability have been based on fluctuating asymmetry (FA) that measures random differences between the left and the right sides of bilateral traits. In this article, we frame an experimental design characterized by a spatial autocorrelation structure for determining the genetic control of developmental instability for those traits that cannot be bilaterally measured. This design allows the residual environmental variance of a quantitative trait to be dissolved into two components due to permanent and random environmental factors. The degree of developmental instability is quantified by the relative proportion of the random residual variance to the total residual variance. We formulate a mixture model to estimate and test the genetic effects of quantitative trait loci (QTL) on the developmental instability of the trait. The genetic parameters including the QTL position, the QTL effects, and spatial autocorrelations are estimated by implementing the EM algorithm within the mixture model framework. Simulation studies were performed to investigate the statistical behavior of the model. A live example for poplar trees was used to map the QTL that control root length growth and its developmental instability from cuttings in water culture.

QTL analyses of seed weight during the development of soybean (Glycine max L. Merr.)

“…(2) Highly heritable traits tend to be more repeatable and stable across multiple environments (Paterson et al, 1991). Though swC2_1, swC2_2 and swC2_3 effect were very small, increasingly more evidences have been observed that the accumulation of minor gene expressions may have played an important role in the ultimate formation of a complex trait (Wu et al, 2007). Thus, we believed that MAS with QTL swC2_1, swC2_2 and swC2_3 have good potential to increase the efficiency of breeding programs seeking higher 100-seed weight genotypes (Tables 3 and 4).…”

A dynamic framework for quantifying the genetic architecture of phenotypic plasticity

“…Consider a mapping population, derived from the cross between two contrasting inbred lines, which contains n progeny that can be replicated. Such a population can be composed of doubled haploids (DH), recombinant inbred lines, both commonly used in Arabidopsis and crops [23], or hybrid clones derived from cuttings or tissue culture available to many forest trees [10,11]. Here, we assume the use of a DH population for which there are two homozygous genotypes each for an allele inherited from a different parent at each locus [23].…”

“…some genotypes may be more sensitive to environmental change than others [3,4]. Understanding the genetic basis of phenotypic plasticity that guides varying responses for different genotypes to the environment has been the subject of a long-standing debate in biology [5][6][7][8][9][10][11][12][13][14]. To study the degree to which genotypes vary in response to changing environments, the same genotypes are grown under multiple environmental conditions [15,16].…”

A comparative fluctuating asymmetry study between two walnut (Juglans regia L.) populations may contribute as an early signal for bio-monitoring

“…There is some underlying additive genetic variance associated with fluctuating asym metry and relevant QTLs have been identi fied in poplars (Wu et al 2007) and mice (Leamy et al 2002), but the extent of its ge netic basis remains unresolved (Moller & Thornhill 1997a, 1997b, Markow & Clarke 1997, Houle 1997, Van Dongen 2000b. Nevertheless, there is at least circumstantial evidence that has linked fluctuating asym metry to environmental stress (Parsons 1990, 1992, Silva et al 2009, Hodar 2002) and even to genetic bottlenecks (Hoelzel et al 2002).…”